What is Log Aggregation? Getting Started and Best Practices

Imagine if you could efficiently analyze every piece of log data generated across your different servers, applications, network, and cloud resources in one place.

Now imagine you could achieve this without sifting through log files scattered across different environments.

Enter log aggregation.

For set-ups as simple as a single application running on a single server, manually checking the logs may suffice. However, as your systems grow in complexity, such a fragmented approach becomes time-consuming, cumbersome, and error-prone.

To truly understand and utilize the wealth of information logs provide, a more sophisticated approach becomes necessary: one that systematically gathers, standardizes, and centralizes log data.

In this guide, you will learn how log aggregation can help supercharge your approach to effective log management in production.

What is log aggregation?

Log aggregation is an essential aspect of log management that involves collecting logs from the various applications and resources in your production environment, and centralizing them in one place for easy searching and analysis.

Aggregating your logs is about making it easy to observe your entire environment in one place so it's easy to diagnose problems without having to interpret log files individually.

Why is log aggregation important?

Most non-trivial production systems are composed of several distributed components generating copious amount of log data often stored locally in files.

In such complex environments, log aggregation is an way to bring the scattered logs into a centralized repository for easier access, monitoring, and analysis.

Without aggregation, diagnosing issues would be an arduous, time-consuming endeavor and you won't be able to visualize long-term trends and mitigate potential issues through alerting.

If you take nothing else away from the concept of log aggregation, take this:

Just imagine facing an outage where you're left scrambling, SSHing into multiple servers to manually pinpoint the root cause and piece together the issue.

Contrast that with having a system where all your logs are systematically collated and centralized in a log management service, allowing for a swift, comprehensive overview and efficient troubleshooting.

The latter not only saves precious time during critical moments but also reduces the risk of oversight and human error in log analysis.

How to aggregate your logs in five steps

So how do you get started with log aggregation?

First, you need to ensure that your systems are generating useful and well-structured logs to the standard output or a file. Once you've got the basics covered, aggregating your logs should be pretty straightforward in most cases.

In simple terms, you need to:

1. Identify your log sources.
2. Choose a log management solution.
3. Collect the logs.
4. Parse, filter, and transmit the logs.
5. Centralize the logs.

Let's crack on and learn how to build a log aggregation pipeline suitable for production systems.

1. Identify your log sources

Your first objective is to identify the various logs that contain information relevant to your troubleshooting, auditing, and analysis needs. Here are some common logs to consider:

• Application logs
• Logs from web servers like Apache or Nginx
• Container logs
• Network logs
• Database logs
• Logs generated by your cloud resources and functions
• Operating system logs
• Configuration change logs
• Security logs
• Backup logs

While this is not a comprehensive list, it should give you an idea of what you might need to aggregate to ensure complete visibility into your production environment.

2. Choose a log management solution

Before you start aggregating logs from the various sources you've identified, you should know where you're sending them to. There are several log management tools ranging from open source solutions that you can self-host to managed cloud-based services that can be set up within minutes.

We recommend using Better Stack, and we have a completely free plan that you can use to evaluate the service for as long as you wish.

3. Collect the logs

Once you've identified the log sources, you'll need to devise the appropriate aggregation strategy to help you automatically collect data from each of those sources and transport them to the desired destination. Popular approaches include:

• Constructing an automated logging pipeline using tools like Fluentd, Logstash or Vector for collecting, parsing, and shipping the logs (most recommended).
• Collecting logs from the sources using custom integrations or proprietary agents (e.g. AWS Cloudwatch, Datadog agent, etc).
• Configuring your application's logging framework to transmit logs directly to external services.
• Streaming your logs continuously to a centralized platform through Syslog.
• Copying log files over the network at regular intervals using tools like rsync and cron.

Learn more: Log Shippers Explained and How to Choose One

4. Parse, filter, and enrich the logs

When collecting log data from the configured sources, you'll want to apply parsing rules to extract information, standardize key attributes, and filter out irrelevant data.

Since logs flow into the pipeline from different systems or applications that may have different logging standards, it's often necessary to coerce them into a common format to make log analysis and correlation much easier once the data has been centralized.

Some examples include normalizing field names and attribute formats, converting timestamps to UTC time, filtering out or masking sensitive data, merging events spread over multiple log lines, sampling logs to remove duplicate date, or converting unstructured data to a structured format like JSON.

You can also enrich the raw log data by supplementing them with additional context or related information. For instance, an IP address in a log can be enriched with geolocation data, so that you can see a country or city of origin instead of just seeing an IP. You can also group logs by servers, types (app vs system), or version to make them easier to filter later on.

5. Centralizing the logs

After collecting and processing logs, they should be forwarded to the selected central log management platform for analysis, monitoring, and alerting.

To ensure data integrity when sending logs across networks, consider implementing a queuing mechanism. This reduces the risk of data loss during potential interruptions in the aggregation process. An example is Logstash's persistent queues  feature, which safeguard in-transit messages by storing them on disk.

When centralizing logs, it's also crucial to set retention policies. to define how long logs are retained, based on budget constraints, storage limits, or regulatory demands. Once the logs surpass their retention period, they can either be purged or moved to a more cost-effective, albeit slower, archival storage for future reference.

Log aggregation challenges and solutions

Log aggregation is a complex process and can face several challenges. Here are some common reasons or ways in which it can fail:

• Insufficiently scaled infrastructure for self-hosted solutions may struggle to cope with high log volumes.
• Unreliable networks can lead to logs being lost during transmission.
• Overloading the log shipper instance with logs can exceed its processing capacity, resulting in data loss.
• Old logs, if not archived or pruned correctly, can sky-rocket costs and hamper real-time analysis.

To address these challenges, a common approach is to introduce a message queue, such as Apache Kafka, between log sources and aggregation instances. This queue acts as a buffer, holding logs temporarily during disruptions or spikes in volume, thus ensuring offering relief to the log shipper during high influx periods.

By using Kafka's distributed nature and replication features, logs can be stored redundantly ensuring no data loss even if a few Kafka nodes fail. However, it's vital to consider the increased complexity and cost this introduces. Ensure the benefits outweigh the complications before diving into such intricate setups.

How to choose a log aggregation tool

When choosing a log aggregator, look for tools that integrate with your existing systems, services, and applications. You should also verify that it supports the sources and log formats you're working with either natively or through a plugin.

If you're dealing with a high volume of log data, ensure that the tool can efficiently handle such load, while judging its ability to scale further due to potential growth or spikes in log volume.

Another key consideration is the initial and ongoing costs. Open-source aggregators are typically free to use but could incur operational expenses, while proprietary tools often bring recurring fees.

When it comes to aggregating logs from cloud resources, most providers provide a native aggregation tool (such as AWS CloudWatch) but they typically only work with the vendor's services. In a multi-cloud or hybrid environment, it's better to use a universal solution that can work with all the resources in your environment.

Since logs often contain sensitive information, chose tools with strong security features such as in-transit and at-rest encryption, data masking, and redaction. Also choose tools that are highly observable so that you can quickly catch issues if something goes wrong.

Some well-known log aggregation tools include Vector, Logstash (Elastic Stack), Apache Flume, Fluentd, Graylog, Grafana Loki, and many others.

Learn more: Top Log Management and Aggregation Tools

Log aggregation FAQs

Here are answers to a few common questions often asked about log aggregation:

How does log aggregation differ from log management?

Log aggregation and log management are sometimes used interchangeably, but they aren't identical. Log aggregation is a subset of the broader management process which focuses on collecting and centralizing logs. On the other hand, log management encapsulates a broader set of tasks including storage, analysis, monitoring, retention, alerting, and more.

Is log aggregation the same as log collection?

No. Log collectors primarily handle the retrieval of logs from diverse sources, without necessarily structuring them. On the other hand, log aggregation encompasses the full cycle of fetching logs, then processing, filtering, and standardizing them for easier analysis, and consolidating them in one central repository.

It's worth noting that there's often a functional overlap between log collection and log aggregation tools. For instance, Vector can serve solely as a log collector, but it can also represent a complete aggregation pipeline either independently or in tandem with other tools.

How often should logs be aggregated?

Most systems will benefit from real-time aggregation, while scheduled batches may suffice for others but you'll be unable to view your log data in real-time.

Is sending logs directly from the application to the log management service advisable?

Sending logs directly from your application instances to a log management service is a straightforward way to get started with log aggregation if the log volume is relatively small and delivery guarantees are not needed.

However, this approach tightly couples your application to the service, limits preprocessing options, can introduce performance issues if log delivery is sluggish, and poses a high risk of data loss during network disruptions or downtimes.

Final thoughts

Log aggregation is only the first step towards developing a comprehensive production log management strategy.

While it demands significant time and dedication to set up, the investment pays off by maximizing the utility of your logs and helping you manage them in the easiest way possible.

Got any questions or comments on log aggregation? Write me on X (Twitter) .

Thanks for reading!

Article by

Ayooluwa Isaiah

Ayo is the Head of Content at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including LWN.net, Digital Ocean, and CSS-Tricks. When he’s not writing or coding, he loves to travel, bike, and play tennis.

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