Log storage and analysis, data visualization. Today, this is almost a must-have for any Internet project, but only the ELK ecosystem can present data in a form convenient for analysis. It can process gigabytes of data from balancers, hypervisors, and switches and present them in the form of convenient dashboards. In addition, Elasticsearch uses machine learning (ML) capabilities to improve data analysis and search.
Elasticsearch uses indices to organize and store kazakhstan telegram number database data. An index is a logical storage where documents of the same type are organized. Each index consists of one or more shards (parts), which allows data to be distributed across different cluster nodes to ensure fault tolerance and scalability.
In order for data to get into ES indexes, it cannot be simply pulled from the site database "as is". This "raw" data needs to be indexed. To create an index, the system's own API is used, which needs to be called from somewhere. This can be done by event handlers in Bitrix or an agent that will periodically update the data, or a queue server. It is very convenient to work with Kibana from the ELK stack.
Below is an example of an index with manual field mapping. The index can be created using the REST API or a ready-made library for working with it. Mapping can also be dynamic, but usually this thing cannot be fully trusted. The best result is obtained with a combination of dynamic and explicit mapping; you can create your own tricky field mapping rules.
Example index Elasticsearch.png
We load products into the index.
Loading products and index.png
We receive goods in the index.
Let's try to find "Shoes".