Re: Source API requires unbounded distributed storage?

[Fred Reiss]

Created SPARK-16963 to cover this issue.

Fred

On Thu, Aug 4, 2016 at 4:52 PM, Michael Armbrust 
wrote:

> Yeah, this API is in the private execution package because we are planning
> to continue to iterate on it.  Today, we will only ever go back one batch,
> though that might change in the future if we do async checkpointing of
> internal state.
>
> You are totally right that we should relay this info back to the source.
> Opening a JIRA sounds like a good first step.
>
> On Thu, Aug 4, 2016 at 4:38 PM, Fred Reiss  wrote:
>
>> Hi,
>>
>> I've been looking over the Source API in 
>> org.apache.spark.sql.execution.streaming,
>> and I'm at a loss for how the current API can be implemented in a practical
>> way. The API defines a single getBatch() method for fetching records from
>> the source, with the following Scaladoc comments defining the semantics:
>>
>>
>> */**  * Returns the data that is between the offsets (*`*start*`*, *`
>> *end*`*]. When *`*start*` *is *`*None*`
>>
>> *then  * the batch should begin with the first available record. This
>> method must always return the  * same data for a particular *`*start*` *and
>> *`*end*`
>> *pair.  */*
>> * def *getBatch(start: Option[Offset], end: Offset): DataFrame
>>
>> If I read the semantics described here correctly, a Source is required to
>> retain all past history for the stream that it backs. Further, a Source
>> is also required to retain this data across restarts of the process where
>> the Source is instantiated, even when the Source is restarted on a
>> different machine.
>>
>> The current implementation of FileStreamSource follows my reading of the
>> requirements above. FileStreamSource never deletes a file.
>>
>> I feel like this requirement for unbounded state retention must be a
>> mistake or misunderstanding of some kind. The scheduler is internally
>> maintaining a high water mark (StreamExecution.committedOffsets in
>> StreamExecution.scala) of data that has been successfully processed. There
>> must have been an intent to communicate that high water mark back to the
>> Source so that the Source can clean up its state. Indeed, the DataBricks
>> blog post from last week (https://databricks.com/blog/2
>> 016/07/28/structured-streaming-in-apache-spark.html) says that "Only a
>> few minutes’ worth of data needs to be retained; Structured Streaming will
>> maintain its own internal state after that."
>>
>> But the code checked into git and shipped with Spark 2.0 does not have an
>> API call for the scheduler to tell a Source where the boundary of "only a
>> few minutes' worth of data" lies.
>>
>> Is there a JIRA that I'm not aware of to change the Source API? If not,
>> should we maybe open one?
>>
>> Fred
>>
>
>

Re: Source API requires unbounded distributed storage?

[Michael Armbrust]

Yeah, this API is in the private execution package because we are planning
to continue to iterate on it.  Today, we will only ever go back one batch,
though that might change in the future if we do async checkpointing of
internal state.

You are totally right that we should relay this info back to the source.
Opening a JIRA sounds like a good first step.

On Thu, Aug 4, 2016 at 4:38 PM, Fred Reiss  wrote:

> Hi,
>
> I've been looking over the Source API in 
> org.apache.spark.sql.execution.streaming,
> and I'm at a loss for how the current API can be implemented in a practical
> way. The API defines a single getBatch() method for fetching records from
> the source, with the following Scaladoc comments defining the semantics:
>
>
> */**  * Returns the data that is between the offsets (*`*start*`*, *`*end*
> `*]. When *`*start*` *is *`*None*`
>
> *then  * the batch should begin with the first available record. This
> method must always return the  * same data for a particular *`*start*` *and
> *`*end*`
> *pair.  */*
> * def *getBatch(start: Option[Offset], end: Offset): DataFrame
>
> If I read the semantics described here correctly, a Source is required to
> retain all past history for the stream that it backs. Further, a Source
> is also required to retain this data across restarts of the process where
> the Source is instantiated, even when the Source is restarted on a
> different machine.
>
> The current implementation of FileStreamSource follows my reading of the
> requirements above. FileStreamSource never deletes a file.
>
> I feel like this requirement for unbounded state retention must be a
> mistake or misunderstanding of some kind. The scheduler is internally
> maintaining a high water mark (StreamExecution.committedOffsets in
> StreamExecution.scala) of data that has been successfully processed. There
> must have been an intent to communicate that high water mark back to the
> Source so that the Source can clean up its state. Indeed, the DataBricks
> blog post from last week (https://databricks.com/blog/
> 2016/07/28/structured-streaming-in-apache-spark.html) says that "Only a
> few minutes’ worth of data needs to be retained; Structured Streaming will
> maintain its own internal state after that."
>
> But the code checked into git and shipped with Spark 2.0 does not have an
> API call for the scheduler to tell a Source where the boundary of "only a
> few minutes' worth of data" lies.
>
> Is there a JIRA that I'm not aware of to change the Source API? If not,
> should we maybe open one?
>
> Fred
>

Source API requires unbounded distributed storage?

[Fred Reiss]

Hi,

I've been looking over the Source API in
org.apache.spark.sql.execution.streaming, and I'm at a loss for how the
current API can be implemented in a practical way. The API defines a single
getBatch() method for fetching records from the source, with the following
Scaladoc comments defining the semantics:


*/**  * Returns the data that is between the offsets (*`*start*`*, *`*end*`*].
When *`*start*` *is *`*None*`

*then  * the batch should begin with the first available record. This
method must always return the  * same data for a particular *`*start*` *and
*`*end*`
*pair.  */*
* def *getBatch(start: Option[Offset], end: Offset): DataFrame

If I read the semantics described here correctly, a Source is required to
retain all past history for the stream that it backs. Further, a Source is
also required to retain this data across restarts of the process where the
Source is instantiated, even when the Source is restarted on a different
machine.

The current implementation of FileStreamSource follows my reading of the
requirements above. FileStreamSource never deletes a file.

I feel like this requirement for unbounded state retention must be a
mistake or misunderstanding of some kind. The scheduler is internally
maintaining a high water mark (StreamExecution.committedOffsets in
StreamExecution.scala) of data that has been successfully processed. There
must have been an intent to communicate that high water mark back to the
Source so that the Source can clean up its state. Indeed, the DataBricks
blog post from last week (
https://databricks.com/blog/2016/07/28/structured-streaming-in-apache-spark.html)
says that "Only a few minutes’ worth of data needs to be retained;
Structured Streaming will maintain its own internal state after that."

But the code checked into git and shipped with Spark 2.0 does not have an
API call for the scheduler to tell a Source where the boundary of "only a
few minutes' worth of data" lies.

Is there a JIRA that I'm not aware of to change the Source API? If not,
should we maybe open one?

Fred