Transferring data from a Yandex Data Streams source endpoint

Yandex Data Transfer enables you to migrate data from an Data Streams queue and implement various data transfer, processing, and transformation scenarios. To implement a transfer:

1. Explore possible data transfer scenarios.
2. Prepare the Data Streams database for the transfer.
3. Set up a source endpoint in Yandex Data Transfer.
4. Set up one of the supported data targets.
5. Create a transfer and start it.
6. Perform required operations with the database and control the transfer.
7. In case of any issues, use ready-made solutions to resolve them.

Scenarios for transferring data from Data StreamsScenarios for transferring data from Data Streams

1. Migration: Moving data from one storage to another. Migration often means migrating a database from obsolete local databases to managed cloud ones.

   Mirroring data across Data Streams queues is a separate migration task.

2. Data delivery is a process of delivering arbitrary data to target storage. It includes data retrieval from a queue and its deserialization with subsequent transformation to target storage format.

   • YDS to ClickHouse®.
   • YDS to Object Storage.
   • YDS to Managed Service for YDB.
   • YDS in Managed Service for Apache Kafka®.

For a detailed description of possible Yandex Data Transfer scenarios, see Tutorials.

Preparing the source databasePreparing the source database

1. Create a service account with the yds.editor role.

2. Create a data stream.

3. (Optional) Create a processing function.

   Processing function example

   const yc = require("yandex-cloud");
   const { Parser } = require("@robojones/nginx-log-parser");
   module.exports.handler = async function (event, context) {
       const schema =
           '$remote_addr - $remote_user [$time_local] "$request" $status $bytes_sent "$http_referer" "$http_user_agent"';
       const parser = new Parser(schema);
       return {
           Records: event.Records.map((record) => {
               const decodedData = new Buffer(record.kinesis.data, "base64")
                   .toString("ascii")
                   .trim();
               try {
                   const result = parser.parseLine(decodedData);
                   if (result.request == "") {
                       // empty request - drop message
                       return {
                           eventID: record.eventID,
                           invokeIdentityArn: record.invokeIdentityArn,
                           eventVersion: record.eventVersion,
                           eventName: record.eventName,
                           eventSourceARN: record.eventSourceARN,
                           result: "Dropped"
                       };
                   }
                   return {
                       // successfully parsed message
                       eventID: record.eventID,
                       invokeIdentityArn: record.invokeIdentityArn,
                       eventVersion: record.eventVersion,
                       eventName: record.eventName,
                       eventSourceARN: record.eventSourceARN,
                       kinesis: {
                           data: new Buffer(JSON.stringify(result)).toString(
                               "base64"
                           ),
                       },
                       result: "Ok"
                   };
               } catch (err) {
                   // error - fail message
                   return {
                       eventID: record.eventID,
                       invokeIdentityArn: record.invokeIdentityArn,
                       eventVersion: record.eventVersion,
                       eventName: record.eventName,
                       eventSourceARN: record.eventSourceARN,
                       result: "ProcessingFailed",
                   };
               }
           })
       };
   };
   

4. (Optional) Prepare a data schema file in JSON format.

   Sample file with a data schema:

   [
       {
           "name": "<field_name>",
           "type": "<type>"
       },
       ...
       {
           "name": "<field_name>",
           "type": "<type>"
       }
   ]
   

   Supported types:

   • any
   • boolean
   • datetime
   • double
   • int8
   • int16
   • int32
   • int64
   • string
   • uint8
   • uint16
   • uint32
   • uint64
   • utf8

Configuring the Data Streams source endpointConfiguring the Data Streams source endpoint

When creating or updating an endpoint, you can define:

• Stream connection settings in Yandex Data Streams. These are required parameters.
• Additional settings.

Basic settingsBasic settings

Advanced settingsAdvanced settings

Use advanced settings to specify transformation and conversion rules. Data is processed in the following order:

1. Transformation. Data in JSON format is provided to a Yandex Cloud Functions function. The function body contains metadata and raw data from the queue. The function handles the data and sends it back to Data Transfer.

2. Conversion. Data is parsed as a preparation for delivery to the target.

If no transformation rules are set, parsing is applied to raw data from the queue. If no conversion rules are set, the data goes directly to the target.

Configuring the data targetConfiguring the data target

Configure one of the supported data targets:

• PostgreSQL
• MySQL®
• MongoDB
• ClickHouse®
• Greenplum®
• Yandex Managed Service for YDB
• Yandex Object Storage
• Apache Kafka®
• YDS
• Elasticsearch
• OpenSearch

For a complete list of supported sources and targets in Yandex Data Transfer, see Available transfers.

After configuring the data source and target, create and start the transfer.

Troubleshooting data transfer issuesTroubleshooting data transfer issues

• Transfer failure
• Cloud Functions redirects

For more troubleshooting tips, see Troubleshooting.

Transfer failureTransfer failure

A Replication or Snapshot and increment transfer is interrupted with an error.

Error message:

/Ydb.PersQueue.V1.PersQueueService/AddReadRule failed: OVERLOADED

Transfers are aborted due to the cloud quota on the number of operations with Managed Service for YDB.

Solution:

1. Increase the Number of schema transactions per minute property in the Managed Service for YDB quotas for the cloud with the required database and reactivate the transfer.

Cloud Functions redirectsCloud Functions redirects

In rare cases, the following error may occur during transfers from Data Streams or Apache Kafka®:

redirect to SOME_URL is requested but no redirects are allowed.

Possible cause:

The use of the Cloud Functions function is set up on the source. It returns a redirect to another URL rather than data.

Solution:

Such redirects are not allowed for security reasons. Avoid using redirects to Cloud Functions during transfers.