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How to implement the ELK Stack log analysis system architecture, in view of this problem, this article introduces the corresponding analysis and answer in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

"ELK Stack is mentioned when it comes to log analysis architecture, which has basically become the longest-used log analysis architecture. In the daily log analysis field, simple data analysis, data BI and so on are supported."

What is the log system?

In the early server application fault analysis, we may go directly to the server to query the relevant logs and locate the exception. However, with the continuous growth of service and business, more and more machines are deployed. It is even stipulated in the administrative authority that you must not log on to the production server to do any operation. Or it is due to data scrolling problems, disk capacity problems lead to log deletion or compression.

Therefore, we need to make a system that collects all the logs on the server and can retrieve them uniformly. The data can even be presented directly based on visualization. Carries on the report class analysis to the overall operation of the system.

So we need a log system to collect, store, index, visualize and so on.

ELK Stack Architecture and components

ELK Stack may be very familiar to some students, and many companies use this architecture in their log analysis systems. Why did you take ELK Stack out in the first place?

In fact, when I do something, the first thing I want to know is what problems the system, project and product are trying to solve. And then conduct in-depth study.

ElasticSearch talked about it briefly before. This time, the main purpose is to bring out the ELK Stack or the scenes commonly used by ElasticSearch to communicate.

ELK Stack can be seen from the figure above that ELK Stack consists of three components, ElasticSearch, Logstash, and Kibana. It originated from ElasticSearch. ElasticSearch as a search engine, then its own users will certainly need to show the intake and reading of data when using it. So there are Logstash and Kibana. Logstash provides data acquisition and data parsing and implements writing to ElasticSearch. On the other hand, Kibana provides flexible visualization of data.

Logstash is an open source data acquisition and processing tool, relying on its powerful pipeline and plug-in mechanism to achieve a variety of data format collection, analysis, conversion. Can flexibly realize the log parsing work. Commonly used parsing plug-ins such as grok, kv, json and other formats of data processing. Kibana is an open source component designed to provide data visualization for ElasticSearch. It relies on ElasticSearch to analyze the data log directly. Such as grouping, aggregation, percentile, etc. To achieve bar chart, pie chart, line chart and other data visualization chart. Support direct log data filtering, retrieval and so on. Basically can realize the daily data analysis work.

To sum up:

ElasticSearch is an open source search engine, which is characterized by distributed, automatic discovery, index fragmentation, replica, restful interface, multi-source, automatic load and other functions.

Logstash is also an open source data acquisition and parsing processing tool. It can provide text content collection and filtering, processing, and sending data to storage space.

Kibana is also an open source and free visualization tool, which provides Web-related interface for log analysis and processing for ELasticSearch, realizing the display of data, directly retrieving ElasticSearch, summarizing and analyzing data logs.

The answer to the question on how to implement the ELK Stack log analysis system architecture is shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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