Debezium connector for MariaDB

The Debezium MariaDB connector supports the following MariaDB topologies:

When a database client queries a database, the client uses the database’s current schema. However, the database schema can be changed at any time, which means that the connector must be able to identify what the schema was at the time each insert, update, or delete operation was recorded. Also, a connector cannot necessarily apply the current schema to every event. If an event is relatively old, it’s possible that it was recorded before the current schema was applied.

To ensure correct processing of events that occur after a schema change, MariaDB includes in the transaction log not only the row-level changes that affect the data, but also the DDL statements that are applied to the database. As the connector encounters these DDL statements in the binlog, it parses them and updates an in-memory representation of each table’s schema. The connector uses this schema representation to identify the structure of the tables at the time of each insert, update, or delete operation and to produce the appropriate change event. In a separate database schema history Kafka topic, the connector records all DDL statements along with the position in the binlog where each DDL statement appeared.

When the connector restarts after either a crash or a graceful stop, it starts reading the binlog from a specific position, that is, from a specific point in time. The connector rebuilds the table structures that existed at this point in time by reading the database schema history Kafka topic and parsing all DDL statements up to the point in the binlog where the connector is starting.

This database schema history topic is for internal connector use only. Optionally, the connector can also emit schema change events to a different topic that is intended for consumer applications.

When the MariaDB connector captures changes in a table to which a schema change tool such as gh-ost or pt-online-schema-change is applied, there are helper tables created during the migration process. You must configure the connector to capture changes that occur in these helper tables. If consumers do not need the records the connector generates for helper tables, configure a single message transform (SMT) to remove these records from the messages that the connector emits.

You can configure a Debezium MariaDB connector to produce schema change events that describe schema changes that are applied to tables in the database. The connector writes schema change events to a Kafka topic named <topicPrefix>, where topicPrefix is the namespace specified in the topic.prefix connector configuration property. Messages that the connector sends to the schema change topic contain a payload, and, optionally, also contain the schema of the change event message.

The schema for the schema change event has the following elements:

The following example shows a typical schema in JSON format.

The payload of a schema change event message includes the following elements:

The following example shows a typical schema change message in JSON format. The message contains a logical representation of the table schema.

For more information, see schema history topic.

When a Debezium MariaDB connector is first started, it performs an initial consistent snapshot of your database. This snapshot enables the connector to establish a baseline for the current state of the database.

Debezium can use different modes when it runs a snapshot. The snapshot mode is determined by the snapshot.mode configuration property. The default value of the property is initial. You can customize the way that the connector creates snapshots by changing the value of the snapshot.mode property.

The connector completes a series of tasks when it performs the snapshot. The exact steps vary with the snapshot mode and with the table locking policy that is in effect for the database. The Debezium MariaDB connector completes different steps when it performs an initial snapshot that uses a global read lock or table-level locks.

By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.

However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. You might want to perform an ad hoc snapshot after any of the following changes occur in your Debezium environment:

You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling tables. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling table.

When you initiate an ad hoc snapshot of an existing table, the connector appends content to the topic that already exists for the table. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.

Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database. Also, the snapshot can capture a subset of the contents of the table(s) in the database.

You specify the tables to capture by sending an execute-snapshot message to the signaling table. Set the type of the execute-snapshot signal to incremental or blocking, and provide the names of the tables to include in the snapshot, as described in the following table:

You initiate an ad hoc incremental snapshot by adding an entry with the execute-snapshot signal type to the signaling table, or by sending a signal message to a Kafka signaling topic. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table. Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time.

For more information, see Incremental snapshots.

You initiate an ad hoc blocking snapshot by adding an entry with the execute-snapshot signal type to the signaling table or signaling topic. After the connector processes the message, it begins the snapshot operation. The connector temporarily stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot. After the snapshot completes, the connector resumes streaming.

For more information, see Blocking snapshots.

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector. Incremental snapshots are based on the DDD-3 design document.

In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each table in phases, in a series of configurable chunks. You can specify the tables that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1024 rows.

As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each table row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:

When you run an incremental snapshot, Debezium sorts each table by primary key and then splits the table into chunks based on the configured chunk size. Working chunk by chunk, it then captures each table row in a chunk. For each row that it captures, the snapshot emits a READ event. That event represents the value of the row when the snapshot for the chunk began.

As a snapshot proceeds, it’s likely that other processes continue to access the database, potentially modifying table records. To reflect such changes, INSERT, UPDATE, or DELETE operations are committed to the transaction log as per usual. Similarly, the ongoing Debezium streaming process continues to detect these change events and emits corresponding change event records to Kafka.

In some cases, the UPDATE or DELETE events that the streaming process emits are received out of sequence. That is, the streaming process might emit an event that modifies a table row before the snapshot captures the chunk that contains the READ event for that row. When the snapshot eventually emits the corresponding READ event for the row, its value is already superseded. To ensure that incremental snapshot events that arrive out of sequence are processed in the correct logical order, Debezium employs a buffering scheme for resolving collisions. Only after collisions between the snapshot events and the streamed events are resolved does Debezium emit an event record to Kafka.

To assist in resolving collisions between late-arriving READ events and streamed events that modify the same table row, Debezium employs a so-called snapshot window. The snapshot window demarcates the interval during which an incremental snapshot captures data for a specified table chunk. Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic. But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..

For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic. The snapshot records that it captures directly from a table are emitted as READ operations. Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emits UPDATE or DELETE operations for each change.

As the snapshot window opens, and Debezium begins processing a snapshot chunk, it delivers snapshot records to a memory buffer. During the snapshot windows, the primary keys of the READ events in the buffer are compared to the primary keys of the incoming streamed events. If no match is found, the streamed event record is sent directly to Kafka. If Debezium detects a match, it discards the buffered READ event, and writes the streamed record to the destination topic, because the streamed event logically supersede the static snapshot event. After the snapshot window for the chunk closes, the buffer contains only READ events for which no related transaction log events exist. Debezium emits these remaining READ events to the table’s Kafka topic.

The connector repeats the process for each snapshot chunk.

Currently, you can use either of the following methods to initiate an incremental snapshot:

For more advanced uses, you can fine-tune control of the snapshot by implementing one of the following interfaces:

To provide more flexibility in managing snapshots, Debezium includes a supplementary ad hoc snapshot mechanism, known as a blocking snapshot. Blocking snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.

A blocking snapshot behaves just like an initial snapshot, except that you can trigger it at run time.

You might want to run a blocking snapshot rather than use the standard initial snapshot process in the following situations:

When you run a blocking snapshot, Debezium stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot. After the snapshot completes, the streaming is resumed.

You can set the following properties in the data component of a signal:

For example:

A delay might exist between the time that you send the signal to trigger the snapshot, and the time when streaming stops and the snapshot starts. As a result of this delay, after the snapshot completes, the connector might emit some event records that duplicate records captured by the snapshot.

By default, the MariaDB connector writes change events for all of the INSERT, UPDATE, and DELETE operations that occur in a table to a single Apache Kafka topic that is specific to that table.

The connector uses the following convention to name change event topics:

topicPrefix.databaseName.tableName

Suppose that fulfillment is the topic prefix, inventory is the database name, and the database contains tables named orders, customers, and products. The Debezium MariaDB connector emits events to three Kafka topics, one for each table in the database:

The following list provides definitions for the components of the default name:

The connector applies similar naming conventions to label its internal database schema history topics, schema change topics, and transaction metadata topics.

If the default topic name do not meet your requirements, you can configure custom topic names. To configure custom topic names, you specify regular expressions in the logical topic routing SMT. For more information about using the logical topic routing SMT to customize topic naming, see Topic routing.

Debezium can generate events that represent transaction boundaries and that enrich data change event messages.

Debezium generates transaction boundary events for the BEGIN and END delimiters in every transaction. Transaction boundary events contain the following fields:

Unless overridden via the topic.transaction option, the connector emits transaction events to the <topic.prefix>.transaction topic.

When transaction metadata is enabled the data message Envelope is enriched with a new transaction field. This field provides information about every event in the form of a composite of fields:

Following is an example of a message:

A change event’s key contains the schema for the changed table’s key and the changed row’s actual key. Both the schema and its corresponding payload contain a field for each column in the changed table’s PRIMARY KEY (or unique constraint) at the time the connector created the event.

Consider the following customers table, which is followed by an example of a change event key for this table.

Every change event that captures a change to the customers table has the same event key schema. For as long as the customers table has the previous definition, every change event that captures a change to the customers table has the following key structure. In JSON, it looks like this:

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample table that was used to show an example of a change event key:

The value portion of a change event for a change to this table is described for:

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers table:

The value of a change event for an update in the sample customers table has the same schema as a create event for that table. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. Here is an example of a change event value in an event that the connector generates for an update in the customers table:

An UPDATE operation that changes a row’s primary key field(s) is known as a primary key change. For a primary key change, in place of an UPDATE event record, the connector emits a DELETE event record for the old key and a CREATE event record for the new (updated) key. These events have the usual structure and content, and in addition, each one has a message header related to the primary key change:

The value in a delete change event has the same schema portion as create and update events for the same table. The payload portion in a delete event for the sample customers table looks like this:

A delete change event record provides a consumer with the information it needs to process the removal of this row. The old values are included because some consumers might require them in order to properly handle the removal.

MariaDB connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

When a row is deleted, the delete event value still works with log compaction, because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that same key, the message value must be null. To make this possible, after the Debezium MariaDB connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value.

A truncate change event signals that a table has been truncated. The message key of a truncate event is null. The message value resembles the following example:

In case a single TRUNCATE statement applies to multiple tables, one truncate change event record for each truncated table will be emitted.

The following table shows how the connector maps basic MariaDB data types.

Excluding the TIMESTAMP data type, MariaDB temporal types depend on the value of the time.precision.mode connector configuration property. For TIMESTAMP columns whose default value is specified as CURRENT_TIMESTAMP or NOW, the value 1970-01-01 00:00:00 is used as the default value in the Kafka Connect schema.

MariaDB allows zero-values for DATE, DATETIME, and TIMESTAMP columns because zero-values are sometimes preferred over null values. The MariaDB connector represents zero-values as null values when the column definition allows null values, or as the epoch day when the column does not allow null values.

The DATETIME type represents a local date and time such as "2018-01-13 09:48:27". As you can see, there is no time zone information. Such columns are converted into epoch milliseconds or microseconds based on the column’s precision by using UTC. The TIMESTAMP type represents a timestamp without time zone information. It is converted by MariaDB from the server (or session’s) current time zone into UTC when writing and from UTC into the server (or session’s) current time zone when reading back the value. For example:

Such columns are converted into an equivalent io.debezium.time.ZonedTimestamp in UTC based on the server (or session’s) current time zone. The time zone will be queried from the server by default.

If this fails, it must be specified explicitly by the database timezone MariaDB configuration option. For example, if the database’s time zone (either globally or configured for the connector by means of the timezone option) is "America/Los_Angeles", the TIMESTAMP value "2018-06-20 06:37:03" is represented by a ZonedTimestamp with the value "2018-06-20T13:37:03Z".

The time zone setting for the JVM that runs Kafka Connect and Debezium does not affect these conversions.

More details about properties related to temporal values are in the documentation for MariaDB connector configuration properties.

Debezium connectors handle decimals according to the setting of the decimal.handling.mode connector configuration property.

MariaDB handles the BOOLEAN value internally in a specific way. The BOOLEAN column is internally mapped to the TINYINT(1) data type. When the table is created during streaming then it uses proper BOOLEAN mapping as Debezium receives the original DDL. During snapshots, Debezium executes SHOW CREATE TABLE to obtain table definitions that return TINYINT(1) for both BOOLEAN and TINYINT(1) columns. Debezium then has no way to obtain the original type mapping and so maps to TINYINT(1).

To enable you to convert source columns to Boolean data types, Debezium provides a TinyIntOneToBooleanConverter custom converter that you can use in one of the following ways:

Currently, the Debezium MariaDB connector supports the following spatial data types.

By default, during a connector snapshot, the Debezium MariaDB connector obtains column types from the JDBC driver, which assigns the TINYINT(1) type to BOOLEAN columns. Debezium then uses these JDBC column types to define the schema for the snapshot events. After the connector transitions from the snapshot to the streaming phase, the change event schema that results from the default mapping can lead to inconsistent mappings for BOOLEAN columns. To help ensure that MariaDB emits BOOLEAN columns uniformly, you can apply the custom TinyIntOneToBooleanConverter, as shown in the following configuration example.

In the preceding example, the selector and length.checker properties are optional. By default, the converter checks that TINYINT data types conform to a length of 1. If length.checker to false, the converter does not explicitly confirm that the TINYINT data type conforms to a length of 1. The selector designates the tables or columns to convert, based on the supplied regular expression. If you omit the selector property, the converter maps all TINYINT columns to logical BOOL field types. If you do not configure a selector option, and you want to map TINYINT columns to TINYINT(1), omit the length.checker property, or set its value to true.

If you integrate the Debezium JDBC sink connector with a Debezium MariaDB source connector, the MariaDB connector emits some column attributes differently during the snapshot and streaming phases. For the JDBC sink connector to consistently consume changes from both the snapshot and streaming phase, you must include the JdbcSinkDataTypesConverter converter as part of the MariaDB source connector configuration, as shown in the following example:

In the preceding example, the selector.* and treat.real.as.double configuration properties are optional.

The selector.* properties specify comma-separated lists of regular expressions that specify which tables and columns that the converter applies to. By default, the converter applies the following rules apply to all Boolean, real, and string-based column data types, across all tables:

For each data type, you can configure a selector rule to override the default scope and apply the selector to specific tables and columns only. For example, to set the scope of the Boolean converter, add the following rule to the connector configuration, as in the preceding example: converters.jdbc-sink.selector.boolean=.*.MY_TABLE.BOOL_COL

A Debezium MariaDB connector requires a MariaDB user account. This MariaDB user must have appropriate permissions on all databases for which the Debezium MariaDB connector captures changes.

You must enable binary logging for MariaDB replication. The binary logs record transaction updates in a way that enables replicas to propagate those changes.

Global transaction identifiers (GTIDs) uniquely identify transactions that occur on a server within a cluster. Although not required for a Debezium MariaDB connector, using GTIDs simplifies replication and enables you to more easily confirm if primary and replica servers are consistent.

For MariaDB, GTIDs are enabled by default and no additional configuration is necessary.

When an initial consistent snapshot is made for large databases, your established connection could timeout while the tables are being read. You can prevent this behavior by configuring interactive_timeout and wait_timeout in your MariaDB configuration file.

You might want to see the original SQL statement for each binlog event. Enabling the binlog_annotate_row_events option in the MariaDB configuration allows you to do this.

Verify the setting of the binlog_row_value_options variable in the database. To enable the connector to consume UPDATE events, this variable must be set to a value other than PARTIAL_JSON.

Following is an example of the configuration for a connector instance that captures data from a MariaDB server on port 3306 at 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium MariaDB connector in a JSON file by setting the configuration properties that are available for the connector.

You can choose to produce events for a subset of the schemas and tables in a database. Optionally, you can ignore, mask, or truncate columns that contain sensitive data, that are larger than a specified size, or that you do not need.

For the complete list of the configuration properties that you can set for the Debezium MariaDB connector, see MariaDB connector configuration properties.

You can send this configuration with a POST command to a running Kafka Connect service. The service records the configuration and starts one connector task that performs the following actions:

To start running a MariaDB connector, configure a connector configuration, and add the configuration to your Kafka Connect cluster.

After the connector starts, it performs a consistent snapshot of the MariaDB databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming change event records to Kafka topics.

The Debezium MariaDB connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values.

Information about MariaDB connector configuration properties is organized as follows:

Debezium connectors expose metrics via the MBean name for the connector. These metrics, which are specific to each connector instance, provide data about the behavior of the connector’s snapshot, streaming, and schema history processes.

By default, when you deploy a correctly configured connector, Debezium generates a unique MBean name for each of the different connector metrics. To view the metrics for a connector process, you configure your observability stack to monitor its MBean. But these default MBean names depend on the connector configuration; configuration changes can result in changes to the MBean names. A change to the MBean name breaks the linkage between the connector instance and the MBean, disrupting monitoring activity. In this scenario, you must reconfigure the observability stack to use the new MBean name if you want to resume monitoring.

To prevent monitoring disruptions that result from MBean name changes, you can configure custom metrics tags. You configure custom metrics by adding the custom.metric.tags property to the connector configuration. The property accepts key-value pairs in which each key represents a tag for the MBean object name, and the corresponding value represents the value of that tag. For example: k1=v1,k2=v2. Debezium appends the specified tags to the MBean name of the connector.

After you configure the custom.metric.tags property for a connector, you can configure the observability stack to retrieve metrics associated with the specified tags. The observability stack then uses the specified tags, rather than the mutable MBean names to uniquely identify connectors. Later, if Debezium redefines how it constructs MBean names, or if the topic.prefix in the connector configuration changes, metrics collection is uninterrupted, because the metrics scrape task uses the specified tag patterns to identify the connector.

A further benefit of using custom tags, is that you can use tags that reflect the architecture of your data pipeline, so that metrics are organized in a way that suits you operational needs. For example, you might specify tags with values that declare the type of connector activity, the application context, or the data source, for example, db1-streaming-for-application-abc. If you specify multiple key-value pairs, all of the specified pairs are appended to the connector’s MBean name.

The following example illustrates how tags modify the default MBean name.

The MBean is debezium.mariadb:type=connector-metrics,context=snapshot,server=<topic.prefix>.

Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.

The following table lists the snapshot metrics that are available.

The connector also provides the following additional snapshot metrics when an incremental snapshot is executed:

The Debezium MariaDB connector also provides the HoldingGlobalLock custom snapshot metric. This metric is set to a Boolean value that indicates whether the connector currently holds a global or table write lock.

The Debezium MariaDB connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect provide.

Debezium monitoring documentation provides details for how to expose these metrics by using JMX.

The MBean is debezium.mariadb:type=connector-metrics,context=streaming,server=<topic.prefix>.

The following table lists the streaming metrics that are available.

The Debezium MariaDB connector also provides the following additional streaming metrics:

The MBean is debezium.mariadb:type=connector-metrics,context=schema-history,server=<topic.prefix>.

The following table lists the schema history metrics that are available.