Shulou(Shulou.com)05/31 Report--

What this article shares with you is about how to analyze the log analysis framework of Elastic Stack. The editor thinks it is very practical, so I share it with you to learn. I hope you can get something after reading this article.

What is Beats?

ELK Stack is mainly composed of three components: Logstash, ElasticSearch and Kibana. It provides the ability of data collection, storage analysis and data visualization. These components meet the common needs in the field of log management and analysis.

The location of Logstash in ELK Stack is not only the data collection Agent but also the data parsing and processing tool. The final data is sent to ElasticSearch. This will cause Logstash to occupy higher resources for data collection, enrichment, analysis and so on in the real environment. At the same time, Logstash has inherent performance problems.

Beats is a set of open source log collectors. Write in the go language (compare with Logstash's Java). Different Beat is used to collect different log data, including Filebeat, Winlogbeat, Packetbeat, Heartbeat, and so on. Beat is written in GE language and is mainly responsible for log collection in Elastic Stack. For example, Filebeat is used to collect data of text type, and Packetbeat is used to collect data of real-time network packets. Beats provides a simple way to parse the data. If you need to achieve more complex data parsing, you can send the data to Logstash through Beats for parsing. Logstash provides a powerful data parsing processing plug-in. So for Elastic Stack, it becomes

With the addition of beats, the problem of data acquisition becomes less. Normally, for example, filbeats only needs to deal with the collection of single-line data or the combined collection of multi-line data. After the completion of data acquisition, beats directly uses TCP to send the data to Logstash, and then Logstash carries out complex data analysis and enrichment problems. This basically isolates the production service from the resources of the log processing framework. And the implementation is very simple and does not require too much additional configuration. Can follow the previous configuration of Logstash.

Elastic Stack integrates with message queuing

The above can be used in small data analysis and processing scenarios. In order to deal with a large amount of data in production, other factors need to be considered, such as elastic expansion, safety, peak trimming, reparsing and so on. Then message queuing can be introduced.

The introduction of Kafka or other message queues can achieve the cache of data and ensure that it will not be lost. ElasticSearch is the core of the whole system, but it is very vulnerable to load when indexing a large amount of data. When ElasticSearch is very busy, Logstash will be affected by it and slow down. Therefore, Kafka is generally added to ensure better availability of the data.

Elastic Stack adds Beats to separate the data acquisition and analysis side, or Beats provides a better data acquisition, simple parsing and sending scheme with lower resource occupancy. Make the current Elastic Stack more suitable for production.

The above is how to analyze the log analysis framework of Elastic Stack. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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