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From "Tupshin Harper (JIRA)" <>
Subject [jira] [Created] (CASSANDRA-8844) Change Data Capture (CDC)
Date Sat, 21 Feb 2015 01:22:11 GMT
Tupshin Harper created CASSANDRA-8844:

             Summary: Change Data Capture (CDC)
                 Key: CASSANDRA-8844
             Project: Cassandra
          Issue Type: New Feature
          Components: Core
            Reporter: Tupshin Harper
             Fix For: 3.1

"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 [~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.

The mechanism:
We propose a durable logging mechanism that functions similar to a commitlog, with the following
- 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
- 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.

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