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Debezium 0.8 Final Is Released

I’m very happy to announce the release of Debezium 0.8.0.Final!

The key features of Debezium 0.8 are the first work-in-progress version of our Oracle connector (based on the XStream API) and a brand-new parser for MySQL DDL statements. Besides that, there are plenty of smaller new features (e.g. propagation of default values to corresponding Connect schemas, optional propagation of source queries in CDC messages and a largely improved SMT for sinking changes from MongoDB into RDBMS) as well as lots of bug fixes (e.g. around temporal and numeric column types, large transactions with Postgres).

Please see the previous announcements (Beta 1, CR 1) to learn about all the changes in more depth. The Final release largely resembles CR1; apart from further improvements to the Oracle connector (DBZ-792) there’s one nice addition to the MySQL connector contributed by Peter Goransson: when doing a snapshot, it will now expose information about the processed rows via JMX (DBZ-789), which is very handy when snapshotting larger tables.

Please take a look at the change log for the complete list of changes in 0.8.0.Final and general upgrade notes.

What’s next?

We’re continuing our work on the Oracle connector. The work on initial snapshotting is well progressing and it should be part of the next release. Other improvements will be support for structural changes to captured tables after the initial snapshot has been made, more extensive source info metadata and more. Please track DBZ-716 for this work; the improvements are planned to be released incrementally in the upcoming versions of Debezium.

We’ve also started to explore ingesting changes via LogMiner. This is more involved in terms of engineering efforts than using XStream, but it comes with the huge advantage of not requiring a separate license (LogMiner comes with the Oracle database itself). It’s not quite clear yet when we can release something on this front, and we’re also actively exploring further alternatives. But we are quite optimistic and hope to have something some time soon.

The other focus of work is a connector for SQL Server (see DBZ-40). Work on this has started as well, and there should be an Alpha1 release of Debezium 0.9 with a first drop of that connector within the next few weeks.

To find out about some more long term ideas, please check out our roadmap and get in touch with us, if you got any ideas or suggestions for future development.

About Debezium

Debezium is an open source distributed platform that turns your existing databases into event streams, so applications can see and respond almost instantly to each committed row-level change in the databases. Debezium is built on top of Kafka and provides Kafka Connect compatible connectors that monitor specific database management systems. Debezium records the history of data changes in Kafka logs, so your application can be stopped and restarted at any time and can easily consume all of the events it missed while it was not running, ensuring that all events are processed correctly and completely. Debezium is open source under the Apache License, Version 2.0.

Get involved

We hope you find Debezium interesting and useful, and want to give it a try. Follow us on Twitter @debezium, chat with us on Gitter, or join our mailing list to talk with the community. All of the code is open source on GitHub, so build the code locally and help us improve ours existing connectors and add even more connectors. If you find problems or have ideas how we can improve Debezium, please let us know or log an issue.


Debezium 0.8.0.CR1 Is Released

A fantastic Independence Day to all the Debezium users in the U.S.! But that’s not the only reason to celebrate: it’s also with great happiness that I’m announcing the release of Debezium 0.8.0.CR1!

Following our new release scheme, the focus for this candidate release of Debezium 0.8 has been to fix bug reported for last week’s Beta release, accompanied by a small number of newly implemented features.

Thanks a lot to everyone testing the new Antlr-based DDL parser for the MySQL connector; based on the issues you reported, we were able to fix a few bugs in it. As announced recently, for 0.8 the legacy parser will remain the default implementation, but you are strongly encouraged to test out the new one (by setting the connector option ddl.parser.mode to antlr) and report any findings you may have. We’ve planned to switch to the new implementation by default in Debezium 0.9.

In terms of new features, the CR1 release brings support for CITEXT columns in the Postgres connector (DBZ-762). All the relational connectors support it now to convey the original name and length of captured columns using schema parameters in the emitted change messages (DBZ-644). This can come in handy to properly size columns in a sink database for types such as VARCHAR.

Thanks a lot to the following community members who contributed to this release: Andreas Bergmeier, Olavi Mustanoja and Orr Ganani.

Please take a look at the change log for the complete list of changes in 0.8.0.CR1 and general upgrade notes.

What’s next?

Barring any unforeseen issues and critical bug reports, we’ll release Debezium 0.8.0.Final next week.

Once that’s out, we’ll continue work on the Oracle connector (e.g. exploring alternatives to using XStream for ingesting changes from the database as well as initial snapshotting), which remains a "tech preview" component as of 0.8.

We’ll also work towards a connector for SQL Server (see DBZ-40), for which the first steps just have been made today by preparing a Docker-based setup with a CDC-enabled SQL Server instance, allowing to implement and test the connector in the following.

To find out about some more long term ideas, please check out our roadmap and get in touch with us, if you got any ideas or suggestions for future development.

About Debezium

Debezium is an open source distributed platform that turns your existing databases into event streams, so applications can see and respond almost instantly to each committed row-level change in the databases. Debezium is built on top of Kafka and provides Kafka Connect compatible connectors that monitor specific database management systems. Debezium records the history of data changes in Kafka logs, so your application can be stopped and restarted at any time and can easily consume all of the events it missed while it was not running, ensuring that all events are processed correctly and completely. Debezium is open source under the Apache License, Version 2.0.

Get involved

We hope you find Debezium interesting and useful, and want to give it a try. Follow us on Twitter @debezium, chat with us on Gitter, or join our mailing list to talk with the community. All of the code is open source on GitHub, so build the code locally and help us improve ours existing connectors and add even more connectors. If you find problems or have ideas how we can improve Debezium, please let us know or log an issue.


Debezium 0.8.0.Beta1 Is Released

It’s with great excitement that I’m announcing the release of Debezium 0.8.0.Beta1!

This release brings many exciting new features as well as bug fixes, e.g. the first drop of our new Oracle connector, a brand new DDL parser for the MySQL connector, support for MySQL default values and the update to Apache Kafka 1.1.

Due to the big number of changes (the release contains exactly 42 issues overall), we decided to alter our versioning schema a little bit: going forward we may do one or more Beta and CR ("candidate release") releases before doing a final one. This will allow us to get feedback from the community early on, while still completing and polishing specific features. Final (stable) releases will be named like 0.8.0.Final etc.

This release would not have been possible without our outstanding community; a huge "thank you" goes out to the following open source enthusiasts who all contributed to the new version: Echo Xu, Ivan Vucina, Listman Gamboa, Omar Al-Safi, Peter Goransson, Roman Kuchar (who did a tremendous job with the new DDL parser implementation!), Sagar Rao, Saulius Valatka, Sairam Polavarapu, Stephen Powis and WenZe Hu.

Thank you all very much for your help!

Now let’s take a closer look at some of the features new in Debezium 0.8.0.Beta1; as always, you can find the complete list of changes of this release in the change log. Plese take a special look at the breaking changes and the upgrade notes.

XStream-based Oracle Connector (Tech Preview)

Support for a Debezium Oracle connector has been one of the most asked for features for a long time (its original issue number is DBZ-20!). So we are very happy that we eventually can release a first work-in-progress version of that connector. At this point this code is still very much evolving, so it should be considered as a first tech preview. This means it’s not feature complete (most notably, there’s no support for initial snapshots yet), the emitted message format may still change etc. So while we don’t recommend using it in production quite yet, you should definitely give it a try and report back about your experiences.

One challenge for the Oracle connector is how to get the actual change events out of the database. Unlike with MySQL and Postgres, there’s unfortunately no free-to-use and easy-to-work-with API which would allow to do the same for Oracle. After some exploration we decided to base this first version of the connector on the Oracle XStream API. While this (kinda) checks the box for "easy-to-work-with", it doesn’t do so for "free-to-use": using this API requires you to have a license for Oracle’s separate GoldenGate product. We’re fully aware of this being not ideal, but we decided to still go this route as a first step, allowing us to get some experiences with Oracle and also get a connector into the hands of those with the required license handy. Going forward, we are going to explore alternative approaches. We already have some ideas and discussions around this, so please stay tuned (the issue to track is DBZ-137).

The Oracle connector is going to evolve within the next 0.8.x releases. To learn more about it, please check its connector documentation page.

Antlr-based MySQL DDL Parser

In order to build up an internal meta-model of the captured database’s structure, the Debezium MySQL connector needs to parse all issued DDL statements (CREATE TABLE etc.). This used to be done with a hand-written DDL parser which worked reasonably well, but over time it also revealed some shortcomings; as the DDL language is quite extensive, we saw repeatedly bug reports caused by some specific DDL constructs not being parseable.

So we decided to go back to the drawing board and came up with a brand new parser design. Thanks to the great work of Roman Kuchar, we now have a completely new DDL parser which is based on the proven and very mature Antlr parser generator (luckily, the Antlr project provides a complete MySQL grammar). So we should see much less issue reports related to DDL parsing going forward.

For the time being, the old parser still is in place and remains to be the default parser for Debezium 0.8.x. You are very encouraged though to test the new implementation by setting the connector option ddl.parser.mode to antlr and report back if you run into any issues doing so. We plan to improve and polish the Antlr parser during the 0.8.x release line (specifically we’re going to measure its performance and optimize as needed) and switch to it by default as of Debezium 0.9. Eventually, the old parser will be removed in a future release after that.

Further MySQL Connector Changes

The MySQL Connector propagates column default values to corresponding Kafka Connect schemas now (DBZ-191). That’s beneficial when using Avro as serialization format and the schema registry with compatibility checking enabled.

By setting the include.query connector option to true, you can add the original query that caused a data change to the corresponding CDC events (DBZ-706). While disabled by default, this feature can be a useful tool for analyzing and interpreting data changes captured with Debezium.

Some other changes in the MySQL connector include configurability of the heartbeat topic name (DBZ-668), fixes around timezone handling for TIMESTAMP (DBZ-578) and DATETIME columns (DBZ-741) and correct handling of NUMERIC column without an explicit scale value (DBZ-727).

Postgres Connector

The Debezium Connector for Postgres has seen quite a number of bugfixes, including the following ones:

  • wal2json can handle transactions now that are bigger than 1Gb (DBZ-638)

  • the transaction ID is consistently handled as long now (DBZ-673)

  • multiple fixes related to temporal column types (DBZ-681, DBZ-696)

  • OIDs are handled correctly as unsigned int now (DBZ-697, DBZ-701)

MongoDB Connector

Also for the MongoDB Connector a number of small feature implementations and bugfixes has been done:

  • Tested against MongoDB 3.6 (DBZ-529)

  • Nested documents can be flattened using a provided SMT now (DBZ-561), which is useful when sinking changes from MongoDB into a relational database

  • The unwrapping SMT can be used together with Avro now (DBZ-650)

  • The unwrapping SMT can handle arrays with mixed element types (DBZ-649)

  • When interrupted during snapshotting before completion, the connector will redo the snapshot after restarting (DBZ-712)

What’s next?

As per the new Beta/CR/Final release scheme, we hope to get some feedback by the community (i.e. you :) on this Beta release. Depending on the number of issues reported, we’ll either release another Beta or go to CR1 with the next version. The 0.8.0.Final version will be released within a few weeks. Note that the Oracle connector will remain a "tech preview" component also in the final version.

After that, we’ve planned to do a few 0.8.x releases with bug fixes mostly, while work on Debezium 0.9 will commence in parallel. For that we’ve planned to work on a connector for SQL Server (see DBZ-40). We’d also like to explore means of creating consistent materializations of joins from multiple tables' CDC streams, based on the ids of originating transactions. Also there’s the idea and a first prototype of exposing Debezium change events as a reactive event stream (DBZ-566), which might be shipped eventually.

Please take a look at the roadmap for some more long term ideas and get in touch with us, if you got thoughts around that.

About Debezium

Debezium is an open source distributed platform that turns your existing databases into event streams, so applications can see and respond almost instantly to each committed row-level change in the databases. Debezium is built on top of Kafka and provides Kafka Connect compatible connectors that monitor specific database management systems. Debezium records the history of data changes in Kafka logs, so your application can be stopped and restarted at any time and can easily consume all of the events it missed while it was not running, ensuring that all events are processed correctly and completely. Debezium is open source under the Apache License, Version 2.0.

Get involved

We hope you find Debezium interesting and useful, and want to give it a try. Follow us on Twitter @debezium, chat with us on Gitter, or join our mailing list to talk with the community. All of the code is open source on GitHub, so build the code locally and help us improve ours existing connectors and add even more connectors. If you find problems or have ideas how we can improve Debezium, please let us know or log an issue.


Querying Debezium Change Data Events With KSQL

Last year we have seen the inception of a new open-source project in the Apache Kafka universe, KSQL, which is a streaming SQL engine build on top of Kafka Streams. In this post, we are going to try out KSQL querying with data change events generated by Debezium from a MySQL database.

As a source of data we will use the database and setup from our tutorial. The result of this exercise should be similar to the recent post about aggregation of events into domain driven aggregates.

Entity diagram

First let’s look at the entities in the database and the relations between them.

Entity diagram
Figure 1: Entity diagram of the example entities

 

The picture above shows the full ER diagram for the inventory database in the example MySQL instance. We are going to focus on two entities:

  • customers - the list of customers in the system

  • orders - the list of orders in the system

There is a 1:n relation between customers and orders, modelled by the purchaser column in the orders table, which is a foreign key to the customers table.

Configuration

We are going to use a Docker Compose file for the deployment of the environment. The deployment consists of the following Docker images:

We also need the KSQL client. To make things simple we are going to use a pre-built Docker image but you can download and directly use the client from the KSQL download page.

Example

First we need to start the Debezium and Kafka infrastructure. To do so, clone the debezium-examples GitHub repository and start the required components using the provided Compose file:

export DEBEZIUM_VERSION=0.7
git clone https://github.com/debezium/debezium-examples.git
cd debezium-examples/tutorial/
docker-compose -f docker-compose-mysql.yaml up

Next we must register an instance of the Debezium MySQL connector to listen to changes in the database:

curl -i -X POST -H "Accept:application/json" -H  "Content-Type:application/json" http://localhost:8083/connectors/ -d @- <<-EOF
{
    "name": "inventory-connector",
    "config": {
        "connector.class": "io.debezium.connector.mysql.MySqlConnector",
        "tasks.max": "1",
        "database.hostname": "mysql",
        "database.port": "3306",
        "database.user": "debezium",
        "database.password": "dbz",
        "database.server.id": "184055",
        "database.server.name": "dbserver",
        "database.whitelist": "inventory",
        "database.history.kafka.bootstrap.servers": "kafka:9092",
        "database.history.kafka.topic": "schema-changes.inventory",
        "transforms": "unwrap",
        "transforms.unwrap.type": "io.debezium.transforms.UnwrapFromEnvelope",
        "key.converter": "org.apache.kafka.connect.json.JsonConverter",
        "key.converter.schemas.enable": "false",
        "value.converter": "org.apache.kafka.connect.json.JsonConverter",
        "value.converter.schemas.enable": "false"
    }
}
EOF

Now we should have all components up and running and initial data change events are already streamed into Kafka topics. There are multiple properties that are especially important for our use case:

  • The UnwrapFromEnvelope SMT is used. This allows us to directly map fields from the after part of change records into KSQL statements. Without it, we would need to use EXTRACTJSONFIELD for each field to be extracted from the after part of messages.

  • Schemas are disabled for the JSON converter. The reason is the same as above. With schemas enabled, for JSON the record is encapsulated in a JSON structure that contains the fields schema (with schema information) and payload (with the actual data itself). We would again need to use EXTRACTJSONFIELD to get to the relevant fields. There is no such issue with Avro converter so this option does not need to be set when Avro is used.

Next we are going to start the KSQL command shell. We will run a local engine in the CLI. Also please note --net parameter. This guarantees that KSQL container runs in the same network as Debezium containers and allows proper DNS resolution.

docker run -it --net tutorial_default confluentinc/ksql-cli ksql-cli local --bootstrap-server kafka:9092

First we will list all Kafka topics that exist in the broker:

ksql> LIST TOPICS;

 Kafka Topic                         | Registered | Partitions | Partition Replicas
------------------------------------------------------------------------------------
 connect-status                      | false      | 5          | 1
 dbserver                            | false      | 1          | 1
 dbserver.inventory.addresses        | false      | 1          | 1
 dbserver.inventory.customers        | false      | 1          | 1
 dbserver.inventory.orders           | false      | 1          | 1
 dbserver.inventory.products         | false      | 1          | 1
 dbserver.inventory.products_on_hand | false      | 1          | 1
 ksql__commands                      | true       | 1          | 1
 my_connect_configs                  | false      | 1          | 1
 my_connect_offsets                  | false      | 25         | 1
 schema-changes.inventory            | false      | 1          | 1

The topics we are interested in are dbserver.inventory.orders and dbserver.inventory.customers.

KSQL processing by default starts with latest offsets. We want to process the events already in the topics so we switch processing from earliest offsets.

ksql> SET 'auto.offset.reset' = 'earliest';
Successfully changed local property 'auto.offset.reset' from 'null' to 'earliest'

First we need to create streams from the topics containing the Debezium data change events. A stream in KSQL and Kafka Streams terminology is an unbounded incoming data set with no state.

ksql> CREATE STREAM orders_from_debezium (order_number integer, order_date string, purchaser integer, quantity integer, product_id integer) WITH (KAFKA_TOPIC='dbserver.inventory.orders',VALUE_FORMAT='json');

 Message
----------------
 Stream created
ksql>
ksql> CREATE STREAM customers_from_debezium (id integer, first_name string, last_name string, email string) WITH (KAFKA_TOPIC='dbserver.inventory.customers',VALUE_FORMAT='json');

 Message
----------------
 Stream created

Partitioning

Our deployment uses only one partition per topic. In a production system there will likely be multiple partitions per topic and we need to ensure that all events belonging to our aggregated object end up in the same partition. The natural partioning in our case is per customer id. We are going to repartition the orders_from_debezium stream according to the purchaser field that contains the customer id. The repartitioned data are written into a new topic ORDERS_REPART:

ksql> CREATE STREAM orders WITH (KAFKA_TOPIC='ORDERS_REPART',VALUE_FORMAT='json',PARTITIONS=1) as SELECT * FROM orders_from_debezium PARTITION BY PURCHASER;

 Message
----------------------------
 Stream created and running
ksql> LIST TOPICS;

 Kafka Topic                         | Registered | Partitions | Partition Replicas
------------------------------------------------------------------------------------
...
 ORDERS_REPART                       | true       | 1          | 1
...

We are going to execute the same operation for customers too. It is necessary for two reasons:

  • The current key is a struct that contains a field named id with the customer id. This is different from the repartitioned order topic which contains only the id value as the key, so the partitions would not match.

  • When we will create a JOIN later, there is a limitation that requires the key to have the same value as a key field in the table. The table field contains a plain value but the key contains a struct so they would not match. See this KSQL issue for more details.

ksql> CREATE STREAM customers_stream WITH (KAFKA_TOPIC='CUSTOMERS_REPART',VALUE_FORMAT='json',PARTITIONS=1) as SELECT * FROM customers_from_debezium PARTITION BY ID;

 Message
----------------------------
 Stream created and running
ksql> LIST TOPICS;

 Kafka Topic                         | Registered | Partitions | Partition Replicas
------------------------------------------------------------------------------------
...
 CUSTOMERS_REPART                    | true       | 1          | 1
...

To verify that records have a new key and are thus repartioned we can issue few statements to compare the results:

ksql> SELECT * FROM orders_from_debezium LIMIT 1;
1524034842810 | {"order_number":10001} | 10001 | 16816 | 1001 | 1 | 102
LIMIT reached for the partition.
Query terminated
ksql> SELECT * FROM orders LIMIT 1;
1524034842810 | 1001 | 10001 | 16816 | 1001 | 1 | 102
LIMIT reached for the partition.
Query terminated

The second column contains ROWKEY which is the key of the message.

Customer/order join

So far we were only declaring streams as an unbounded stateless data set. In our use case the order is really an event that comes and goes. But customer is an entity that can be updated and generally is a part of a state fo the system. Such quality is represented in KSQL or Kafka Streams as table. We are going to create a table of customers from the topic containing repartitioned customers.

ksql> CREATE TABLE customers (id integer, first_name string, last_name string, email string) WITH (KAFKA_TOPIC='CUSTOMERS_REPART',VALUE_FORMAT='json',KEY='id');

 Message
---------------
 Table created

Now we have everything in place to make a join between customer and its orders and create a query that will monitor incoming orders and list them with associated customer fields.

ksql> SELECT order_number,quantity,customers.first_name,customers.last_name FROM orders left join customers on orders.purchaser=customers.id;
10001 | 1 | Sally | Thomas
10002 | 2 | George | Bailey
10003 | 2 | George | Bailey
10004 | 1 | Edward | Walker

Let’s apply a few changes to the database, which will result in corresponding CDC events being emitted by Debezium:

docker-compose -f docker-compose-mysql.yaml exec mysql bash -c 'mysql -u $MYSQL_USER -p$MYSQL_PASSWORD inventory'

mysql> INSERT INTO orders VALUES(default,NOW(), 1003,5,101);
Query OK, 1 row affected, 1 warning (0.02 sec)

mysql> UPDATE customers SET first_name='Annie' WHERE id=1004;
Query OK, 1 row affected (0.02 sec)
Rows matched: 1  Changed: 1  Warnings: 0

mysql> UPDATE orders SET quantity=20 WHERE order_number=10004;
Query OK, 1 row affected (0.02 sec)
Rows matched: 1  Changed: 1  Warnings: 0

You may notice that only changes in the orders table have triggered changes in the joined stream. This is a product of the stream/table join. We would need a stream/stream join to trigger changes if any of input streams is modified.

So the final result of the select after the database is modified is

10001 | 1 | Sally | Thomas
10002 | 2 | George | Bailey
10003 | 2 | George | Bailey
10004 | 1 | Edward | Walker
10005 | 5 | Edward | Walker
10004 | 20 | Edward | Walker

Summary

We have successfully started a KSQL instance. We have mapped KSQL streams to Debezium topics filled by Debezium and made a join between them. We have also discussed the problem of repartioning in streaming applications.

If you’d like to try out this example with Avro encoding and schema registry then you can use our Avro example. Also for further details and more advanced usages just refer to the KSQL syntax reference.

In case you need help, have feature requests or would like to share your experiences with this example, please let us know in the comments below.

About Debezium

Debezium is an open source distributed platform that turns your existing databases into event streams, so applications can see and respond almost instantly to each committed row-level change in the databases. Debezium is built on top of Kafka and provides Kafka Connect compatible connectors that monitor specific database management systems. Debezium records the history of data changes in Kafka logs, so your application can be stopped and restarted at any time and can easily consume all of the events it missed while it was not running, ensuring that all events are processed correctly and completely. Debezium is open source under the Apache License, Version 2.0.

Get involved

We hope you find Debezium interesting and useful, and want to give it a try. Follow us on Twitter @debezium, chat with us on Gitter, or join our mailing list to talk with the community. All of the code is open source on GitHub, so build the code locally and help us improve ours existing connectors and add even more connectors. If you find problems or have ideas how we can improve Debezium, please let us know or log an issue.


Debezium 0.7.5 Is Released

It’s my pleasure to announce the release of Debezium 0.7.5!

This is a bugfix release to the 0.7 release line, which we decided to do while working towards Debezium 0.8. Most notably it fixes an unfortunate bug introduced in 0.7.3 (DBZ-663), where the internal database history topic of the Debezium MySQL connector could be partly deleted under some specific conditions. Please see the dedicated blog post on this issue to find out whether this affects you and what you should do to prevent this issue.

Together with this, we released a couple of other fixes and improvements. Thanks to Maciej Brynski, the performance of the logical table routing SMT has been improved significantly (DBZ-655). Another fix contributed by Maciej is for DBZ-646 which lets the MySQL connector handle CREATE TABLE statements for the TokuDB storage engine now.

And we got some more bugfixes by our fantastic community: Long-term community member Peter Goransson fixed an issue about the snapshot JMX metrics of the MySQL connector, which are now also accessible after the snapshot has been completed (DBZ-640). Andrew Tongen spotted and fixed an issue for the Debezium embedded engine (DBZ-665) which caused offsets to be committed more often than needed. And Matthias Wessendorf upgraded the Debezium dependencies and Docker images to Apache Kafka 1.0.1 (DBZ-647).

Thank you all for your help!

Please refer to the change log for the complete list of changes in Debezium 0.7.5.

What’s next?

Please see the previous release announcement for the next planned features. Due to the unplanned 0.7.5 release, though, the schedule of the next one will likely be extended a little bit.

About Debezium

Debezium is an open source distributed platform that turns your existing databases into event streams, so applications can see and respond almost instantly to each committed row-level change in the databases. Debezium is built on top of Kafka and provides Kafka Connect compatible connectors that monitor specific database management systems. Debezium records the history of data changes in Kafka logs, so your application can be stopped and restarted at any time and can easily consume all of the events it missed while it was not running, ensuring that all events are processed correctly and completely. Debezium is open source under the Apache License, Version 2.0.

Get involved

We hope you find Debezium interesting and useful, and want to give it a try. Follow us on Twitter @debezium, chat with us on Gitter, or join our mailing list to talk with the community. All of the code is open source on GitHub, so build the code locally and help us improve ours existing connectors and add even more connectors. If you find problems or have ideas how we can improve Debezium, please let us know or log an issue.


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