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https://issues.apache.org/jira/browse/CASSANDRA-8844?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14522461#comment-14522461
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Martin Kersten edited comment on CASSANDRA-8844 at 4/30/15 11:29 PM:
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I would argue not for logs but for 'listener' queries for each table.
If a client want to listen for a certain or all changes he is free to submit
where clauses. So every time a changed row of that table fulfills the where
clause the client listener gets notified.
A client may issue many where clauses and would be able to query its active
where clauses for a table. By removing all where clauses the listener will
actually remove itself from the listener list for that table.
One could even extend that by submitting real select statements working on only
the currently change row. Maybe the listener may even add a timing setting
allowing a node to aggregate multiple update events and send one single
notification for multiple changed rows (if this makes sense for a system using
hashing for partition / sharding).
Since many clients may listen using the same where clause the performance would
be manageable and not depending on how many clients are listening but how many
different select statements where listened to by all clients.
By being able to name the listening where clauses one could even generated
named events where a combination of name and where clause makes it unique.
An additional option is being able to add an additional value for each row
containing the names of all event names associated with the listener select
statements (queries).
Pro:
* Easy to understand
* Easy to manage
* Fine-tuning is possible (like a single client listens to only a single user
or a list of particular users)
* Lot of reuse capabilities (just query over the changed row(s) not all rows,
grammar etc.)
* Works on shared tables
* Avoids maintaining disk logs
* Only three operations are necessary to implement (add listener query,
remove listener query and get all active listener queries).
* Performance improvements possible by combining (almost) similar where
clauses and by adding special cases (like where clauses for certain user IDs
will result in a huge lists.
* All in memory operation no disk writes necessary
Contra:
* Ensuring being notified exactly once might be challenging
* Only works on changes about to happen afterwards and not changes happened
in recent time (no travel back in time like it would be possible while having
change logs).
was (Author: martin kersten):
I would argue not for logs but for 'listener' queries for each table.
If a client want to listen for a certain or all changes he is free to submit
where clauses. So every time a changed row of that table fulfills the where
clause the client listener gets notified.
A client may issue many where clauses and would be able to query its active
where clauses for a table. By removing all where clauses the listener will
actually remove itself from the listener list for that table.
One could even extend that by submitting real select statements working on only
the currently change row. Maybe the listener may even add a timing setting
allowing a node to aggregate multiple update events and send one single
notification for multiple changed rows (if this makes sense for a system using
hashing for partition / sharding).
Since many clients may listen using the same where clause the performance would
be manageable and not depending on how many clients are listening but how many
different select statements where listened to by all clients.
By being able to name the listening where clauses one could even generated
named events where a combination of name and where clause makes it unique.
An additional option is being able to add an additional value for each row
containing the names of all event names associated with the listener select
statements (queries).
Pro:
* Easy to understand
* Easy to manage
* Fine-tuning is possible (like a single client listens to only a single user
or a list of particular users)
* Lot of reuse capabilities (just query over the changed row(s) not all rows,
grammar etc.)
* Works on shared tables
* Avoids maintaining disk logs
* Only three operations are necessary to implement (add listener query,
remove listener query and get all active listener queries).
* Performance improvements possible by combining (almost) similar where
clauses and by adding special cases (like where clauses for certain user IDs
will result in a huge lists.
* All in memory operation no disk writes necessary
Contra:
* Ensuring being notified exactly once might be challenging
* Only works on changes about to happen afterwards and not changes happened
in recent time.
> Change Data Capture (CDC)
> -------------------------
>
> Key: CASSANDRA-8844
> URL: https://issues.apache.org/jira/browse/CASSANDRA-8844
> Project: Cassandra
> Issue Type: New Feature
> Components: Core
> Reporter: Tupshin Harper
> Fix For: 3.x
>
>
> "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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