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Overview

Change Data Capture (CDC) pipeline allows extracting changes in real-time from the databases which support CDC and loading them into Amazon Redshift.

We use heavily modified embedded Debezium engine for CDC.

Supported source databases

MySQL
SQL Server
PostgreSQL
Oracle
DB2
MongoDB
AS400 (IBMI platfroms)

When to use this pipeline

Use the pipeline described in this article to extract data from the CDC-enabled database and load it into Amazon Redshift in [almost] real-time.

Flows optimized for Redshift

Flow type		When to use
Any to Redshift: Database to Redshift File to Redshift Queue to Redshift Web service to Redshift Well-known API to Redshift		When you need to extract data from any source, transform it and load it into Redshift.
Bulk load files in S3 into Redshift		When you need to bulk-load files that already exist in S3 without applying any transformations. The flow automatically generates the COPY command and MERGEs data into the destination.
Stream CDC events into Redshift	You are here	When you need to stream updates from the database which supports Change Data Capture (CDC) into Redshift in real-time.
Stream messages from queue into Redshift		When you need to stream messages from the message queue which supports streaming into Redshift in real-time.
COPY files into Redshift		When you need to bulk-load data from the file-based or cloud storage, API, or NoSQL database into Redshift without applying any transformations. This flow requires providing the user-defined COPY command. Unlike Bulk load files in S3 into Redshift, this flow does not support automatic MERGE.

How it works

The end-to-end CDC pipeline extracts data from a CDC-enabled database and loads it into Redshift.

There are two options when creating a pipeline:

1. A single flow that streams CDC events directly into Redshift.

This flow streams CDC events into the designated S3 bucket in real-time and periodically (as often as every second) loads the data into Redshift in parallel with the stream. The advantage of the pipeline with a single flow is simplicity. There is just one flow to configure, schedule, and monitor. The disadvantage is the fact that if the streaming fails, the load also fails, and vice versa. Note that the flow is fault tolerant. After fixing the issue the stream and load resume from the last successful checkpoint.

2. A pipeline with independent Extract and Load flows

Extract Flow: this Flow extracts data from a CDC-enabled database.
Load Flow: this Flow loads data into Redshift.

The extract and load Flows are running in parallel which guarantees a very high processing speed and low latency. In fact, the actual pipeline can include multiple independent extract and load Flows which allows it to scale horizontally between multiple processing nodes.

Prerequisites

CDC is enabled for the source database.
The Redshift is up and running and available from the Internet.
The Redshift user hasINSERTprivilege for the table(s).
The Amazon S3 bucket is created, and Redshift is able to access the bucket.

A pipeline with a single flow

Redshift CDC flow streams CDC events into the designated S3 bucket in real-time and periodically (as often as every second) loads the data into Redshift in parallel with the stream.

There is no need to create a separate Flow for the initial load. The first time it connects to a CDC-enabled source database, it reads a consistent snapshot of all of the included databases and tables. When that snapshot is complete, the Flow continuously reads the changes that were committed to the transaction log and generates the corresponding insert, update, and delete events.

Step 1. Create a CDC Connection for the source.

Read how to create a CDC connection.

Step 2. Create connection for staging files in AWS S3

Redshift loads staged files from S3 storage. Read how to create S3 connection.

Step 3. Create a Redshift connection for the destination.

Step 4. Create a connection for history and offset files.

Read how to create CDC Offset and History connection.

Etlworks CDC connectors store the history of DDL changes for the monitored database in the history file and the current position in the transaction log in the offset file.

Typical CDC extract flow starts by snapshotting the monitored tables (A) or starts from the oldest known position in the transaction (redo) log (B), then proceeds to stream changes in the source database (C). If the Flow is stopped and restarted, it resumes from the last recorded position in the transaction log. The connection created in this step can be used to reset the CDC pipeline and restart the process from scratch.

The connection, by default, points to the directory{app.data}/debezium_data.

Step 5. Create Redshift CDC flow.

In Flows clickAdd flow. Type incdc in Select Flow type. SelectStream CDC events into Amazon Redshift.

Left to right: select the CDC connection created in step 1, select tables to monitor in FROM, select the Redshift connection created in step 3, and select or enter the Redshift table name in TO. When streaming data from multiple source tables set the destination table using a wildcard template in the following format:schema.prefix_*_suffix, whereschemais a Redshift schema to load data into.

Step 6. Set connection for staging files in AWS S3.

Select Connections tab, select connection created in step 2 and select CSV format.

Step 7. Configure load parameters

Click theMAPPINGbutton, select theParameterstab.

Set String for SQL NULL

By default, when the CDC connector generates CSV files, it sets null values to "null". This is causing an issue when loading nulls into numeric columns in Redshift.

Enter 'null' in String used to convert top SQL NULL to configure the Redshift COPY command to treat "null" as SQL NULL.

Modify the following Load parameters:

Load data into Redshift every (ms): by default, the flow loads data into Redshift every 5 minutes (300000 milliseconds). The load runs in parallel with the CDC stream, which never stops. Decrease this parameter to load data into Redshift more often or increase it to reduce the number of consumed Redshift credits.
Wait (ms) to let running load finish when CDC stream stops: By default, the flow loads data into Redshift every 5 minutes. The CDC stream and load are running in parallel, so when streaming stops, the flow executes the load last more time to finish loading the remaining data in the queue. It is possible that the load flow is still running when the stream stops. Use this parameter to configure how long the flow should wait before executing the load last time. Clear this parameter to disable the wait. In this case, if the load task is still running, the flow will finish without executing the load one last time. The flow will load the remaining data in the queue on the next run.
Action: the action can beMERGE(default) orINSERT. If the action is set toMERGEthe flow will INSERT records that do not exist in the destination table, UPDATE existing records, and DELETE records that were deleted in the source table.
How to MERGE: the type of SQL to execute when merging data. The following options are available: Native MERGE(preview) - execute native Redshift MERGE SQL. Note that MERGE command is in preview; DELETE/INSERT (default) - DELETE all records in the actual table that also exist in the temp table, then INSERT all records from the temp table into the actual table.
Lookup Fields:MERGEaction requires a list of columns that uniquely identify the record. By default, the flow will attempt to predict the Lookup Fields by checking unique indexes in the source and destination tables, but if there is no unique index in either table it is not guaranteed that the prediction will be 100% accurate. Use this parameter to define the Lookup Fields in the following format:fully.qualified.table1=field1,field2;fully.qualified.table2=field1,field2.

The other parameters are similar or the same as for the flow type Bulk load files in S3 into Redshift.

Step 8. Schedule Redshift CDC flow.

We recommend using a continuous run Schedule type. The idea is that the Flow runs until it is stopped manually, there is an error, or (if configured) there are no more new CDC events for an extended period of time. It restarts automatically after a configurable number of seconds.

Monitor running CDC flow

Read how to monitor running CDC flow.

A pipeline with independent extract and load flows

Create and schedule Extract flow

CDC extract flow extracts data from a CDC-enabled database and creates CSV files with CDC events in the configured location. These files are loaded into the Redshift by Load Flow.

Step 1. Create a CDC Connection for the source database.

Read how to create a CDC connection.

Step 2. Create an S3 connection for CDC events.

Read how to create an S3 connection.

To improve performance when loading data from S3 it is recommended that you enable GZip archiving.

Step 3. Create a connection for history and offset files.

Read how to create CDC Offset and History connection.

Etlworks CDC connectors store the history of DDL changes for the monitored database in the history file and the current position in the transaction log in the offset file.

The connection, by default, points to the directory {app.data}/debezium_data.

Step 4. Create CSV format.

This format is used to create CSV files with CDC events.

Read how to create CSV format.

Enable the following properties:

Always enclose
Escape double-quotes
Save Metadata

Step 5. Create CDC extract flow.

In Flows click Add flow. Type in cdc in Select Flow type. Select Stream CDC events, create files.

Left to right: select CDC connection created in step 1, select tables to monitor in FROM, select connection created in step 2 to stage files with CDC events, select format created in step 4.

You can now execute flow manually or schedule it to run continuously.

To stop CDC Flow manually, click Stop / Cancel.

Note that as configured the CDC flow never stops automatically. It is a recommended configuration. You can configure the CDC Connection to stop when there are no more new CDC events for an extended period of time. Read more.

Step 6. Schedule CDC extract flow.

We recommend using a continuous run Schedule type. The idea is that the extract Flow runs until it is stopped manually, there is an error, or (if configured) there are no more new CDC events for an extended period of time. It restarts automatically after a configurable number of seconds.

Monitor running CDC extract flow

Read how to monitor running CDC extract flow.

Create and schedule Load flow

This Flow is used to bulk load files created by the CDC extract flow into Amazon Redshift.

Step 1. Create a new Redshift connection.

This Connection will be used as a destination.

Step 2. Add new bulk load flow.

InFlows click[+], type inbulk load files in s3 into redshift, and select the Flow.

Step 3. Configure load transformation.

Select or enter the following attributes of the transformation (left to right):

1. S3 Connection for CDC events created in this step.

2. CSV Format created in this step.

3. A wildcard filename that matches the names of the files created by the CDC extract flow: *_cdc_stream_*.csv for uncompressed files or *_cdc_stream_*.gz for compressed files.

Note: If the source connection supports default wildcard templates (parameter Contains CDC events is enabled or the source connection is created using CDC Events connector) the wildcard filename can be selected in FROM.

4. Redshift Connection created in step 1.

5. The wildcard destination table name in the following format:schema.*, whereschemais a Redshift schema to load data into.

Step 4. Configure MERGE into the Redshift tables.

By default bulk load flow INSERTS data into the Redshift table. To configure MERGE:

1. Click the MAPPING button.

2. Set Action to CDC MERGE and enable Predict lookup fields.

Step 5. Set String for SQL NULL

By default, when the CDC connector generates CSV files, it sets null values to "null". This is causing an issue when loading nulls into numeric columns in Redshift.

Enter 'null' in String used to convert top SQL NULL to configure the Redshift COPY command to treat "null" as SQL NULL.

Step 5. Configure flow to automatically handle source schema changes.

The load flow always creates a table in Redshift if it does not exist.

Enable Alter target table if the source has columns that the target table doesn't have to automatically add missing columns to the target table.

You can now execute the Load flow manually.

Step 6. Schedule load flow.

Schedule flow to run as often as needed. These are the options:

Run flow periodically (as often as once a minute).
Run flow continuously (as often as once a second).

Articles in this section

Create pipeline to CDC data into Amazon Redshift