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From DuyHai Doan <doanduy...@gmail.com>
Subject Re: Using CDC Feature to Stream C* to Kafka (Design Proposal)
Date Wed, 12 Sep 2018 09:18:22 GMT
The biggest problem of having CDC working correctly in C* is the
deduplication issue.

Having a process to read incoming mutation from commitlog is not that hard,
having to dedup them through N replicas is much harder

The idea is : why don't we generate the CDC event directly at the
coordinator side ? Indeed, the coordinator is the single source of true for
each mutation request. As soon as the coordinator receives 1
acknowledgement from any replica, the mutation can be considered "durable"
and safely sent downstream to the CDC processor. This approach would
requires to change the write path on the coordinator side and may have
impact on performance (if writing to CDC downstream is blocking or too slow)

My 2 cents

On Wed, Sep 12, 2018 at 5:56 AM, Joy Gao <joyg@wepay.com.invalid> wrote:

> Re Rahul:  "Although DSE advanced replication does one way, those are use
> cases with limited value to me because ultimately it’s still a master slave
> design."
> Completely agree. I'm not familiar with Calvin protocol, but that sounds
> interesting (reading time...).
>
> On Tue, Sep 11, 2018 at 8:38 PM Joy Gao <joyg@wepay.com> wrote:
>
>> Thank you all for the feedback so far.
>>
>> The immediate use case for us is setting up a real-time streaming data
>> pipeline from C* to our Data Warehouse (BigQuery), where other teams can
>> access the data for reporting/analytics/ad-hoc query. We already do this
>> with MySQL
>> <https://wecode.wepay.com/posts/streaming-databases-in-realtime-with-mysql-debezium-kafka>,
>> where we stream the MySQL Binlog via Debezium <https://debezium.io>'s
>> MySQL Connector to Kafka, and then use a BigQuery Sink Connector to stream
>> data to BigQuery.
>>
>> Re Jon's comment about why not write to Kafka first? In some cases that
>> may be ideal; but one potential concern we have with writing to Kafka first
>> is not having "read-after-write" consistency. The data could be written to
>> Kafka, but not yet consumed by C*. If the web service issues a (quorum)
>> read immediately after the (quorum) write, the data that is being returned
>> could still be outdated if the consumer did not catch up. Having web
>> service interacts with C* directly solves this problem for us (we could add
>> a cache before writing to Kafka, but that adds additional operational
>> complexity to the architecture; alternatively, we could write to Kafka and
>> C* transactionally, but distributed transaction is slow).
>>
>> Having the ability to stream its data to other systems could make C* more
>> flexible and more easily integrated into a larger data ecosystem. As Dinesh
>> has mentioned, implementing this in the database layer means there is a
>> standard approach to getting a change notification stream (unlike trigger
>> which is ad-hoc and customized). Aside from replication, the change events
>> could be used for updating Elasticsearch, generating derived views (i.e.
>> for reporting), sending to an audit services, sending to a notification
>> service, and in our case, streaming to our data warehouse for analytics.
>> (one article that goes over database streaming is Martin Kleppman's Turning
>> the Database Inside Out with Apache Samza
>> <https://www.confluent.io/blog/turning-the-database-inside-out-with-apache-samza/>,
>> which seems relevant here). For reference, this turning database into a
>> stream of change events is pretty common in SQL databases (i.e. mysql
>> binlog, postgres WAL) and NoSQL databases that have primary-replica setup
>> (i.e. Mongodb Oplog). Recently CockroachDB introduced a CDC feature as well
>> (and they have master-less replication too).
>>
>> Hope that answers the question. That said, dedupe/ordering/getting full
>> row of data via C* CDC is a hard problem, but may be worth solving for
>> reasons mentioned above. Our proposal is an user approach to solve these
>> problems. Maybe the more sensible thing to do is to build it as part of C*
>> itself, but that's a much bigger discussion. If anyone is building a
>> streaming pipeline for C*, we'd be interested in hearing their approaches
>> as well.
>>
>>
>> On Tue, Sep 11, 2018 at 7:01 AM Rahul Singh <rahul.xavier.singh@gmail.com>
>> wrote:
>>
>>> You know what they say: Go big or go home.
>>>
>>> Right now candidates are Cassandra itself but embedded or on the side
>>> not on the actual data clusters, zookeeper (yuck) , Kafka (which needs
>>> zookeeper, yuck) , S3 (outside service dependency, so no go. )
>>>
>>> Jeff, Those are great patterns. ESP. Second one. Have used it several
>>> times. Cassandra is a great place to store data in transport.
>>>
>>>
>>> Rahul
>>> On Sep 10, 2018, 5:21 PM -0400, DuyHai Doan <doanduyhai@gmail.com>,
>>> wrote:
>>>
>>> Also using Calvin means having to implement a distributed monotonic
>>> sequence as a primitive, not trivial at all ...
>>>
>>> On Mon, Sep 10, 2018 at 3:08 PM, Rahul Singh <
>>> rahul.xavier.singh@gmail.com> wrote:
>>>
>>>> In response to mimicking Advanced replication in DSE. I understand the
>>>> goal. Although DSE advanced replication does one way, those are use cases
>>>> with limited value to me because ultimately it’s still a master slave
>>>> design.
>>>>
>>>> I’m working on a prototype for this for two way replication between
>>>> clusters or databases regardless of dB tech - and every variation I can get
>>>> to comes down to some implementation of the Calvin protocol which basically
>>>> verifies the change in either cluster , sequences it according to impact
to
>>>> underlying data, and then schedules the mutation in a predictable manner
on
>>>> both clusters / DBS.
>>>>
>>>> All that means is that I need to sequence the change before it happens
>>>> so I can predictably ensure it’s Scheduled for write / Mutation. So I’m
>>>> Back to square one: having a definitive queue / ledger separate from
>>>> the individual commit log of the cluster.
>>>>
>>>>
>>>> Rahul Singh
>>>> Chief Executive Officer
>>>> m 202.905.2818
>>>>
>>>> Anant Corporation
>>>> 1010 Wisconsin Ave NW, Suite 250
>>>> <https://maps.google.com/?q=1010+Wisconsin+Ave+NW,+Suite+250+%0D%0AWashington,+D.C.+20007&entry=gmail&source=g>
>>>> Washington, D.C. 20007
>>>>
>>>> We build and manage digital business technology platforms.
>>>> On Sep 10, 2018, 3:58 AM -0400, Dinesh Joshi <dinesh.joshi@yahoo.com.invalid>,
>>>> wrote:
>>>>
>>>> On Sep 9, 2018, at 6:08 AM, Jonathan Haddad <jon@jonhaddad.com> wrote:
>>>>
>>>> There may be some use cases for it.. but I'm not sure what they are.
>>>> It might help if you shared the use cases where the extra complexity is
>>>> required?  When does writing to Cassandra which then dedupes and writes to
>>>> Kafka a preferred design then using Kafka and simply writing to Cassandra?
>>>>
>>>>
>>>> From the reading of the proposal, it seems bring functionality similar
>>>> to MySQL's binlog to Kafka connector. This is useful for many applications
>>>> that want to be notified when certain (or any) rows change in the database
>>>> primarily for a event driven application architecture.
>>>>
>>>> Implementing this in the database layer means there is a standard
>>>> approach to getting a change notification stream. Downstream subscribers
>>>> can then decide which notifications to act on.
>>>>
>>>> LinkedIn's databus is similar in functionality -
>>>> https://github.com/linkedin/databus However it is for heterogenous
>>>> datastores.
>>>>
>>>> On Thu, Sep 6, 2018 at 1:53 PM Joy Gao <joyg@wepay.com.invalid> wrote:
>>>>
>>>>>
>>>>>
>>>>> We have a* WIP design doc
>>>>> <https://wepayinc.box.com/s/fmdtw0idajyfa23hosf7x4ustdhb0ura>*
that
>>>>> goes over this idea in details.
>>>>>
>>>>> We haven't sort out all the edge cases yet, but would love to get some
>>>>> feedback from the community on the general feasibility of this approach.
>>>>> Any ideas/concerns/questions would be helpful to us. Thanks!
>>>>>
>>>>>
>>>> Interesting idea. I did go over the proposal briefly. I concur with Jon
>>>> about adding more use-cases to clarify this feature's potential use-cases.
>>>>
>>>> Dinesh
>>>>
>>>>
>>>

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