It can also absorb message bursts that the output can not handle. The output plugins are listed below: boundaryĪ persistent queue allows Logstash to protect against data loss, storing events on disk so they can be recovered after a restart. One, or many, must be defined, and you can choose from a list similar to but not the same as the input ones. The output plugin is the end of the pipeline. The Logstash filter plugins are listed below: age A Grok filter is also included to extract fields from log lines. You can also do enrichment based on external HTTP addresses, SQL databases, and even Elasticsearch indices. Filter pluginsįilter plugins are optional and enable Logstash to do data processing, from simple things like removing a field to allowing custom ruby code. Some plugins come as standard with Logstash, others need to be installed manually. The following list contains all of the available input plugins for Logstash: azure_event_hubsĮach plugin will have its own set of settings in addition to the common settings, which include add_field, codec, enable_metric, id, tags, and type. The input plugin is the data entry point that enables Logstash to ingest documents from a variety of sources. Second, it offers integration with OpenTelemetry for logs and traces, which is gaining popularity.Īnother interesting aspect of Data Prepper is that it offers OpenSearch distributed tracing support leveraging the OpenTelemetry collectors, which is something Logstash does not provide as it is more generic. First, Data Prepper supports running Logstash configuration files (although the configurations you can run appear to be so restricted that this is unlikely to be workable except in extremely limited circumstances). It uses a similar concept: source, buffer, processor(s), and sink(s) that allow you to read from one source and write to many sinks of data.ĭata Prepper’s catalog of sources/processors/buffers is more limited, but there are two interesting things to note. “Data Prepper is a server-side data collector capable of filtering, enriching, transforming, normalizing, and aggregating data for downstream analytics and visualization.” ĭata Prepper emerged as the official ingestion tool for OpenSearch almost ten years after the launch of Logstash. Logstash has a large catalog of input and output plugins that allows you to read from and write to a multitude of data sources, from HTTP/TCP/CSV to GCS/AWS S3/Elasticsearch.įrom a durability perspective, Logstash offers persistent queuing to temporarily buffer requests that cannot be sent, and dead letter queuing to process documents that failed on ingestion.
It does that by allowing many inputs, filters, and outputs, with the option of chaining the output of one pipeline into another. Logstash is a battle-tested ingestion framework that allows you to build a large number of pipeline patterns.
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“Logstash is a free and open server-side data processing pipeline that ingests data from a multitude of sources, transforms it, and then sends it to your favorite ‘stash.’”
High-level diagram Logstash Data Prepper Overview Logstash This evaluation compares the following aspects of each tool: To give you some context, Logstash was added to ELK in 2012, and Data Prepper was launched in 2021. In this article, we will compare Logstash, the flagship ingestion tool of the ELK (Elasticsearch Logstash Kibana) stack, and Data Prepper, OpenSearch’s ingestion tool response. Comparative table of available filters/processors.It will prevent issues automatically and perform advanced optimizations to keep your search operation running smoothly.
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