How to Efficiently Manage Logstash Output for Unstructured Data in Elasticsearch скачать в хорошем качестве

How to Efficiently Manage Logstash Output for Unstructured Data in Elasticsearch

Learn how to optimize your Logstash configuration for handling unstructured data effectively, addressing common issues and offering solutions for unique event identification within Elasticsearch. --- This video is based on the question https://stackoverflow.com/q/63559567/ asked by the user 'Balabama Krop' ( https://stackoverflow.com/u/14128175/ ) and on the answer https://stackoverflow.com/a/63564828/ provided by the user 'leandrojmp' ( https://stackoverflow.com/u/1123206/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Logstash: elasticsearch output and unstructured data Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Mastering Logstash Output for Unstructured Data in Elasticsearch Managing unstructured data can often feel like navigating a maze. When working with Logstash and Elasticsearch, issues can arise, especially when aggregating data from multiple log files. This guide will guide you through the process of configuring your Logstash setup to efficiently handle logs, ensuring that your data flows smoothly into Elasticsearch. The Challenge The primary challenge revolves around using two filters, elapsed and aggregate, with an Event field that may not be unique across multiple files. This situation can lead to unexpected results during data aggregation, particularly when logs from different users contain the same Event value but are sent simultaneously. Several users have encountered this problem, which is exacerbated when expanding log collection to include more directories. When only a single log file is processed, everything may appear to function correctly. However, introducing multiple directories can cause data corruption, leading to strange aggregation results. Key Issues Encountered: Unpredictable data aggregation in Elasticsearch. Difficulty in ensuring unique identification of log events across various files. Analyzing the Problem The issue at hand arises due to parallel processing by Filebeat, which collects log data concurrently. Despite adjustments in the Logstash configuration with a worker: 1 setting aiming to control data flow, each 'harvester' operates independently. Concurrency Problem: When Filebeat sends logs in bulk, the Event field may be the same across different files, resulting in mishaps where the elapsed filter incorrectly associates a start event from one file with an end event from another. Processing Order: Filebeat does not guarantee the order of events but rather aims for at-least-once delivery. Therefore, it can mix up events when processing multiple files simultaneously. Proposed Solution To effectively resolve this issue and ensure data are correctly aggregated, follow these steps: 1. Create a Unique Identifier The best approach is to generate a unique identifier for each event that combines the Event field with the filename. This unique field will help you differentiate between similar events arising from different logs. Implementation: Add the following mutate filter in your Logstash configuration, ideally before the elapsed filter: [[See Video to Reveal this Text or Code Snippet]] 2. Adjust Your Filters After creating the uniqueEvent, use this new field in your elapsed and aggregate filters. Modifying these filters ensures the right associations are made across various log files. 3. Consider Harvesting Limits Optionally, you might want to limit the number of simultaneous harvesters to prevent excessive parallel processing. You can do this by adding the following to your filebeat.yml configuration: [[See Video to Reveal this Text or Code Snippet]] However, be aware that while this may resolve some issues, it could slow down data processing. Conclusion By consolidating the Event and filename into a single unique identifier, you can better manage log data aggregation and prevent the complications arising from the parallel processing of log files. This adjustment will significantly enhance your Logstash configuration, making it more robust when dealing with unstructured data, ultimately leading to more reliable and comprehensible results in your Elasticsearch output. Feel free to reach out with your experiences or questions on handling Logstash configurations for unstructured data!