Resolving Grok Syntax Issues: Handling Multi-line Entries in Logstash

Discover how to effectively manage and filter log entries with `Grok syntax` in Logstash, especially when dealing with multi-line exceptions and distinguishing new log entries from continuations.
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This video is based on the question https://stackoverflow.com/q/77103419/ asked by the user 'Wojciech Szabowicz' ( https://stackoverflow.com/u/1662139/ ) and on the answer https://stackoverflow.com/a/77110518/ provided by the user 'Wojciech Szabowicz' ( https://stackoverflow.com/u/1662139/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding the Issue with Grok Syntax in Logstash

When working with logs in Logstash, it can often be challenging to properly parse and filter multi-line entries. This challenge is particularly evident when dealing with.exceptions, which can extend beyond a single line. In this guide, we’ll address a common problem of distinguishing new log entries from those that are continuations of previous ones.

Consider the following log entries:

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The problem arises when you attempt to filter these logs using Grok. You may notice that instead of reading them as distinct entries, Logstash concatenates them into a single entry, making log analysis much more cumbersome.

Solution: Configuring Grok and Multiline Codec

To handle multi-line log entries appropriately, you can use a combination of Grok syntax and the multiline codec. Here’s how to effectively configure your Logstash setup.

Step 1: Define Your Multiline Codec

To begin with, it’s essential to set up your input configuration correctly to recognize which lines belong together. Here’s an example configuration that you can adopt:

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pattern: This regex pattern specifies that a new log entry starts with a timestamp. Lines that do not match this pattern will be treated as a continuation of the previous entry.

negate: Setting this to true means that any non-matching lines will be appended to the previous entry.

what: This option specifies that the continuation lines belong to the "previous" entry.

Step 2: Set Up Your Grok Filter

Once you have configured the multiline codec, you’ll want to set up your Grok filter to process the log entries. Here’s an example of a Grok expression that you can use:

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In this expression:

TIMESTAMP_ISO8601: Matches the log timestamp.

ISO8601_TIMEZONE: Captures the time zone.

WORD: Captures the log level (e.g., WARNING, ERROR).

GREEDYDATA: Captures the source and message parts of the log.

Recap: Benefits of Correctly Handling Multi-line Log Entries

Implementing the aforementioned configurations will allow Logstash to accurately parse multi-line log entries. This configuration not only improves readability but also enhances the ability to analyze logs effectively. Properly distinguishing between new log entries and their continuations ensures that you have a more detailed view of your log data, allowing you to troubleshoot issues more efficiently.

In conclusion, aligning the Grok syntax with the appropriate multiline codec settings is crucial for successful log parsing. By addressing the parsing challenge, you can streamline your log analysis process, making it easier to gain insights from your logs.
With this guidance, you should now be able to confidently configure Logstash to handle multi-line log entries appropriately, ensuring a clearer understanding of your log data.

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