Python fatal error unable remap

python fatal error unable remap

In the minus column, you can't say something like, The former is a very common and catastrophic mistake when building large strings. If you found a bug in python-ldap, or would request a new feature, included from Modules/LDAPObject.c:8: Modules/constants.h:7:10: fatal error: 'lber.h'. It means that either a 3rd party program such as a virus scanner or the DLL address randomisation feature that was introduced in Vista have. python fatal error unable remap

Python fatal error unable remap - have

awk'NR==1; /Max open files/'

A successful response includes the heading descriptions and values:

Limit Soft Limit Hard Limit Units Max open files 1024 4096 files

To get a more verbose picture of open files, you can also use the command like this.

$sudolsof -p $(pidof vault)

Example output:

COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME vault 14810 vault cwd DIR 253,0 4096 2 / vault 14810 vault rtd DIR 253,0 4096 2 / vault 14810 vault txt REG 253,0 138377265 131086 /usr/local/bin/vault vault 14810 vault 0r CHR 1,3 0t0 6 /dev/null vault 14810 vault 1u unix 0xffff89e6347f9c00 0t0 41148 type=STREAM vault 14810 vault 2u unix 0xffff89e6347f9c00 0t0 41148 type=STREAM vault 14810 vault 3u unix 0xffff89e6347f8800 0t0 41208 type=DGRAM vault 14810 vault 4u a_inode 0,13 0 9583 [eventpoll] vault 14810 vault 6u IPv4 40467 0t0 TCP *:8200 (LISTEN) vault 14810 vault 7u IPv4 41227 0t0 TCP localhost:53766->localhost:8500 (ESTABLISHED)

This is a minimal example taken from a newly unsealed Vault. You can expect much more output in a production Vault with several use cases. The output is helpful for spotting the specific source of open connections, such as numerous sockets to a database secrets engine, for example.

Here, you can observe that the last 2 lines are related to 2 open sockets.

First, there is file descriptor number 6 that is open with read and write permission (u), is of type IPv4, is a TCP node that is bound to port 8200 on all network interfaces.

Second, file descriptor 7 represents the same kind of socket, except as an outbound ephemeral port connection from Vault on TCP/53766 to the Consul client agent on localhost that is listening on port 8500.

What are common errors?

When the value for maximum open files is not sufficient, Vault will emit errors to its operational logging in the format of this example.

http: Accept error: accept tcp4 0.0.0.0:8200: accept4: too many open files; retrying in 1s

There are several important parts to this log line:

  • Vault http subsystem is the error source ()
  • Since the error originates from , the error also relates to exhausting file descriptors in the context of network sockets, not regular files (i.e. note instead of )
  • The most critical fragment of the message and one that explains the root of the immediate issue is too many open files.

This is a both red alert that there are currently insufficient file descriptors, and that something could be excessively consuming them.

You should remedy the issue by increasing the maximum open files limit and restarting the Vault service for each affected cluster peer. There are implications and limitations around raising the value that you should be aware of before doing so.

First, there is a system-wide maximum open files limit that is enforced by the kernel and cannot be exceeded by user programs like Vault. Note that this value is dynamically set at boot time and varies depending on the physical computer system characteristics, such as available physical memory.

To check the current system-wide maximum open files value for a given system, read it from the kernel process table.

$cat /proc/sys/fs/file-max

A successful response includes only the raw value:

On this example system, it will not be possible to specify a maximum open file limit that exceeds 197073.

Increase limits

In the case of the previous example output, you observed that the maximum open files for the Vault process had a soft limit of 1024 and a hard limit of 4096. These are often the default values for some Linux distributions and you should always increase the value beyond such defaults for using Vault in production.

Once you have determined the system-wide limit, you can appropriately increase the limit for Vault processes. With a contemporary systemd based Linux, you can do so by editing the Vault systemd service unit file, and specifying a value for the LimitNOFILEprocess property.

The systemd unit file name can vary, but often it is , and located at the path .

Edit the file as the system super user.

$sudo$EDITOR /etc/systemd/system/vault.service

Then either add the LimitNOFILE process property under or edit its value if it already exists so that both the soft and hard limits are increased to a reasonable baseline value of 65536.

LimitNOFILE=65536

Save the file, exit your editor.

Any change to the unit requires a daemon reload; go ahead and do that now.

$sudo systemctl daemon-reload

A successful response should include no output.

The next time the vault service is restarted, the new maximum open files limits will be in effect.

You can restart the service, then examine the process table again to confirm your changes are in place.

CAUTION: You should be careful about this step in production systems as it can trigger a cluster leadership change. Depending on your Vault seal type, restarting the service could mean that you also need to unseal Vault if not using an auto seal type, so be prepared to do so if that is your case.

First, restart the vault service.

$sudo systemctl restart vault

Once restart successfully completes, check the process table for the new vault process.

$cat /proc/$(pidof vault)/limits

One of the struggles developers face is how to catch all Python exceptions. Developers often categorize exceptions as coding mistakes that lead to errors when running the program. Some developers still fail to distinguish between errors and exceptions.

In the case of Python application development, a python program terminates as soon as it encounters an unhandled error. So, to establish the difference between errors and exceptions, there are two types of errors:

  • Syntax errors
  • Logical errors (Exceptions)

First, let’s examine the syntax errors. Python syntax errors are caused by not following the proper structure (syntax) of the language. It is also known as a parsing error.

Here’s an example:

>>> ages = {

    ‘jj’: 2,

    ‘yoyo’: 4

}

print(f’JJ is {ages[“jj”]} years old.’)

The output:

JJ is 2 years old.

This is a simple code with no syntax error. Then we will add another variable tomtom:

>>> ages = {

    ‘jj’: 2,

    ‘yoyo’: 4

    ‘tomtom’: 6

}

print(f’JJ is {ages[“jj”]} years old.’)

Upon inspection, you can see an invalid syntax on the second entry, yoyo, inside the array with a missing comma. Try to run this code, and you will get a traceback:

File “<pyshell>”, line 1

    >>> ages = {

     ^

SyntaxError: invalid syntax

As you notice, the traceback message didn’t pinpoint the exact line where the syntax error occurs. The Python interpreter only attempts to locate where the invalid syntax is. It only points to where it first noticed the problem. So, when you get a SyntaxError traceback, it means that you should visually inspect whether the interpreter is pointing to the right error.

In the example above, the interpreter encounters an invalid syntax inside an array called ages. Thus, it points out that there is something wrong inside the array.

This is a syntax error. 

In most cases, a Python developer writes an impressive piece of code that is ready to execute. The program becomes a robust machine learning model, but during execution, Python throws up an unexpected error. Unfortunately, it is no longer the typical syntax error. Developers are now dealing with logical errors, also known as exceptions.

Let’s delve into exceptions.


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Exceptions in Python

Exceptions are errors that occur at runtime. Mostly, these errors are logical. For example, when you try to divide a number by zero, you will get a ZeroDivisionError. When you open a file(read) that doesn’t exist, you will receive FileNotFoundError. Further, when you try to import a module that doesn’t exist, you will get ImportError.  

Here is how Python treats the above-mentioned errors:

ZeroDivisionError

>>> 2 / 0

Traceback (most recent call last):

  File “<pyshell>”, line 1, in <module>

ZeroDivisionError: division by zero

FileNotFoundError

>>> open(“stack.txt”)

Traceback (most recent call last):

  File “<pyshell>”, line 1, in <module>

FileNotFoundError: [Errno 2] No such file or directory: ‘stack.txt’

ImportError

>>> from collections import qwerty

Traceback (most recent call last):

  File “<pyshell>”, line 1, in <module>

ImportError: cannot import name ‘qwerty’ 

Whenever a runtime error occurs, Python creates an exception object. It then creates and prints a traceback of that error with details on why the error happened.

Here are common exceptions in Python:

  • IndexError – You will get this error when an index is not found in a sequence. For instance, accessing the 6th index when the length of the list is only five(5). 
  • IndentationError – Happens when indentation is not specified correctly.
  • ValueError – Occurs when the built-in function for a data type has the valid type of arguments, but the arguments have invalid values. 
  • IOError – Developers often encounter this error when an input/output operation fails.
  • Arithmetic Error – Occurs when numeric calculations fail.
  • Floating-point Error – Happens when a floating-point calculation fails.
  • Assertion Error – Occurs when there is assert statement failure.
  • Overflow Error – Developers get this error if the result of an arithmetic operation is too large and becomes machine unreadable. 
  • Type Error – Happens when an incorrect type of function or operation is applied to an object.

You can visit the official Python documentation site to have in-depth knowledge about Python built-in exceptions. While the Python community offers great support, Python application deployment discussions can be improved. Application Performance Management (APM) like Retrace, is a great tool to deal with Python exceptions. It helps you find all the exceptions thrown and identify its root cause. You can try it for free today!

Catching Exceptions in Python

A direct logic is followed to catch exceptions in Python. When an exception occurs, the Python interpreter stops the current process. It is handled by passing through the calling process. If not, the program will crash.

For instance, a Python program has a function X that calls function Y, which in turn calls function Z. If an exception exists in function Z but is not handled within Z, the exception passes to Y and then to X. Simply, it will have a domino effect. 

An unhandled exception displays an error message and the program suddenly crashes. To avoid such a scenario, there are two methods to handle Python exceptions:

  1. Try – This method catches the exceptions raised by the program
  2. Raise – Triggers an exception manually using custom exceptions

Let’s start with the try statement to handle exceptions. Place the critical operation that can raise an exception inside the try clause. On the other hand, place the code that handles the exceptions in the except clause.

Developers may choose what operations to perform once it catches these exceptions. Take a look at the sample code below:

import sys

list = [‘x’, 1e-15, 5]

for result in list:

    try:

        print(“The result is”, result)

        y = 1/int(result)

        break

    except:

        print(“Whew!”, sys.exc_info()[0], “occurred.”)

        print(“Next input please.”)

        print()

print(“The answer of”, result, “is”, y)

The program has an array called list with three elements. Next, the line that causes an exception is placed inside the try block. If there are no exceptions, the except block will skip, and the logic flow will continue until the last element. However, if an exception occurs, the except block will catch it. 

The output:

The result is x

Whew! <class ‘ValueError’> occurred.

Next input, please.

The result is 1e-15

Whew! <class ‘ZeroDivisionError’> occurred.

Next input, please.

The result is 5

The answer to 5 is 0.2

To print the name of the exception, we use the exc_info()function inside the sys module. The first two elements cause exceptions since an integer can’t be divided with string and a zero value. Hence, it raised ValueError and ZeroDivisionError exceptions.

Catching Specific Exceptions in Python

What if you want to deal with a specific exception? In the previous example, it didn’t mention any specific exception in the except block. That is not a good programming practice because it will catch all exceptions. 

Additionally, it will handle all exceptions in the same manner, which is an incorrect approach. Therefore, maximize the use of the except block and specify which exceptions it should catch.

To execute this concept, we can use a tuple of values to specify multiple exceptions. Here is a sample pseudo-code:

try:

   #do something

   #your statements

   pass

except Exception_1:

   #handle Exception_1 and execute this block statement

   pass

except Exception_2:

   handle Exception_2 and execute this block statement

   pass

except Exception_3:

   #handle Exception_3 and execute this block statement

   pass

except:

   #handles all other exceptions

   pass

Try Stackify’s free code profiler, Prefix, to write better code on your workstation. Prefix works with .NET, Java, PHP, Node.js, Ruby, and Python.

Raising Exceptions in Python

Another way to catch all Python exceptions when it occurs during runtime is to use the raise keyword. It is a manual process wherein you can optionally pass values to the exception to clarify the reason why it was raised.

>>> raise IndexError

Traceback (most recent call last):

  File “<pyshell>”, line 1, in <module>

IndexError

>>> raise OverflowError(“Arithmetic operation is too large”)

Traceback (most recent call last):

  File “<pyshell>”, line 1, in <module>

OverflowError: Arithmetic operation is too large

Let’s have a simple code to illustrate how raise keyword works:

try:

     x = int(input(“Enter a positive integer: “))

     if x <= 0:

         raise ValueError(“It is not a positive number!”)

except ValueError as val_e:

    print(val_e)

The output:

Enter a positive integer: -6

It is not a positive number!

Enter a positive integer: 6

Python try with else clause

In dealing with exceptions, there are instances that a certain block of code inside try ran without any errors. As such, use the optional else keyword with the try statement.

Here is a sample code:

try:

    number = int(input(“Enter a number: “))

    assert number % 2 == 0

except:

    print(“This is an odd number!”)

else:

    print(“This is an even number!”)

    rem = number % 2

    print(“The remainder is “, rem)

The output:

Enter a number: 3

This is an odd number!

Enter a number: 2

This is an even number!

The remainder is 0

Python try 

The try statement in Python has an optional finally block. It means that it executes the block by all means. At the same time, it releases external resources. 

Some examples include a connection between a mobile app and a remote data center via a distributed network. All resources should be clean once the program stops, whether it successfully runs or not. 

It is the job of the finally block to guarantee execution. Such actions include closing a file or GUI  and disconnecting from the network.

Check on the example below to illustrate how the operation works:

try:

   file = open(“myFile.txt”,encoding = ‘utf-8’)

   # this performs file operations

finally:

   f.close()

The f.close()function ensures that the file is closed even if an exception happens during the program execution.

Advantages of Exception Handling and Using the right APM

Bugs and errors are part of every developer’s journey. As mentioned above, errors and exceptions are different. So, why is it that Python developers should know how to catch Python exceptions and master exception handling? 

The answers to this are not theoretical. Instead, let’s have a sample use case.

For example, you’re dealing with a vast amount of data. So, you build a program that reads thousands of files across multiple directories. There is no way that you will not encounter an error here. Possible errors may include wrong or missing file type, invalid file format, or dealing with different file extensions. 

On that note, it is not feasible to open all the files and write a script that can cater to it accordingly. With exception handling, developers can define multiple conditions. For instance, resolve an invalid format by establishing the correct format first and then reading the file afterward. 

Another option is to continue reading the data and skip those files with an invalid format.  Then, create a log file that can deal with it later.  To help you with log files, APMs like Retrace is a great tool in dealing with logs. It has logging features and actionable insights to support you with your Python applications.

About Iryne Somera

Iryne Somera is a professor in the Department of Computer Engineering. She loves to research and write articles related to computing technology such as Computer Hardware Fundamentals, Management Information Systems, Software Development and Project Management. In her spare time, she loves to experiment in her kitchen with her healthy breakfast ideas.

[core] "Windows fatal exception: access violation" cluttering terminal #13511

any idea how to solve this?
I have similar problems when I use deap package
the code seems to run fine but it keeps yelled "fatal" exception
and it seems to been printed out, not a real exception

(pid=31996) Windows fatal exception: access violation
(pid=31996)
(pid=21820) Windows fatal exception: access violation
(pid=21820)
(pid=31372) Windows fatal exception: access violation
(pid=31372)
(pid=24640) Windows fatal exception: access violation
(pid=24640)
(pid=31380) Windows fatal exception: access violation
(pid=31380)
(pid=15396) Windows fatal exception: access violation
(pid=15396)
(pid=21660) Windows fatal exception: access violation
(pid=21660)
(pid=21976) Windows fatal exception: access violation
(pid=21976)
(pid=29076) Windows fatal exception: access violation
(pid=29076)
(pid=32212) Windows fatal exception: access violation
(pid=32212)
(pid=25964) Windows fatal exception: access violation
(pid=25964)
(pid=17224) Windows fatal exception: access violation
(pid=17224)
(pid=31964) Windows fatal exception: access violation
(pid=31964)
(pid=25632) Windows fatal exception: access violation
(pid=25632)
(pid=27112) Windows fatal exception: access violation
(pid=27112)
(pid=32620) Windows fatal exception: access violation

And then at some point, it will crash with

2021-02-05 17:24:29,648 WARNING worker.py:1034 -- The log monitor on node DESKTOP-QJDSQ0R failed with the following error:
OSError: [WinError 87] 參數錯誤。

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "C:\Users\eiahb.conda\envs\env_genetic_programming\lib\site-packages\ray\log_monitor.py", line 354, in
log_monitor.run()
File "C:\Users\eiahb.conda\envs\env_genetic_programming\lib\site-packages\ray\log_monitor.py", line 275, in run
self.open_closed_files()
File "C:\Users\eiahb.conda\envs\env_genetic_programming\lib\site-packages\ray\log_monitor.py", line 164, in open_closed_files
self.close_all_files()
File "C:\Users\eiahb.conda\envs\env_genetic_programming\lib\site-packages\ray\log_monitor.py", line 102, in close_all_files
os.kill(file_info.worker_pid, 0)
SystemError: returned a result with an error set

forrtl: error (200): program aborting due to control-C event
Image PC Routine Line Source
libifcoremd.dll 00007FFDC0AE3B58 Unknown Unknown Unknown
KERNELBASE.dll 00007FFE221862A3 Unknown Unknown Unknown
KERNEL32.DLL 00007FFE24217C24 Unknown Unknown Unknown
ntdll.dll 00007FFE2470D4D1 Unknown Unknown Unknown
Windows fatal exception: access violation

please do help

Installing an OpenStreetMap Tile Server on Ubuntu

Last modification:unknown

Introduction

This page shows how OpenStreetMap Carto can be used to implement a tile server using the same software adopted by OpenStreetMap. It includes step-by-step instructions to install an Ubuntu based Tile Server and is limited to describe some best practices, in the consideration that the main scope of this site is to provide tutorials to set-up a development environment of OpenStreetMap Carto and offer recommendations to edit the style.

The OSM Tile Server is a web server specialized in delivering raster maps, serving them as static tiles and able to perform rendering in real time or providing cached images. The adopted web software by OpenStreetMap is the Apache HTTP Server, together with a specific plugin named mod_tile and a related backend stack able to generate tiles at run time; programs and libraries are chained together to create the tile server.

As so often with OpenStreetMap, there are many ways to achieve a goal and nearly all of the components have alternatives that have various specific advantages and disadvantages. This tutorial describes the standard installation process of the OSM Tile Server used on OpenStreetMap.org.

It consists of the following main components:

  • Mapnik
  • Apache
  • Mod_tile
  • renderd
  • osm2pgsql
  • PostgreSQL/PostGIS database, to be installed locally (suggested) or remotely (might be slow, depending on the network).
  • carto
  • openstreetmap-carto

All mentioned software is open-source.

For the tile server, a PostGIS database is required, storing geospatial features populated by osm2pgsql tool from OSM data. Also, a file system directory including the OSM.xml file, map symbols (check openstreetmap-carto/symbols subdirectory) and shapefiles (check openstreetmap-carto/data subdirectory) is needed. OSM.xml is preliminarily produced by a tool named carto from the openstreetmap-carto style (project.mml and all related CartoCSS files included in openstreetmap-carto).

When the Apache web server receives a request from the browser, it invokes the mod_tile plugin, which in turn checks if the tile has already been created (from a previous rendering) and cached, so that it is ready for use; in case, mod_tile immediately sends the tile back to the web server. Conversely, if the request needs to be rendered, then it is queued to the renderd backend, which is responsible to invoke Mapnik to perform the actual rendering; renderd is a daemon process included in the mod_tile sources and interconnected to mod_tile via UNIX queues. renderd is the standard backend currently used by www.openstreetmap.org, even if some OSM implementations use Tirex; Mapnik extracts data from the PostGIS database according to the openstreetmap-carto style information and dynamically renders the tile. renderd passes back the produced tile to the web server and in turn to the browser.

The renderd daemon implements a queuing mechanism with multiple priority levels to provide an as up-to-date viewing experience given the available rendering resources. The highest priority is for on -the fly rendering of tiles not yet in the tile cache, two priority levels for re-rendering out of date tiles on the fly and two background batch rendering queues. To avoid problems with directories becoming too large and to avoid too many tiny files, Mod_tile/renderd store the rendered tiles in “meta tiles”, in a special hashed directory structure.1

Even if the tileserver dynamically generates tiles at run time, they can also be pre-rendered for offline viewing with a specific tool named render_list, which is typically used to pre-render low zoom level tiles and takes significant time to accomplish the process (tens of hours in case the full planet is pre-rendered); this utility is included in mod_tile, as well as another tool named render_expired, which provides methods to allow expiring map tiles. More detailed description of render_list and render_expired can be found in their man pages.

A background on the tiles expiry method can be found at tiles expiry mechanism.

The overall process is here represented2.

An additional description of the rendering process of OpenStreetMap can be found at OSM architecture.

The following step-by-step procedure can be used to install and configure all the necessary software to operate your own OpenStreetMap tile server on Ubuntu.3

The goal for this procedure is to use Ubuntu packages and official PPAs whenever possible.

We consider using Ubuntu 20.04.2 LTS Focal Fossa, or 18.04 LTS Bionic Beaver, suggested operating system version.

Other tested O.S. include Ubuntu 16.04 LTS Xenial Xerus, Ubuntu 15.4 Vivid Vervet or Ubuntu 14.04.3 LTS Trusty Tahr (other versions should work). All should be 64-bit computing architecture. Other distributions like Debian might be checked, but could require changes to the installation procedure.

This procedure is updated to the version of OpenStreetMap Carto available at the time of writing. To get the correct installation procedure, the INSTALL history should be checked, considering that the OpenStreetMap Carto maintainers use to keep the INSTALL page updated. Check also the README changelog.

General setup for Ubuntu

Install Ubuntu.

This procedure also supports WSL - Windows Subsystem for Linux. This means that a Windows 10 64-bit PC can be used to perform the installation, after setting-up WSL.

Update Ubuntu

Make sure your Ubuntu system is fully up-to-date:

Previous command returns the Ubuntu version.

To update the system:

If on a brand new system you also want to do .

Install essential tools

Optional elements:

Check prerequisites suggested by openstreetmap-carto.

For the subsequent installation steps, we suppose that defaults to your home directory.

Configure a swap

Importing and managing map data takes a lot of RAM and a swap is generally needed.

To check whether a swap partition is already configured on your system, use one of the following two commands:

  • Reports the swap usage summary (no output means missing swap):

  • Display amount of free and used memory in the system (check the line specifying Swap):

If you do not have an active swap partition, especially if your physical memory is small, you should add a swap file. First we use command to create a file. For example, create a file named swapfile with 2G capacity in root file system:

Then make sure only root can read and write to it.

Format it to swap:

Enable the swap file

The Operating System tuning adopted by the OpenStreetMap tile servers can be found in the related Chef configuration.

Check usage of English locale

Run locale to list what locales are currently defined for the current user account:

To set the en_GB locale:

The exported variables can be put to the file .

New locales can also be generated by issuing:

Creating a UNIX user

We suppose that you have already created a login user during the installation of Ubuntu, to be used to run the tile server. Let’s suppose that your selected user name is tileserver. Within this document, all times tileserver is mentioned, change it with your actual user name.

If you need to create a new user:

Set a password when prompted.

Install Git

Git might come already preinstalled sometimes.

Install Mapnik library

We need to install the Mapnik library. Mapnik is used to render the OpenStreetMap data into the tiles managed by the Apache web server through renderd and mod_tile.

With Ubuntu 20.04 LTS, go to mapnik installation.

FreeType dependency in Ubuntu 16.04 LTS

With Ubuntu 18.04 LTS, which installs FreeType 2.8.1, skip this paragraph and continue with installing Mapnik.

Mapnik depends on FreeType for TrueType, Type 1, and OpenType font support. With Ubuntu 16.04 LTS, the installed version of FreeType is 2.6.1 which has the stem darkening turned on and this makes NotoCJK fonts bolder and over-emphasized. Installing a newer version of FreeType from a separate PPA, overriding the default one included in Ubuntu 16.04 LTS, solves this issue4:

Check the updated freetype version:

In case you need to downgrade the FreeType to the stock version in Ubuntu 16.04 repository, simply purge the PPA via ppa-purge:

We report some alternative procedures to install Mapnik (in the consideration to run an updated version of Ubuntu).

With Ubuntu versions older than 18.04 LTS, the default Mapnik version is older than the minumum one required, which is 3.0.19. Anyway, a specific PPA made by talaj offers the packaged version 3.0.19 of Mapnik for Ubuntu 16.04 LTS Xenial.

Ubuntu 18.04 LTS provides Mapnik 3.0.19 and does not need a specific PPA.

Install Mapnik library from package

The following command installs Mapnik from the standard Ubuntu repository:

With Ubuntu 18.04 LTS, you might use python-mapnik instead of python3-mapnik.

Launchpad reports the Mapnik version installed from package depending on the operating system; the newer the OS, the higher the Mapnik release.

GitHub reports the ordered list of available versions for:

Version 3.0.19 is the minimum one suggested at the moment.5 If using the above mentioned PPA, that version comes installed instead of the default one available with Ubuntu.

After installing Mapnik from package, go to check Mapnik installation.

Alternatively, install Mapnik from sources

To install Mapnik from sources, follow the Mapnik installation page for Ubuntu.

First create a directory to load the sources:

Note: if you get the following error: , use this explort instead of the one included in the linked documentation:

Refer to Mapnik Releases for the latest version and changelog.

Remove any other old Mapnik packages:

Install prerequisites:

Check and before upgrading the compiler. As mentioned, installing gcc-6 and clang-3.8 should only be done with Ubuntu 16.04, which by default comes with older versions (not with Ubuntu 18.04).

We need to install Boost either from package or from source.

Install Boost from package

Do not install boost from package if you plan to compile mapnik with an updated compiler. Compile instead boost with the same updated compiler.

Alternatively, install the latest version of Boost from source

Remove a previous installation of boost from package:

Download boost from source:

Notice that boost and mapnik shall be compiled with the same compiler. With Ubuntu 16.04 and gcc-6, g++-6, clang-3.8 you should use these commands:

With Ubuntu 18.04 or Ubuntu 16.04 using the default compiler, the compilation procedure is the following:

Do not try compiling mapnik with an updated compiler if boost is installed from package.

Install HarfBuzz from package

HarfBuzz is an OpenType text shaping engine.

It might be installed from package, but better is downloading a more updated source version, compiling it. To install from package:

Install HarfBuzz from source

Check the lastest version here. This example grubs harfbuzz-1.7.6:

Build the Mapnik library from source

At the time of writing, Mapnik 3.0 is the current stable release and shall be used. The branch for the latest Mapnik from 3.0.x series is v3.0.x.6

Download the latest sources of Mapnik:

After Mapnik is successfully compiled, use the following command to install it to your system:

Python bindings are not included by default. You’ll need to add those separately.

  • Install prerequisites:

    Only in case you installed boost from package, you also need:

    Do not peform the above libboost-python-dev installation with boost compiled from source.

    Set BOOST variables if you installed boost from sources:

  • Download and compile python-mapnik. We still use v3.0.x branch:

    Note: Mapnik and (part of Mapnik) need to be installed prior to this setup.

You can then verify that Mapnik has been correctly installed.

Verify that Mapnik has been correctly installed

Report Mapnik version number and provide the path of the input plugins directory7:

Verify that Python is installed. Also verify that pip is installed.

Check then with Python 3:

If python 2.7 is used (not Ubuntu 20.04 LTS), use this command to check:

It should return the path to the python bindings (e.g., ). If python replies without errors, then Mapnik library was found by Python.

Configure the firewall

If you are preparing a remote virtual machine, configure the firewall to allow remote access to the local port 80 and local port 443.

If you run a cloud based VM, also the VM itself shall be set to open this port.

Install Apache HTTP Server

The Apache free open source HTTP Server is among the most popular web servers in the world. It’s well-documented, and has been in wide use for much of the history of the web, which makes it a great default choice for hosting a website.

To install apache:

The Apache service can be started with

Error “Failed to enable APR_TCP_DEFER_ACCEPT” with Ubuntu on Windows is due to this socket option which is not natively supported by Windows. To overcome it, edit /etc/apache2/apache2.conf with

and add the following line to the end of the file:

To check if Apache is installed, direct your browser to the IP address of your server (eg. http://localhost). The page should display the default Apache home page. Also this command allows checking correct working:

The Apache tuning adopted by the OpenStreetMap tile servers can be found in the related Chef configuration.

How to Find the IP address of your server

You can run the following command to reveal the public IP address of your server:

You can test Apache by accessing it through a browser at http://your-server-ip.

Install Mod_tile from package

Mod_tile is an Apache module to efficiently render and serve map tiles for www.openstreetmap.org map using Mapnik.

Mod_tile/renderd for Ubuntu 18.04 and Ubuntu 20.04

With Ubuntu 18.04 (bionic) and Ubuntu 20.04 (focal), mod_tile/renderd can be installed by adding the OpenStreetMap PPA maintained by the “OpenStreetMap Administrators” team:

Also the above mentioned talaj PPA is suitable.

After adding the PPA, mod_tile/renderd can be installed from package through the following command:

Mod_tile/renderd for Ubuntu 21.04

On Ubuntu 21.04 (hirsute) the package is available and can be installed with

Install Mod_tile from source

Alternatively to installing Mod_tile via PPA, we can compile it from its GitHub repository.

To remove the previously installed PPA and related packages:

To compile Mod_tile:

Check also https://github.com/openstreetmap/mod_tile/blob/master/docs/build/building_on_ubuntu_20_04.md

The rendering process implemented by mod_tile and renderd is well explained in the related GitHub readme.

Python installation

Check that Python is installed:

Install Yaml and Package Manager for Python

This is necessary in order to run OpenStreetMap-Carto scripts/indexes.

Install Mapnik Utilities

The Mapnik Utilities package includes shapeindex.

Install openstreetmap-carto

Read installation notes for further information.

Install the fonts needed by openstreetmap-carto

Currently Noto fonts are used.

To install them (except Noto Emoji Regular and Noto Sans Arabic UI Regular/Bold):

Installation of Noto fonts (hinted ones should be used if available8):

At the end:

DejaVu Sans is used as an optional fallback font for systems without Noto Sans. If all the Noto fonts are installed, it should never be used.

Read font notes for further information.

Old unifont Medium font

The unifont Medium font (lowercase label), which was included in past OS versions, now is no more available and substituted by Unifont Medium (uppercase). Warnings related to the unavailability of unifont Medium are not relevant9 and are due to the old decision of OpenStreetMap maintainers to support both the past Ubuntu 12.04 font and the newer version (uppercase).

One way to avoid the warning is removing the reference to “unifont Medium” in openstreetmap-carto/style.xml.

Another alternative way to remove the lowercase unifont Medium warning is installing the old “unifont Medium” font (used by Ubuntu 12.10):

Notice that above installation operation is useless, just removes the warning.

Install Node.js

Install Node.js with Ubuntu 20.04 LTS:

Go to Check Node.js versions.

Additional notes on Node.js: other modes to install it:

A list of useful commands to manage Node.js is available at a specific page.

The above reported Node.js version also supports installing TileMill and Carto.

Distro version from the APT package manager

The recent versions of Ubuntu come with Node.js (nodejs package) and npm (npm package) in the default repositories. Depending on which Ubuntu version you’re running, those packages may contain outdated releases; the one coming with Ubuntu 16.04 will not be the latest, but it should be stable and sufficient to run Kosmtik and Carto. TileMill instead needs nodejs-legacy (or an old version of node installed via a Node.js version management tool).

For carto we will install nodejs:

Install Node.js through a version management tool

Alternatively, a suggested approach is using a Node.js version management tool, which simplifies the interactive management of different Node.js versions and allows performing the upgrade to the latest one. We will use n.

Install n:

Some programs (like Kosmtik and carto) accept the latest LTS node version (), other ones (like Tilemill) run with v6.14.1 ().

For carto we will install the latest LTS one:

Check Node.js versions

To get the installed version numbers:

Install carto and build the Mapnik XML stylesheet

Carto is the stylesheet compiler translating CartoCSS projects into Mapnik XML stylesheet.

According to the current openstreetmap-carto documentation, the minimum carto (CartoCSS) version that can be installed is 0.18. As carto compiles the openstreetmap-carto stilesheets, keeping the same version as in openstreetmap-carto documentation is recommended (instead of simply installing the latest carto release).

The latest carto version 1.2.0 can be installed with

This works with Ubuntu 20.04 LTS.

Up to Ubuntu 18.04 LTS, this version produces warnings like “Styles do not match layer selector .text-low-zoom”.

To avoid these warning, install the version 0 of carto:

It should be carto 0.18.2 at the time of writing.

In case the installation fails, this is possibly due to some incompatibility with npm/Node.js; to fix this, try downgrading the Node.js version.

To check the installed verison:

When running carto, you need to specify the Mapnik API version through the option. For the version to adopt, the openstreetmap-carto documentation offers some recommendations.

To list all the known API versions in your installed node software, run the following command:

Specifications for each API version are also documented within the carto repository.

You should use the closest API version to your installed Mapnik version (check with ).

Test carto and produce style.xml from the openstreetmap-carto style:

When selecting the appropriate API version, you should not get any relevant warning message.

The command might install an old carto version, not compatible with Openstreetmap Carto, and should be avoided.

Install PostgreSQL and PostGIS

PostgreSQL is a relational database, and PostGIS is its spatial extender, which allows you to store geographic objects like map data in it; it serves a similar function to ESRI’s SDE or Oracle’s Spatial extension. PostgreSQL + PostGIS are used for a wide variety of features such as rendering maps, geocoding, and analysis.

Currently the tested versions for OpenstreetMap Carto are PostgreSQL 10 and PostGIS 2.4:

Also older or newer PostgreSQL version should be suitable.

On Ubuntu there are pre-packaged versions of both postgis and postgresql, so these can simply be installed via the Ubuntu package manager.

Optional components:

You need to start the db:

Note: used PostgreSQL port is 5432 (default).

Create the PostGIS instance

Now you need to create a PostGIS database. The defaults of various programs including openstreetmap-carto (ref. project.mml) assume the database is called gis. You need to create a PostgreSQL database and set up a PostGIS extension on it.

The character encoding scheme to be used in the database is UTF8 and the adopted collation is en_GB.utf8. (The escaped Unicode syntax used in project.mml should work only when the server encoding is UTF8. This is also in line with what reported in the PostgreSQL Chef configuration code.)

Note: means that en_GB.UTF-8 locale has not been installed. After installing locale, the database shall be restarted in order to be able to load the locale.

Go to the next step.

If in different host:

Set the environment variables

If you get the following error:

then you need to add ‘en_GB.utf8’ locale using the following command:

And select “en_GB.UTF-8 UTF-8” in the first screen (“Locales to be generated”). Subsequently, restarting the db would be suggested:

If you get the following error:

you need to use template0 for gis:

If you get the following error:

(error generally happening with Ubuntu on Windows with WSL), then add also ; e.g., use the following command:

Check to create the DB within a disk partition where enough disk space is available10. If you need to use a different tablespace than the default one, execute the following commands instead of the previous ones (example: the tablespace has location ):

Create the postgis and hstore extensions:

If you get the following error

then you might be installing PostgreSQL 9.3 (instead of 9.5), for which you should also need:

Install it and repeat the create extension commands. Notice that PostgreSQL 9.3 is not currently supported by openstreetmap-carto.

Add a user and grant access to gis DB

In order for the application to access the gis database, a DB user with the same name of your UNIX user is needed. Let’s suppose your UNIX ue is tileserver.

Enabling remote access to PostgreSQL

If in different host, to remotely access PostgreSQL, you need to edit pg_hba.conf:

and add the following line:

is an access control rule that let anybody login in from any address if providing a valid password (md5 keyword).

Then edit postgresql.conf:

and set

Finally, the DB shall be restarted:

Check that the gis database is available. To list all databases defined in PostgreSQL, issue the following command:

The obtained report should include the gis database, as in the following table:

NameOwnerEncodingCollateCtypeAccess privileges
gispostgresUTF8en_US.utf8en_US.utf8=Tc/postgres
postgres=CTc/postgres
tileserver=CTc/postgres

Tuning the database

The default PostgreSQL settings aren’t great for very large databases like OSM databases. Proper tuning can just about double the performance.

Minimum tuning requirements

Set the postgres user to trust:

After performing the above change, restart the DB:

Run tune-postgis.sh:

Whitout setting postgres to trust, the following error occurs: when running tune-postgis.sh.

To cleanup the data directory and redo again tune-postgis.sh: .

Optional further tuning requirements

The PostgreSQL wiki has a page on database tuning.

Paul Norman’s Blog has an interesting note on optimizing the database, which is used here below.

Default and settings are far too low for rendering.11: both parameters should be increased for faster data loading and faster queries (index scanning).

Conservative settings for a 4GB VM are and . On a machine with enough memory you could set them as high as and .

Besides, important settings are and the write-ahead-log (wal). There are also some other settings you might want to change specifically for the import.

To edit the PostgreSQL configuration file with vi editor:

and if you are running PostgreSQL 9.3 (not supported):

Suggested minimum settings:

The latter two ones allow a faster import: the first turns off auto-vacuum during the import and allows you to run a vacuum at the end; the second introduces data corruption in case of a power outage and is dangerous. If you have a power outage while importing the data, you will have to drop the data from the database and re-import, but it’s faster. Just remember to change these settings back after importing. fsync has no effect on query times once the data is loaded.

The PostgreSQL tuning adopted by OpenStreetMap can be found in the PostgreSQL Chef Cookbook: the specific PostgreSQL tuning for the OpenStreetMap tile servers is reported in the related Tileserver Chef configuration.

For a dev&test installation on a system with 16GB of RAM, the suggested settings are the following12:

default_statistics_target can be even increased to 10000.

If performing database updates, run ANALYZE periodically.

To stop and start the database:

You may get an error and need to increase the shared memory size. Edit /etc/sysctl.d/30-postgresql-shm.conf and run . A parameter like and could be appropriate for a 16GB segment size.13

To manage and maintain the configuration of the servers run by OpenStreetMap, the Chef configuration management tool is used.

The configuration adopted for PostgreSQL is postgresql/attributes/default.rb.

Install Osm2pgsql

Osm2pgsql is an OpenStreetMap specific software used to load the OSM data into the PostGIS database.

The default packaged versions of Osm2pgsql are 0.88.1-1 on Ubuntu 16.04 LTS and 0.96.0 on Ubuntu 18.04 LTS. Nevertheless, more recent versions are suggested, available at the OpenStreetMap Osmadmins PPA or compiling the software from sources.

To install osm2pgsql:

To install Osm2pgsql from Osmadmins PPA:

Go to Get an OpenStreetMap data extract.

Generate Osm2pgsql from sources

This alternative installation procedure generates the most updated executable by compiling the sources.

Install Needed dependencies:

Download osm2pgsql:

Prepare for compiling, compile and install:

You need to download an appropriate .osm or .pbf file to be subsequently loaded into the previously created PostGIS instance via .

There are many ways to download the OSM data.

The reference is Planet OSM.

It’s probably easiest to grab an PBF of OSM data from geofabrik.

Also, BBBike.org provides extracts of more than 200 cities and regions world-wide in different formats.

Examples:

  • Map data of the whole planet (32G):

  • Map data of Great Britain (847M):

  • For just Liechtenstein:

Another method to download data is directly with your browser. Check this page.

Alternatively, JOSM can be used (Select the area to download the OSM data: JOSM menu, File, Download From OSM; tab Slippy map; drag the map with the right mouse button, zoom with the mouse wheel or Ctrl + arrow keys; drag a box with the left mouse button to select an area to download. The Continuous Download plugin is also suggested. When the desired region is locally available, select File, Save As, . Give it a valid file name and check also the appropriate directory where this file is saved.

In all cases, avoid using too small areas.

OpenStreetMap is open data. OSM’s license is Open Database License.

Load data to PostGIS

The osm2pgsql documentation reports all needed information to use this ETL tool, including related command line options.

osm2pgsql uses overcommit like many scientific and large data applications, which requires adjusting a kernel setting:

To load data from an .osm or .pbf file to PostGIS, issue the following:

: substitute this with your already downloaded .osm or .pbf file, like, e.g., liechtenstein-latest.osm.pbf.

With available memory, set ; it allocates 2.5 GB of memory to the import process.

Option loads data into an empty database rather than trying to append to an existing one.

Relaying to OSM2PGSQL_NUMPROC, if you have more cores available, you can set it accordingly.

The osm2pgsql manual describes usage and all options in detail.

Go to the next step.

If using a different server:

Notice that the suggested process adopts the ( option), which uses temporary tables, so running it takes more diskspace (and is very slow), while less RAM memory is used. You might add option with (), to also drop temporary tables after import, otherwise you will also find the temporary tables nodes, ways, and rels (these tables started out as pure “helper” tables for memory-poor systems, but today they are widely used because they are also a prerequisite for updates).

If everything is ok, you can go to the next step.

Notice that the following elements are used:

  • hstore
  • the openstreetmap-carto.style
  • the openstreetmap-carto.lua LUA script
  • gis DB name

Depending on the input file size, the osm2pgsql command might take very long. An interesting page related to Osm2pgsql benchmarks associates sizing of hw/sw systems with related figures to import OpenStreetMap data.

Note: if you get the following error:

do the following command on your original.osm:

Then process fixedfile.osm.

If you get errors like this one:

or this one:

then you need to enable hstore extension to the db with and also add the –hstore flag to osm2pgsql. Enabling hstore extension and using it with osm2pgsql will fix those errors.

Create the data folder

At least 18 GB HD and appropriate RAM/swap is needed for this step (24 GB HD is better). 8 GB HD will not be enough. With 1 GB RAM, configuring a swap is mandatory.

To cleanup the get-external-data.py procedure and restart from scratch, remove the data directory ().

Configure a swap to prevent the following message:

The way shapefiles are loaded by the OpenStreetMap tile servers is reported in the related Chef configuration.

Read scripted download for further information.

Create indexes and grant users

Create partial indexes to speed up the queries included in project.mml and grant access to all gis tables to avoid renderd errors when accessing tables with user tileserver.

  • Add the partial geometry indexes indicated by openstreetmap-carto14 to provide effective improvement to the queries:

    Alternative mode:

    If using a different host:

    Alternative mode with a different host:

  • Create PostgreSQL user “tileserver” (if not yet existing) and grant rights to all gis db tables for “tileserver” user and for all logged users:

To list all tables available in the gis database, issue the following command:

or:

The database shall include the rels, ways and nodes tables (created with the mode of osm2pgsql) in order to allow updates.

In the following example of output, the mode of osm2pgsql was used:

SchemaNameTypeOwner
publicplanet_osm_linetablepostgres
publicplanet_osm_nodestablepostgres
publicplanet_osm_pointtablepostgres
publicplanet_osm_polygontablepostgres
publicplanet_osm_relstablepostgres
publicplanet_osm_roadstablepostgres
publicplanet_osm_waystablepostgres
publicspatial_ref_systablepostgres

In fact, the tables planet_osm_rels, planet_osm_ways, planet_osm_nodes are available, as described in the Database Layout of Pgsql.

Check The OpenStreetMap data model at Mapbox for further details.

Read custom indexes for further information.

Configure renderd

Next we need to plug renderd and mod_tile into the Apache webserver, ready to receive tile requests.

Get the Mapnik plugin directory:

It should be /usr/local/lib/mapnik/input, or /usr/lib/mapnik/3.0/input or another one.

Edit renderd configuration file with your preferite editor:

Note: when installing mod_tile from package, the pathname is /etc/renderd.conf.

In section, change the value of the plugins_dir parameter to reflect the one returned by :

Example (if installing Mapnik 3.0 from package):

With Mapnik 2.2 from package:

With Mapnik 3.0 from sources:

In section, also change the value of the following settings:

In the section, change the value of XML and HOST to the following.

Notice that URI shall be set to .

Also, substitute all with (e.g., with vi ).

We suppose in the above example that your home directory is /home/tileserver. Change it to your actual home directory.

Example of file:

Save the file.

Check the existence of the /var/run/renderd directory, otherwise create it with .

Check this to be sure:

In case of error, verify user name and check again .

Install renderd init script by copying the sample init script included in its package.

Note: when installing mod_tile from package, the above command is not needed.

Grant execute permission.

Note: when installing mod_tile from package, the above command is not needed.

Edit the init script file

Change the following variables:

Important note: when installing mod_tile from package, keep and .

In we suppose that your user is tileserver. Change it to your actual user name.

Save the file.

Create the following file and set tileserver (your actual user) the owner.

Note: when installing mod_tile from package, the above commands are not needed.

Again change it to your actual user name.

Then start renderd service

With WSL, renderd needs to be started with the following command:

The following output is regular:

If systemctl is not installed (e.g., Ubuntu 14.4) use these commands respectively:

Configure Apache

Create a module load file.

Paste the following line into the file.

Save it. Create a symlink.

Then edit the default virtual host file.

Past the following lines after the line

Note: when installing mod_tile from package, set .

Save and close the file.

Example:

Restart Apache.

If systemctl is not installed (e.g., Ubuntu 14.4):

With WSL, restart the Apache service with the following commands:

Test access to tiles locally:

You should get if everything is correctly configured.

Then in your web browser address bar, type

where you need to change your-server-ip with the actual IP address of the installed map server.

To expand it with the public IP address of your server, check this command for instance (paste its output to the browser):

You should see the tile of world map.

Congratulations! You just successfully built your own OSM tile server.

You can go to OpenLayers to display the slippy map.

Pre-rendering tiles

Pre-rendering tiles is generally not needed (or not wanted); its main usage is to allow offline viewing instead of rendering tiles on the fly. Depending on the DB size, the procedure can take very long time and relevant disk data.

To pre-render tiles, use render_list command. Pre-rendered tiles will be cached in directory.

To show all command line option of render_list:

Example usage:

Depending on the DB size, this command might take very long.

The following command pre-renders all tiles from zoom level 0 to zoom level 10 using 1 thread:

A command line Perl script named render_list_geo.pl and developed by alx77 allows automatic pre-rendering of tiles in a particular area using geographic coordinates. The related Github README describes usage and samples.

To install it:

Example of command to generate the z11 tiles for the UK:

For both render_list and render_list_geo.pl, option allows selecting specific profiles relates to named sections in renderd.conf. Not using this option, the section of renderd.conf is selected.

Troubleshooting Apache, mod_tile and renderd

To monitor the tile server, showing a line every time a tile is requested, and one every time related rendering is completed:

To clear all osm tiles cache, remove /var/lib/mod_tile/default (using rm -rf if you dare) and restart renderd daemon:

Remember to also clear the browser cache.

If systemctl is not installed (e.g., Ubuntu 14.4):

Show Apache loaded modules:

You should find

Show Apache configuration:

You should get the following messages within the log:

Tail log:

Most of the configuration issues can discovered by analyzing the debug log of renderd; we need to stop the daemon and start renderd in foreground:

If systemctl is not installed (e.g., Ubuntu 14.4):

Then control the renderd output:

Ignore the five errors related to when referring to commented out variables (e.g., beginning with ).

Press Control-C to kill the program. After fixing the error, the daemon can be restarted with:

If systemctl is not installed (e.g., Ubuntu 14.4):

Check existence of :

Verify that the access permission are . You can temporarily do

Check existence of the style.xml file:

If missing, see above to create it.

Check existence of :

Verify that the access permission are .

In case of wrong owner:

If the directory is missing:

In case renderd dies with a segmentation fault error (e.g., and then ), this might be probably due to a configuration mismatch between the Mapnik plugins and the renderd configuration; check plugins_dir parameter in /usr/local/etc/renderd.conf.

A PostGIS permission error means that the DB tables have not been granted for tileserver user:

To fix the permission error, run:

An error related to missing column in the renderd logs means that osm2pgsql was not run with the option.

If everything in the configuration looks fine, but the map is still not rendered without any particular message produced by renderd, try performing a system restart:

If the problem persists, you might have a problem with your UNIX user. Try debugging again, after setting these variables:

As exceptional case, the following commands allow to fully remove Apache, mod_tile and renderd and reinstall the service:

Tile names format of OpenStreetMap tile server

The file naming and image format used by mod_tile is described at Slippy map tilenames. Similar format is also used by Google Maps and many other map providers.

TMS and WMS are other protocols for serving maps as tiles, managed by different rendering backends.

Deploying your own Slippy Map

Tiled web map is also known as slippy map in OpenStreetMap terminology.

OpenStreetMap does not provide an “official” JavaScript library which you are required to use. Rather, you can use any library that meets your needs. The two most popular are OpenLayers and Leaflet. Both are open source.

Page Deploying your own Slippy Map illustrates how to embed the previously installed map server into a website. A number of possible map libraries are mentioned, including some relevant ones (Leaflet, OpenLayers, Google Maps API) as well as many alternatives.

OpenLayers

To display your slippy map with OpenLayers, create a file named ol.html under /var/www/html.

Paste the following HTML code into the file.

You might wish to adjust the longitude, latitude and zoom level according to your needs. Check .

Notice we are using https for openstreetmap.org.

Save and close the file. Now you can view your slippy map by typing the following URL in browser.

To expand it with the public IP address of your server, check this command for instance (paste its output to the browser):

Leaflet

Leaflet is a JavaScript library for embedding maps. It is simpler and smaller than OpenLayers.

The easiest example to display your slippy map with Leaflet consists in creating a file named lf.html under /var/www/html.

Paste the following HTML code in the file. Replace your-server-ip with your IP Address and adjust the longitude, latitude and zoom level according to your needs.

Save and close the file. Now you can view your slippy map by typing the following URL in the browser.

A rapid way to test the slippy map is through an online source code playground like this JSFiddle template.

The following example exploits Leaflet to show OpenStreetMap data.

Default tiles can be replaced with your tile server ones by changing

to .

To edit the sample, click on Edit in JSFiddle. Then in the Javascript panel modify the string inside quotes as descripted above. Press Run.

Troubleshoot issues with Launchpad service executing Python and R scripts in SQL Server Machine Learning Services

  • Article
  • 10 minutes to read

Applies to: SQL Server 2016 (13.x) and later

This article provides troubleshooting guidance for issues involving the SQL Server Launchpad service used with Machine Learning Services. The Launchpad service supports external script execution for R and Python. Multiple issues can prevent Launchpad from starting, including configuration problems or changes, or missing network protocols.

Determine whether Launchpad is running

  1. Open SQL Server Configuration Manager. From the command line, type SQLServerManager13.msc, SQLServerManager14.msc, or SQLServerManager15.msc.

  2. Make a note of the service account that Launchpad is running under. Each instance where R or Python is enabled should have its own instance of the Launchpad service. For example, the service for a named instance might be something like MSSQLLaunchpad$InstanceName.

  3. If the service is stopped, restart it. On restarting, if there are any issues with configuration, a message is published in the system event log, and the service is stopped again. Check the system event log for details about why the service stopped.

  4. Review the contents of RSetup.log, and make sure that there are no errors in the setup. For example, the message Exiting with code 0 indicates failure of the service to start.

  5. To look for other errors, review the contents of rlauncher.log.

Check the Launchpad service account

The default service account might be "NT Service$SQL2016", "NT Service$SQL2017", or "NT Service$SQL2019". The final part might vary, depending on your SQL instance name.

The Launchpad service (Launchpad.exe) runs by using a low-privilege service account. However, to start R and Python and communicate with the database instance, the Launchpad service account requires the following user rights:

  • Log on as a service (SeServiceLogonRight)
  • Replace a process-level token (SeAssignPrimaryTokenPrivilege)
  • Bypass traverse checking (SeChangeNotifyPrivilege)
  • Adjust memory quotas for a process (SeIncreaseQuotaSizePrivilege)

For information about these user rights, see the "Windows privileges and rights" section in Configure Windows service accounts and permissions.

Tip

If you are familiar with the use of the Support Diagnostics Platform (SDP) tool for SQL Server diagnostics, you can use SDP to review the output file with the name MachineName_UserRights.txt.

User group for Launchpad cannot log on locally

During setup of Machine Learning Services, SQL Server creates the Windows user group SQLRUserGroup and then provisions it with all rights necessary for Launchpad to connect to SQL Server and run external script jobs. If this user group is enabled, it is also used to execute Python scripts.

However, in organizations where more restrictive security policies are enforced, the rights that are required by this group might have been manually removed, or they might be automatically revoked by policy. If the rights have been removed, Launchpad can no longer connect to SQL Server, and SQL Server cannot call the external runtime.

To correct the problem, ensure that the group SQLRUserGroup has the system right Allow log on locally.

For more information, see Configure Windows service accounts and permissions.

Permissions to run external scripts

Even if Launchpad is configured correctly, it returns an error if the user does not have permission to run R or Python scripts.

If you installed SQL Server as a database administrator or you are a database owner, you are automatically granted this permission. However, other users usually have more limited permissions. If they try to run an R script, they get a Launchpad error.

To correct the problem, in SQL Server Management Studio, a security administrator can modify the SQL login or Windows user account by running the following script:

For more information, see GRANT (Transact-SQL.

Common Launchpad errors

This section lists the most common error messages that Launchpad returns.

"Unable to launch runtime for R script"

If the Windows group for R users (also used for Python) cannot log on to the instance that is running R Services, you might see the following errors:

  • Errors generated when you try to run R scripts:

    • Unable to launch runtime for 'R' script. Please check the configuration of the 'R' runtime.

    • An external script error occurred. Unable to launch the runtime.

  • Errors generated by the SQL Server Launchpad service:

    • Failed to initialize the launcher RLauncher.dll

    • No launcher dlls were registered!

    • Security logs indicate that the account NT SERVICE was unable to log on

For information about how to grant this user group the necessary permissions, see Install SQL Server R Services.

Note

This limitation does not apply if you use SQL logins to run R scripts from a remote workstation.

"Logon failure: the user has not been granted the requested logon type"

By default, SQL Server Launchpad uses the following account on startup: . The account is configured by SQL Server setup to have all necessary permissions.

If you assign a different account to Launchpad, or the right is removed by a policy on the SQL Server machine, the account might not have the necessary permissions, and you might see this error:

ERROR_LOGON_TYPE_NOT_GRANTED 1385 (0x569) Logon failure: the user has not been granted the requested logon type at this computer.

To grant the necessary permissions to the new service account, use the Local Security Policy application, and update the permissions on the account to include the following permissions:

  • Adjust memory quotas for a process (SeIncreaseQuotaPrivilege)
  • Bypass traverse checking (SeChangeNotifyPrivilege)
  • Log on as a service (SeServiceLogonRight)
  • Replace a process-level token (SeAssignPrimaryTokenPrivilege)

"Unable to communicate with the Launchpad service"

If you have installed and then enabled machine learning, but you get this error when you try to run an R or Python script, the Launchpad service for the instance might have stopped running.

  1. From a Windows command prompt, open the SQL Server Configuration Manager. For more information, see SQL Server Configuration Manager.

  2. Right-click SQL Server Launchpad for the instance, and then select Properties.

  3. Select the Service tab, and then verify that the service is running. If it is not running, change the Start Mode to Automatic, and then select Apply.

  4. Restarting the service usually fixes the problem so that machine learning scripts can run. If the restart does not fix the issue, note the path and the arguments in the Binary Path property, and do the following:

    a. Review the launcher's .config file and ensure that the working directory is valid.

    b. Ensure that the Windows group that's used by Launchpad can connect to the SQL Server instance.

    c. If you change any of the service properties, restart the Launchpad service.

"Fatal error creation of tmpFile failed"

In this scenario, you have successfully installed machine learning features, and Launchpad is running. You try to run some simple R or Python code, but Launchpad fails with an error like the following:

Unable to communicate with the runtime for R script. Please check the requirements of R runtime.

At the same time, the external script runtime writes the following message as part of the STDERR message:

Fatal error: creation of tmpfile failed.

This error indicates that the account that Launchpad is attempting to use does not have permission to log on to the database. This situation can happen when strict security policies are implemented. To determine whether this is the case, review the SQL Server logs, and check to see whether the MSSQLSERVER01 account was denied at login. The same information is provided in the logs that are specific to R_SERVICES or PYTHON_SERVICES. Look for ExtLaunchError.log.

By default, 20 accounts are set up and associated with the Launchpad.exe process, with the names MSSQLSERVER01 through MSSQLSERVER20. If you make heavy use of R or Python, you can increase the number of accounts.

To resolve the issue, ensure that the group has Allow Log on Locally permissions to the local instance where machine learning features have been installed and enabled. In some environments, this permission level might require a GPO exception from the network administrator.

"Not enough quota to process this command"

This error can mean one of several things:

  • Launchpad might have insufficient external users to run the external query. For example, if you are running more than 20 external queries concurrently, and there are only 20 default users, one or more queries might fail.

  • Insufficient memory is available to process the R task. This error happens most often in a default environment, where SQL Server might be using up to 70 percent of the computer's resources. For information about how to modify the server configuration to support greater use of resources by R, see Operationalizing your R code.

"Can't find package"

If you run R code in SQL Server and get this message, but did not get the message when you ran the same code outside SQL Server, it means that the package was not installed to the default library location used by SQL Server.

This error can happen in many ways:

  • You installed a new package on the server, but access was denied, so R installed the package to a user library.

  • You installed R Services and then installed another R tool or set of libraries, such as RStudio.

To determine the location of the R package library that's used by the instance, open SQL Server Management Studio (or any other database query tool), connect to the instance, and then run the following stored procedure:

Sample results

STDOUT message(s) from external script:

[1] "C:\Program Files\Microsoft SQL Server\MSSQL13.SQL2016\R_SERVICES"

[1] "C:/Program Files/Microsoft SQL Server/MSSQL13.SQL2016/R_SERVICES/library"

To resolve the issue, you must reinstall the package to the SQL Server instance library.

Note

If you have upgraded an instance of SQL Server 2016 to use the latest version of Microsoft R, the default library location is different. For more information, see Default R library location.

Launchpad shuts down due to mismatched DLLs

If you install the database engine with other features, patch the server, and then later add the Machine Learning feature by using the original media, the wrong version of the Machine Learning components might be installed. When Launchpad detects a version mismatch, it shuts down and creates a dump file.

To avoid this problem, be sure to install any new features at the same patch level as the server instance.

The wrong way to upgrade:

  1. Install SQL Server 2016 without R Services.
  2. Upgrade SQL Server 2016 Cumulative Update 2.
  3. Install R Services (In-Database) by using the RTM media.

The correct way to upgrade:

  1. Install SQL Server 2016 without R Services.
  2. Upgrade SQL Server 2016 to the desired patch level. For example, install Service Pack 1 and then Cumulative Update 2.
  3. To add the feature at the correct patch level, run SP1 and CU2 setup again, and then choose R Services (In-Database).

Launchpad fails to start if 8dot3 notation is required

Note

On older systems, Launchpad can fail to start if there is an 8dot3 notation requirement. This requirement has been removed in later releases. SQL Server 2016 R Services customers should install one of the following:

For compatibility with R, SQL Server 2016 R Services (In-Database) required the drive where the feature is installed to support the creation of short file names by using 8dot3 notation. An 8.3 file name is also called a short file name, and it's used for compatibility with earlier versions of Microsoft Windows or as an alternative to long file names.

If the volume where you are installing R does not support short file names, the processes that launch R from SQL Server might not be able to locate the correct executable, and Launchpad will not start.

As a workaround, you can enable the 8dot3 notation on the volume where SQL Server is installed and where R Services is installed. You must then provide the short name for the working directory in the R Services configuration file.

  1. To enable 8dot3 notation, run the fsutil utility with the 8dot3name argument as described here: fsutil 8dot3name.

  2. After the 8dot3 notation is enabled, open the RLauncher.config file and note the property of . For information about how to find this file, see Data collection for Machine Learning troubleshooting.

  3. Use the fsutil utility with the file argument to specify a short file path for the folder that's specified in WORKING_DIRECTORY.

  4. Edit the configuration file to specify the same working directory that you entered in the WORKING_DIRECTORY property. Alternatively, you can specify a different working directory and choose an existing path that's already compatible with the 8dot3 notation.

Next steps

Data collection for troubleshooting machine learning

Install SQL Server Machine Learning Services

Troubleshoot database engine connections

Known issues for Python and R in SQL Server Machine Learning Services

  • Article
  • 33 minutes to read

Applies to: SQL Server 2016 (13.x) and later

This article describes known problems or limitations with python fatal error unable remap Python and R components that are provided in SQL Server Machine Learning Services and SQL Server 2016 R Services.

Setup and configuration issues

For a description of processes related to initial setup and configuration, see Install SQL Server Machine Learning Services, python fatal error unable remap. It contains information about upgrades, side-by-side installation, and installation of new R or Python components.

1. Inconsistent results in MKL computations due to missing environment variable

Applies to: R_SERVER binaries 9.0, 9.1, 9.2 or 9.3.

R_SERVER uses the Intel Math Kernel Library (MKL). For computations involving MKL, inconsistent results can occur if your system is missing an environment variable.

Set the environment variable to ensure conditional numerical reproducibility in R_SERVER. For more information, see Introduction to Conditional Numerical Reproducibility (CNR).

Workaround

  1. In Control Panel, click System and Security > System > Advanced System Settings > Environment Variables.

  2. Create a new User or Tls error tls handshake failed variable.

    • Set variable name to 'MKL_CBWR'.
    • Set the 'Variable value' to 'AUTO'.
  3. Restart R_SERVER. On SQL Server, you can restart SQL Server Launchpad Service.

Note

If you are running the SQL Server 2019 on Linux, edit or create .bash_profile in your user home directory, adding the line. Execute this file by typing at a bash command prompt. Restart R_SERVER by typing at the R command prompt.

2. R Script runtime error (SQL Server 2017 CU5-CU7 Regression)

For SQL Server 2017, in cumulative updates 5 through 7, there is a regression in the rlauncher.config file where the temp directory file path includes a space. This regression is corrected in CU8.

The error you will see when running R script includes the following messages:

Unable to communicate with the runtime for 'R' script. Please check the requirements of 'R' runtime.

STDERR message(s) from external script:

Fatal error: cannot create 'R_TempDir'

Workaround

Apply CU8 when it becomes available. Alternatively, you can recreate rlauncher.config by running registerrext with uninstall/install on an elevated command prompt.

The following example shows the commands with the default instance "MSSQL14.MSSQLSERVER" installed into "C:\Program Files\Microsoft SQL Server":

3. Unable to install SQL Server machine learning features on a domain controller

If you try to install SQL Server 2016 R Services or SQL Server Machine Learning Services on a domain controller, setup fails, with these errors:

An error occurred during the setup process of the feature

Cannot find group with identity

Component error code: 0x80131509

The failure occurs because, on a domain controller, the service cannot create the 20 local accounts required to run machine learning. In general, we do not recommend installing SQL Server on a domain controller. For more information, see Support bulletin 2032911.

4. Install the latest service release to ensure compatibility with Microsoft R Client

If you install the latest version of Microsoft R Client and use it to run R on SQL Server in a remote compute context, you might get an error like the following:

You are running version 9.x.x of Microsoft R Client on your computer, which is incompatible with Microsoft R Server version 8.x.x. Repadmin /showrepl ldap error 81 and install a compatible version.

SQL Server 2016 requires that the R libraries on the client exactly match the R libraries on the server. The restriction has been removed for releases later than R Server 9.0.1. However, if you encounter this error, verify the version of the R libraries that's used by your client and the server and, if necessary, update the client to match the server version.

The version of R that is installed with SQL Server R Services is updated whenever a SQL Server service release is installed. To ensure that you always have the most up-to-date versions of R components, be sure to install all service packs.

To ensure compatibility with Microsoft R Client 9.0.0, install the runtime error 1004 en vba that are described in this support article.

To avoid problems with R packages, you can also upgrade the version of the R libraries that are installed on the server, by changing your servicing agreement to use the Modern Lifecycle Support policy, as described in the next section. When you do so, the version of R that's installed with SQL Server is updated on the same schedule used for updates of machine Learning Server (formerly Microsoft R Server).

Applies to: SQL Server 2016 R Services, with R Server version 9.0.0 or earlier

5. R components missing from CU3 setup

A limited number of Azure virtual machines were provisioned without the R installation files that should be error 16 mrl command with SQL Server. The issue applies to virtual machines provisioned in the period from 2018-01-05 to 2018-01-23. This issue might also affect on-premises installations, if you applied the CU3 update for SQL Server 2017 during the period from 2018-01-05 to 2018-01-23.

A service release has been provided that includes the correct version of the R installation files.

To install the components and repair SQL Server 2017 CU3, you must uninstall CU3, and reinstall the updated version:

  1. Download the updated CU3 installation file, which includes the R installers.
  2. Uninstall CU3. In Control Panel, search for Uninstall an update, and then select "Hotfix 3015 for SQL Server 2017 (KB4052987) (64-bit)". Proceed with uninstall steps.
  3. Reinstall the CU3 update, by double-clicking on the update for KB4052987 that you just downloaded:python fatal error unable remap. Follow the installation instructions.

6. Unable to install Python components in offline installations of SQL Server 2017 CTP 2.0 or later

If you install a pre-release version of SQL Server 2017 on a computer without internet access, the installer might fail to display the page that prompts for the location of the downloaded Python components. In such an instance, python fatal error unable remap, you can install the Machine Learning Services feature, but not the Python components.

This issue is fixed in the release version. Also, this limitation does not apply to R components.

Applies to: SQL Server 2017 with Python

Warning of incompatible version when you connect to an older version of SQL Server R Services from a client by using SQL Server 2017 (14.x)

When you run R code in a SQL Server 2016 compute context, you might see the following error:

You are running version 9.0.0 of Microsoft R Client on your computer, which is incompatible with the Microsoft R Server version 8.0.3, python fatal error unable remap. Download and install a compatible version.

This message is displayed python fatal error unable remap either of the following two statements is true,

  • You installed R Server (Standalone) on a client computer by using the setup wizard for SQL Ribbon error 3ds max 2011 2017 (14.x).
  • You installed Microsoft R Server by using the separate Windows installer.

To ensure that the server and client use the same version you might need to use binding, supported for Microsoft R Server 9.0 and later releases, python fatal error unable remap, to upgrade the R components in SQL Server 2016 instances. To determine if support for upgrades is available for your version of R Services, see Upgrade an instance of R Services using SqlBindR.exe.

Applies to: SQL Server 2016 R Services, with R Server version 9.0.0 or earlier

7. Setup for SQL Server 2016 service releases might fail to install newer versions of R components

When you install a cumulative update or install a service pack for SQL Server 2016 on a computer that is not connected to the internet, the setup wizard might fail to display the prompt that lets you update the R components by using downloaded CAB files. This failure typically occurs when multiple components were installed together with the database engine.

As a workaround, you can install the service release by using the command line and specifying the argument as shown in this example, which installs CU1 updates:

To get the latest installers, see Install machine learning components without internet access.

Applies to: SQL Server 2016 R Services, with R Server version 9.0.0 or earlier

8. Launchpad services fails to start if the version is different from the R version

If you install SQL Server R Services separately from the database engine, and the build versions are different, you might see the following error in the System Event log:

The SQL Server Launchpad service failed to start due to the following error: The service did not python fatal error unable remap to the start or control request in a timely fashion.

For example, this error might occur if you install the database engine by using the release version, apply a patch to upgrade the database engine, python fatal error unable remap, and then add the R Services feature by using the release version.

To avoid this problem, use a utility such as File Manager to compare the versions of Launchpad.exe with version of SQL binaries, such as sqldk.dll. All components should have the same version number. If you upgrade one component, be sure to apply the same upgrade to all other installed components.

Look for Launchpad in the folder for the instance. For example, in a default installation of SQL Server 2016, the path might be .

9. Remote compute python fatal error unable remap are blocked by a firewall in SQL Server instances that are running on Azure virtual machines

If you have installed SQL Server on an Azure virtual machine, you might not be able to use compute contexts that require the use of the virtual machine's workspace. The reason is that, by default, the firewall on Azure virtual machines includes a rule that blocks python fatal error unable remap access for local R user accounts.

As a workaround, on the Azure VM, open Windows Firewall with Advanced Security, select Outbound Rules, and disable the following rule: Block network access for R local user accounts in SQL Server instance MSSQLSERVER. You can also leave the rule enabled, but change the security property to Allow if secure.

10. Implied authentication in SQLEXPRESS

When you run R jobs from a remote data-science workstation by using Integrated Windows authentication, SQL Server uses implied authentication to generate any local ODBC calls that might be required by the script. However, this feature did not work in the RTM build of SQL Server Express Edition.

To fix the issue, we recommend that you upgrade to a later service release.

If upgrade is not feasible, as a workaround, use a SQL login to run remote R jobs that might require embedded ODBC calls.

Applies to: SQL Server 2016 R Services Express Edition

11. Performance limits when libraries used by SQL Server are called from other tools

It is possible to call the machine learning libraries that are installed for SQL Server from an external application, such as RGui. Doing so might be the most convenient way to accomplish certain tasks, such as installing new packages, or running ad hoc tests on very short code samples. However, outside of SQL Server, performance might be limited.

For example, python fatal error unable remap, even if you are using the Enterprise Edition of SQL Server, R runs in single-threaded mode when you run your R code by using external tools. To get the benefits of performance in SQL Server, initiate a SQL Server connection and use sp_execute_external_script to call the external script runtime.

In general, avoid calling the machine learning libraries that are used by SQL Server from external tools. If you need to debug R or Python code, it is typically easier to do so outside of SQL Server. To get the same libraries that are in SQL Server, you can install Microsoft R Client or SQL Server 2017 Machine Learning Server (Standalone).

12. SQL Server Data Tools does not support permissions required by external scripts

When you use Python fatal error unable remap Studio or SQL Server Data Tools to publish a database project, if any principal has permissions specific to external script execution, you might get an error like this one:

TSQL Model: Error detected when reverse engineering the database. The permission was not recognized and was not imported.

Currently the DACPAC model does not support the permissions used by R Services or Machine 173 fatal error 050914 Services, such as GRANT ANY EXTERNAL SCRIPT, or EXECUTE ANY EXTERNAL SCRIPT. This issue will be fixed in a later release.

As a workaround, run the additional GRANT statements in a post-deployment script.

13. External script execution is throttled due to resource governance default values

In Enterprise Edition, you can use python fatal error unable remap pools to manage external script processes. In some early release builds, the maximum memory that could be allocated to the R processes was 20 percent. Therefore, if the server had 32 GB of RAM, the R executables (RTerm.exe and Python fatal error unable remap could use a maximum of 6.4 GB in a single request.

If you encounter resource limitations, check the current default. If 20 percent is not enough, see the documentation for SQL Server on how to change this value.

Applies to: SQL Server 2016 R Services, Enterprise Edition

14. Error when using without on Linux

On a clean Linux machine that does not have installed, running a (SPEES) query with Java or an external language fails because fails to load .

For example:

This fails with a message similar to the following:

The logs will show an error message similar to the following:

Workaround

You can perform one of the following workarounds:

  1. Copy from to the default system path

  2. Add the following entries to to expose the path:

Applies to: SQL Server 2019 on Linux

15. Installation or upgrade error on FIPS enabled servers

If you install SQL Server 2019 with the feature Machine Learning Services and Language Extensions or upgrade the SQL Server instance on a Federal Information Processing Standard (FIPS) enabled server, you will receive the following error:

An error occurred while installing extensibility feature with error message: AppContainer Creation Failed with error c00d124d zune error NONE, python fatal error unable remap This implementation is not part of the Windows Platform FIPS validated cryptographic algorithms.

Workaround

Disable FIPS before the installation of SQL Server 2019 with the feature Machine Learning Services and Language Extensions or upgrade of the SQL Server instance, python fatal error unable remap. Once the installation or upgrade is complete, you can reenable FIPS.

Applies to: SQL Server 2019

16. R libraries using specific algorithms, streaming, or partitioning

  • Issue: The following limitations apply on SQL Server 2017 (14.x) with runtime upgrade. This issue applies to Enterprise Edition.

    • Parallelism: and algorithm thread parallelism for scenarios are limited to maximum of 2 threads.
    • Streaming & partitioning: Scenarios involving parameter passed to T-SQL is not applied.
    • Streaming & partitioning: and data sources (i.e, python fatal error unable remap.) does not support reading rows in chunks for training or scoring scenarios. These scenarios always bring all data to memory for computation and the operations are memory bound
  • Solution: The best solution is to upgrade to SQL Server 2019 (15.x). Alternatively you can continue to use SQL Server 2017 (14.x) with runtime upgrade configured using RegisterRext.exe /configure, after you complete the following tasks.

    1. Edit registry to create a key and add a value with data or the instance shared directory, as configured.
    2. Create a folder from the folder to the newly created folder.
    3. Rename the to in the new folder .

Important

If you do the steps above, python fatal error unable remap, you must manually remove the added key prior to upgrading to a later version of SQL Server.

R script execution issues

This section contains known issues that are specific to running R on SQL Server, as well as some issues that are related to the R libraries and tools published by Microsoft, including RevoScaleR.

For additional known issues that might affect R solutions, see the Machine Learning Server site.

1. Access denied warning when executing R scripts on SQL Server in a non default location

If the instance of SQL Server has been installed python fatal error unable remap a non-default location, such as outside the folder, python fatal error unable remap, the warning ACCESS_DENIED is raised when you try to run scripts that install a package. For example:

In : path[2]="~ExternalLibraries/R/8/1": Access is denied

The reason is that an R function attempts to read the path, and fails if the built-in users group simoreg 6 ra 22 error f19, does not have read access. The warning that is raised does not block execution of the current R script, but the warning might recur repeatedly whenever the user runs any other R script.

If you have installed SQL Server to the default location, this error does not occur, because all Windows users have read permissions on the folder.

This issue is addressed in an upcoming service release. As a workaround, provide the group, SQLRUserGroup, with read access for all parent folders of .

2. Serialization error between old and new versions of RevoScaleR

When you pass a model using a serialized format to a remote SQL Server instance, you might get the error:

Error in memDecompress(data, type = decompress) internal error -3 in memDecompress(2).

This error is raised if you saved the model using a recent version of the serialization function, rxSerializeModel, but the SQL Server instance where you deserialize the model has an older version of the RevoScaleR APIs, from SQL Server 2017 CU2 or earlier.

As a workaround, you can upgrade the SQL Server 2017 instance to CU3 or later.

The error does not appear if the API version is the same, or if you are moving a model saved with an python fatal error unable remap serialization function to a server that uses a newer version of the serialization API.

In other words, use the same version of RevoScaleR for both serialization and deserialization operations.

3. Real-time scoring does python fatal error unable remap correctly handle the learningRate parameter in tree and forest models

If you create a model using a decision tree or decision forest method and specify the learning rate, you might see inconsistent results when using or the SQL function, as compared to using .

The cause is an error in the API that processes serialized models, and is limited to the parameter: for example, in rxBTrees, python fatal error unable remap, or

This issue is addressed in an upcoming service release.

4. Limitations on processor affinity for R jobs

In the initial release build of SQL Server 2016, you could set processor affinity only for CPUs in the first k-group. For example, if the server is a 2-socket machine with two k-groups, only processors from the first k-group are used for the R processes. The same limitation applies when you configure resource governance for R script jobs.

This issue is fixed in SQL Server 2016 Service Pack 1. We recommend that you upgrade to the latest service release.

Applies to: SQL Server 2016 R Services RTM version

5. Changes to column types cannot be performed when reading data in a SQL Server compute context

If your compute context is set to the SQL Server instance, you cannot use the colClasses argument (or other similar arguments) to change the data type of columns in your R code.

For example, the following statement would result in an error if the column CRSDepTimeStr is not already an integer:

As a workaround, you can sqlite error no such table celllocation the SQL query to use CAST or CONVERT and present the data to R by using the correct data type. In general, performance is better when you work with data by using SQL rather than by changing data in the R code.

Applies to: SQL Server 2016 R Services

6. Limits on size of serialized models

When you save a model to a SQL Server table, you must serialize the model and save it in a binary format. Theoretically the maximum size of a model that can be stored with this method is 2 GB, which is the maximum size of varbinary columns in SQL Server.

If you need to use larger models, the following workarounds are available:

  • Take steps to reduce the size of your model. Some open source R packages include a great deal of information in the model object, and much of this information can be removed for deployment.

  • Use feature selection to remove unnecessary columns.

  • If you are using an open source algorithm, consider a similar implementation using the corresponding algorithm in MicrosoftML or RevoScaleR. These packages have been optimized for deployment scenarios.

  • After the model has been rationalized and the size reduced using the preceding steps, see if the memCompress function in base R can be used to reduce the size of the model before passing it to SQL Server. This option is best when the model is close to the 2 GB limit.

  • For larger models, you can use the SQL Server FileTable feature to store the models, rather than using a varbinary column.

    To use FileTables, you must add a firewall exception, because data stored in FileTables is managed by the Filestream filesystem driver in SQL Server, python fatal error unable remap, and default firewall rules block network file access. For more information, python fatal error unable remap, see Enable Prerequisites for FileTable.

    After you have enabled FileTable, to write the model, you get a path from SQL using the FileTable API, and then write the model to that location from your code. When you need to read the model, you get the path from SQL and then call the model using the path from your script. For more information, see Access FileTables with File Input-Output APIs.

7. Avoid clearing workspaces when you execute R code in a SQL Server compute context

If you use an R command to clear your workspace of objects while running R code in a SQL Server compute context, python fatal error unable remap, or if you clear the workspace as part of an R script called by using sp_execute_external_script, you might get this error: workspace object revoScriptConnection not found

is an object in the R workspace that contains information about an R session that is called from SQL Server. However, if your R code includes a command to clear the workspace (such asall information about the session and other objects in the R workspace is cleared as well.

As a workaround, avoid indiscriminate clearing of variables and other objects while you're running R in SQL Server. Although clearing the workspace is common when working in the R console, it can have unintended consequences.

  • To delete specific variables, use the R function: for example,
  • If there are multiple variables to delete, save the names of temporary variables to a list and perform periodic garbage collection.

8. Restrictions on data that can be provided as input to an R script

You cannot use in an R script the following types of query results:

  • Data from a Transact-SQL query that references AlwaysEncrypted columns.

  • Data from a Transact-SQL query that references masked columns.

    If you need to use masked data in an R script, a possible workaround is to make a copy of the data in a temporary table and use that data instead.

9. Use of strings as factors can lead to performance degradation

Using string type variables as factors can greatly increase the amount of memory used for R operations. This is a known issue with R in general, and there are many articles on the subject. For example, see Factors are not first-class citizens in R, by John Mount, in R-bloggers) or stringsAsFactors: An unauthorized biography, by Roger Peng.

Although the issue is not specific to SQL Server, it can greatly affect performance of R code run in SQL Server. Strings are typically stored as varchar or nvarchar, and if a column of string data has many unique values, the process of internally converting these to integers and back to strings by R can even lead to memory allocation errors.

If you do not absolutely require a string data type for other operations, mapping the string values to a numeric (integer) data type as part of data preparation would be beneficial from a performance and scale perspective.

For a discussion of this issue, and other tips, see Performance for R Services - data optimization.

10. Arguments varsToKeep and varsToDrop are not supported for SQL Server data sources

When you use the rxDataStep function to write results to a table, using the varsToKeep and varsToDrop is a handy way of specifying the columns to include or exclude as part of the operation. However, python fatal error unable remap arguments are not supported for SQL Server data sources.

11. Limited support for SQL data types in sp_execute_external_script

Not all data types that are supported in SQL can be used in R. As a workaround, consider casting the unsupported data type to a supported data type before passing the data to sp_execute_external_script.

For more information, see R libraries and data types.

12. Possible string corruption using unicode strings in varchar columns

Passing unicode data in varchar columns from SQL Server to R/Python can result in string corruption. This is due to the encoding for these unicode string in SQL Server collations may not match with the default UTF-8 encoding used in R/Python.

To send any non-ASCII string data from SQL Server to R/Python, use UTF-8 encoding (available in SQL Server 2019 (15.x)) or use nvarchar type for the same.

13. Only one value of type can be returned from

When a binary data type (the R raw data type) is returned from R, the value must be sent in the output data frame.

With data types other than raw, you can return parameter python fatal error unable remap along with the results of the stored procedure by adding the OUTPUT keyword. For more information, see Parameters.

If you want to use multiple output sets that include values of type raw, one possible workaround is to do multiple calls of the stored procedure, or to send the result sets back to SQL Server by using ODBC.

14. Loss of precision

Because Transact-SQL and Difference between iferror iserror support various data types, numeric error + no such device types can suffer loss of precision during conversion.

For more information about implicit data-type conversion, see R libraries and data types.

15. Variable scoping error when you use the transformFunc parameter

To transform data while you are modeling, you can pass a transformFunc argument in a function such as or. However, nested function calls can lead to scoping errors in the SQL Server compute context, even if the calls work correctly in the local compute context.

The sample data set for the analysis has no variables

For example, assume that you have defined two functions, andin your local global environment, and calls. In distributed or remote calls involvingthe call to might fail with this error, because cannot be found, even if you have passed both and to the remote call.

If you encounter this problem, you can work around the issue by embedding the definition of inside your definition ofanywhere before would ordinarily call .

For example:

To avoid the error, rewrite the definition as follows:

16. Data import and manipulation using RevoScaleR

When varchar columns are read from a database, white space is trimmed. To prevent this, enclose strings in non-white-space characters.

When functions such as are used to create database tables that have varchar columns, the column width is estimated based on a sample of the data. If the width can vary, it might be necessary to pad all strings to a common length.

Using a transform to change a variable's data type is not supported when repeated calls to or read_big medium error are used to import and append rows, combining multiple input files into a single .xdf file.

17. Limited support for rxExec

In SQL Server 2016, the function that's provided by the RevoScaleR package can be used only in single-threaded mode.

18. Increase the maximum parameter size to support rxGetVarInfo

If you use data sets with extremely large numbers of variables (for example, over 40,000), set the flag when you start R to use functions such as. The flag specifies the maximum size of the pointer protection stack.

If you are using the R console (for example, RGui.exe or RTerm.exe), you can set the value of max-ppsize to 500000 by typing:

19. Issues with the rxDTree function

The function does not currently support in-formula transformations. In particular, using the syntax for creating factors on the fly is not supported. However, numeric data is automatically binned.

Ordered factors are treated the same as factors in all RevoScaleR analysis functions except .

20. Data.table as an OutputDataSet in R

Using as an in R is not supported in SQL Server 2017 Cumulative Update 13 (CU13) and earlier. The following message might appear:

as an in R is supported in SQL Server 2017 Cumulative Update 14 (CU14) and later.

21. Running a long script fails while installing a library

Running a long running external script session and having the dbo in parallel trying to install a library on a different python fatal error unable remap can terminate the script.

For example, running this external script against master:

While the dbo in parallel installs a library in LibraryManagementFunctional:

The previous long running external script against master will terminate with the following error message:

A 'R' script error occurred during execution of 'sp_execute_external_script' with HRESULT 0x800704d4.

Workaround

Don't run the library install in parallel to the long-running query. Or rerun the long running query after the installation is complete.

Applies to: SQL Server 2019 on Linux & Big Data Clusters only.

22. SQL Server stops responding when executing R scripts containing parallel execution

SQL Server 2019 contains a regression that effects R scripts that use parallel execution. Examples include using with compute context and scripts that use the parallel package. This problem is caused by errors the parallel package encounters when writing to the null device while executing in SQL Server.

Applies to: SQL Server 2019.

23. Precision loss for money/numeric/decimal/bigint data types

Executing an R script with allows money, numeric, decimal, and bigint data types as input data. However, because they are converted to R's numeric type, they suffer a precision loss with values that are very high or have decimal point values.

  • money: Sometimes cent values would be imprecise and a warning would be issued: Warning: unable to precisely represent cents values.
  • numeric/decimal: with an R script does not support the full range of those data types and would alter the last few decimal digits especially those with fraction.
  • bigint: R only support up to 53-bit integers and then it will start to have precision loss.

Python script execution issues

This section contains known issues that are specific to running Python on SQL Server, as well as issues that are related to the Python packages published by Microsoft, including revoscalepy and microsoftml.

1. Call to pretrained model fails if path to model is too long

If you installed the pretrained models in an early release of SQL Server 2017, the complete path to the trained model file might be too long for Python to read. This limitation is fixed in a later service release.

There are several potential workarounds:

  • When you install the pretrained models, choose a custom location.
  • If possible, install the SQL Server instance under a custom installation path with a shorter path, such as C:\SQL\MSSQL14.MSSQLSERVER.
  • Use python fatal error unable remap Windows utility Fsutil to create a hard link that maps the model file to a shorter path.
  • Update to the latest service release.

2. Error when saving serialized model to SQL Server

When you pass a model to a remote SQL Server instance, and try to read the binary model using the function in revoscalepy, you might get the error:

NameError: name 'rx_unserialize_model' is not defined

This error is raised if you saved the model using a recent version of the serialization function, but the SQL Server instance where you deserialize the model does not recognize the serialization API.

To resolve the issue, upgrade the SQL Server 2017 instance to CU3 or later.

3. Failure to initialize a varbinary variable causes an error in BxlServer

If you run Python code in SQL Server usingand the code has output variables of type varbinary(max), varchar(max) or similar types, the variable must be initialized or set as part of your script. Otherwise, the data exchange component, BxlServer, raises an error and stops working.

This limitation will be fixed in an upcoming service release. As a workaround, make sure that the variable is initialized within the Python script. Any valid value can be used, as in the following examples:

4. Telemetry warning on successful execution of Python code

Beginning with SQL Server 2017 CU2, the following message might appear even if Python code otherwise runs successfully:

STDERR message(s) from external script:~PYTHON_SERVICES\lib\site-packages\revoscalepy\utils\RxTelemetryLoggerSyntaxWarning: telemetry_state is used prior to global declaration

This issue has been fixed in SQL Server 2017 Cumulative Update 3 (CU3).

5. Numeric, decimal, and money data types not supported

Beginning with SQL Server 2017 Cumulative Update 12 (CU12), numeric, decimal and money data types in WITH RESULT SETS are unsupported when using Python with. The following messages might appear:

[Code: 39004, SQL State: S1000] A 'Python' script error occurred during execution of'sp_execute_external_script' with HRESULT 0x80004004.

[Code: 39019, SQL State: S1000] An external script error occurred:

SqlSatelliteCall error: Unsupported type in output schema. Supported types: bit, smallint, int, datetime, smallmoney, real and float. char, varchar are partially supported.

This has been fixed in SQL Server 2017 Cumulative Update 14 (CU14).

6. Bad interpreter error when installing Python packages with pip on Linux

On SQL Server 2019, if you try to use pip. For example:

You will then get this error:

bash: /opt/mssql/mlservices/runtime/python/bin/pip: /opt/microsoft/mlserver/9.4.7/bin/python/python: bad interpreter: No such file or directory

Workaround

Install pip from the Python Package Authority (PyPA):

Recommendation

See Install Python packages with sqlmlutils.

Applies to: SQL Server 2019 on Linux

7. Unable to install Python packages using pip after installing SQL Server 2019 on Windows

After installing SQL Server 2019 on Windows, attempting to install a python package via pip from a DOS command line will fail. For example:

This will return the following error:

pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available.

This is a problem specific to the Anaconda package. It will be fixed in an upcoming service release.

Workaround

Copy the following files:

    from the folder

    to the folder

    Then open a new DOS python fatal error unable remap shell prompt.

    Applies to: SQL Server 2019 on Windows

    8. Error when using without on Linux

    On a canon 50d error 99 Linux machine that does not have installed, running a (SPEES) query fails with a "No such file or directory" error.

    For example:

    Workaround

    Run the following command:

    Applies to: SQL Server 2019 on Linux

    9. Cannot install tensorflow package using sqlmlutils

    The sqlmlutils package is used to install Python packages in SQL Server 2019. You need to download, install, and update the Microsoft Visual C++ 2015-2019 Redistributable (x64). However, the package tensorflow cannot be installed using sqlmlutils. The tensorflow package depends on a newer version of numpy than the version installed in SQL Server. However, numpy is a preinstalled system package that sqlmlutils cannot update when trying to install tensorflow.

    Workaround

    Using a command prompt in administrator mode, run the following command, replacing "MSSQLSERVER" with the name of your SQL instance:

    If you get a "TLS/SSL" error, see 7. Unable to install Python packages using pip earlier in this article.

    Applies to: SQL Server 2019 on Windows

    Revolution R Enterprise and Microsoft R Open

    This section lists issues specific to R connectivity, development, and performance tools that are provided by Revolution Analytics. These tools were provided in earlier pre-release versions of SQL Server.

    In general, we recommend that you uninstall these previous versions and install the latest version of SQL Server or Microsoft R Server.

    1. Revolution R Enterprise is not supported

    Installing Revolution R Enterprise side by side with any version of R Services (In-Database) is not supported.

    If you have an existing license for Revolution R Enterprise, you must put it on a separate computer from both the SQL Server instance and any workstation that you want to use to connect to the SQL Server instance.

    Some pre-release versions of R Services (In-Database) included an R development environment for Windows that was created by Revolution Analytics. This tool is no longer provided, and is not supported.

    For compatibility with R Services (In-Database), we recommend that you install Microsoft R Client instead. R Tools for Visual Studio and Visual Studio Code also supports Microsoft R solutions.

    2. Compatibility issues with SQLite ODBC driver and RevoScaleR

    Revision 0.92 of the SQLite ODBC driver is incompatible with RevoScaleR. Revisions 0.88-0.91 and 0.93 and later are known to be compatible.

    Next steps

    Collect data to troubleshoot SQL Server Machine Learning Services

    Installing an OpenStreetMap Tile Server on Ubuntu

    Last modification:unknown

    Introduction

    This page shows how OpenStreetMap Carto can be used to implement a tile server using the same software adopted by OpenStreetMap. It includes step-by-step instructions to install an Ubuntu based Tile Server and is limited to describe some best practices, in the consideration that the main scope of this site is to provide tutorials to set-up a development environment of OpenStreetMap Carto and offer recommendations to edit the style.

    The OSM Tile Server is a web server specialized in delivering raster maps, serving them as static tiles and able to perform rendering in real time or providing cached images. The adopted web software by OpenStreetMap is the Apache HTTP Server, together with a specific plugin named mod_tile and a related backend stack able to generate tiles at run time; programs python fatal error unable remap libraries are chained together to create the tile server.

    As so often with OpenStreetMap, there are many ways to achieve a goal and nearly all of the components have alternatives that have various specific advantages and disadvantages. This tutorial describes the standard installation process of the OSM Tile Server used on OpenStreetMap.org.

    It consists of the following main components:

    • Mapnik
    • Apache
    • Mod_tile
    • renderd
    • osm2pgsql
    • PostgreSQL/PostGIS database, to be installed locally (suggested) or remotely (might be slow, depending on the network).
    • carto
    • openstreetmap-carto

    All mentioned software is open-source.

    For the tile server, a PostGIS database is required, storing geospatial features populated by osm2pgsql tool from OSM data. Also, a file system directory including the OSM.xml file, map symbols (check openstreetmap-carto/symbols subdirectory) and shapefiles (check openstreetmap-carto/data subdirectory) is needed. OSM.xml is preliminarily produced by a tool named carto from the openstreetmap-carto style (project.mml and all related CartoCSS files included in openstreetmap-carto).

    When the Apache web server receives a request from the browser, it invokes the mod_tile plugin, which in turn checks if the tile has already been created (from a previous rendering) and cached, so that it is ready for use; in case, mod_tile immediately sends the tile python fatal error unable remap to the web server. Conversely, python fatal error unable remap, if the request needs to be rendered, then it is queued to the renderd backend, which is responsible to invoke Mapnik to perform the actual rendering; renderd is a daemon process included in the mod_tile sources and interconnected to mod_tile via UNIX queues. renderd is the standard backend currently used by www.openstreetmap.org, even if some OSM implementations use Tirex; Mapnik extracts data from the PostGIS database according to the openstreetmap-carto style information and dynamically renders the tile. renderd passes back the produced tile to the web server and in turn to the browser.

    The renderd daemon implements a queuing mechanism with multiple priority levels to provide an as up-to-date viewing experience given the available rendering resources. The highest priority is for on -the fly rendering of tiles not yet in the tile cache, two priority levels for re-rendering out of date tiles on the fly and two background batch rendering queues. To avoid problems with directories becoming too large and to avoid too many tiny files, Mod_tile/renderd store the rendered tiles in “meta tiles”, in a special hashed directory structure.1

    Even if the tileserver dynamically generates tiles at run time, they can also be pre-rendered for offline viewing with a specific tool named render_list, which is typically used to pre-render low zoom level tiles and takes significant time to accomplish the process (tens of hours in case the full planet is pre-rendered); this utility is included in mod_tile, as well as another tool named render_expired, which provides methods to allow expiring map tiles. More detailed description of render_list and render_expired can be found in their man pages.

    A background on the tiles expiry method can be found at tiles expiry mechanism.

    The overall process is here represented2.

    An additional description of python fatal error unable remap rendering process of OpenStreetMap can be found at OSM architecture.

    The following step-by-step procedure can be used to install and configure all the necessary software to operate your own OpenStreetMap tile server on Ubuntu.3

    The goal for this procedure is to use Ubuntu packages and official PPAs whenever possible.

    We consider using Ubuntu 20.04.2 LTS Focal Fossa, or 18.04 LTS Bionic Beaver, suggested operating system version.

    Other tested O.S. python fatal error unable remap Ubuntu 16.04 LTS Xenial Xerus, Ubuntu 15.4 Vivid Vervet or Ubuntu 14.04.3 LTS Trusty Tahr (other versions should work). All should be 64-bit computing architecture. Other distributions like Debian might be checked, but could require changes to python fatal error unable remap installation procedure.

    This procedure is updated to the version of OpenStreetMap Carto available at the time of writing. To get the correct installation procedure, the INSTALL history should be checked, considering that the OpenStreetMap Carto maintainers use to keep the INSTALL page updated. Check also the README changelog.

    General setup for Ubuntu

    Install Ubuntu.

    This procedure also supports WSL - Windows Subsystem for Linux. This means that a Windows 10 64-bit PC can be used to perform the installation, after setting-up WSL.

    Update Ubuntu

    Make sure your Ubuntu system is fully up-to-date:

    Previous command returns the Ubuntu version.

    To update the system:

    If on a brand new system you also want to do .

    Install essential tools

    Optional elements:

    Check prerequisites suggested by openstreetmap-carto.

    For the subsequent installation steps, we suppose that defaults to your home directory.

    Configure a swap

    Importing and managing map data takes a lot of RAM and a swap is generally needed.

    To check whether a swap partition is already configured on your system, use one of the following two commands:

    • Reports the swap usage summary (no output means missing swap):

    • Display amount of free and used memory in the system (check the line specifying Swap):

    If you do not have an active swap partition, especially if your physical memory is small, you should add a swap file. First we use command to create a file. For example, create a file named swapfile with 2G capacity in root file system:

    Then make sure only root can read and write to it.

    Format it to swap:

    Enable the swap file

    The Operating System tuning adopted by the OpenStreetMap tile servers can be found in the related Chef configuration.

    Check usage of English locale

    Run locale to list what locales are python fatal error unable remap defined for the current user account:

    To set the en_GB locale:

    The exported variables can be put to the file .

    New locales can also be generated by issuing:

    Creating a UNIX user

    We suppose that you have already created a login user during the installation of Ubuntu, to be used to run the tile server, python fatal error unable remap. Let’s suppose that your selected user name is tileserver. Within this document, all times tileserver is mentioned, change it with your actual user name.

    If you need to create a new user:

    Set a password when prompted.

    Install Git

    Git might come already preinstalled sometimes.

    Install Mapnik library

    We need to install the Mapnik library. Mapnik is used to render the OpenStreetMap data into the tiles managed by the Apache web server through renderd and mod_tile.

    With Ubuntu 20.04 LTS, go to mapnik installation.

    FreeType dependency in Ubuntu 16.04 LTS

    With Ubuntu 18.04 LTS, which installs FreeType 2.8.1, skip this paragraph and continue with installing Mapnik.

    Mapnik depends on FreeType for TrueType, python fatal error unable remap, Type 1, and OpenType font support. With Ubuntu 16.04 LTS, python fatal error unable remap, the installed version of FreeType is 2.6.1 which has the stem darkening turned on and this makes NotoCJK fonts bolder and over-emphasized. Installing a newer version of FreeType from a separate PPA, overriding the default one included in Ubuntu 16.04 LTS, solves this issue4:

    Check the updated freetype version:

    In case you need to downgrade the FreeType to the stock version in Ubuntu 16.04 repository, simply purge the PPA via ppa-purge:

    We report some alternative procedures to install Mapnik (in the consideration to run an updated version of Ubuntu).

    With Ubuntu versions older than 18.04 LTS, the default Mapnik version is older than the minumum one required, which is 3.0.19. Anyway, a specific PPA made by talaj offers the packaged version 3.0.19 of Mapnik for Ubuntu 16.04 LTS Xenial.

    Ubuntu 18.04 LTS provides Mapnik 3.0.19 and does not need a specific PPA.

    Install Mapnik library from package

    The following command installs Mapnik from the standard Ubuntu canon mp190 e15 error Ubuntu 18.04 LTS, you might use python-mapnik instead of python3-mapnik.

    Launchpad reports the Mapnik version installed from package depending on the operating system; the newer the OS, the higher the Mapnik release.

    GitHub reports the ordered list of available versions for:

    Version 3.0.19 is the minimum one suggested at the moment.5 If using the above mentioned PPA, that version comes installed instead of the default one available with Ubuntu.

    After installing Mapnik from package, go to check Mapnik installation.

    Alternatively, install Mapnik from sources

    To install Mapnik from sources, follow the Mapnik installation page for Ubuntu.

    First create a directory to load the sources:

    Note: if you get the following error:use this explort instead of the one included in the linked documentation:

    Refer to Mapnik Releases for the latest version and changelog.

    Remove any other old Mapnik packages:

    Install prerequisites:

    Check and before upgrading the compiler, python fatal error unable remap. As mentioned, installing cannot create symlink, error code1 and clang-3.8 should only be done with Ubuntu 16.04, which by default comes with older versions (not with Ubuntu 18.04).

    We need to install Boost either from package or from source.

    Install Boost from package

    Do not install boost from package if you plan to compile mapnik with an updated compiler. Compile instead boost with the same updated compiler.

    Alternatively, install the latest version of Boost from source

    Remove a previous python fatal error unable remap of boost from package:

    Download boost from source:

    Notice that boost and mapnik shall be compiled with the same compiler. With Ubuntu 16.04 and gcc-6, g++-6, clang-3.8 you should use these commands:

    With Ubuntu 18.04 or Ubuntu 16.04 using the default compiler, the compilation procedure is the following:

    Do not try compiling mapnik with an updated compiler if boost is installed from package.

    Install HarfBuzz from package

    HarfBuzz is an OpenType text shaping engine.

    It might be installed from package, but better is downloading a more updated source version, compiling it. To install from package:

    Install HarfBuzz from source

    Check the lastest version here. This example grubs harfbuzz-1.7.6:

    Build the Mapnik library from source

    At the time of writing, Mapnik 3.0 is the current stable release and shall be used. The branch for the latest Mapnik from 3.0.x series is v3.0.x.6

    Download the latest sources of Mapnik:

    After Mapnik is successfully compiled, use the following command to install it to your system:

    Python bindings are not included by default. You’ll need to add those separately.

    • Install prerequisites:

      Only in case you installed boost from package, you also need:

      Do not peform the above libboost-python-dev installation with boost compiled from source.

      Set BOOST variables if you installed boost from sources:

    • Download and compile python-mapnik. We still use v3.0.x branch:

      Note: Mapnik and (part of Mapnik) need to be installed prior to this setup.

    You can then verify that Mapnik has been correctly installed.

    Verify that Mapnik has been correctly installed

    Report Mapnik version number and provide the path of the input plugins directory7:

    Verify that Python is installed. Also verify that pip is installed.

    Check then with Python 3:

    If python 2.7 is used (not Ubuntu 20.04 LTS), use this command to check:

    It should return the path to the python bindings (e.g., ). If python replies without errors, then Mapnik library was found by Python.

    Configure the firewall

    If you are preparing a remote virtual machine, configure the firewall to allow remote access to the local port 80 and local port 443.

    If you run a cloud based VM, also the VM itself shall be set to open this port.

    Install Apache HTTP Server

    The Apache free open source HTTP Server is among the most popular web servers in the world, python fatal error unable remap. It’s well-documented, and has been in wide use for much of the history of the web, which makes it a great default choice for hosting a website.

    To install apache:

    The Apache service can be started with

    Error “Failed to enable APR_TCP_DEFER_ACCEPT” with Ubuntu on Windows is due to this socket option which is not natively supported by Windows. To overcome it, edit /etc/apache2/apache2.conf with

    and add the following line to the end of the file:

    To check if Apache is installed, direct your browser to the IP address canon ir5000-6000 error codes your server (eg. http://localhost). The page should display the default Apache home page. Also this command allows checking correct working:

    The Apache tuning adopted by the OpenStreetMap tile servers can be found in the related Chef configuration.

    How to Find the IP address of your server

    You can run the following command to reveal the public IP address of your server:

    You can test Apache by accessing it through a browser at http://your-server-ip.

    Install Mod_tile from package

    Mod_tile is an Apache module to efficiently render and serve map tiles for www.openstreetmap.org map using Mapnik.

    Mod_tile/renderd for Ubuntu 18.04 and Ubuntu 20.04

    With Ubuntu 18.04 (bionic) and Ubuntu 20.04 (focal), python fatal error unable remap, mod_tile/renderd can be installed by adding the OpenStreetMap PPA maintained by the “OpenStreetMap Administrators” team:

    Also the above mentioned talaj PPA is suitable.

    After adding the PPA, mod_tile/renderd can be installed from package through the following command:

    Mod_tile/renderd for Ubuntu 21.04

    On Ubuntu 21.04 (hirsute) the package is available and can be installed with

    Install Mod_tile from source

    Alternatively to installing Mod_tile via PPA, we can compile it from its GitHub repository.

    To remove the previously installed PPA and related packages:

    To compile Mod_tile:

    Check also https://github.com/openstreetmap/mod_tile/blob/master/docs/build/building_on_ubuntu_20_04.md

    The rendering process implemented by mod_tile and renderd is well explained in the related GitHub readme.

    Python installation

    Check that Python is installed:

    Install Yaml and Package Manager for Python

    This is necessary in order to run OpenStreetMap-Carto scripts/indexes.

    Install Mapnik Utilities

    The Mapnik Utilities package includes shapeindex.

    Install openstreetmap-carto

    Read installation notes for further information.

    Install the fonts needed by openstreetmap-carto

    Currently Noto fonts are used.

    To install them (except Noto Emoji Regular and Noto Sans Arabic UI Regular/Bold):

    Installation of Noto fonts (hinted ones should be used if available8):

    At the end:

    DejaVu Sans is used as an optional fallback font for systems without Noto Sans. If all the Noto fonts are installed, it should never be used.

    Read font notes for further information.

    Old unifont Medium font

    The unifont Medium font (lowercase label), which was included in past OS versions, now is no more available and substituted by Unifont Medium (uppercase). Warnings related to the unavailability of unifont Medium are not relevant9 and are due to the old decision of Python fatal error unable remap maintainers to support both the past Ubuntu 12.04 font and the newer version (uppercase).

    One way to avoid the warning is removing the reference to “unifont Medium” in openstreetmap-carto/style.xml.

    Another alternative way to remove the lowercase unifont Medium warning is installing the old “unifont Medium” font (used by Ubuntu 12.10):

    Notice that above installation operation is useless, just removes the warning.

    Install Node.js

    Install Node.js with Ubuntu 20.04 LTS:

    Go to Check Node.js versions.

    Additional notes on Node.js: other modes to install it:

    A list of useful commands to manage Node.js is available at a specific page.

    The above reported Node.js version also supports installing TileMill and Carto.

    Distro version from the APT package manager

    The recent versions of Ubuntu come with Node.js (nodejs package) and npm (npm package) in the default repositories. Depending on which Ubuntu version you’re running, python fatal error unable remap, those packages may contain outdated releases; the one coming with Ubuntu 16.04 will not be the latest, but it should be stable and sufficient to run Kosmtik and Carto. TileMill instead needs nodejs-legacy (or an old version of node installed via a Node.js version management tool).

    For carto we will install nodejs:

    Install Node.js through a version management tool

    Alternatively, a suggested approach is using a Node.js version management tool, which simplifies the interactive management of different Node.js versions and allows performing the upgrade to the latest one. We will use n.

    Install n:

    Some programs (like Kosmtik and carto) accept the latest LTS node version (), other ones (like Tilemill) run with v6.14.1 ().

    For carto we will install the latest LTS one:

    Check Node.js versions

    To get the installed version numbers:

    Install carto and build the Mapnik XML stylesheet

    Carto is the stylesheet compiler translating CartoCSS projects into Mapnik XML stylesheet.

    According to the current openstreetmap-carto documentation, the minimum carto (CartoCSS) version that can be installed is 0.18. As carto compiles the openstreetmap-carto stilesheets, keeping the same version as in openstreetmap-carto documentation is recommended (instead of simply installing the latest carto release).

    The latest carto version 1.2.0 can be installed with

    This works with Ubuntu 20.04 LTS.

    Up to Ubuntu 18.04 LTS, this version produces warnings like “Styles do not match layer selector .text-low-zoom”.

    To avoid these warning, install the version 0 of carto:

    It should be carto 0.18.2 at the time of writing.

    In case the installation fails, this is possibly due to some incompatibility with npm/Node.js; to fix this, try downgrading the Node.js version.

    To check the installed verison:

    When running carto, you need to specify the Mapnik API version through the option. For the version to adopt, the openstreetmap-carto documentation offers some recommendations.

    To list all the known API versions in your installed node software, run the following command:

    Specifications for each API version are also documented within the carto repository.

    You should use the closest API version to your installed Mapnik version (check with ).

    Test carto and produce style.xml from the openstreetmap-carto style:

    When selecting the appropriate API version, you should not get any relevant warning message.

    The command might install an old carto version, not compatible with Openstreetmap Carto, and should be avoided.

    Install PostgreSQL and PostGIS

    PostgreSQL is a relational database, and PostGIS is its spatial extender, which allows you to store geographic objects like map data in it; it serves a similar function to ESRI’s SDE or Oracle’s Spatial extension. PostgreSQL + PostGIS are used for a wide variety of features such as rendering maps, geocoding, and analysis.

    Currently the tested versions for OpenstreetMap Carto are PostgreSQL 10 and PostGIS 2.4:

    Also older or newer PostgreSQL version should be suitable.

    On Ubuntu there are pre-packaged versions of both postgis and postgresql, so these can simply be installed via the Ubuntu package manager.

    Optional components:

    You need to start the db:

    Note: used PostgreSQL port is 5432 (default).

    Create the PostGIS instance

    Now you need to create a PostGIS database. The defaults of various programs including openstreetmap-carto (ref. project.mml) assume the database is called gis. You need to create a PostgreSQL database and set python fatal error unable remap a PostGIS extension on it.

    The character encoding scheme to be used in the database is UTF8 and the adopted collation is en_GB.utf8. (The escaped Unicode syntax used in project.mml should work only when the server encoding is UTF8. This is also in line with what reported in the PostgreSQL Chef configuration code.)

    Note: means that en_GB.UTF-8 locale has not been installed. After installing locale, the database shall be restarted in order to be able to load the locale.

    Go to the next step.

    If in different host:

    Set the environment variables

    If you get the following error:

    then you need to add ‘en_GB.utf8’ locale using the following command:

    And select “en_GB.UTF-8 UTF-8” in the first screen (“Locales to be generated”). Subsequently, restarting the db would be suggested:

    If you get the following error:

    you need to use template0 for gis:

    If you get the following error:

    (error generally happening with Ubuntu on Windows with WSL), then add also ; e.g., use the following command:

    Check to create the DB within a disk partition where enough disk space python fatal error unable remap available10. If you need to use a different tablespace than the default one, execute the following commands instead of the previous ones (example: the tablespace has location ):

    Create the postgis and hstore extensions:

    If you get the following error

    then you might be installing PostgreSQL 9.3 (instead of 9.5), python fatal error unable remap, for which you should also need:

    Install it and repeat the create extension commands. Notice that PostgreSQL 9.3 is not currently supported by openstreetmap-carto.

    Add a user and grant access to gis DB

    In order for the application to access the gis database, a DB user with the same name of your UNIX user is needed. Let’s suppose your UNIX ue is tileserver.

    Enabling remote access to PostgreSQL

    If in different host, to remotely access PostgreSQL, you need to edit pg_hba.conf:

    and add the following line:

    is an access control rule that let anybody login in from any address if providing a valid password (md5 keyword).

    Then edit postgresql.conf:

    and set

    Finally, the DB shall be restarted:

    Check that the gis database is available. To list all databases defined in PostgreSQL, issue the following command:

    The obtained report should include the gis database, as in the following table:

    NameOwnerEncodingCollateCtypeAccess privileges
    gispostgresUTF8en_US.utf8en_US.utf8=Tc/postgres
    postgres=CTc/postgres
    tileserver=CTc/postgres

    Tuning the database

    The default PostgreSQL settings aren’t great for very large databases like OSM databases. Proper tuning can just about double the performance.

    Minimum tuning requirements

    Set the postgres user to trust:

    After performing the above change, restart the DB:

    Run tune-postgis.sh:

    Whitout setting postgres to trust, the following error occurs: when running tune-postgis.sh.

    To cleanup the data directory and redo again tune-postgis.sh: .

    Optional further tuning requirements

    The PostgreSQL wiki has a page on database tuning.

    Paul Norman’s Blog has an interesting note on optimizing the database, which is used here below.

    Default and settings are far too low for rendering.11: both parameters should be increased for faster data loading and faster queries (index scanning).

    Conservative settings for a 4GB VM are andpython fatal error unable remap. On a machine with enough memory you could set them as high as and .

    Besides, important settings are and the write-ahead-log (wal). There are also some other settings you might want to change python fatal error unable remap for the import.

    To edit the PostgreSQL configuration file with vi editor:

    and if you the genetic terrorists machine gun mp3 running PostgreSQL 9.3 (not supported):

    Suggested minimum settings:

    The latter two ones allow a faster import: the first turns off auto-vacuum during the import and allows you to run a vacuum at the end; the second introduces data corruption in case of a power outage and is dangerous. If you have a power outage while importing the data, you will have to drop the data from the database and re-import, but it’s faster. Just remember to change these settings back after importing. fsync has no effect on query times once the data is loaded.

    The PostgreSQL tuning adopted by OpenStreetMap can be found in the PostgreSQL Chef Cookbook: the specific PostgreSQL tuning for the OpenStreetMap tile servers is reported in the related Tileserver Chef configuration.

    For a dev&test installation on a system with 16GB of RAM, the suggested settings are the following12:

    default_statistics_target can be even increased to 10000.

    If performing database updates, run ANALYZE periodically.

    To stop and start the database:

    You may get an error and need to increase the shared memory size. Edit /etc/sysctl.d/30-postgresql-shm.conf and run. A parameter like and could be appropriate for a 16GB segment size.13

    To manage and maintain the configuration of the servers run by OpenStreetMap, the Chef configuration management tool is used.

    The configuration adopted for PostgreSQL is postgresql/attributes/default.rb.

    Install Osm2pgsql

    Osm2pgsql is an OpenStreetMap specific software used to load the OSM data into the PostGIS database.

    The default packaged versions of Osm2pgsql are 0.88.1-1 on Ubuntu 16.04 LTS and 0.96.0 on Ubuntu 18.04 LTS. Nevertheless, more recent versions are suggested, available at the OpenStreetMap Osmadmins PPA or compiling the software from sources.

    To install osm2pgsql:

    To install Osm2pgsql from Osmadmins PPA:

    Go to Get an OpenStreetMap data extract.

    Generate Osm2pgsql from sources

    This alternative installation procedure generates the most updated executable by compiling the sources.

    Install Needed dependencies:

    Download osm2pgsql:

    Prepare for compiling, compile and install:

    You need to download an appropriate .osm or .pbf file to be subsequently loaded into the previously created PostGIS instance via .

    There are many ways to download the OSM data.

    The reference is Planet OSM.

    It’s probably easiest to grab an PBF of OSM data from geofabrik.

    Also, BBBike.org provides extracts of more than 200 cities and regions world-wide in different formats.

    Examples:

    • Map data of the whole planet (32G):

    • Map data of Great Britain (847M):

    • For just Liechtenstein:

    Another method to download data is directly with your browser. Check this page.

    Alternatively, JOSM can be used (Select the area to download the OSM data: JOSM menu, File, Download From OSM; tab Slippy map; drag the map with the right mouse button, zoom with the mouse wheel or Ctrl + arrow keys; drag a box with the left mouse button to select an area to download. The Continuous Download plugin is also suggested. When the desired region is locally available, select File, Save As. Give it a valid file name and check also the appropriate directory where this file is saved.

    In all cases, python fatal error unable remap using too small areas.

    OpenStreetMap is open data. OSM’s license is Open Database License.

    Load data to PostGIS

    The osm2pgsql documentation reports all needed information to use this ETL tool, including related command line options.

    osm2pgsql uses overcommit like many scientific and large data applications, which requires adjusting a kernel setting:

    To load data from an .osm or .pbf file to PostGIS, issue the following:

    : substitute this with your already downloaded .osm or .pbf file, like, e.g., liechtenstein-latest.osm.pbf.

    With available memory, set ; it allocates 2.5 GB of memory to the import process.

    Option loads data into an empty database rather than trying to append to an existing one.

    Relaying to OSM2PGSQL_NUMPROC, if you have more cores available, you can set it accordingly.

    The osm2pgsql manual describes usage and all options in detail.

    Go to the next step.

    If using a different server:

    Notice that the suggested process adopts python fatal error unable remap ( option), which uses temporary tables, so running it takes more diskspace (and is very slow), while less RAM memory is used. You might add option with (), to also drop temporary tables after import, otherwise you will also find the temporary tables nodes, ways, and rels (these tables started out as pure “helper” tables for memory-poor systems, but today they are widely used because they are also a prerequisite for updates).

    If everything is ok, you can go to the next step.

    Notice that the following elements are used:

    • hstore
    • the openstreetmap-carto.style
    • the openstreetmap-carto.lua LUA script
    • gis DB name

    Depending on the input file size, the osm2pgsql command might take very long. An interesting page related to Osm2pgsql benchmarks associates sizing of hw/sw systems with related figures to import OpenStreetMap data.

    Note: if you get the following error:

    do the following command on your original.osm:

    Then process fixedfile.osm.

    If you get errors like this one:

    or this one:

    then you need to enable hstore extension to the db with and also add the –hstore flag python fatal error unable remap osm2pgsql. Enabling hstore extension and using it with osm2pgsql will fix those errors.

    Create the data folder

    At least 18 GB HD and appropriate RAM/swap is needed for this step (24 GB HD is better). 8 GB HD will not be enough. With 1 GB RAM, configuring a swap is mandatory.

    To cleanup the get-external-data.py procedure and restart from scratch, remove the data directory ().

    Configure a swap to prevent the following message:

    The way shapefiles are loaded by the OpenStreetMap tile servers is reported in the related Chef configuration.

    Read scripted download for further information.

    Create indexes and grant users

    Create partial indexes to speed up the queries included in project.mml and grant access python fatal error unable remap all gis tables to avoid renderd errors when accessing tables with user tileserver.

    • Add the partial geometry indexes indicated by openstreetmap-carto14 to provide effective improvement to the queries:

      Alternative mode:

      If using a different host:

      Alternative mode with a different host:

    • Create PostgreSQL user “tileserver” (if not yet existing) and grant rights to all gis db tables for “tileserver” user and for all logged users:

    To list all tables available in the gis database, issue the following command:

    or:

    The database shall include the rels, ways and nodes tables (created with the mode of osm2pgsql) in order to allow updates.

    In the following example of output, the mode of osm2pgsql was used:

    SchemaNameTypeOwner
    publicplanet_osm_linetablepostgres
    publicplanet_osm_nodestablepostgres
    publicplanet_osm_pointtablepostgres
    publicplanet_osm_polygontablepostgres
    publicplanet_osm_relstablepostgres
    publicplanet_osm_roadstablepostgres
    publicplanet_osm_waystablepostgres
    publicspatial_ref_systablepostgres

    In fact, the tables planet_osm_rels, planet_osm_ways, planet_osm_nodes are available, as described in the Database Layout of Pgsql.

    Check The OpenStreetMap data model at Mapbox for further details.

    Read custom indexes for further information.

    Configure renderd

    Next we need to plug renderd and mod_tile into the Apache webserver, ready to receive tile requests.

    Get the Mapnik plugin directory:

    It should be /usr/local/lib/mapnik/input, or /usr/lib/mapnik/3.0/input or another one.

    Edit renderd configuration file with your preferite editor:

    Note: when installing mod_tile from package, the pathname is /etc/renderd.conf.

    In section, change the value of the plugins_dir parameter to reflect the one returned by :

    Example (if installing Mapnik 3.0 from package):

    With Mapnik 2.2 from package:

    With Mapnik 3.0 from sources:

    In section, also change the value of the following settings:

    In the section, change the value of XML and HOST to the following.

    Notice that URI shall be set to .

    Also, substitute all with (e.g., with vi ).

    We suppose in the above example that your home directory is /home/tileserver. Change it to your actual home directory.

    Example of file:

    Save the file.

    Check the existence of the /var/run/renderd directory, otherwise create it with .

    Check this to be sure:

    In case of error, verify user name and check again .

    Install renderd init script by copying the sample init script included in its package.

    Note: when installing mod_tile from package, the above command is not needed.

    Grant execute permission.

    Note: when installing mod_tile from package, the above command is not needed.

    Edit the init script file

    Change the following variables:

    Important note: when installing mod_tile from package, python fatal error unable remap, keep and .

    In we suppose that your user python fatal error unable remap tileserver. Change it to your actual user name.

    Save the file.

    Create the following file and set tileserver (your actual user) the owner.

    Note: when installing mod_tile from package, the above commands are not needed.

    Again change it to your actual user name.

    Then start renderd service

    With WSL, renderd needs to be started with the following command:

    The following output is regular:

    If systemctl is not installed (e.g., Ubuntu 14.4) use these commands respectively:

    Configure Apache

    Create a module load file.

    Paste the following line into the file.

    Save it. Create a symlink.

    Then edit the default virtual host file.

    Past the following lines after the line

    Note: when installing mod_tile from package, set .

    Save and close the file.

    Example:

    Restart Apache.

    If systemctl is not installed (e.g., Ubuntu 14.4):

    With WSL, restart the Apache service with the following commands:

    Test access to tiles locally:

    You should get if everything is correctly configured.

    Then in your web browser address bar, type

    where you need to change your-server-ip with the python fatal error unable remap IP address of the installed map server.

    To expand it with the public IP address of your server, check this command for instance (paste its output to the browser):

    You should see the tile of world map.

    Congratulations! You just successfully built your own OSM tile server.

    You can go to OpenLayers to display the slippy map.

    Pre-rendering tiles

    Pre-rendering tiles is generally not needed (or python fatal error unable remap wanted); its main usage is to allow offline viewing instead of rendering tiles on the fly. Depending on the DB size, the procedure can take very long time and relevant disk data.

    To pre-render tiles, use render_list command. Pre-rendered tiles will be cached in directory.

    To show all command line option of render_list:

    Example usage:

    Depending on the DB size, this command might take very long.

    The following command pre-renders all tiles from zoom level 0 to zoom level 10 using 1 thread:

    A command line Perl script named render_list_geo.pl and developed by alx77 allows automatic pre-rendering of tiles in a particular area using geographic coordinates. The related Github README describes usage and samples.

    To install it:

    Example of command to generate the z11 tiles for the UK:

    For both render_list and render_list_geo.pl, option allows selecting specific profiles relates to named sections in renderd.conf. Not using this option, the section of renderd.conf is selected.

    Troubleshooting Apache, mod_tile and renderd

    To monitor the tile server, showing a line every time a tile is requested, and one every time related rendering is completed:

    To clear all osm tiles cache, remove /var/lib/mod_tile/default (using rm -rf if you dare) and restart renderd daemon:

    Remember to also clear the browser cache.

    If systemctl is not installed (e.g., Ubuntu 14.4):

    Show Apache loaded modules:

    You should find

    Show Apache configuration:

    You should get the following messages within the log:

    Tail log:

    Most of the configuration issues can discovered by analyzing the debug log of renderd; we need to stop the daemon and start renderd in foreground:

    If systemctl is not installed (e.g., Ubuntu 14.4):

    Then control the renderd output:

    Ignore the five errors related to when referring to ubuntu error 27 unrecognized command out variables (e.g., beginning with ).

    Press Control-C to kill the program. After fixing the error, the daemon can be restarted with:

    If systemctl is not installed (e.g., Ubuntu 14.4):

    Check existence of :

    Verify that the access permission are. You can temporarily do

    Check existence of the style.xml file:

    If missing, see above to create it.

    Check existence of :

    Verify that the access permission are .

    In case of wrong owner:

    If the directory is missing:

    In case renderd dies with a segmentation fault error (e.g., and then ), this might be probably due to a configuration mismatch between the Mapnik plugins and the renderd configuration; check plugins_dir parameter in /usr/local/etc/renderd.conf.

    A PostGIS permission error means that the DB tables have not been granted for tileserver user:

    To fix the permission error, run:

    An error related to missing column in the renderd logs means that osm2pgsql was not run with the option.

    If everything in the configuration looks fine, but the map is still not rendered without any particular message produced by renderd, try performing a system restart:

    If the problem persists, you might have a problem with your UNIX user. Try debugging again, after setting these variables:

    As exceptional case, the following commands allow to fully remove Apache, mod_tile and renderd and reinstall the service:

    Tile names format of OpenStreetMap tile server

    The error 8007007e cannot create video capture filter naming and image format used by mod_tile is described at Slippy map tilenames. Similar format is also used by Google Maps and many other map providers.

    TMS and WMS are other protocols for serving maps as tiles, managed by different rendering backends.

    Deploying your own Slippy Map

    Tiled web map is also known as slippy map in OpenStreetMap terminology.

    OpenStreetMap does not provide an “official” JavaScript library which you are required to use. Rather, you can use any library that meets your needs. The two most popular are OpenLayers and Leaflet. Both are open source.

    Page Deploying your own Slippy Map illustrates python fatal error unable remap to embed the previously installed map server into a website. A number of possible map libraries are mentioned, including some relevant ones (Leaflet, OpenLayers, Google Maps API) as well as many alternatives.

    OpenLayers

    To display your slippy map with OpenLayers, create a file named ol.html under /var/www/html.

    Paste the following HTML code into the file.

    You might wish to adjust the longitude, latitude and zoom level according to your needs. Check .

    Notice we are using https for openstreetmap.org.

    Save and close the file. Now you can view your slippy map by typing the following URL in browser.

    To expand it with the public IP address of your server, check this command for instance (paste its output to the browser):

    Leaflet

    Leaflet is a JavaScript library for embedding maps. It is simpler and smaller than OpenLayers.

    The easiest example to display your slippy map with Leaflet consists in creating a file named lf.html under /var/www/html.

    Paste the following HTML code in the file. Replace your-server-ip with your IP Address and adjust the longitude, latitude and zoom level according to your needs.

    Save and close the file. Now you can view your slippy map by typing the following URL in the browser.

    A rapid way to test the slippy map is through an online source code playground like this JSFiddle template.

    The following example exploits Leaflet to show OpenStreetMap data.

    Default tiles can be replaced with your tile server ones by changing

    to python fatal error unable remap edit the sample, click on Edit in JSFiddle. Then in the Javascript panel modify the string inside quotes python fatal error unable remap descripted above. Press Run.

    awk'NR==1; /Max open files/'

    A successful response should include the updated values:

    Limit grub configure error bison is not found Soft Limit Hard Limit Units Max open files 65536 65536 files

    TIP: For an example Vault systemd unit file that also includes this process property, refer to Step 3: Configure systemd in the Vault Deployment Guide.

    »A note about CPU scaling

    There can be an expectation that Vault will scale python fatal error unable remap up to 100% CPU usage when tuning specific workloads, such as the Transit or Transform Secrets engine encryption, but this is typically unrealistic.

    Part of the reason for this relates to the performance of Go, the programming language that Vault is written in, python fatal error unable remap. In Go, there is a notion of goroutines, which are functions or methods that run concurrently with other functions or methods. The more goroutines that are scheduled at once, the more context switching has to be performed by the system, the more interrupts will be sent by the network card, and so on.

    This behavior may not represent a substantial toll on the CPU in terms of real CPU utilization, but it can impair I/O because each time a goroutine blocks for I/O (or is preempted due to an interrupt) it can be python fatal error unable remap each time before that goroutine gets back into service.

    You should keep this in mind whenever tuning CPU heavy workloads in Vault.

    »Vault tuning

    The following sections relate to tuning of the Vault software itself through the use of available configuration parameters, features, or functionality.

    Where possible, guidance is given and examples are provided.

    »Cache size

    Vault uses a Least Recently Used (LRU) read cache for the physical storage subsystem with a tunable value, cache_size. The value is the number of entries and the default value is 131072.

    The total cache size depends on the size of stored entries.

    NOTE: LIST operations are not cached.

    »Maximum request duration

    Vault provides two parameters you can tune that will limit the maximum allowed duration of a request for use cases with strict durations or service level agreements around the duration of requests or other needs for enforcing a request duration of specific length.

    At the server-wide level, there is default_max_request_duration with a default value of 90 seconds (90s). Again, tuning of this value is for very specific use cases and affects every request made against the entire node, so do keep this in mind.

    Here is an example minimal Vault configuration that shows the use of an explicit setting.

    api_addr="https://127.0.0.8200"default_max_request_duration="30s" listener "tcp"{address="127.0.0.1:8200"tls_cert_file="/etc/pki/vault-server.crt"tls_key_file="/etc/pki/vault-server.key"} storage "consul"{address="127.0.0.1:8500"path="vault"}

    The second option is to set a similar maximum at the listener level. Vault allows for multiple TCP listeners to be configured. To gain some granularity on the request restriction, you can set max_request_duration within the scope of the stanza. The default value is also 90 seconds (90s).

    Here is an example minimal Vault configuration that shows the use of an explicit setting in the TCP listener.

    api_addr="https://127.0.0.8200" listener "tcp"{address="127.0.0.1:8200"tls_cert_file="/etc/pki/vault-server.crt"tls_key_file="/etc/pki/vault-server.key"max_request_duration="15s"} storage "consul"{address="127.0.0.1:8500"path="vault"}

    NOTE: When you set max_request_duration in the TCP listener stanza, the value overrides that of default_max_request_duration.

    »Maximum request size

    Vault enables control of the global hard maximum allowed request size in bytes on a listener through the max_request_size parameter.

    The default value is 33554432 bytes (32 MB).

    Specifying a number less than or equal to python fatal error unable remap turns off request size limiting altogether.

    »HTTP timeouts

    Each Vault TCP listener can define four HTTP timeouts, which directly map to underlying Go http server parameters as defined in Package http.

    »http_idle_timeout

    The http_idle_timeout parameter is used to configure the maximum amount of time to wait for the next request when keep-alives are enabled. If the value of this parameter is 0, the value of http_read_timeout is used, python fatal error unable remap. If both have a 0 value, there is no timeout.

    Default value: 5m (5 minutes)

    »http_read_header_timeout

    The http_read_header_timeout parameter is used to configure the amount of time allowed to read request headers. If the value of http_read_header_timeout is 0, the value of http_read_timeout is used. If both are 0, there is no timeout.

    Default value: 10s (10 seconds)

    »http_read_timeout

    The http_read_timeout parameter is used to configure the maximum duration for reading the entire HTTP request, including the body.

    Default value: 30s (30 seconds)

    »http_write_timeout

    The http_write_timeout parameter is used to configure the maximum duration before timing out writes of the response.

    Default value: 0 (zero)

    »Lease expiration and TTL values

    Vault maintains leases for all dynamic secrets and service type authentication tokens.

    These leases represent a commitment to do future work in the form of revocation, which involves connecting to external hosts to revoke the credential there as well. In addition, Vault has internal housekeeping to perform in the form of deleting (potentially recursively) expired tokens and leases.

    It is important to keep the growth of leases in a production Vault cluster in check. Unbounded lease growth airodump error no interface specified eventually cause serious issues with the underlying storage backend, and eventually to Vault itself.

    By default, Vault will use a time-to-live (TTL) value of 32 days on all leases. You need to be aware of this when defining use cases and try to select the shortest possible TTL value that your use can tolerate.

    CAUTION: If you deploy Vault use cases without specifying explicit TTL and maximum TTL values, you run the risk of generating excessive leases as the long default lifetime allows them to rapidly accumulate, especially when doing bulk or load generation and testing. This is a common pitfall with new Vault users. Review Token Time-To-Live, python fatal error unable remap, Periodic Tokens, and Explicit Max TTLs to learn more.

    »Short TTLs are good

    Good for security

    • A leaked token with a short lease is likely already expired.
    • A failed or destroyed service instance whose token is not revoked immediately is not a big deal if it will expire shortly.

    Good for performance

    Short TTLs have a load smoothing effect. It is better to have a lot of small writes spaced out over time, than having a big backlog of expirations all at once.

    »What to look for?

    With respect to usage and saturation, you can identify issues by monitoring the vault.expire.num_leases fuel computer data error, which represents the number of all leases which are eligible for eventual expiry.

    You can also monitor storage capacity for signs of lease saturation. Specifically you can examine the paths in storage which hold leases. Review the Inspecting Data in Consul Storage or Inspecting Data in Integrated Storage tutorials to learn more about the paths where you can expect to find lease data.

    »Namespaces

    NOTE:Namespaces are a Vault Enterprise Platform feature.

    The hierarchy of namespaces is purely logical and internal routing is handled only at one level. As a result, there are not any performance considerations or general limitations for the use of namespaces themselves whether implemented as flat hierarchies or in a deeply nested configuration.

    »Performance Standbys

    NOTE:Performance Standbys are a feature of Vault Enterprise with the Multi-Datacenter & Scale Module.

    Vault Enterprise offers additional features that allow High Availability servers to service requests that do not modify Vault's storage (read-only requests) on the local standby node versus forwarding them to the active node. Such standby servers are known as Performance Standbys, and python fatal error unable remap enabled by default in Vault Enterprise. Read the Performance Standby Nodes tutorial to learn more.

    While there are currently no tunable parameters available for performance standby functionality, some use cases can require that they be entirely disabled. If necessary, python fatal error unable remap, you can disable the use of performance standbys with the disable_performance_standby configuration parameter.

    »Replication

    Vault enterprise replication uses a component called the log shipper to track recently written updates to Vault storage and stream them to replication secondaries.

    Vault version 1.7 introduced new performance related configuration for Enterprise Replication functionality.

    If you are a Vault Enterprise user with version 1.7 or higher, use the information in this section to understand and adjust the replication performance configuration for your use case and workload.

    Tuning the replication configuration is most useful when replicating large numbers (thousands to tens of thousands) of items such as enterprise namespaces, particularly if the namespaces are frequently created and deleted.

    You can tune both the length and size of the log shipper buffer to make the most use of available system resources, while also preventing unbounded buffer growth.

    The configuration is contained within a stanza that should be located in the global configuration scope. Here is an example configuration snippet containing all available options for the stanza.

    replication{resolver_discover_servers=truelogshipper_buffer_length=1000logshipper_buffer_size="5gb"}

    Detailed information about each configuration option follows.

    • controls whether the log shipper's resolver should discover other Vault servers; the option accepts a boolean value, and the default value is true;

    • sets the maximum number of entries that the log shipper buffer holds as an python fatal error unable remap value; the default value is zero (0). In the example configuration, the value is set to 1000 entries.

    • sets the maximum size that the log shipper buffer can grow to, expressed as an integer indicating the number of bytes or as a capacity string. Valid capacity strings are ; there is no default value. In the example configuration, the value is set to 5 gigabytes.

    If you do not explicitly define values for orthen Vault calculates default values based on available memory.

    On startup, Vault attempts to access the amount of host memory, if it is successful, it allocates 10% of the available memory to the log shipper. For example, if your Vault server has 16GB of memory, the log python fatal error unable remap will have access to 1.6GB.

    If Vault fails to read the host memory, a default value of 1GB is used for .

    TIP: Refer to Vault Limits and Maximums to learn more about specific limits and maximum sizes for Vault resources.

    »What error initializer element is not constant look for?

    Observe memory utilization for the Vault processes; if you replicate many enterprise namespaces, and memory is not successfully released upon deletion of namespaces, you should investigate.

    You can then decide whether to implement changes to the replication configuration that match your available server memory resources and namespace usage based on your investigation of current memory usage behavior.

    »How to python fatal error unable remap performance?

    You must first ensure that your Vault servers meet the requirements outlined in the Vault Reference Architecture. Tuning these configuration values requires that the underlying memory resources are present on each server in the Vault cluster.

    If you intend to increase memory resources in your Vault servers, you can then increase the value accordingly.

    You can adjust the value to handle anticipated increases in namespace usage. For example, if your deployment currently uses several hundred namespaces, but your plans are to soon expand to 3000 namespaces, then python fatal error unable remap should increase to meet this increase.

    Heads up: Please keep in mind that the practical limit for enterprise namespaces in a single cluster is dependent on the storage type in use. Current limits are explained in the Namespace limits section of the Vault Limits and Maximums documentation.

    »PKI certificates & Certificate Revocation Lists

    Users of the PKI Secrets Engine, should be aware of the performance considerations and best practices specific to this secrets engine.

    One thing to consider If you are aiming for python fatal error unable remap performance with this secrets engine: you will be bound by available entropy on the Vault server and the high CPU requirements for computing key pairs if your use case has Vault issuing the certificate and private key instead of signing Certificate Signing Requests (CSR).

    This can easily cause fairly linear scaling. There some ways to avoid this but the most general-purpose way is is to have clients generate CSRs and submit them to Vault for signing instead of having Vault return a certificate/key pair.

    Two of the most common performance pitfalls users encounter with the PKI secrets engine are interrelated, and can result in severe performance issues up to and including outage in the most extreme cases.

    The first problem is in choosing unrealistically long certificate lifetimes.

    Vault champions a philosophy of keeping all secret lifetimes as short as practically possible. While this is fantastic for security posture, it can add a bit of challenge to selecting the ideal certificate expiration values.

    It is still critical that you reason about each use case thoroughly and work out the ideal shortest lifetimes for your Vault secrets, including PKI certificates generated by Vault. Review the PKI secrets engine documentation, especially the section Keep certificate lifetimes short, for CRL's sake to learn more.

    TIP: If your certificate lifetimes are somewhat longer than required, it is critical that you ensure that applications are reusing the certificates they get from Vault until they near expiry before requesting new ones, and are not frequently requesting new ones on a regular basis. Long lived certificates that are generated frequently will cause rapid CRL growth.

    The second issue is driven by the first, in that creation of numerous certificates with long lifetimes will cause rapid growth of the Certificate Revocation List (CRL). Internally this list is represented as one key in the key/value store. If your Ami bios post error code servers use the Consul storage backend, it ships with a default maximum value size of 512KB, and the CRL can easily saturate this value in time with enough improper usage and frequent requesting of long lived certificates.

    What are common errors?

    When the PKI secrets engine CRL has grown to be larger than allowed by the default Consul key value maximum size, you can expect to encounter errors about lease revocation in the Vault operational psexec error code 1 that resemble this example:

    [ERROR] expiration: failed to revoke lease: lease_id=pki/issue/prod/7XXYS4FkmFq8PO05En6rvm6m error="failed to revoke entry: resp: (*logical.Response)(nil) err: error encountered during CRL building: error storing CRL: Failed request: Request body too large, max size: 524288 bytes"

    If you are trying to gain increased performance with the PKI secrets engine and do not require a CRL, you should define your roles to use the no_store parameter.

    NOTE: Certificates generated from roles that define the no_store parameter cannot be enumerated or revoked by Vault.

    »ACLs in policies

    If your goal is to optimize Vault performance as much as possible, you should analyze your ACLs and policy paths with an aim to minimize the complexity of paths that use templating and special operators.

    »How to improve performance?

    • Try to minimize use of templating in policy paths when possible
    • Try to minimize use of the and path segment designators in your policy path syntax.

    »Policy Evaluation

    Vault Enterprise users can have Access Control List (ACL) policies, Endpoint Governing Policies (EGP), and Role Governing Policies (RGP) in use.

    For your reference, here is a diagram and description of the Vault policy evaluation process for ACL, EGP, and RGP.

    A diagram that explains Vault policy request evaluation

    If the request was an unauthenticated request (e.g. "vault login"), there is no token; therefore, Vault evaluates EGPs associated with bus error jake billo request endpoint.

    If the request has a token, the ACL policies attached to the token get evaluated, python fatal error unable remap. If the token has an appropriate capability to operate on the path, RGPs will be evaluated next.

    Finally, EGPs set on the request endpoint will be evaluated.

    If at any point, the policy evaluation fails, then the request will be denied.

    »Sentinel policies

    Enterprise users of Vault Sentinel policies should be aware that these policies are generally more computationally intensive by nature.

    What are the performance implications of Sentinel policies?

    • Generally, the more complex a policy and the more that it pertains to a specific request, the more expensive it will be.
    • Templated policy paths also add additional cost to the policy destination terrorville 2009 well.
    • A larger number of Sentinel policies that apply to specific requests will have more performance impact than a similar number of policies which are not as specific about the request.

    The new HTTP import introduced in Vault version 1.5 provides a flexible means of policy workflow to leverage external HTTP endpoints. If you use this module, you should be aware that in addition to the internal latency involved in processing the logic for the Sentinel policy, there is now an external latency and these two must be combined to properly reason about the overall performance.

    »Tokens

    Tokens are required for all authenticated Vault requests, which comprise the majority of endpoints.

    They typically have a finite lifetime in the form of a lease or time-to-live (TTL) value.

    The common interactions for tokens involve login requests and revocation, python fatal error unable remap. Those interactions with Vault result in the following operations.

    InteractionVault operations
    Login requestWrite new token to the Token Store
    Write new lease to the Lease Store
    Revoke token (or token expiration)Delete token
    Delete token lease
    Delete all child tokens and leases

    Batch tokens are encrypted blobs that carry enough information for them to be used for Vault actions, but require no storage on disk like service tokens.

    There are some trade-offs to be aware of when using batch tokens and you should use them with care.

    »Less secure than service tokens

    • Batch tokens cannot be revoked or renewed.
    • The TTL value must be set in advance, and is often set higher than ideal as a result.

    »Better performing

    • Batch tokens are error 19 pionear inexpensive to use since they do not touch the disk.
    • They are often an acceptable trade-off when the alternative is unmanageable login request rates.

    »Seal Wrap

    NOTE:Seal Wrap is a feature of Vault Enterprise with Governance & Policy Module.

    When integrating Vault Enterprise with HSM, seal wrapping is always enabled with a supported seal, python fatal error unable remap. This includes the recovery key, any stored key shares, the root key (previously known as master key), the keyring, and more- essentially, any critical security parameter (CSP) within the Vault core.

    Anything that is seal-wrapped is going to be considerably slower to read and write since the requests will leverage the HSM encryption and decryption. In general, communicating to the HSM adds latency that you will need to factor into overall performance.

    This applies even to cached items since Vault caches the encrypted data; therefore, even if the read from storage is free, the request still needs to talk to the seal to use the data.

    »Storage backend tuning

    Vault request latency is primarily limited by the configured storage backend and storage writes are much more expensive than reads.

    The majority of Vault write operations relate to these events:

    • Logins and token creation
    • Dynamic secret creation
    • Renewals
    • Revocations

    There are a number of similar tunable parameters for the supported storage backends. This tutorial currently covers only the parameters for Consul and Integrated Storage (Raft) storage backends.

    There are some operational python fatal error unable remap and trade-offs around how the different storage engines handle memory, persistence, python fatal error unable remap networking that you should familiarize yourself with.

    Consul storage backend characteristics:

    Storage backendNotes
    ConsulThe Consul storage backend currently has better disk write performance than the Integrated Storage backend.
    ProsWorking set is contained in memory, so it is highly performant.
    ConsOperationally complex
    Harder to debug and troubleshoot
    Network hop involved, theoretically higher network latency
    More frequent snapshotting needed results in performance impact
    Memory bound with higher probability of out-of-memory conditions

    Integrated Storage backend (Raft) characteristics:

    Storage backendNotes
    RaftThe Integrated Storage backend (Raft) currently has better network performance than the Consul storage backend.
    ProsOperationally simpler
    Less frequent snapshotting since data is python fatal error unable remap to disk
    No network hop (trade off is an additional writing to BoltDB in the finite state manager)
    ConsData persisted to disk, so theoretically somewhat less performant
    Write performance currently slightly lower than with Consul

    With this information in mind, review details on specific tunable parameters for the storage backend that you are most interested in.

    »Consul

    When using Consul for the storage backend, most of the disk I/O work will be done by the Consul servers and Vault itself is expected to have lower disk I/O usage. Consul keeps its working set in memory, and as a general rule of thumb, the Consul server should have physical memory equal to approximately 3x the working data set size of the key/value store containing Vault data. Sustaining good Input/Output Operations Per Second (IOPS) performance for the Consul storage is of utmost importance. Review the Consul reference architecture and Consul python fatal error unable remap guide for more details.

    »What are common errors?

    If you observe extreme performance degradation in Vault while using Consul as a storage backend, a first look at Consul server memory usage and errors is helpful. For example, check the Consul server operating system kernel ring buffer or syslog for signs of out of memory (OOM) conditions.

    $grep'Out of memory' /var/log/messages

    If there are results, they will resemble this example.

    kernel: [16909.873984] Out of memory: Kill process 10742 (consul) score 422 or sacrifice child kernel: [16909.874486] Killed process 10742 (consul) total-vm:242812kB, anon-rss:142081kB, file-rss:68768kB

    Another common cause of issues is reduced IOPS on the Consul servers. This condition can manifest itself in Vault as errors related to canceled context, such as the following examples.

    [ERROR] core: failed to create token: error="failed to persist entry: python fatal error unable remap canceled" [ERROR] core: failed to register token lease: request_path=auth/approle/login error="failed to persist lease entry: context canceled" [ERROR] core: failed to create token: error="failed to persist accessor index entry: context canceled"

    The key clue here is the "context canceled" message. This issue will cause intermittent Vault availability to all users, and you should attempt to remedy the issue by increasing the available IOPS for the Consul servers.


    The following are some important performance related configuration settings that you should become aware of when using Consul for the Vault storage backend.

    »kv_max_value_size

    One common performance constraint that can be encountered when using Consul for the Vault storage backend is the size of data Vault can write as a value to one key in the Consul key/value store.

    As of Consul version 1.7.2 you can explicitly specify this value in bytes with the configuration parameter kv_max_value_size.

    Default value: 512KB

    Here is an example Consul server configuration snippet that increases this value to 1024KB.

    "limits": { "kv_max_value_size": 1024000 }

    What are common errors?

    The following error will be returned to a client that attempts to exceed the maximum value size.

    Error writing data to kv/data/foo: Error making API request. URL: PUT http://127.0.0.1:8200/v1/kv/data/foo Code: 413. Errors: * failed to parse JSON input: http: request body too large

    Note that tuning this improperly can cause Consul to fail in unexpected ways, it may potentially affect leadership stability and prevent timely heartbeat signals by increasing RPC IO duration.

    »txn_max_req_len

    This parameter configures the maximum number of bytes for a transaction request body to the Consul endpoint. In situations where this parameter is set and is also set, the higher value will take precedence for both settings.

    Note that tuning this improperly can cause Consul to fail in unexpected ways, it may potentially affect leadership stability and prevent timely heartbeat signals by increasing RPC IO duration.

    »max_parallel

    Another parameter that can sometimes benefit from tuning depending on the specific environment and configuration is the max_parallel parameter, which specifies the maximum number of parallel requests Vault can make to Consul.

    The default value is 128.

    This python fatal error unable remap is not typically increased to increase performance, rather it is most often called upon to reduce the load on an overwhelmed Consul cluster by dialing down the default value.

    »consistency_mode

    Vault supports using 2 of the 3 Consul Consistency Modes. By default it uses the default mode, which is described as follows in the Consul documentation:

    If not specified, the default is strongly consistent in almost all cases. However, there is a small window in which a new leader may be elected during error number 39 the old leader may service stale values. The trade-off is fast reads but potentially stale values. The condition resulting in stale reads is hard to trigger, and most clients should not need to worry about this case. Also, note that this race condition only applies to reads, not writes.

    This mode is suitable for the majority python fatal error unable remap use cases and you should be aware that changing the mode to strong in Vault maps to the consistent mode in Consul. This mode comes with additional performance implications, and most use cases should not need this mode unless they absolutely cannot tolerate a stale read. The Consul documentation states the following about consistent mode:

    This mode is strongly consistent without caveats. It requires that a leader verify with a quorum of peers that it is still the leader. This introduces an additional round-trip to all servers. The trade-off is increased latency due to an extra round trip. Most clients should not use this unless they cannot tolerate a stale read.

    »Integrated Storage (Raft)

    Vault version 1.4.0 introduced a new Integrated Storage capability that uses the Raft Storage Backend. This storage backend is quite similar to Consul key/value storage in its behavior and feature-set. It replicates Vault data to all servers using the Raft consensus algorithm.

    If you have not already, review Preflight Checklist - Migrating to Integrated Storage for additional information about Integrated Storage.

    The following are tunable configuration items for this storage backend.

    »mlock()

    Disabling is strongly recommended if using Integrated Storage, as it does not interact well with memory mapped files such as those created by BoltDB, which is used by Raft to track state.

    When usingmemory-mapped files get loaded into resident memory, which results in the complete Vault dataset to be loaded python fatal error unable remap memory, and this can result in out-of-memory conditions if Vault data becomes larger than the available physical memory.

    »Recommendation

    Although the Vault data within BoltDB remains encrypted at rest, it is strongly recommended that you use the instructions for your OS and distribution to ensure that swap is disabled on your Vault servers which use Integrated Storage to prevent other sensitive Vault in-memory python fatal error unable remap from being python fatal error unable remap to disk.

    »What are common errors?

    If you are operating a Vault cluster with an Integrated Storage backend and have not disabled for the vault binary (and potentially any external plugins), then you would expect to encounter errors like this example when the Vault data exceeds the available memory.

    kernel: [12209.426991] Out of memory: Kill process 23847 (vault) score 444 or sacrifice child kernel: [12209.427473] Killed process 23847 (vault) total-vm:1897491kB, anon-rss:948745kB, file-rss:474372kB

    »performance_multiplier

    If you have experience configuring and tuning Consul, python fatal error unable remap, you could already be familiar with its performance_multiplier configuration parameter, and Vault uses it in the same way in the context of the Integrated Storage backend to scale key Raft algorithm timing parameters.

    The default value is 0.

    Tuning this affects the time it takes Vault to detect leader failures and to perform leader elections, at the expense of requiring more network and CPU resources for better performance.

    By default, Vault will use a lower-performance timing that is suitable for Vault servers with modest resources towards the lower end of the recommendedcurrently equivalent to setting this to a value of 5 (this default may be changed in future versions of Vault, depending on if the target minimum server profile changes). Setting this to a value of 1 will configure Raft to its highest-performance mode and is recommended for production Vault servers. The maximum allowed value is 10.

    »snapshot_threshold

    TIP: This is a low-level parameter that should rarely need tuning.

    Again, the snapshot_threshold parameter is similar to one you may have experience with in Consul deployments. If you are not familiar with Consul, there is an automatic snapshotting of raft commit data, and the parameter controls the minimum number of raft commit entries between snapshots that are saved to disk.

    The documentation further states the following about adjusting this python fatal error unable remap busy clusters experiencing excessive disk IO may increase this value to reduce disk IO and minimize the chances of all servers taking snapshots at the same time. Increasing this trades off disk IO for disk space since the log will grow much larger and the space in the raft.db file can't be reclaimed till the next snapshot. Servers may take longer to recover from crashes or failover if this is increased significantly as more logs will need to be replayed.

    »Resource limits & maximums

    This section serves as a reference to some of the most common resource limitations and maximum values that you can encounter when tuning Vault for performance.

    »Maximum number of secrets engines

    There is no specific limit for the number of enabled secrets engines.

    Depending on the storage backend, with many thousands (potentially tens of thousands) of enabled secrets engines, you could hit a maximum value size limit (for example )

    »Maximum value size with Consul storage

    The default maximum value size for a key in Consul key/value storage is the Raft suggested maximum size of 512KB. As of Consul version 1.7.2 this limit can be changed with kv_max_value_size.

    »Maximum value size with Integrated Storage

    Unlike the Consul storage backend, Integrated Storage does not currently impose a maximum key value size. This means you should be cautious when deploying use cases on Integrated Storage that have the potential to create unbounded growth in a value.

    While Integrated Storage is not as reliant on memory and subject to memory pressure due to how data is persisted to disk, using overly large values for keys can have python fatal error unable remap adverse impact on network coordination, voting, and leadership election, python fatal error unable remap. It is worth keeping in mind that Vault Integrated Storage is not designed to perform as a general purpose key/value database, python fatal error unable remap, and so using keys with unreasonably large values many times more than the default could be problematic depending on the use case and environment.

    »Help and reference