"In databases, change data capture (CDC) is a set of software design patterns used to determine (and track) the data that has changed so that action can be taken using the changed data. Also, Change data capture (CDC) is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources."
As Cassandra is increasingly being used as the Source of Record (SoR) for mission critical data in large enterprises, it is increasingly being called upon to act as the central hub of traffic and data flow to other systems. In order to try to address the general need, we (cc Brian Hess), propose implementing a simple data logging mechanism to enable per-table CDC patterns.
- Use CQL as the primary ingestion mechanism, in order to leverage its Consistency Level semantics, and in order to treat it as the single reliable/durable SoR for the data.
- To provide a mechanism for implementing good and reliable (deliver-at-least-once with possible mechanisms for deliver-exactly-once ) continuous semi-realtime feeds of mutations going into a Cassandra cluster.
- To eliminate the developmental and operational burden of users so that they don't have to do dual writes to other systems.
- For users that are currently doing batch export from a Cassandra system, give them the opportunity to make that realtime with a minimum of coding.
We propose a durable logging mechanism that functions similar to a commitlog, with the following nuances:
- Takes place on every node, not just the coordinator, so RF number of copies are logged.
- Separate log per table.
- Per-table configuration. Only tables that are specified as CDC_LOG would do any logging.
- Per DC. We are trying to keep the complexity to a minimum to make this an easy enhancement, but most likely use cases would prefer to only implement CDC logging in one (or a subset) of the DCs that are being replicated to
- In the critical path of ConsistencyLevel acknowledgment. Just as with the commitlog, failure to write to the CDC log should fail that node's write. If that means the requested consistency level was not met, then clients should experience UnavailableExceptions.
- Be written in a Row-centric manner such that it is easy for consumers to reconstitute rows atomically.
- Written in a simple format designed to be consumed directly by daemons written in non JVM languages
I strongly suspect that the following features will be asked for, but I also believe that they can be deferred for a subsequent release, and to guage actual interest.
- Multiple logs per table. This would make it easy to have multiple "subscribers" to a single table's changes. A workaround would be to create a forking daemon listener, but that's not a great answer.
- Log filtering. Being able to apply filters, including UDF-based filters would make Casandra a much more versatile feeder into other systems, and again, reduce complexity that would otherwise need to be built into the daemons.
- Cassandra would only write to the CDC log, and never delete from it.
- Cleaning up consumed logfiles would be the client daemon's responibility
- Logfile size should probably be configurable.
- Logfiles should be named with a predictable naming schema, making it triivial to process them in order.
- Daemons should be able to checkpoint their work, and resume from where they left off. This means they would have to leave some file artifact in the CDC log's directory.
- A sophisticated daemon should be able to be written that could
- Catch up, in written-order, even when it is multiple logfiles behind in processing
- Be able to continuously "tail" the most recent logfile and get low-latency(ms?) access to the data as it is written.
In order to make consuming a change log easy and efficient to do with low latency, the following could supplement the approach outlined above
- Instead of writing to a logfile, by default, Cassandra could expose a socket for a daemon to connect to, and from which it could pull each row.
- Cassandra would have a limited buffer for storing rows, should the listener become backlogged, but it would immediately spill to disk in that case, never incurring large in-memory costs.
With all of the above, still relevant:
- instead (or in addition to) using the other logging mechanisms, use CQL transport itself as a logger.
- Extend the CQL protoocol slightly so that rows of data can be return to a listener that didn't explicit make a query, but instead registered itself with Cassandra as a listener for a particular event type, and in this case, the event type would be anything that would otherwise go to a CDC log.
- If there is no listener for the event type associated with that log, or if that listener gets backlogged, the rows will again spill to the persistent storage.
Pros: No syntax extesions
Cons: doesn't make it easy to capture the various permutations (i'm happy to be proven wrong) of per-dc logging. also, the hypothetical multiple logs per table would break this
Pros: Expressive and allows for easy DDL management of all aspects of CDC
Cons: Syntax additions. Added complexity, partly for features that might not be implemented