Logstash Tutorial: Configure input and output plug-ins and data type conversion

SQL and nosql database data migration

Logstash is a powerful data processing engine that can be used for data migration between SQL and NoSQL databases.

Logstash provides a variety of input and output plugins that can read data from various sources and send it to various destinations. For SQL databases, Logstash provides some specific input and output plug-ins, such as JDBC input plug-ins and JDBC output plug-ins, which can facilitate data interaction with relational databases. By configuring Logstash's input and output plug-ins, you can define data source and target connection information, query statements, or write operations to achieve data migration.

For NoSQL databases, Logstash also provides some specific input and output plug-ins, such as Elasticsearch input plug-ins and Elasticsearch output plug-ins, which can interact with Elasticsearch data. In addition, Logstash also supports other NoSQL databases, such as MongoDB and Cassandra, and corresponding plug-ins can be used for data migration.

Configure Logstash's input and output plugins

Configuring Logstash's input and output plugins requires editing the Logstash configuration file. Here is a simple example showing how to configure Logstash's input and output plugins:

1. Open the Logstash configuration file, usually located /etc/logstash/conf.d/in a directory, and create a new configuration file, eg myconfig.conf.

2. Add the configuration of the input plugin in the configuration file. The following is an example of reading data from a MySQL database:

input {
  jdbc {
    jdbc_connection_string => "jdbc:mysql://localhost:3306/mydatabase"
    jdbc_user => "username"
    jdbc_password => "password"
    jdbc_driver_library => "/path/to/mysql-connector-java.jar"
    jdbc_driver_class => "com.mysql.jdbc.Driver"
    statement => "SELECT * FROM mytable"
  }
}

In the above example, we use jdbcthe input plugin to connect to the MySQL database, specifying the connection string, username, password, driver library and driver class. statementFields define the query statement to execute.

3. Add configuration for output plugins. Here is an example of sending data to Elasticsearch:

output {
  elasticsearch {
    hosts => ["localhost:9200"]
    index => "myindex"
    document_type => "_doc"
  }
}

In the above example, we used elasticsearchthe output plugin to send data to the local Elasticsearch instance. hostsThe field specifies the address and port of Elasticsearch, indexthe field specifies the index name to write to, and document_typethe field specifies the type of document.

4. Save the configuration file and start Logstash. You can start Logstash with the following command:

logstash -f /etc/logstash/conf.d/myconfig.conf

The above is a simple configuration example, you can make more complex configurations according to actual needs, including data conversion, field mapping, filters and conditions, etc.

Note: Logstash's configuration syntax is based on Ruby, and you can use conditions, loops, and other Ruby code in configuration files to implement more advanced logic and processing operations.

This is just a simple starting point. For more configuration options and usage of Logstash's input and output plugins, please refer to the official Logstash documentation.

Configure data type conversion in input and output

When processing PostgreSQL byteatype data and mapping it to Elasticsearch data types, you can use Logstash mutatefilters and Elasticsearch index mapping to achieve.

First, you can convert the field to a string type using the option mutatein the filter . This converts the data into a readable string format for processing in Logstash. Here is an example:convertbyteabytea

filter {
    
    
  mutate {
    
    
    convert => {
    
    
      "bytea_field" => "string"
    }
  }
}

In the above example, we cast bytea_fieldthe field named from byteatype to string type.

Next, before sending the data to Elasticsearch, you can define the data type of the field in Elasticsearch's index mapping binary. This will ensure that Elasticsearch handles the field's data type correctly. Here is an example:

PUT myindex
{
    
    
  "mappings": {
    
    
    "properties": {
    
    
      "bytea_field": {
    
    
        "type": "binary"
      }
    }
  }
}

In the above example, we created an index called and set the datatype of the field to myindexin the mapping .bytea_fieldbinary

byteaThrough the above steps, Logstash will convert the PostgreSQL field to a string type when reading it , then send the data to Elasticsearch, and set the data type of the field to according to the index mapping binary. byteaThis way, you can process and store the correct type of data in Elasticsearch . Note that Elasticsearch binarytypes store raw byte data and do not parse or process it.

Depending on your specific needs, you may also need to add other logic and filters to the Logstash configuration to further process and manipulate the data. These examples only show byteathe basic methods of processing type data, and you can adjust and extend them appropriately according to the actual situation.