* Renaming ConfigurationHelper to Instantiator
* Doc improvements and typo fixes
* Bringing getInstance() methods into consistent order
* Raising exception instead of logging error if instantion fails
Changed the events’ `source` structure to optionally contain the identifier of the MySQL thread where appropriate. The thread is included on each `BEGIN` binlog event, so these are captured and added to all of the associated change events produced for that transaction.
The version of the DB server required for this to work is at least 9.4. To be able to stream logical changes, the code relies on enhancements to the JDBC driver which are not yet public. Therefore, the current codebase includes the sources for the JDBC driver.
The commit also updates the general DBZ build system for:
* custom checkstyle package exclusions - required by the Postgres driver the protobuf code for now
* adds support for debugging Surefire and Failsafe
The Travis-CI builds run the Maven build using the `assembly` profile, and this has been failing quite a bit lately.
The first problem appears to be that the Travis-CI environment recently changed to have port 3306 taken, which means that our build fails to start any Docker containers for MySQL that attempt to use this port. A simple fix is to use different ports for the assembly build.
However, trying to change the port numbers for some of the profiles caused a lot of problems, and to correct these required refactoring how the properties are set. The Docker Maven plugin is now configured with separate properties that are set once (depending upon the profile) to determine the port assignments of the various Docker containers. The Failsafe plugin executions then use these Maven properties when setting the system variables (e.g., `database.host`) needed in the integration tests. This appears to have worked, but it still is a bit fragile. For example, the assembly profile defines several Failsafe executions, and during this profile these should be the only executions run; however, if not all the properties are set properly, the build seems to also run the default Failsafe execution in addition to the other `assembly` profile executions. (I think properties can’t only be defined in the execution, but need to also be defined in the Failsafe configuration.)
The “alternative” MySQL Docker images were removed, since they basically should not provide any different behavior than the `mysql/mysql-server` images we normally used. The extra containers required a lot more resources to run and dramatically increased the complexity of the build.
A few other trivial changes were made.
It also updates EmbeddedEngine to use the Kafka commit callbacks introduced after 0.10 and updates AbstractConnectorTest to better synchronize with the embedded engine
Added tests to verify whether the connector is properly restarting in the binlog when previously the connector failed or stopped in the middle of a transaction. The tests showed that the connector is not able to properly start when using or not using GTIDs, since restarting from an arbitrary binlog event causes problems since the TABLE_MAP events for the affected tables are skipped.
The logic was changed significantly to record in the offsets the binlog coordinates at the start of the transaction, which should work whether or not GTIDs are used. Upon restart, the connector may have to re-read the events that were previously processed, but now the offset also includes the number of events that were previously processed so that these can be skipped upon restart.
This has an unforunate side effect since the offsets capture a transaction was completed only when it generates a source record for the subsequent transaction. This is because the connector generates source records (with their offsets) for the binlog events in the transaction before the transaction's commit is seen. And, since no additional source records are produced for the transaction commit, the recorded offsets will show that the prior transaction is complete and that all of the events in the subsequent transaction are to be skipped. Thus, upon restart the connector has to re-read (but ignore) all of the binlog events associated with the completed transaction. This shouldn’t be a problem, and will only slow restarts for very large transactions.
Changed the MySQL connector to have several new configuration properties for setting up the SSL key store and trust store (which can be used in place of System or JDK properties) used for MySQL secure connections, and another property to specify what kind of SSL connection be used.
Modified several integration tests to ensure all MySQL connections are made with `useSSL=false`.
Upgraded from Kafka 0.9.0.1 to Kafka 0.10.0. The only required change was to override the `Connector.config()` method, which returns `null` or a `ConfigDef` instance that contains detailed metadata for each of the configuration fields, including supporting recommended values and marking fields as not visible (e.g., if they don't make sense given other configuration field values). This can be used by user interfaces to data-drive the configuration of a connector. Also, the default validation logic of the Connector implementations uses a `Validator` that is pretty restrictive in its functionality.
Debezium already had a fairly decent and simple `Configuration` framework. After several attempts to try and merge these concepts, reconciling the two validation mechanisms was very complicated and involved a lot of changes. It was easier to simply continue Debezium-specific validation and to override the `Connector.validate(...)` method to use Debezium's `Configuration`-based validation. Connector-based validation logic includes determining recommended values, so Debezium's `Field` class (used to define each configuration property) was enhanced with a new `Recommender` class that is similar to Kafka's.
Additional integration tests were added to verify that the `ConfigDef` result is acceptable and that the new connector validation logic works as expected, including getting recommended values for some fields (e.g., database names, table/collection names) from MySQL and MongoDB by connecting and dynamically reading the values. This was done in a way that remains backward compatible with the regular expression formats of these fields, but in a user interface that uses the `ConfigDef` mechanism the user can simply select the databases and table/collection identifiers.
Added a new `debezium-connector-mongodb` module that defines a MongoDB connector. The MongoDB connector can capture and record the changes within a MongoDB replica set, or when seeded with addresses of the configuration server of a MongoDB sharded cluster, the connector captures the changes from the each replica set used as a shard. In the latter case, the connector even discovers the addition of or removal of shards.
The connector monitors each replica set using multiple tasks and, if needed, separate threads within each task. When a replica set is being monitored for the first time, the connector will perform an "initial sync" of that replica set's databases and collections. Once the initial sync has completed, the connector will then begin tailing the oplog of the replica set, starting at the exact point in time at which it started the initial sync. This equivalent to how MongoDB replication works.
The connector always uses the replica set's primary node to tail the oplog. If the replica set undergoes an election and different node becomes primary, the connector will immediately stop tailing the oplog, connect to the new primary, and start tailing the oplog using the new primary node. Likewise, if connector experiences any problems communicating with the replica set members, it will try to reconnect (using exponential backoff so as to not overwhelm the replica set) and continue tailing the oplog from where it last left off. In this way the connector is able to dynamically adjust to changes in replica set membership and to automatically handle communication failures.
The MongoDB oplog contains limited information, and in particular the events describing updates and deletes do not actually have the before or after state of the documents. Instead, the oplog events are all idempotent, so updates contain the effective changes that were made during an update, and deletes merely contain the deleted document identifier. Consequently, the connector is limited in the information it includes in its output events. Create and read events do contain the initial state, but the update contain only the changes (rather than the before and/or after states of the document) and delete events do not have the before state of the deleted document. All connector events, however, do contain the local system timestamp at which the event was processed and _source_ information detailing the origins of the event, including the replica set name, the MongoDB transaction timestamp of the event, and the transactions identifier among other things.
It is possible for MongoDB to lose commits in specific failure situations. For exmaple, if the primary applies a change and records it in its oplog before it then crashes unexpectedly, the secondary nodes may not have had a chance to read those changes from the primary's oplog before the primary crashed. If one such secondary is then elected as primary, it's oplog is missing the last changes that the old primary had recorded and no longer has those changes. In these cases where MongoDB loses changes recorded in a primary's oplog, it is possible that the MongoDB connector may or may not capture these lost changes.
The snapshot mode within the offsets now are marked as complete with the last source record produced during the snapshot. This is the only sure way to update the offset.
Note that the `source` field shows the snapshot is in effect for _all_ records produced during the snapshot, including the very last one. This distinction w/r/t the offset was made possible due to recent changes for DBZ-73.
Previously, when the snapshot reader completed all generation of records, it then attempted to record an empty DDL statement. However, since this statement had no net effect on the schemas, no source record was produced and thus the offset's snapshot mode was never changed. Consequently, if the connector were stopped immediately after the snapshot completed but before other events could be read or produced, upon restart the connector would perform another snapshot.
Refactored the MySQL connector to break out the logic of reading the binlog into a separate class, added a similar class to read a full snapshot, and then updated the MySQL connector task class to use both. Added several test cases and updated the existing tests.
Previously, the DDL statements were being filtered and recorded based upon the name of the database that appeared in the binlog. However, that database name is actually the name of the database to which the client submitting the operation is connected, and is not necessarily the database _affected_ by the operation (e.g., when an operation includes a fully-qualified table name not in the connected-to database).
With these changes, the table/database affected by the DDL statements is now being used to filter the recording of the statements. The order of the DDL statements is still maintained, but since each DDL statement can apply to a separate database the DDL statements are batched (in the same original order) based upon the affected database. For example, two statements affecting "db1" will get batched together into one schema change record, followed by one statement affecting "db2" as a second schema change record, followed by another statement affecting "db1" as a third schema record.
Meanwhile, this change does not affect how the database history records the changes: it still records them as submitted using a single record for each separate binlog event/position. This is much safer as each binlog event (with specific position) is written atomically to the history stream. Also, since the database history stream is what the connector uses upon recovery, the database history records are now written _after_ any schema change records to ensure that, upon recovery after failure, no schema change records are lost (and instead have at-least-once delivery guarantees).
The new envelope Struct contains fields for the local time at which the connector processed the event, the kind of operation (e.g., read, insert, update, or delete), the state of the record before and after the change, and the information about the event source. The latter two items are connector-specific. The timestamp is merely the time using the connector's process clock, and no guarantees are provided about accuracy, monotonicity, or relationship to the original source event.
The envelope structure is now used as the value for each event message in the MySQL connector; they keys of the event messages remain unchanged. Note that to facilitate Kafka log compaction (which requires a null value), a delete event containing the envelope with details about the deletion is followed by a "tombstone" event that contains the same key but null value.
An example of a message value with this new envelope is as follows:
{
"schema" : {
"type" : "struct",
"fields" : [ {
"type" : "struct",
"fields" : [ {
"type" : "int32",
"optional" : false,
"name" : "org.apache.kafka.connect.data.Date",
"version" : 1,
"field" : "order_date"
}, {
"type" : "int32",
"optional" : false,
"field" : "purchaser"
}, {
"type" : "int32",
"optional" : false,
"field" : "quantity"
}, {
"type" : "int32",
"optional" : false,
"field" : "product_id"
} ],
"optional" : true,
"name" : "connector_test.orders",
"field" : "before"
}, {
"type" : "struct",
"fields" : [ {
"type" : "int32",
"optional" : false,
"name" : "org.apache.kafka.connect.data.Date",
"version" : 1,
"field" : "order_date"
}, {
"type" : "int32",
"optional" : false,
"field" : "purchaser"
}, {
"type" : "int32",
"optional" : false,
"field" : "quantity"
}, {
"type" : "int32",
"optional" : false,
"field" : "product_id"
} ],
"optional" : true,
"name" : "connector_test.orders",
"field" : "after"
}, {
"type" : "struct",
"fields" : [ {
"type" : "string",
"optional" : false,
"field" : "server"
}, {
"type" : "string",
"optional" : false,
"field" : "file"
}, {
"type" : "int64",
"optional" : false,
"field" : "pos"
}, {
"type" : "int32",
"optional" : false,
"field" : "row"
} ],
"optional" : false,
"name" : "io.debezium.connector.mysql.Source",
"field" : "source"
}, {
"type" : "string",
"optional" : false,
"field" : "op"
}, {
"type" : "int64",
"optional" : true,
"field" : "ts"
} ],
"optional" : false,
"name" : "kafka-connect-2.connector_test.orders",
"version" : 1
},
"payload" : {
"before" : null,
"after" : {
"order_date" : 16852,
"purchaser" : 1003,
"quantity" : 1,
"product_id" : 107
},
"source" : {
"server" : "kafka-connect-2",
"file" : "mysql-bin.000002",
"pos" : 2887680,
"row" : 4
},
"op" : "c",
"ts" : 1463437199134
}
}
Notice how the Schema is significantly larger, since it must describe all of the envelope's fields even when those fields are not used. In this case, the event signifies that a record was created as the 4th record of a single event recorded in the binlog.
When a row is updated in the database and the primary/unique key for that table is changed, the MySQL connector continues to generate an update event with the new key and new value, but now also generates a tombstone event for the old key. This ensures that when a Kafka topic is compacted, all prior events with the old key will (eventually) be removed. It also ensures that consumers see that the row represented by the old key has been removed.
Changed the MySQL connector to use comma-separated lists of regular expressions for the database
and table whitelist/blacklists. Literals are still accepted and will match fully-qualified table names,
although the '.' character used as a delimiter is also a special character in regular expressions and
therefore may need to be escaped with a double backslash ('\\') to more carefully match fully-qualified
table names.
Added several new configuration properties for the MySQL connector that instruct it to hide,
truncate, and/or mask certain columns. The properties' values are all lists of regular expressions
or literal fully-qualified column names. For example, the following configuration property:
column.blacklist=server.users.picture,server.users.other
will cause the connector to leave out of change event messages for the `server.users` table those
fields that correspond to the `picture` and `others` columns. This capability can be used to
This capability can be used to prevent dissemination of sensitive information in the change event
stream.
An alternative to blacklisting is masking. The following configuration property:
column.mask.with.10.chars=server\\.users\\.(\\w*email)
will cause the connector to mask in the change event messages for the `server.users` table
all values for columns whose name ends in `email`. The values will be replaced in this case with
a constant string of 10 asterisk ('*') characters, even when the email value is null.
This capability can also be used to prevent dissemination of sensitive information in the change event
stream.
Another option is to truncate string values for specific columns. The following configuration
property:
column.truncate.to.120.chars=server[.]users[.](description|biography)
will cause the connector to truncate to at most 120 characters the values of the `description` and
`biography` columns in the change event messages for the `server.users` table. Although this example
used a limit of 120 characters, any positive length can be specified; separate properties should
be used when different lengths are required. Note how the '.' delimiter in the fully-qualified names
is escaped since that same character is a special character in regular expressions. This capability
can be used to reduce the size of change event messages.
Changed the EmbeddedConnector framework to initialize all major components via configuration properties rather than through the public builder. This increases the size of the configurations, but it simplifies what embedding applications must do to obtain an EmbeddedConnector instance.
The DatabaseHistory framework was also changed to be configurable in similar ways to the OffsetBackingStore. Essentially, connectors that want to use it (like the MySqlConnector) will describe it as part of the connector's configuration, allowing more flexibility in which DatabaseHistory implementation is used and how it is configured whether in Kafka Connector or as part of the EmbeddedConnector.
Added a README.md to `debezium-embedded` to provide documentation and sample code showing how to use the EmbeddedConnector.
Adds a small framework for recording the DDL operations on the schema state (e.g., Tables) as they are read and applied from the log, and when restarting the connector task to recover the accumulated schema state. Where and how the DDL operations are recorded is an abstraction called `DatabaseHistory`, with three options: in-memory (primarily for testing purposes), file-based (for embedded cases and perhaps standalone Kafka Connect uses), and Kafka (for normal Kafka Connect deployments).
The `DatabaseHistory` interface methods take several parameters that are used to construct a `SourceRecord`. The `SourceRecord` type was not used, however, since that would result in this interface (and potential extension mechanism) having a dependency on and exposing the Kafka API. Instead, the more general parameters are used to keep the API simple.
The `FileDatabaseHistory` and `MemoryDatabaseHistory` implementations are both fairly simple, but the `FileDatabaseHistory` relies upon representing each recorded change as a JSON document. This is simple, is easily written to files, allows for recovery of data from the raw file, etc. Although this was done initially using Jackson, the code to read and write the JSON documents required a lot of boilerplate. Instead, the `Document` framework developed during Debezium's very early prototype stages was brought back. It provides a very usable API for working with documents, including the ability to compare documents semantically (e.g., numeric values are converted to be able to compare their numeric values rather than just compare representations) and with or without field order.
The `KafkaDatabaseHistory` is a bit more complicated, since it uses a Kafka broker to record all database schema changes on a single topic with single partition, and then upon restart uses it to recover the history from the dedicated topics. This implementation also records the changes as JSON documents, keeping it simple and independent of the Kafka Connect converters.
