Скачать или смотреть Handling UPSERT with Different Keys in Confluent Elasticsearch Sink Connector

Explore how to manage UPSERTs in Confluent Elasticsearch Sink Connector when writing from different topics. Learn effective solutions for using non-ID fields.
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Handling UPSERT with Different Keys in Confluent Elasticsearch Sink Connector

In today’s data-driven world, managing and syncing data across different systems can often become challenging. One of the particular cases arises when using the Confluent Elasticsearch Sink Connector to integrate data from several topics into a single Elasticsearch index. A common scenario is needing to handle both INSERT and UPSERT operations efficiently.

The Challenge

You may find yourself in a situation where you want to write documents to the same Elasticsearch index from different Kafka topics. For instance:

First Topic (INSERT): This topic contains new records that you want to insert directly.

Second Topic (UPSERT): This topic not only updates existing records but also might need to reference a field other than the default _id for the updating process.

The main question here is: Is it possible to perform UPSERT operations based on a different field instead of _id?

The Solution

Yes, it is possible! Here’s a detailed breakdown of how to configure the Confluent Elasticsearch Sink Connector to achieve that.

Step 1: Configure Key Handling

To ensure that your connector accurately performs UPSERT operations based on fields other than _id, follow these steps:

Set Key Handling: You need to set the key.ignore configuration to false. This configuration indicates to the connector that it should utilize the keys from the incoming Kafka records when writing to Elasticsearch.

Step 2: Utilize Existing Primary Keys

Instead of relying solely on the _id field, you can map the existing primary key columns from your JSON documents to the _id. This means:

Maintain Existing Keys: Ensure that each JSON document has an existing field that can act as a primary key.

Preserve Document Identity: When Kafka produces records, the connector will use these existing primary key fields to uniquely identify and update the documents in Elasticsearch.

Example Configuration

Here’s a simple example configuration for your connector:

[[See Video to Reveal this Text or Code Snippet]]

Ensure that key.ignore is set to false.

Verify that the keys from your topics are mapped correctly to the schema in Elasticsearch.

Conclusion

By configuring the Confluent Elasticsearch Sink Connector in this way, you can efficiently manage documents from different topics. The combination of allowing key handling and mapping existing fields to _id helps in achieving the desired UPSERT functionality.

Now you can seamlessly integrate and update your data, allowing your applications to remain agile and responsive to changes without losing critical information. Happy data processing!