cassandra-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Branimir Lambov (JIRA)" <>
Subject [jira] [Commented] (CASSANDRA-8844) Change Data Capture (CDC)
Date Thu, 14 Apr 2016 13:52:28 GMT


Branimir Lambov commented on CASSANDRA-8844:

First round of comments (I haven't looked at the read/replay part yet):

- I was a fan of the {{ReplayPosition}} name. It stands for a more general concept which happens
be the commit log position for us. Further to this, it should be a {{CommitLogPosition}} rather
than {{..SegmentPosition}} as it does not just specify a position within a given segment but
an overall position in the log (for a specific keyspace). I am also wondering if it should
not include a keyspace id / reference now that it is keyspace-specific to be able to fail
fast on mismatch.
- I'd prefer to throw the {{WriteTimeoutException}} directly from {{allocate}} (instead of
catching null in {{CommitLog}} and doing the same). Doing the check inside the {{while}} loop
will avoid the over-allocation and do less work in the common case.
- Do we really need to have separate buffer pools per manager? Static (or not) shared will
offer slightly better cache locality, and it's better to block both commit logs if we're running
beyond allowed memory (we may want to double the default limit).
- [{{segmentManagers}} array|]:
An {{EnumMap}} (which boils down to the same thing) would be cleaner and should not have any
performance impact.
- [{{shutdownBlocking}}|]:
Better shutdown in parallel, i.e. initiate and await termination separately.
- [{{reCalculating}} cas in {{maybeUpdateCDCSizeCounterAsync}}|]
is fishy: makes you think it would clear on exception in running update, which isn't the case.
The {{updateCDCDirectorySize}} body should be wrapped in {{try ... finally}} as well to do
- You could use a scheduled executor to avoid the explicit delays. Or a {{RateLimiter}} (we'd
prefer to update ASAP when triggered, but not too often) instead of the delay.
- [{{updateCDCOverflowSize}}|]:
use {{while (!reCalculating.compareAndSet(false, true)) {};}}. You should reset the value
- I don't get the {{DirectorySizeCalculator}}. Why the {{alive}} and {{visited}} sets, the
{{listFiles}} step? Either list the files and just loop through them, or do the {{walkFileTree}}
operation -- you are now doing the same work twice. Use a plain long instead of the atomic
as the class is still thread-unsafe.
- {{CDCSizeCalculator.calculateSize}} should return the size, and maybe made synchronized
for a bit of additional safety.
- [Scrubber change|]
should be reverted.
- "Permissible" changed to "permissable" at some places in the code; the latter is a misspelling.

> Change Data Capture (CDC)
> -------------------------
>                 Key: CASSANDRA-8844
>                 URL:
>             Project: Cassandra
>          Issue Type: New Feature
>          Components: Coordination, Local Write-Read Paths
>            Reporter: Tupshin Harper
>            Assignee: Joshua McKenzie
>            Priority: Critical
>             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
> 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}
> {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

This message was sent by Atlassian JIRA

View raw message