Backfilling From Kafka Topics With Varied Ingestion Rates

[Akshay Aggarwal]

Hi Flink Users,

We have a scenario where we're reading from multiple kafka topics using a single kafka consumer. Each topic has a very different ingestion rate, like CheckoutTopic has 500 rec/sec, PageViewTopic has 10,000 rec/sec. We are performing ordering of these events across topics using a keyed process function (keyed on userId) and a EVENT_TIME watermark which is based on the ingestionTs of the record captured just before it is produced into kafka. 

On live data this pipeline works perfectly, but if I restart the job to process from an old savepoint (say 24hrs old), the job fills up the state, a full back pressure (ratio 1) gets created on the source operators, checkpoints start failing and the job eventually dies. My hypothesis is that the data from both the topics are read at the max rate possible, but since the watermark from the PageViewTopic will lag significantly behind the CheckoutTopic overall watermarks don't progress, excessive data from CheckoutTopic fills up the state and results in the failure mentioned above.

I also observed this while backfilling from a savepoint using a single topic, even though watermarks do progress faster than before, the job has the same fate. In this case I'm assuming the offsets/watermarks of the individual partitions go out-of-sync with respect to time leading to a similar situation mentioned above.

Is this understanding correct? is there a known solution for this? And if not, what is the suggested approach to tackle this problem?

Thanks & Regards,

Akshay Aggarwal

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Our organization accepts no liability for the content of this email, or for the consequences of any actions taken on the basis of the information provided, unless that information is subsequently confirmed in writing. If you are not the intended recipient, you are notified that disclosing, copying, distributing or taking any action in reliance on the contents of this information is strictly prohibited.

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[Aljoscha Krettek]

Hi,

I'm afraid your analysis is 100% correct. Currently there's no
out-of-box feature for dealing with this but our work on a new source
interface ([1]) will enable us to add a feature that we call "event-time
alignment" where source readers would slow down reading from certain
source partitions if their watermark advances to far beyond the minimum
watermark over all source partitions.

Best,
Aljoscha

On 07.02.20 13:36, Akshay Aggarwal wrote:

> Hi Flink Users,
>
> We have a scenario where we're reading from multiple kafka topics using a
> single kafka consumer. Each topic has a very different ingestion rate, like
> CheckoutTopic has 500 rec/sec, PageViewTopic has 10,000 rec/sec. We are
> performing ordering of these events across topics using a keyed process
> function (keyed on userId) and a EVENT_TIME watermark which is based on the
> ingestionTs of the record captured just before it is produced into kafka.
>
> On live data this pipeline works perfectly, but if I restart the job to
> process from an old savepoint (say 24hrs old), the job fills up the state,
> a full back pressure (ratio 1) gets created on the source operators,
> checkpoints start failing and the job eventually dies. My hypothesis is
> that the data from both the topics are read at the max rate possible, but
> since the watermark from the PageViewTopic will lag significantly behind
> the CheckoutTopic overall watermarks don't progress, excessive data
> from CheckoutTopic fills up the state and results in the failure mentioned
> above.
>
> I also observed this while backfilling from a savepoint using a single
> topic, even though watermarks do progress faster than before, the job has
> the same fate. In this case I'm assuming the offsets/watermarks of the
> individual partitions go out-of-sync with respect to time leading to a
> similar situation mentioned above.
>
> Is this understanding correct? is there a known solution for this? And if
> not, what is the suggested approach to tackle this problem?
>
> Thanks & Regards,
> Akshay Aggarwal
>

[Akshay Aggarwal]

Thanks Aljoscha. Is there a JIRA where this is getting tracked? 

~Akshay

On Wed, Feb 12, 2020 at 1:56 PM Aljoscha Krettek <[hidden email]> wrote:

Hi,

I'm afraid your analysis is 100% correct. Currently there's no
out-of-box feature for dealing with this but our work on a new source
interface ([1]) will enable us to add a feature that we call "event-time
alignment" where source readers would slow down reading from certain
source partitions if their watermark advances to far beyond the minimum
watermark over all source partitions.

Best,
Aljoscha

On 07.02.20 13:36, Akshay Aggarwal wrote:
> Hi Flink Users,
>
> We have a scenario where we're reading from multiple kafka topics using a
> single kafka consumer. Each topic has a very different ingestion rate, like
> CheckoutTopic has 500 rec/sec, PageViewTopic has 10,000 rec/sec. We are
> performing ordering of these events across topics using a keyed process
> function (keyed on userId) and a EVENT_TIME watermark which is based on the
> ingestionTs of the record captured just before it is produced into kafka.
>
> On live data this pipeline works perfectly, but if I restart the job to
> process from an old savepoint (say 24hrs old), the job fills up the state,
> a full back pressure (ratio 1) gets created on the source operators,
> checkpoints start failing and the job eventually dies. My hypothesis is
> that the data from both the topics are read at the max rate possible, but
> since the watermark from the PageViewTopic will lag significantly behind
> the CheckoutTopic overall watermarks don't progress, excessive data
> from CheckoutTopic fills up the state and results in the failure mentioned
> above.
>
> I also observed this while backfilling from a savepoint using a single
> topic, even though watermarks do progress faster than before, the job has
> the same fate. In this case I'm assuming the offsets/watermarks of the
> individual partitions go out-of-sync with respect to time leading to a
> similar situation mentioned above.
>
> Is this understanding correct? is there a known solution for this? And if
> not, what is the suggested approach to tackle this problem?
>
> Thanks & Regards,
> Akshay Aggarwal
>

-----------------------------------------------------------------------------------------

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Any views or opinions presented in this email are solely those of the author and do not necessarily represent those of the organization. Any information on shares, debentures or similar instruments, recommended product pricing, valuations and the like are for information purposes only. It is not meant to be an instruction or recommendation, as the case may be, to buy or to sell securities, products, services nor an offer to buy or sell securities, products or services unless specifically stated to be so on behalf of the Flipkart group. Employees of the Flipkart group of companies are expressly required not to make defamatory statements and not to infringe or authorise any infringement of copyright or any other legal right by email communications. Any such communication is contrary to organizational policy and outside the scope of the employment of the individual concerned. The organization will not accept any liability in respect of such communication, and the employee responsible will be personally liable for any damages or other liability arising.

 

Our organization accepts no liability for the content of this email, or for the consequences of any actions taken on the basis of the information provided, unless that information is subsequently confirmed in writing. If you are not the intended recipient, you are notified that disclosing, copying, distributing or taking any action in reliance on the contents of this information is strictly prohibited.

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[rmetzger0]

You can find more information here: https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface

On Wed, Feb 12, 2020 at 11:30 AM Akshay Aggarwal <[hidden email]> wrote:

Thanks Aljoscha. Is there a JIRA where this is getting tracked? 

~Akshay

On Wed, Feb 12, 2020 at 1:56 PM Aljoscha Krettek <[hidden email]> wrote:

Hi,

I'm afraid your analysis is 100% correct. Currently there's no
out-of-box feature for dealing with this but our work on a new source
interface ([1]) will enable us to add a feature that we call "event-time
alignment" where source readers would slow down reading from certain
source partitions if their watermark advances to far beyond the minimum
watermark over all source partitions.

Best,
Aljoscha

On 07.02.20 13:36, Akshay Aggarwal wrote:
> Hi Flink Users,
>
> We have a scenario where we're reading from multiple kafka topics using a
> single kafka consumer. Each topic has a very different ingestion rate, like
> CheckoutTopic has 500 rec/sec, PageViewTopic has 10,000 rec/sec. We are
> performing ordering of these events across topics using a keyed process
> function (keyed on userId) and a EVENT_TIME watermark which is based on the
> ingestionTs of the record captured just before it is produced into kafka.
>
> On live data this pipeline works perfectly, but if I restart the job to
> process from an old savepoint (say 24hrs old), the job fills up the state,
> a full back pressure (ratio 1) gets created on the source operators,
> checkpoints start failing and the job eventually dies. My hypothesis is
> that the data from both the topics are read at the max rate possible, but
> since the watermark from the PageViewTopic will lag significantly behind
> the CheckoutTopic overall watermarks don't progress, excessive data
> from CheckoutTopic fills up the state and results in the failure mentioned
> above.
>
> I also observed this while backfilling from a savepoint using a single
> topic, even though watermarks do progress faster than before, the job has
> the same fate. In this case I'm assuming the offsets/watermarks of the
> individual partitions go out-of-sync with respect to time leading to a
> similar situation mentioned above.
>
> Is this understanding correct? is there a known solution for this? And if
> not, what is the suggested approach to tackle this problem?
>
> Thanks & Regards,
> Akshay Aggarwal
>

-----------------------------------------------------------------------------------------

This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error, please notify the system manager. This message contains confidential information and is intended only for the individual named. If you are not the named addressee, you should not disseminate, distribute or copy this email. Please notify the sender immediately by email if you have received this email by mistake and delete this email from your system. If you are not the intended recipient, you are notified that disclosing, copying, distributing or taking any action in reliance on the contents of this information is strictly prohibited.

 

Any views or opinions presented in this email are solely those of the author and do not necessarily represent those of the organization. Any information on shares, debentures or similar instruments, recommended product pricing, valuations and the like are for information purposes only. It is not meant to be an instruction or recommendation, as the case may be, to buy or to sell securities, products, services nor an offer to buy or sell securities, products or services unless specifically stated to be so on behalf of the Flipkart group. Employees of the Flipkart group of companies are expressly required not to make defamatory statements and not to infringe or authorise any infringement of copyright or any other legal right by email communications. Any such communication is contrary to organizational policy and outside the scope of the employment of the individual concerned. The organization will not accept any liability in respect of such communication, and the employee responsible will be personally liable for any damages or other liability arising.

 

Our organization accepts no liability for the content of this email, or for the consequences of any actions taken on the basis of the information provided, unless that information is subsequently confirmed in writing. If you are not the intended recipient, you are notified that disclosing, copying, distributing or taking any action in reliance on the contents of this information is strictly prohibited.

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