Every developer’s worst nightmare is having to dig through a huge log file trying to pinpoint problems. The troubleshooting most likely won’t stop there. They’ll either have to follow the trail to multiple other log files and possibly on other servers. The log files may even be in different formats. This may go on until one loses themselves completely. Log aggregation is what you need to stop this seemingly never-ending cycle.
In this post, we will cover everything you need to know about how log aggregation works, why it is important, and how to do it to avoid future log-related headaches.
What Is Log Aggregation: A Simpler Definition
Log aggregation is the step in the overall log management process in which logs are imported from many sources across your company’s infrastructure and collected into a central location. That is why it’s also called log centralization.
By using log shippers that send log events from infrastructure, applications, containers, databases, and whatever else one may think of, the logs are aggregated and stored in a central location.
Log events have fields and can be used to group, filter, and search through your logs in the log management software. Typical default fields usually include timestamp, source, status, severity, host, origin, message, and any other information one needs to be able to analyze, monitor, and search through log events.
All log events that are sent to the log management software get indexed in a document database, Elasticsearch or Solr being the most popular. The logs are stored, and archived, making it easier for you to search and analyze your data. Having all logs in one place, and access to them via one single user interface without the hassle of connecting to machines and running grep is why log management is so powerful and makes developers’ lives so much easier.
Why Is Log Aggregation Important?
Log aggregation is critical for an efficient log management process – and here’s why:
Logs are in a centralized location
Whatever business you may be in, your software applications and infrastructure generate logs documenting the activity of who did what on said systems. However, it definitely wouldn’t be easy for developers to deal with copious amounts of data to pinpoint the root of the problem whenever one occurs. It would be like looking for a needle in a haystack – painfully time-, money- and nerve-consuming, not to mention, error-prone and not scalable.
Enter log aggregation, which brings your logs together to a central location.
Text files become meaningful data
Log files contain data that you can extract, organize, and then query to turn it into valuable information you can use to improve your business operations.
However, these are not simple text files that you can do a quick search on. Log aggregation does the parsing for you, which means it turns the raw information from your log files into structured data.
Working with meaningful log messages and structured log formats are only a few of the logging best practices we recommend you follow for easier troubleshooting, which makes log aggregation an important step in the logging and monitoring process.
Better real-time monitoring
Instead of going tailing each log file to do real-time monitoring, when you aggregate your logs, you get to search within a single location containing all of the structured, organized, and meaningful data.
In other words, you get real-time access to a live stream of activity, enabling you to troubleshoot and identify trends and patterns to prevent errors from happening.
Sophisticated search capabilities
Given that you now have a set of meaningful data, not just text, you can also get smarter and more refined with your queries.
During log aggregation, your code is treated as data, meaning that it is indexed and organized in a conceptual schema, allowing you to do fast semantic searches based on the nature of the data.
How to Aggregate Your Logs?
There are multiple ways you can set up a centralized logging solution:
A simple and straightforward option is to copy your log files to a central location using simple tools such as rsync and cron. However, although it does bring together all of your logs, this option is not really the same as an aggregation, but more of a “co-location.” Furthermore, since you need to follow a cron schedule, in the long-term, file replication is not a good solution as you don’t get real-time access to your log data.
Syslog, rsyslog, or syslog-ng
The second approach is to use syslog since you probably have already installed it on your system, or the two syslog implementations, rsyslog or syslog-ng. They allow processes to send log entries to them that they’ll then redirect to a central location.
You need to set up a central syslog daemon on your network as well as the clients’. The client logging daemons will forward these messages to the daemons.
Syslog is also a simple method to aggregate your logs since you have already installed it and you only have to configure it. The catch is to make sure the central syslog server is available and figure out how to scale it.
If you want to learn more about syslog and its implementations and see them in action, you might also be interested in:
- Recipe: rsyslog + Redis + Logstash
- Recipe: rsyslog + Elasticsearch + Kibana
- Recipe: How to Integrate rsyslog with Kafka and Logstash
- Recipe: Apache Logs + rsyslog (parsing) + Elasticsearch
- Structured Logging with rsyslog and Elasticsearch
- Centralized Logging with rsyslog eBook
Log Aggregation Tools: Open-source & Commercial
Syslog, rsyslog and syslog-ng work great, but log aggregation tools work even better and with fewer limitations. They have extra features that make log collecting better and more efficient. Most of these tools are general-purpose log management solutions that also include logging as a functionality.
They are, of course, different, but rely on a similar architecture, involving logging clients and/or agents on each host that forward messages to collectors which further forward them to a central location. Unlike syslog options, with tools, this collection tier is horizontally scalable to grow as the data volume increases over time.
Here are a few examples of such open-source log aggregation tools:
- Logstash – an open-source tool that enables you to ship, parse, and index logs from different sources. It works by defining inputs, filters, and outputs and features a UI that allows you to easily access, view, and search your data.
- Fluentd – just like Logstash, this log aggregation tool manages log data from different sources. However, it doesn’t feature a storage tier but allows you to configure the destination.
- Flume – an Apache project that can collect, aggregate, and move vast amounts of data. It can also store it on the HDFS on Hadoop.
- Graylog2 – this tool stores your logs in MongoDB or Elasticsearch that you can then search and analyze via their UI.
- Scribe – a C++ tool that was released by Facebook as open-source on GitHub. It’s compatible with any language and is a reliable and scalable log aggregation server.
- Splunk – this is one of the veteran log management tools which handles not only log aggregation but also log search and analysis, visualization, and reporting.
If you’re not familiar with these log aggregation tools or don’t know which one is best, we wrote a blog post about Logstash alternatives where we compare most of them. Check it out if you’re interested in that!
Managed or Hosted Log Aggregation Services
If you want a solution that requires minimum involvement on your part, you should try a hosted “logging as a service” provider. These solutions are responsible for maintaining and setting up any infrastructure you may need, as well as manage the collection, storage, and access to log data. You only need to configure your syslog daemons or agents and trust the rest to these providers.
A few log management service providers are:
- Sematext Logs – gives you hassle-free log management and analytics platform where you can correlate logs with events and metrics, live-tail logs, add alerts to logs, and use Google-like syntax for filtering. Sematext’s auto-discovery of logs and services lets you automatically start forwarding logs from both log files and containers directly through the user interface. We offer a 14-day free trial so give it a try!
- Logz.io – provides you with machine data analytics built on ELK and Kibana.
- Logentries (now Rapid7 InsightOps) – this log management platform covers log aggregation and analysis, at the same time enabling you to keep track and visualize your log data in real-time.
- Loggly – it enables you to access and analyze all of your log data fast and simple while giving you real-time insights on how to improve your code.
- Papertrail – with Papertrail, you can aggregate, search, and analyze any type of log file, text log file, or syslog in real-time.
Sematext Logs as a Log Management and Aggregation Tool
When it comes to log aggregation, Sematext Logs is compatible with a wide range of logging frameworks, allowing you to bring all your log events to one central location as they happen. You get a real-time view of your logs, so you can pinpoint anomalies as they are logged.
With Sematext Logs, you get more powerful searching and filtering capabilities that allow you to do full-text searching. You also get something called log context. When you search for log events and pinpoint the one you wanted to find, you still see all log events that occurred before it and after that particular log event.
Sematext’s auto-discovery of logs and services lets you automatically start forwarding logs from both log files and containers directly through the user interface.
In a nutshell, Sematext Logs is a hassle-free log management and analytics solution that improves efficiency and grants you actionable insights faster. Use the 14-day free trial to explore all its features. Try it out!
Log aggregation is a core part of log management. Companies are constantly creating more complex infrastructures containing a multitude of software and applications making them inherently more susceptible to bugs and errors.
Working with hundreds of log files on hundreds of servers makes it close to impossible to detect anomalies and solve them before they reach the end user. By aggregating logs, you save time, money, and gain better insights into your consumers’ behavior.
Related log aggregation articles: