I am using spark 2.3.0 and Kafka 0.10.2.0 so I assume structured streaming
using Direct API's although I am not sure? If it is direct API's the only
parameters that are relevant are below according to this
<https://www.linkedin.com/pulse/enable-back-pressure-make-your-spark-streaming-production-lan-jiang>
article
- spark.conf("spark.streaming.backpressure.enabled", "true")
- spark.conf("spark.streaming.kafka.maxRatePerPartition", "10000")
I set both of these and I run select count * on my 10M records I still
don't see any output until it finishes the initial batch of 10M and this
takes a while. so I am wondering if I miss something here?
On Tue, Mar 20, 2018 at 6:09 AM, Geoff Von Allmen <ge...@ibleducation.com>
wrote:
> The following
> <http://spark.apache.org/docs/latest/configuration.html#spark-streaming>
> settings
> may be what you’re looking for:
>
> - spark.streaming.backpressure.enabled
> - spark.streaming.backpressure.initialRate
> - spark.streaming.receiver.maxRate
> - spark.streaming.kafka.maxRatePerPartition
>
>
>
> On Mon, Mar 19, 2018 at 5:27 PM, kant kodali <kanth...@gmail.com> wrote:
>
>> Yes it indeed makes sense! Is there a way to get incremental counts when
>> I start from 0 and go through 10M records? perhaps count for every micro
>> batch or something?
>>
>> On Mon, Mar 19, 2018 at 1:57 PM, Geoff Von Allmen <ge...@ibleducation.com
>> > wrote:
>>
>>> Trigger does not mean report the current solution every 'trigger
>>> seconds'. It means it will attempt to fetch new data and process it no
>>> faster than trigger seconds intervals.
>>>
>>> If you're reading from the beginning and you've got 10M entries in
>>> kafka, it's likely pulling everything down then processing it completely
>>> and giving you an initial output. From here on out, it will check kafka
>>> every 1 second for new data and process it, showing you only the updated
>>> rows. So the initial read will give you the entire output since there is
>>> nothing to be 'updating' from. If you add data to kafka now that the
>>> streaming job has completed it's first batch (and leave it running), it
>>> will then show you the new/updated rows since the last batch every 1 second
>>> (assuming it can fetch + process in that time span).
>>>
>>> If the combined fetch + processing time is > the trigger time, you will
>>> notice warnings that it is 'falling behind' (I forget the exact verbiage,
>>> but something to the effect of the calculation took XX time and is falling
>>> behind). In that case, it will immediately check kafka for new messages and
>>> begin processing the next batch (if new messages exist).
>>>
>>> Hope that makes sense -
>>>
>>>
>>> On Mon, Mar 19, 2018 at 13:36 kant kodali <kanth...@gmail.com> wrote:
>>>
>>>> Hi All,
>>>>
>>>> I have 10 million records in my Kafka and I am just trying to
>>>> spark.sql(select count(*) from kafka_view). I am reading from kafka and
>>>> writing to kafka.
>>>>
>>>> My writeStream is set to "update" mode and trigger interval of one
>>>> second (Trigger.ProcessingTime(1000)). I expect the counts to be
>>>> printed every second but looks like it would print after going through all
>>>> 10M. why?
>>>>
>>>> Also, it seems to take forever whereas Linux wc of 10M rows would take
>>>> 30 seconds.
>>>>
>>>> Thanks!
>>>>
>>>
>>
>
