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From "Joshua McKenzie (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (CASSANDRA-8844) Change Data Capture (CDC)
Date Tue, 17 May 2016 00:29:16 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-8844?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15285499#comment-15285499
] 

Joshua McKenzie edited comment on CASSANDRA-8844 at 5/17/16 12:28 AM:
----------------------------------------------------------------------

If either of you ([~carlyeks] / [~blambov]) have started reviewing, there are a couple of
logical flaws in the way I've used the value of "combined un-flushed cdc-containing segment
size and cdc_raw". Currently a non-cdc mutation could succeed in an allocation during a full
CDC time-frame while cdc allocations fail, advance in allocatingFrom(), and allow subsequent
cdc-based mutations to succeed since the new code doesn't check for {{atCapacity}} until the
case of allocation failure. The current logic strictly precludes allocation of a new CommitLogSegment
by a cdc-containing Mutation allocation so it works when tested on cdc-only streams of Mutations
but not mixed; I'll be writing a unit test to prove that shortly. Problem #2: if we track
un-flushed full cdc-containing segment size in {{atCapacity}} and use that as part of metric
to reject CDC-containing Mutations *before* that allocation attempt, we would then prematurely
reject cdc mutations in the final CommitLogSegment created in the chain before filling it.

I'm going to need to spend some more time thinking about this. My initial hunch is that we
may be unable to track un-flushed segment size w/CDC data in them as a meaningful marker of
future CDC-data, thus meaning we cannot guarantee adherence to the user-specified disk-space
restrictions for CDC due to in-flight data not yet being counted.  As new segment allocation
takes place in the management thread and the current logic is strongly coupled to the invariant
that new segment allocation always succeeds (even if back-pressured by compression buffer
usage), the approach of forcibly failing is less palatable to me than us being a little loose
with our interpretation of cdc_total_space_in_mb by 1-N segment units, assuming N tends to
be low single digits leading to <5% violation in the default case. This should hold true
unless flushing gets wildly backed up relative to ingest of writes; I don't know enough about
that code to speak to that but will likely read into it a bit.

Anyway - figured I'd point that out in case either of you came across it and registered it
mentally as a concern or if either of you have any immediate ideas on this topic.


was (Author: joshuamckenzie):
If either of you ([~carlyeks] / [~blambov]) have started reviewing, there are a couple of
logical flaws in the way I've used the value of "combined un-flushed cdc-containing segment
size and cdc_raw". Currently a non-cdc mutation could fail an allocation, advance in allocatingFrom(),
and allow subsequent cdc-based mutations to succeed since the new code doesn't check for {{atCapacity}}
until the case of allocation failure. The current logic strictly precludes allocation of a
new CommitLogSegment by a cdc-containing Mutation allocation so it works when tested on cdc-only
streams of Mutations but not mixed; I'll be writing a unit test to prove that shortly. Problem
#2: if we track un-flushed full cdc-containing segment size in {{atCapacity}} and use that
as part of metric to reject CDC-containing Mutations *before* that allocation attempt, we
would then prematurely reject cdc mutations in the final CommitLogSegment created in the chain
before filling it.

I'm going to need to spend some more time thinking about this. My initial hunch is that we
may be unable to track un-flushed segment size w/CDC data in them as a meaningful marker of
future CDC-data, thus meaning we cannot guarantee adherence to the user-specified disk-space
restrictions for CDC due to in-flight data not yet being counted.  As new segment allocation
takes place in the management thread and the current logic is strongly coupled to the invariant
that new segment allocation always succeeds (even if back-pressured by compression buffer
usage), the approach of forcibly failing is less palatable to me than us being a little loose
with our interpretation of cdc_total_space_in_mb by 1-N segment units, assuming N tends to
be low single digits leading to <5% violation in the default case. This should hold true
unless flushing gets wildly backed up relative to ingest of writes; I don't know enough about
that code to speak to that but will likely read into it a bit.

Anyway - figured I'd point that out in case either of you came across it and registered it
mentally as a concern or if either of you have any immediate ideas on this topic.

> 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.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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