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Well-structured logs are the foundation for effective log analysis. Whatever logging tool you choose, the structure makes it easier for you to find, analyze, and visualize the data. Additionally, the structure provides context for your data. This structure should, if possible, be customized for the application-level logs. In other situations, such as infrastructure and system logs, you are responsible for giving logs shape through processing.
An open-source program called Logstash was initially created to manage the streaming of a significant volume of log data from several sources. It became the backbone of the ELK Stack after being added to it, processing log messages as well as improving and messaging them before sending them to a specified location for storage (stashing).
Logstash may be used to gather, enrich, and transform a broad variety of various data types because it has a robust ecosystem of plugins. For Logstash, there are more than 200 distinct plugins available, and a sizable community uses its extendable capabilities.
Logstash has not always had an easy ride. Users have occasionally complained about Logstash over the years because of certain inherent performance problems and architectural defects. Alternative log aggregators started competing with Logstash, and side projects like Lumberjack, Logstash-Forwarder, and Beats were created to address some of these problems.
Nevertheless, despite these drawbacks, Logstash is still an essential part of the stack. By making significant changes to Logstash itself, such as the brand-new execution engine made available in version 8.0, significant progress has been made to try and ease these pains. As a result, logging with ELK is now considerably more reliable than it formerly was.
The three stages of collection, processing, and dispatching are applied to events that Logstash aggregates and processes. In a Logstash configuration file that specifies the pipeline, the types of data that are gathered, how they are processed, and where they are sent are all specified.
The Logstash configuration file defines each of these steps using so-called plugins: “Input” plugins for data collecting, “Filter” plugins for processing, and “Output” plugins for dispatching. You can encrypt or decrypt your data using codecs that are supported by the input and output plugins (e.g., JSON, multiline, plain).
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The ability of Logstash to combine logs and events from many sources is one of the factors that contribute to its strength. Logstash can be configured to gather and analyze data from a variety of platforms, databases, and applications and transmit it to other systems for archival and analysis using more than 50 input plugins.
The most popular inputs are file, beats, syslog, HTTP, TCP, UDP, and stdin, but there are many other sources from which you can consume data.
You may enhance, alter, and process logs using a variety of incredibly strong filter plugins that Logstash offers. Because of the strength of these filters, Logstash is an extremely useful and adaptable tool for parsing log data.
To take an action when a certain criterion is satisfied, filters can be used in conjunction with conditional statements.
The four most frequently used inputs are: grok, date, mutate, and drop.
Like the input plugins, Logstash offers a variety of output plugins that let you push your data to other platforms, services, and locations. You can use outputs like File, CSV, and S3 to store events, convert them into messages with RabbitMQ and SQS, or send them to several other services like HipChat, PagerDuty, or IRC. Logstash is a very flexible event transformer because of the variety of input and output configurations available.
Events in Logstash might originate from a variety of sources, therefore it’s crucial to determine whether they should be handled by a specific output. If you do not specify an output, Logstash will generate a stdout output on its own. A single event may go via several output plugins.
Both inputs and outputs can use codecs. Data decoding before entering the input is made simple by input codecs. Data can be conveniently encoded using output codecs before it leaves the output.
Typical codecs include:
- The “plain” codec by default only supports plain text and does not separate events.
- The “JSON” codec is used to encode JSON events in inputs and decode JSON messages in outputs; take note that if the payloads received are not in a proper JSON format, they will fall back to plain text.
The “JSON lines” codec enables you to decode JSON messages delimited by n in outputs or to receive and encode JSON events delimited by n.
Logstash is a crucial component of your ELK Stack, but you must understand how to use it both alone and in conjunction with the other elements of the stack.
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WRITTEN BY Suraj Srinivas
Suraj Srinivas works as a Research Associate at CloudThat. He loves to learn and work more on Linux Infrastructure. He likes to learn new technologies to keep myself updated. Suraj is skilled in Virtualization, Samba Active Directory, Cloud administration.
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