Logstash ships with many input, codec, filter, and output plugins that can be used to retrieve, transform, filter, and send logs and events from various applications, servers, and network channels. 

In the previous tutorials, we discussed how to use Logstash to ship Redis logsindex emails using Logstash IMAP input plugin, and many other use cases. 

In this article, we continue our journey into the rich world of Logstash input plugins focusing on the Beats family (e.g., Filebeat and Metricbeat), various file and system input plugins, network, email, and chat protocols, cloud platforms, web applications, and message brokers/platforms. Logstash currently supports over 50 input plugin -- and more are coming -- so covering all of them in one article is not possible. Therefore, we decided to overview some of the most popular input plugin categories to give you a general picture of what you can do with Logstash. 

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In the previous tutorial, we learned how to set up a Qbox Cluster with the ES-Hadoop connector to interface with Hadoop’s data warehouse component, Hive, to perform SQL queries on top of Elasticsearch. The benefits of offloading and manipulating ES indices with Hive enable a multitude of possibilities for high-performing, deeper analysis across large data sets.  

In this tutorial we will take it a step further, by using Logstash to import an existing data set in the form of a CSV file into Elasticsearch in order to perform later batch-analytics in Hadoop’s powerful ecosystem.

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Scaling Elasticsearch is not an easy task. In this article, we go over different methods to make a High-Availability Logstash Indexing Solution using Qbox Hosted Elasticsearch.

Logstash Indexer is the component that indexes events and sends them to Elasticsearch for faster searches. We will use multiple logstash indexers with the exact same configuration. Having multiple indexers with the same configuration opens up different possibilities to make a highly available logstash solution for your ELK stack. These indexer nodes with identical configuration can easily be created using configuration management tools like Puppet or Chef.

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Effective log management involves a possibility to instantly draw useful insights from millions of log entries, identify issues as they arise, and visualize/communicate patterns that emerge out of your application logs. Fortunately, ELK stack (Elasticsearch, Logstash, and Kibana) makes it easy to ship logs from your application to ES collections for storage and analysis. 

Recently, Elastic infrastructure was extended by useful tools for shipping logs called Beats. Filebeat is a part of Beats tool set that can be configured to send log events either to Logstash (and from there to Elasticsearch), or even directly to the Elasticsearch. The tool turns your logs into searchable and filterable ES documents with fields and properties that can be easily visualized and analyzed.

In a previous post, we discussed how to use Filebeat to ship Linux system logs. Now, it's time to show how to ship logs from your MySQL database via Filebeat transport to your Elasticsearch cluster. Making MySQL general and slow logs accessible via Kibana and Logstash will radically improve your database management, log analysis and pattern discovery leveraging the full potential of ELK stack.

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Although elasticsearch can scale indefinitely, you should store required data only. This will speed up the search operation, as well as response time to retrieve the data, and even reduce resource utilization substantially.

Elasticsearch uses an “Inverted Index” to retrieve data that you are searching for. Although this algorithm is one of the best when it comes to text searching, keeping only the data that you need in the index is the best approach.

In this tutorial, we discuss data retention techniques that you can use in elasticsearch. This will obviously depend on the kind of data and your application, because some might need longer retention policies compared to others. 

Imagine an application that deals with finance and money transactions. Such applications will need all of the records forever. But, do these records need to always exist in elasticsearch? Does all of this data need to be quickly searchable?

Logstash provides methods where you can segregate different events, and then store it in standard file storage rather than elasticsearch for long-term storage.

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Filebeat is extremely lightweight compared to its predecessors when it comes to efficiently sending log events. It uses lumberjack protocol, compression, and is easy to configure using a yaml file. It can send events directly to elasticsearch as well as logstash. It keeps track of files and position of its read, so that it can resume where it left of. 

The goal of this tutorial is to set up a proper environment to ship Linux system logs to Elasticsearch with Filebeat. It then shows helpful tips to make good use of the environment in Kibana.

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