Do you really want to build all of that and open yourself to bugs when you
can just use foreachBatch? Here are your options:

1. Build it yourself

// Read offsets from some store
prevOffsets = readOffsets()
latestOffsets = getOffsets()

df = spark.read.format("kafka").option("startOffsets",
prevOffsets).option("endOffsets", latestOffsets).load()
batchLogic(df)

saveOffsets(latestOffsets)

2. Structured Streaming + Trigger.Once + foreachBatch

spark.readStream.format("kafka").load().writeStream.foreachBatch((df,
batchId) => batchLogic(df)).trigger("once").start()

With Option (1), you're going to have to (re)solve:
 a) Tracking and consistency of offsets
 b) Potential topic partition mismatches
 c) Offsets that may have aged out due to retention
 d) Re-execution of jobs and data consistency. What if your job fails as
you're committing the offsets in the end, but the data was already stored?
Will your getOffsets method return the same offsets?

I'd rather not solve problems that other people have solved for me, but
ultimately the decision is yours to make.

Best,
Burak




On Tue, Feb 4, 2020 at 4:41 PM Ruijing Li <liruijin...@gmail.com> wrote:

> Thanks Anil, I think that’s the approach I will take.
>
> Hi Burak,
>
> That was a possibility to think about, but my team has custom dataframe
> writer functions we would like to use, unfortunately they were written for
> static dataframes in mind. I do see there is a ForEachBatch write mode but
> my thinking was at that point it was easier to read from kafka through
> batch mode.
>
> Thanks,
> RJ
>
> On Tue, Feb 4, 2020 at 4:20 PM Burak Yavuz <brk...@gmail.com> wrote:
>
>> Hi Ruijing,
>>
>> Why do you not want to use structured streaming here? This is exactly why
>> structured streaming + Trigger.Once was built, just so that you don't build
>> that solution yourself.
>> You also get exactly once semantics if you use the built in sinks.
>>
>> Best,
>> Burak
>>
>> On Mon, Feb 3, 2020 at 3:15 PM Anil Kulkarni <anil...@gmail.com> wrote:
>>
>>> Hi Ruijing,
>>>
>>> We did the below things to read Kafka in batch from spark:
>>>
>>> 1) Maintain the start offset (could be db, file etc)
>>> 2) Get the end offset dynamically when the job executes.
>>> 3) Pass the start and end offsets
>>> 4) Overwrite the start offset with the end offset. (Should be done post
>>> processing the data)
>>>
>>> Currently to make it work in batch mode, you need to maintain the state
>>> information of the offsets externally.
>>>
>>>
>>> Thanks
>>> Anil
>>>
>>> -Sent from my mobile
>>> http://anilkulkarni.com/
>>>
>>> On Mon, Feb 3, 2020, 12:39 AM Ruijing Li <liruijin...@gmail.com> wrote:
>>>
>>>> Hi all,
>>>>
>>>> My use case is to read from single kafka topic using a batch spark sql
>>>> job (not structured streaming ideally). I want this batch job every time it
>>>> starts to get the last offset it stopped at, and start reading from there
>>>> until it caught up to the latest offset, store the result and stop the job.
>>>> Given the dataframe has a partition and offset column, my first thought for
>>>> offset management is to groupBy partition and agg the max offset, then
>>>> store it in HDFS. Next time the job runs, it will read and start from this
>>>> max offset using startingOffsets
>>>>
>>>> However, I was wondering if this will work. If the kafka producer
>>>> failed an offset and later decides to resend it, I will have skipped it
>>>> since I’m starting from the max offset sent. How does spark structured
>>>> streaming know to continue onwards - does it keep a state of all offsets
>>>> seen? If so, how can I replicate this for batch without missing data? Any
>>>> help would be appreciated.
>>>>
>>>>
>>>> --
>>>> Cheers,
>>>> Ruijing Li
>>>>
>>> --
> Cheers,
> Ruijing Li
>

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