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https://issues.apache.org/jira/browse/CASSANDRA-8844?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Tupshin Harper updated CASSANDRA-8844:
--------------------------------------
Description:
"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.
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
was:
"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.
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.
> 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: 3.1
>
>
> "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."
> -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.
> 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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