Elevate Your Data Processing and Synchronization to Alibaba Cloud Elasticsearch using Realtime Compute for Apache Flink

In this article, we explore how to use Realtime Compute for Apache Flink to process log data and synchronize it with an Alibaba Cloud Elasticsearch cl.

Processing and synchronizing data in real-time is crucial for businesses that rely on up-to-the-minute information for decision-making and operational efficiency. In this article, we explore how to use Realtime Compute for Apache Flink to process log data and synchronize it with an Alibaba Cloud Elasticsearch cluster, thereby creating a powerful log retrieval system.

Prerequisites

Before diving into the procedure, ensure you have the following:

Activated Realtime Compute for Apache Flink and created a project.
Created an Alibaba Cloud Elasticsearch cluster. For more details, visit the Create an Alibaba Cloud Elasticsearch cluster page.
Activated Simple Log Service, and created both a project and a Logstore.

Understanding the Environment

Realtime Compute for Apache Flink, a service based on Flink and provided by Alibaba Cloud, supports a variety of input and output systems, including Kafka and Elasticsearch. By utilizing Realtime Compute for Apache Flink in conjunction with Alibaba Cloud Elasticsearch, you're equipped to process and search data in real time, transforming your business into a real-time service.

Implementing Your Solution

Here’s how to set up your log retrieval system:

1）Log into the Realtime Compute for Apache Flink console.

2）Create a Realtime Compute job. For more assistance, refer to the Job Development section in the Blink SQL Development Guide.

3）Write Flink SQL statements. Start by creating a source table for Simple Log Service:

CREATE TABLE sls_stream(
  a INT,
  b INT,
  c VARCHAR
)
WITH (
  type ='sls',  
  endPoint ='<yourEndpoint>',
  accessId ='<yourAccessId>',
  accessKey ='<yourAccessKey>',
  startTime = '<yourStartTime>',
  project ='<yourProjectName>',
  logStore ='<yourLogStoreName>',
  consumerGroup ='<yourConsumerGroupName>'
);

4）Create an Elasticsearch result table.Important: This feature is supported in Realtime Compute V3.2.2 and later.

CREATE TABLE es_stream_sink(
  a INT,
  cnt BIGINT,
  PRIMARY KEY(a)
)
WITH(
  type ='elasticsearch-7',
  endPoint = 'http://<instanceid>.public.elasticsearch.aliyuncs.com:<port>',
  accessId = '<yourAccessId>',
  accessKey = '<yourAccessSecret>',
  index = '<yourIndex>',
  typeName = '<yourTypeName>'
);

5）Define the data consumption logic and synchronize the data.

INSERT INTO es_stream_sink
SELECT 
  a,
  count(*) as cnt
FROM sls_stream GROUP BY a

6）Publish and start the job. Once the job is live, data stored in Simple Log Service will be aggregated and imported into your Elasticsearch cluster.

Conclusion

By following the steps outlined above, you can leverage Realtime Compute for Apache Flink and Alibaba Cloud Elasticsearch to create a robust real-time search service. For more complex data synchronization needs, consider exploring user-defined sinks within Realtime Compute for Apache Flink.

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Elevate Your Data Processing and Synchronization to Alibaba Cloud Elasticsearch using Realtime Compute for Apache Flink

Prerequisites

Understanding the Environment

Implementing Your Solution

Conclusion

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