Suppose you were asked to dig in through a vast log file and pinpoint problems. Quite frustrating right? However, the troubleshooting doesn’t stop here. You will also have to follow the trail to multiple log files (of different formats) and possibly on other servers. 

Don’t worry! You can streamline all this frustrating work using log aggregation. This blog is a detailed overview of aggregating the log using an appropriate log aggregation tool.   

What Is Log Aggregation?

The process of collecting and standardizing log events from multiple sources all around the IT infrastructure for immediate log analysis is called log aggregation. You can use log shippers to store logs in a central location, because of which is also called log centralization. 

These constitute fields that can be used to group, filter, and search through the logs in the log management software. The log events sent to this tool are indexed in a document database. You can then effortlessly search and analyze the data stored in the archived. 

Importance Of Log Aggregation

Aggregating log is significant due to the following reasons:

Aggregated In A Centralized Location

Businesses of all sizes and types generate a log that documents the activity of the workers working in it. However, it is relatively arduous to deal with a lot of data to pinpoint the root of the problem whenever it occurs. It is a very time-consuming, frustrating, and painful task. It is also error-prone and not scalable. But, by aggregating logs, one can store them in a centralized location. 

Text Files Become Meaningful Data

The data stored in log files can easily be extracted, organized, and then queried to turn them into valuable information. This information can be used to improve business operations. However, these text files are not straightforward. Log aggregation turns these log files into structured data. Therefore, for seamless troubleshooting, one has to aggregate logs and monitor the process. 

Revolutionized Search Capabilities

Apart from setting meaningful data, you can also get smarter with the queries. While aggregating logs you need to treat logs as code. It is indexed and organized in a conceptual schema which fastens the semantic searches based on the nature of the data. 

Appropriate Real-Time Monitoring

Log aggregation enables you to search within a single location that constitutes all the structured, organized, and meaningful data for real-time monitoring. In other words, you can get real-time access to a live stream of activity, thereby enabling you to troubleshoot and identify trends and patterns to prevent errors from happening. 

How To Aggregate Your Log?

Logs can be aggregated in the following several ways:

Identifying Log Sources

Since sophisticated distributed enterprise application constitutes several moving pieces, identifying all the components required for aggregating logs is significant. Choosing only specific events or importance help in making the logs manageable. 

Collecting Logs

The next process involves collecting the logs. Although it is an automatic process, you can use multiple ways given below:

  • Using standard messaging logging protocol to stream the logs continuously to a centralized location. 
  • Installing custom integration or collectors on servers that will read logs and sent them to the logging platform. 
  • Capturing messages via code instrumentation from specific program parts. 
  • The log management system will directly access source systems and even copy log files over the network. 

Parsing Logs

Logs should be analyzed before using them to gain meaningful insight. The parser extracts important information from each recorded event and puts it into a shared format, which is then stored for later analysis. Reports can be very large and contain a lot of useless data. Decomposition only extracts relevant data while discarding the rest.

Processing Logs

Log aggregation can do other things in input processing.

Indexing creates a map of data that, like a database index, is scanned and stored in the column. Indexing queries from your reports makes it easier and faster. Unique profiles also delete duplicate log data.

Data enrichment can also be very useful to get more information from your reports. Masking is when sensitive data such as encryption keys, privacy, authentication tokens, credentials, etc., are changed in the recorded message.

Then, the log is stored in a centralized location. It not only saves time and money but also provides meaningful insight into the customer’s behavior. 

Also Read: Data Science and AI In Healthcare