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https://issues.apache.org/jira/browse/CASSANDRA-8844?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15873020#comment-15873020
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Yasuharu Goto commented on CASSANDRA-8844:
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[~jbellis] Sorry, It seems that I unexpectedly changed the assignment with a
keyboard shortcut. Thank you for your fix.
> Change Data Capture (CDC)
> -------------------------
>
> Key: CASSANDRA-8844
> URL: https://issues.apache.org/jira/browse/CASSANDRA-8844
> Project: Cassandra
> Issue Type: New Feature
> Components: Coordination, Local Write-Read Paths
> Reporter: Tupshin Harper
> Assignee: Joshua McKenzie
> Priority: Critical
> Fix For: 3.8
>
>
> "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."
> -Wikipedia
> 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 [~brianmhess]), propose
> implementing a simple data logging mechanism to enable per-table CDC patterns.
> h2. The goals:
> # 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.
> h2. The mechanism:
> 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
> h2. Nice-to-haves
> 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.
> h2. Format and Consumption
> - 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.
> h2. Alternate approach
> 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.
> h2. Additional consumption possibility
> 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.
> h2. Possible Syntax
> {code:sql}
> CREATE TABLE ... WITH CDC LOG
> {code}
> 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
> {code:sql}
> CREATE CDC_LOG mylog ON mytable WHERE MyUdf(mycol1, mycol2) = 5 with
> DCs={'dc1','dc3'}
> {code}
> 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
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