Rewrote how the MySQL connector converts temporal values to use schemas with names that identify the semantic
type of temporal value, and customized how the MySQL binlog client library creates Java object values from the
raw binlog events.
Several new "semantic" schema types were defined:
* `io.debezium.time.Year` represents a year number as an INT32 value (e.g., 2016, -345, etc.).
* `io.debezium.time.Date` represents a date by storing the epoch seconds (that is, the number of seconds past the epoch) as an INT64 value.
* `io.debezium.time.Time` represents a time by storing the milliseconds past midnight as an INT32 value.
* `io.debezium.time.MicroTime` represents a time by storing the microsconds past midnight as an INT32 value.
* `io.debezium.time.NanoTime` represents a time by storing the nanoseconds past midnight as an INT32 value.
* `io.debezium.time.Timestamp` represents a date and time (without timezone information) by storing the milliseconds past epoch as an INT64 value.
* `io.debezium.time.MicroTimestamp` represents a date and time (without timezone information) by storing the microseconds past epoch as an INT64 value.
* `io.debezium.time.NanoTimestamp` represents a date and time (without timezone information) by storing the nanoseconds past epoch as an INT64 value.
* `io.debezium.time.ZonedTime` represents a time with timezone and optional fractions of a second (but no date) by storing the ISO8601 form as a STRING value (e.g., `10:15:30+01:00`)
* `io.debezium.time.ZonedTimestamp` represents a date and time with timezone and optional fractions of a second by storing the ISO8601 form as a STRING value (e.g., `2011-12-03T10:15:30.030431+01:00`)
This range of semantic types allows for a far more accurate representation in the events of the temporal values stored within the database. The MySQL connector chooses the semantic type based upon the precision of the MySQL type (e.g., `TIMESTAMP(6)` will be represented with `io.debezium.time.MicroTimestamp`, whereas `TIMESTAMP(3)` will be represented with `io.debezium.time.Timestamp`). This ensures that the events do not lose precision and that the semantics of the database column values are retained in the events even though the values are represented with primitive values.
Obviously these Kafka Connect schema representations are different and more precise than the built-in `org.apache.kafka.connect.data.Date`, `org.apache.kafka.connect.data.Time`, and `org.apache.kafka.connect.data.Timestamp` logical types provided by Kafka Connect and used by the MySQL connector in all 0.2.x and 0.1.x versions. Migration to the new MySQL connector should be possible, although consumers may still need to know about these types to properly handle temporal values and the correct precision (i.e., consumers can just assume all date INT64 values represent milliseconds).
The MySQL binlog client library converted the raw binary event information to JDBC types using a local Calendar instance, which obviously incorporates the local timezone and cannot retain more than millisecond precision. This change extends the library's deserializers to instead use the Java 8 `javax.time` classes and to retain the exact semantics of the database values and to not lose any precisions (since the `javax.time` classes have nanosecond precision).
The same logic is also used to convert the JDBC values obtained during a snapshot from the MySQL Connect/J JDBC driver. The latter has a few quirks, such as not returning any fractional seconds for `TIME` columns, even though `java.sql.Time` can store up to milliseconds.
Most of the logic of the conversions of values and mapping to Kafka Connect schemas is handled in the new `JdbcValueConverters`, which was extracted from the existing `TableSchemaBuilder`. The MySQL connector reuses and actually extends the `JdbcValueConverters` class with its own `MySqlValueConverters` class that also adds support for MySQL-specific types such as `YEAR`. Other connectors whose values are based on JDBC types should be able to reuse and/or extend the `JdbcValueConverters` class.
Integration tests that deal with temporal types were modified to use proper expected values and comparisons.
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 an integration test case to diagnose the loss of the fractional seconds from MySQL temporal values. The problem appears to be a bug in the MySQL Binary Log Connector library that we used, and this bug was reported as https://github.com/shyiko/mysql-binlog-connector-java/issues/103. That was fixed in version 0.3.2 of the library, which Stanley was kind enough to release for us.
During testing, though, several issues were discovered in how temporal values are handled and converted from the MySQL events, through the MySQL Binary Log client library, and through the Debezium MySQL connector to conform with Kafka Connect's various temporal logical schema types. Most of the issues involved converting most of the temporal values from local time zone (which is how they are created by the MySQL Binary Log client) into UTC (which is how Kafka Connect expects them). Really, java.util.Date doesn't have time zone information and instead tracks the number of milliseconds past epoch, but the conversion of normal timestamp information to the milliseconds past epoch in UTC depends on the time zone in which that conversion happens.
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 `VerifyRecord` utility class has methods that will verify a `SourceRecord`, and is used in many of our integration tests to check whether records are constructed in a valid manner. The utility already checks whether the records can be serialized and deserialized using the JSON converter (provided with Kafka Connect); this change also checks with the Avro Converter (which produces much smaller records and is more suitable for production).
Note that version 3.0.0 of the Confluent Avro Converter is required; version 2.1.0-alpha1 could not properly handle complex Schema objects with optional fields (see https://github.com/confluentinc/schema-registry/pull/280).
Also, the names of the Kafka Connect schemas used in MySQL source records has changed.
# The record's envelope Schema used to be "<serverName>.<database>.<table>" but is now "<serverName>.<database>.<table>.Envelope".
# The Schema for record keys used to be named "<database>.<table>/pk", but the '/' character is not valid within a Avro name, and has been changed to "<serverName>.<database>.<table>.Key".
# The Schema for record values used to be named "<database>.<table>", but to better fit with the other Schema names it has been changed to "<serverName>.<database>.<table>.Value".
Thus, all of the Schemas for a single database table have the same Avro namespace "<serverName>.<database>.<table>" (or "<topicName>") with Avro schema names of "Envelope", "Key", and "Value".
All unit and integration tests pass.
Modified the 'docs' profile to build and attach JavaDoc JARs for each module's source and test source artifacts. The profile will be automatically used when releasing.
This utility is only suitable for unit tests and therefore is defined in the test JAR of the `debezium-core` module. It certainly should never be used for production purposes.
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.
This initial commit defines several modules for ingesting from JDBC and specifically from PostgreSQL and MySQL. The two latter modules define separate unit tests and integration tests, and prior to running the integration tests create a Docker image with the respective database and start a Docker container. Any *.sql or *.sh files are run on database, allowing the modules to easily create and populate databases used in the tests. The integration tests are then run (using the failsafe maven plugin), and regardless of whether there are any failures the Docker container is always shutdown (at least when running `mvn install`). See the modules' README files for details.
