How to use Elasticsearch data analysis and polymerization functions
How to use Elasticsearch data analysis and polymerization functions
Overview:
Elasticsearch is a distributed open source search and analysis engine. It provides applications with high -efficiency and powerful data analysis and aggregation capabilities through full -text retrieval, structured search, analysis, and visualization functions.This article will introduce how to analyze and aggregate the data of Elasticsearch, including related programming code and configuration.
Step 1: Installation and configuration Elasticsearch
1. Download and install Elasticsearch.According to your operating system, select the appropriate installation package and install it in accordance with the official document.
2. Configure Elasticsearch.In the `Config` folder in the installation directory, open the` elasticsearch.yml` file to modify some important configuration options, such as cluster names, node names, port slogans, etc.
3. Start Elasticsearch.Perform the startup command and wait for Elasticsearch to start.
Step 2: Create indexes and insert data
In Elasticsearch, the index is similar to the table in the database.First create an index and insert data into the index.
1. Create an index.By sending HTTP requests to the RESTFUL API of Elasticsearch, a new index can be created.For example, execute the following commands to create an index called `My_index`:
PUT /my_index
2. Insert data.You can use Restful API to insert data into the index.For example, execute the following command to insert the data into the `my_type` type of` my_index` index:
PUT /my_index/my_type/1
{
"name": "John Doe",
"age": 30,
"city": "Shanghai"
}
Step 3: Data analysis and aggregation
Elasticsearch provides rich data analysis and aggregation functions, which can be used to statistics, filtering and aggregating data in the index.
1. Statistics number of documents.By executing the following commands, the number of indexing Chinese documents can be counted:
GET /my_index/_count
2. Query data.You can use Elasticsearch's powerful query syntax to make data filtering and query.For example, if you want to query all cities as "SHANGHAI" data, you can execute the following commands:
GET /my_index/_search
{
"query": {
"match": {
"city": "Shanghai"
}
}
}
3. Polymerization data.Elasticsearch provides a variety of aggregate functions, such as maximum values, minimum values, average values, reconciliation, etc.For example, if you want to calculate the average value of the age field, you can execute the following command:
GET /my_index/_search
{
"aggs": {
"avg_age": {
"avg": {
"field": "age"
}
}
}
}
4. Visualized data.By integrating the Kibana tool of Elasticsearch, the data can be visually displayed in real time, so as to understand and analyze data more intuitively.
Precautions:
-Brilling before data analysis and aggregation, make sure that the Elasticsearch index is created and inserted in the data.
-At the execution command, you can use CURL, Postman or other tools to send HTTP requests.
-In the actual situation, the index name, type name, field name, etc. need to be modified.
Summarize:
This article introduces the basic methods of data analysis and aggregation using Elasticsearch for data analysis and aggregation.From the beginning of installation and configuration Elasticsearch, to creating indexes, inserting data, to statistics, query and aggregation data, each step gives corresponding command examples.By using Elasticsearch's data analysis and aggregation function, a large amount of data can be processed more efficiently, and valuable insights can be obtained from it.
