Log Aggregation: Everything You Need to Know for Aggregating Log Data

Log aggregation is the process of consolidating log data from all sources — network nodes, microservices and application components — into a unified centralized repository. It is an important function of the continuous and end-to-end log management process where log aggregation is followed by log analysis, reporting and disposal.

In this article, let’s take a look at the process of log aggregation as well as the benefits. Really, log aggregation is an important foundation that supports all sorts of goals and outcomes for organizations.

Benefits of log aggregation

The biggest benefit of aggregating logs is all the things it enables you to do. What makes log aggregation an important part of your system monitoring and observability strategy?

When developers write software applications and hardware engineers develop networking systems, they include built-in event logging capabilities. The log footprint is generated automatically and continuously, describing how a computing event involves the use of these resources. This information can be used to:

• Monitor for anomalous behavior
• Troubleshooting issues
• Debugging unexpected errors

Aggregating logs is also used to understand how systems and components interact with each other. This in particular allows engineers to develop and understand how these systems should behave under optimal conditions and use this state information to compare unexpected performance deviations and behavior.

Types of log data

Common types of log data include application logs, system logs, network logs and security logs.

Application logs

Logs from applications includes:

• Application exceptions
• Error events such as startup, stop
• SQL logs
• Warnings
• Debugging information

System logs

System logs includes:

• OS-related events descriptions
• Error events, warnings, debugging information, network and application activity
• Performance metrics such as CPU, storage and compute resource utilization

Network logs

Network logs include any data related to network and traffic activity. These include:

• TCP/IP protocols data
• Source and destination IP addresses
• Connection events such as attempts, timeout
• User activity data such as login attempts and time
• Performance metrics such as packet loss, latency, bandwidth
• Application activity such as data access and processing
• Warnings, errors and debugging information

Security logs

Security logs will include information generated by systems, application components and networks. These may include all log application logs, system logs and network logs. Additionally, this can include:

• User activity data related network, data and application access and modification
• Custom metrics that allows users to audit events for compliance with regulatory standards and security policies

Steps in the log aggregation process

OK, so now we know that these logs are generated by applications, systems and devices in silos. Additionally, all this data is likely in different structural formats and requires additional preprocessing for transformation into a consumable format by third-party monitoring and analytics tools.

So, let’s review how the log aggregation process unfolds:

1. Identification

The first step for log aggregation involves planning for the metrics and KPIs relevant to your log analysis. In this step, you’ll identify the log files that contain information on your chosen metrics and select the sources of interest — such as network nodes, application components and system devices.

(Understand the difference between logs & metrics.)

2. Collection, indexing & normalization

Next up, the selected data sources are programmatically accessed and the necessary data transformation procedures are followed. The imported data must follow a fixed predefined format for efficient indexing and later analysis. Indexation depends on:

• The nature of the log files
• Importance of metrics
• Source selection

At this point, you’ll need a log management tool to implement an efficient indexing and sorting mechanism.

3. Processing: Parsing, data enrichment & masking

Log parsing is performed in conjunction with log data normalization. Since only the most useful and complete data points can be analyzed, the parsing process removes irrelevant pieces of information.

Parsing may also involve importing other data points that complement the aggregated and indexed log data streams. For example:

• Geolocation may be added to a set of IP addresses.
• HTTP status codes may be replaced by the associated message content.
• Network session details may be imported from applications and cloud-based services.

If the data is subject to security policies, it may be masked or encrypted (later to be decrypted prior to analytics processing). Sensitive details such as login details and authentication tokens are automatically redacted, depending on the applicable security and privacy policies.

4. Storage

Depending on your data platform and pipeline strategy, the data may be transformed into a unified format and compressed prior to storage. Archived log data may be removed from the storage platform once it is exported or consumed by a third-party log analysis tool.

This is the final phase of the log aggregation process. At this stage, all aggregated data is either already in consumable format or can follow additional ETL (Extract Transform Load) processing depending on the tooling specifications and the schema models such as schema-on-read.

Log data storage best practices

Considering the volume, variety and veracity of log data generated in real-time from a large number of sources, your storage requirements can grow exponentially. Here are a few considerations to make the process more efficient:

• Use a cloud-based scalable data lake platform that can ingest real-time data in a variety of formats at scale, and only require the additional transformation steps prior to log analysis, with a schema-on-read model.
• Use monitoring and observability tools to discover and acquire all nodes and data sources relevant to your analysis.
• Use AI-enabled tools for data augmentation following an extensive data compression process that shrinks data streams to manageable levels.
• Look for changing patterns in log metrics using advanced log analysis tools.

Making meaning, context from log data

An efficient log aggregation process can help engineering teams proactively manage incidents and monitor for anomalous activities within the network. The next step involves embedding meaning and context into log data — and the insights produced using log analysis.

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