Thanks Tathagata for your answer.
The reason I was asking about controlling data size is that the javadoc
indicate you can use foreach or collect on the dataframe. If the data is very
large then a collect may result in OOM.
>From your answer it appears that the only way to control the size (in 2.2)
>would be control the trigger interval. However, in my case, I have to dedup
>the elements in one minute interval, which I am using a trigger interval and
>cannot reduce it. Do you have any other suggestion/recommendation ?
Also, do you have any timeline for the availability of DataSourceV2/Spark 2.3 ?
Thanks again.
On Wednesday, January 3, 2018 2:27 PM, Tathagata Das
<tathagata.das1...@gmail.com> wrote:
1. It is all the result data in that trigger. Note that it takes a DataFrame
which is a purely logical representation of data and has no association with
partitions, etc. which are physical representations.
2. If you want to limit the amount of data that is processed in a trigger, then
you should either control the trigger interval or use the rate limit options on
sources that support it (e.g. for kafka, you can use the option
"maxOffsetsPerTrigger", see the guide).
Related note, these APIs are subject to change. In fact in the upcoming release
2.3, we are adding a DataSource V2 API for
batch/microbatch-streaming/continuous-streaming sources and sinks.
On Wed, Jan 3, 2018 at 11:23 PM, M Singh <mans2si...@yahoo.com.invalid> wrote:
Hi:
The documentation for Sink.addBatch is as follows:
/** * Adds a batch of data to this sink. The data for a given `batchId` is
deterministic and if * this method is called more than once with the same
batchId (which will happen in the case of * failures), then `data` should
only be added once. * * Note 1: You cannot apply any operators on `data`
except consuming it (e.g., `collect/foreach`). * Otherwise, you may get a
wrong result. * * Note 2: The method is supposed to be executed
synchronously, i.e. the method should only return * after data is consumed by
sink successfully. */ def addBatch(batchId: Long, data: DataFrame): Unit
A few questions about the data is each DataFrame passed as the argument to
addBatch - 1. Is it all the data in a partition for each trigger or is it all
the data in that trigger ? 2. Is there a way to control the size in each
addBatch invocation to make sure that we don't run into OOM exception on the
executor while calling collect ?
Thanks
