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    Ultimate Guide to Logging

    Your open-source resource for understanding, analyzing, and troubleshooting system logs

    Centralizing Python Logs

    Before the cloud computing era, applications often logged to files on a server. Administrators would need to log in to dozens, hundreds, or even thousands of systems to search through log files. Modern applications can have thousands of different components running on dozens of different services, making file-based logs obsolete. Instead, developers can use centralization to consolidate logs into a single location.

    Using centralization tools and services:

    • Collects and stores log data in a single location for easier access
    • Provides tools for processing, indexing, and searching log data
    • Allows for the use of data analysis and visualization tools, like charts and dashboards
    • Makes it easier to set retention policies, access controls, and other rules

    This section explains how to centralize logs from standalone Python applications, as well as Python applications running in Docker. We’ll demonstrate these methods using MDN Local Library, a Django-based application provided by MDN. Django uses the standard Python logging module, which is also used by many other Python frameworks.

    Methods of Centralizing Python Logs

    Two of the most common methods for centralizing Python logs are syslog and dedicated log management solutions.


    Syslog is a widely-used standard for formatting, storing, and sending logs on Unix-based systems. Syslog runs as a service that collects logs from applications and system processes, then writes them to a file or another syslog server. This makes it incredibly useful for centralization.

    However, syslog does have limitations. The syslog format—while widely supported—is a mostly unstructured format with little support for non-standard fields or multiline logs. Since logs are stored as plaintext, searching can be slow and difficult. And because logs are stored on a single host, there’s risk of losing log data if the host fails or the file becomes corrupt.

    Logging to Syslog from Python

    The SysLogHandler logs directly from Python to a syslog server. In this example, we’ll send logs of all levels to a local syslog server over UDP:

    LOGGING = {
    ‘version’: 1,
    ‘handlers’: {
    ‘syslog’: {
    ‘level’: ‘DEBUG’,
    ‘class’: ‘logging.handlers.SysLogHandler’,
    ‘facility’: ‘local7’,
    ‘address’: (‘localhost’, 514)
    ‘loggers’: {
    ‘django’: {
    ‘handlers’: [’syslog’],
    ‘level’: ‘DEBUG’

    After starting the application, logs will start to appear in /var/log/syslog:

    $ sudo grep ‘django’ /var/log.syslog
    Sep 26 11:24:36 localhost (0.000) SELECT “django_migrations.””app,” “django_migrations.””name” FROM “django_migrations”; args=()
    Sep 26 11:24:43 localhost (0.000) SELECT “django_session.””session_key,” “django_session.””session_data.”..
    Sep 26 11:24:43 Exception while resolving variable ‘is_paginated’ in template ‘index.html’.#012Traceback (most recent call last):#012...
    Sep 26 11:24:43 localhost (0.000) UPDATE “django_session” SET “session_data” =...

    Log Management Solutions

    Log management solutions, such as SolarWinds? Loggly? are built to ingest, parse, index, and store large volumes of log data. Compared to syslog, they provide better scalability, better protection against data loss, improved search performance, and more ways of interacting with log data. For example, Loggly provides a web-based user interface, querying language, real-time monitoring, and third-party integrations to name a few.

    Many logging solutions support logging directly from code using a custom library or one of the standard library’s built-in handlers. In this example, we use the Loggly Python handler in combination with python-json-logger to send JSON-formatted logs to Loggly over HTTPS. Using JSON allows Loggly to automatically parse out each field while keeping the logs both readable and compact. Before using this code snippet, make sure to replace TOKEN in the URL field with your actual Loggly token. You can also set a custom tag by replacing “python” in the URL.

    LOGGING = {
    ‘version’: 1,
    ‘formatters’: {
    ‘json’: {
    ‘class’: ‘pythonjsonlogger.jsonlogger.JsonFormatter’
    ‘handlers’: {
    ‘loggly’: {
    ‘class’: ‘loggly.handlers.HTTPSHandler’,
    ‘level’: ‘DEBUG’,
    ‘formatter’: ‘json’,
    ‘url’: ‘https://logs-01.loggly.com/inputs/TOKEN/tag/python’,
    ‘loggers’: {
    ‘django’: {
    ‘handlers’: [’loggly’],
    ‘level’: ‘DEBUG’

    When you run the application, the parsed logs will appear in Loggly:

    Viewing Django logs in SolarWinds Loggly. ? 2019 SolarWinds, Inc. All rights reserved.

    Centralizing Logs from Docker

    The challenge with logging Docker containers is they run as isolated, ephemeral processes. Many common logging methods don’t work as effectively in this architecture. However, there are still ways to centralize Docker logs.

    Logging via the Logspout Container

    The Logspout container collects logs from other containers running on a host and forwards them to a syslog server or other destination. This is the easiest way to collect logs since it automatically works for all containers and requires extremely little configuration. The only requirement is containers must log to standard output (STDOUT and STDERR).

    For example, the following command routes all logs to a local syslog server:

    $ docker run—name=”logspout”—volume=/var/run/docker.sock:/var/run/docker.sock gliderlabs/logspout syslog://syslog.server:514

    There is also a version that routes logs to Loggly. Before running this command, make the following replacements:

    • <token>: Your Loggly customer token.
    • <tags>: A comma-separated list of tags to apply to each log event.
    • <filter>: Which containers to log. Remove the FILTER_NAME parameter to log all containers.
    $ docker run -e ‘LOGGLY_TOKEN=<token>’ -e ‘LOGGLY_TAGS=<tags>’ -e ‘FILTER_NAME=<filter>’—volume /var/run/docker.sock:/tmp/docker.sock iamatypeofwalrus/logspout-loggly

    Logging via the Docker Logging Driver

    The Docker logging driver is a service that automatically collects container logs written to STDOUT and STDERR. The driver logs to a file by default, but you can change this to a syslog server or other destination. However, logging to a file allows you to use the docker logs command to quickly view a container’s logs.

    Configuring the logging driver can be problematic with multiple servers, since each server must be individually configured. Because of this, we don’t recommend it for large or production deployments.

    Logging via the Application

    Lastly, you can use a logging library to send logs directly from your application. This method is discouraged since it means connecting each individual container to your centralization service, which consumes resources and makes it harder to deploy changes. You also lose valuable metadata that’s only available using other methods, such as the container name and hostname.

    Recommendations for Centralization

    When developing your centralization strategy, consider these recommendations:

    • Use a log management solution. Log management solutions like Loggly are fast, scalable, and offer many tools for managing log data. They enable faster searching, real-time alerts, collaboration with other team members, and easily support decentralized applications.
    • Log in JSON format. JSON’s structured format makes it easier for services like Loggly to parse individual fields.
    • Log Docker containers to STDOUT and STDERR, and use Logspout to centralize them. Logging containers to standard output allows both the Docker logging driver and Logspout to read and manage your container logs.
    • Centralize all logs, not just application logs. Logging your infrastructure alongside your applications gives you a complete view and can help with detecting problems, performing root cause analysis, and tracing events.
    • Use alerts to monitor for problems. Alerts provide real-time monitoring and notifications, keeping you aware of high-priority issues such as system failures, errors, and exceptions. For more on troubleshooting, read the troubleshooting Python logs section of this guide.
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