RE: Are these numbers abnormal for spark streaming?
Another quick question... I've got 4 nodes with 2 cores each. I've assinged the
streaming app 4 cores. It seems to be using one per node. I imagine forwarding
from the receivers to the executors are causing unnecessary processing. Is
there a way to specify that I want 2 cores from the same machines to be
involved (even better if this can be specified during spark-submit)?
Thanks,
Ashic.
From: as...@live.com
To: gerard.m...@gmail.com; asudipta.baner...@gmail.com
CC: user@spark.apache.org; tathagata.das1...@gmail.com
Subject: RE: Are these numbers abnormal for spark streaming?
Date: Thu, 22 Jan 2015 15:40:17 +
Yup...looks like it. I can do some tricks to reduce setup costs further, but
this is much better than where I was yesterday. Thanks for your awesome input :)
-Ashic.
From: gerard.m...@gmail.com
Date: Thu, 22 Jan 2015 16:34:38 +0100
Subject: Re: Are these numbers abnormal for spark streaming?
To: asudipta.baner...@gmail.com
CC: as...@live.com; user@spark.apache.org; tathagata.das1...@gmail.com
Given that the process, and in particular, the setup of connections, is bound
to the number of partitions (in x.foreachPartition{ x=> ???}), I think it would
be worth trying reducing them.
Increasing the 'spark.streaming.BlockInterval' will do the trick (you can read
the tuning details here: http://www.virdata.com/tuning-spark/#Partitions)
-kr, Gerard.
On Thu, Jan 22, 2015 at 4:28 PM, Gerard Maas wrote:
So the system has gone from 7msg in 4.961 secs (median) to 106msgs in 4,761
seconds. I think there's evidence that setup costs are quite high in this case
and increasing the batch interval is helping.
On Thu, Jan 22, 2015 at 4:12 PM, Sudipta Banerjee
wrote:
Hi Ashic Mahtab,
The Cassandra and the Zookeeper are they installed as a part of Yarn
architecture or are they installed in a separate layer with Apache Spark .
Thanks and Regards,
Sudipta
On Thu, Jan 22, 2015 at 8:13 PM, Ashic Mahtab wrote:
Hi Guys,
So I changed the interval to 15 seconds. There's obviously a lot more messages
per batch, but (I think) it looks a lot healthier. Can you see any major
warning signs? I think that with 2 second intervals, the setup / teardown per
partition was what was causing the delays.
StreamingStarted at: Thu Jan 22 13:23:12 GMT 2015Time since start: 1 hour 17
minutes 16 secondsNetwork receivers: 2Batch interval: 15 secondsProcessed
batches: 309Waiting batches: 0
Statistics over last 100 processed batchesReceiver
StatisticsReceiverStatusLocationRecords in last batch[2015/01/22
14:40:29]Minimum rate[records/sec]Median rate[records/sec]Maximum
rate[records/sec]Last ErrorRmqReceiver-0ACTIVEVDCAPP53.foo.local2.6
K29106295-RmqReceiver-1ACTIVEVDCAPP50.bar.local2.6 K29107291-Batch Processing
StatisticsMetricLast batchMinimum25th percentileMedian75th
percentileMaximumProcessing Time4 seconds 812 ms4 seconds 698 ms4 seconds 738
ms4 seconds 761 ms4 seconds 788 ms5 seconds 802 msScheduling Delay2 ms0 ms3 ms3
ms4 ms9 msTotal Delay4 seconds 814 ms4 seconds 701 ms4 seconds 739 ms4 seconds
764 ms4 seconds 792 ms5 seconds 809 ms
Regards,
Ashic.
From: as...@live.com
To: gerard.m...@gmail.com
CC: user@spark.apache.org
Subject: RE: Are these numbers abnormal for spark streaming?
Date: Thu, 22 Jan 2015 12:32:05 +
Hi Gerard,
Thanks for the response.
The messages get desrialised from msgpack format, and one of the strings is
desrialised to json. Certain fields are checked to decide if further processing
is required. If so, it goes through a series of in mem filters to check if more
processing is required. If so, only then does the "heavy" work start. That
consists of a few db queries, and potential updates to the db + message on
message queue. The majority of messages don't need processing. The messages
needing processing at peak are about three every other second.
One possible things that might be happening is the session initialisation and
prepared statement initialisation for each partition. I can resort to some
tricks, but I think I'll try increasing batch interval to 15 seconds. I'll
report back with findings.
Thanks,
Ashic.
From: gerard.m...@gmail.com
Date: Thu, 22 Jan 2015 12:30:08 +0100
Subject: Re: Are these numbers abnormal for spark streaming?
To: tathagata.das1...@gmail.com
CC: as...@live.com; t...@databricks.com; user@spark.apache.org
and post the code (if possible).In a nutshell, your processing time > batch
interval, resulting in an ever-increasing delay that will end up in a crash.
3 secs to process 14 messages looks like a lot. Curious what the job logic is.
-kr, Gerard.
On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das
wrote:
This is not normal. Its a huge scheduling delay!! Can you tell me more about
the application?- cluser setup, number of receivers, whats the computation, etc.
On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote:
Hate to do this...but...erm...bump? Would really appreci
RE: Are these numbers abnormal for spark streaming?
Yup...looks like it. I can do some tricks to reduce setup costs further, but
this is much better than where I was yesterday. Thanks for your awesome input :)
-Ashic.
From: gerard.m...@gmail.com
Date: Thu, 22 Jan 2015 16:34:38 +0100
Subject: Re: Are these numbers abnormal for spark streaming?
To: asudipta.baner...@gmail.com
CC: as...@live.com; user@spark.apache.org; tathagata.das1...@gmail.com
Given that the process, and in particular, the setup of connections, is bound
to the number of partitions (in x.foreachPartition{ x=> ???}), I think it would
be worth trying reducing them.
Increasing the 'spark.streaming.BlockInterval' will do the trick (you can read
the tuning details here: http://www.virdata.com/tuning-spark/#Partitions)
-kr, Gerard.
On Thu, Jan 22, 2015 at 4:28 PM, Gerard Maas wrote:
So the system has gone from 7msg in 4.961 secs (median) to 106msgs in 4,761
seconds. I think there's evidence that setup costs are quite high in this case
and increasing the batch interval is helping.
On Thu, Jan 22, 2015 at 4:12 PM, Sudipta Banerjee
wrote:
Hi Ashic Mahtab,
The Cassandra and the Zookeeper are they installed as a part of Yarn
architecture or are they installed in a separate layer with Apache Spark .
Thanks and Regards,
Sudipta
On Thu, Jan 22, 2015 at 8:13 PM, Ashic Mahtab wrote:
Hi Guys,
So I changed the interval to 15 seconds. There's obviously a lot more messages
per batch, but (I think) it looks a lot healthier. Can you see any major
warning signs? I think that with 2 second intervals, the setup / teardown per
partition was what was causing the delays.
StreamingStarted at: Thu Jan 22 13:23:12 GMT 2015Time since start: 1 hour 17
minutes 16 secondsNetwork receivers: 2Batch interval: 15 secondsProcessed
batches: 309Waiting batches: 0
Statistics over last 100 processed batchesReceiver
StatisticsReceiverStatusLocationRecords in last batch[2015/01/22
14:40:29]Minimum rate[records/sec]Median rate[records/sec]Maximum
rate[records/sec]Last ErrorRmqReceiver-0ACTIVEVDCAPP53.foo.local2.6
K29106295-RmqReceiver-1ACTIVEVDCAPP50.bar.local2.6 K29107291-Batch Processing
StatisticsMetricLast batchMinimum25th percentileMedian75th
percentileMaximumProcessing Time4 seconds 812 ms4 seconds 698 ms4 seconds 738
ms4 seconds 761 ms4 seconds 788 ms5 seconds 802 msScheduling Delay2 ms0 ms3 ms3
ms4 ms9 msTotal Delay4 seconds 814 ms4 seconds 701 ms4 seconds 739 ms4 seconds
764 ms4 seconds 792 ms5 seconds 809 ms
Regards,
Ashic.
From: as...@live.com
To: gerard.m...@gmail.com
CC: user@spark.apache.org
Subject: RE: Are these numbers abnormal for spark streaming?
Date: Thu, 22 Jan 2015 12:32:05 +
Hi Gerard,
Thanks for the response.
The messages get desrialised from msgpack format, and one of the strings is
desrialised to json. Certain fields are checked to decide if further processing
is required. If so, it goes through a series of in mem filters to check if more
processing is required. If so, only then does the "heavy" work start. That
consists of a few db queries, and potential updates to the db + message on
message queue. The majority of messages don't need processing. The messages
needing processing at peak are about three every other second.
One possible things that might be happening is the session initialisation and
prepared statement initialisation for each partition. I can resort to some
tricks, but I think I'll try increasing batch interval to 15 seconds. I'll
report back with findings.
Thanks,
Ashic.
From: gerard.m...@gmail.com
Date: Thu, 22 Jan 2015 12:30:08 +0100
Subject: Re: Are these numbers abnormal for spark streaming?
To: tathagata.das1...@gmail.com
CC: as...@live.com; t...@databricks.com; user@spark.apache.org
and post the code (if possible).In a nutshell, your processing time > batch
interval, resulting in an ever-increasing delay that will end up in a crash.
3 secs to process 14 messages looks like a lot. Curious what the job logic is.
-kr, Gerard.
On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das
wrote:
This is not normal. Its a huge scheduling delay!! Can you tell me more about
the application?- cluser setup, number of receivers, whats the computation, etc.
On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote:
Hate to do this...but...erm...bump? Would really appreciate input from others
using Streaming. Or at least some docs that would tell me if these are expected
or not.
From: as...@live.com
To: user@spark.apache.org
Subject: Are these numbers abnormal for spark streaming?
Date: Wed, 21 Jan 2015 11:26:31 +
Hi Guys,
I've got Spark Streaming set up for a low data rate system (using spark's
features for analysis, rather than high throughput). Messages are coming in
throughout the day, at around 1-20 per second (finger in the air estimate...not
analysed yet). In the spark streaming UI for the application, I'm getting the
following after 17 hours.
StreamingSta
Re: Are these numbers abnormal for spark streaming?
Given that the process, and in particular, the setup of connections, is
bound to the number of partitions (in x.foreachPartition{ x=> ???}), I
think it would be worth trying reducing them.
Increasing the 'spark.streaming.BlockInterval' will do the trick (you can
read the tuning details here:
http://www.virdata.com/tuning-spark/#Partitions)
-kr, Gerard.
On Thu, Jan 22, 2015 at 4:28 PM, Gerard Maas wrote:
> So the system has gone from 7msg in 4.961 secs (median) to 106msgs in
> 4,761 seconds.
> I think there's evidence that setup costs are quite high in this case and
> increasing the batch interval is helping.
>
> On Thu, Jan 22, 2015 at 4:12 PM, Sudipta Banerjee <
> asudipta.baner...@gmail.com> wrote:
>
>> Hi Ashic Mahtab,
>>
>> The Cassandra and the Zookeeper are they installed as a part of Yarn
>> architecture or are they installed in a separate layer with Apache Spark .
>>
>> Thanks and Regards,
>> Sudipta
>>
>> On Thu, Jan 22, 2015 at 8:13 PM, Ashic Mahtab wrote:
>>
>>> Hi Guys,
>>> So I changed the interval to 15 seconds. There's obviously a lot more
>>> messages per batch, but (I think) it looks a lot healthier. Can you see any
>>> major warning signs? I think that with 2 second intervals, the setup /
>>> teardown per partition was what was causing the delays.
>>>
>>> Streaming
>>>
>>>- *Started at: *Thu Jan 22 13:23:12 GMT 2015
>>>- *Time since start: *1 hour 17 minutes 16 seconds
>>>- *Network receivers: *2
>>>- *Batch interval: *15 seconds
>>>- *Processed batches: *309
>>>- *Waiting batches: *0
>>>
>>>
>>>
>>> Statistics over last 100 processed batchesReceiver Statistics
>>>
>>>- Receiver
>>>
>>>
>>>- Status
>>>
>>>
>>>- Location
>>>
>>>
>>>- Records in last batch
>>>- [2015/01/22 14:40:29]
>>>
>>>
>>>- Minimum rate
>>>- [records/sec]
>>>
>>>
>>>- Median rate
>>>- [records/sec]
>>>
>>>
>>>- Maximum rate
>>>- [records/sec]
>>>
>>>
>>>- Last Error
>>>
>>> RmqReceiver-0ACTIVEVDCAPP53.foo.local2.6 K29106295-RmqReceiver-1ACTIVE
>>> VDCAPP50.bar.local2.6 K29107291-
>>> Batch Processing Statistics
>>>
>>>MetricLast batchMinimum25th percentileMedian75th
>>> percentileMaximumProcessing
>>>Time4 seconds 812 ms4 seconds 698 ms4 seconds 738 ms4 seconds 761 ms4
>>>seconds 788 ms5 seconds 802 msScheduling Delay2 ms0 ms3 ms3 ms4 ms9
>>>msTotal Delay4 seconds 814 ms4 seconds 701 ms4 seconds 739 ms4
>>>seconds 764 ms4 seconds 792 ms5 seconds 809 ms
>>>
>>>
>>> Regards,
>>> Ashic.
>>> --
>>> From: as...@live.com
>>> To: gerard.m...@gmail.com
>>> CC: user@spark.apache.org
>>> Subject: RE: Are these numbers abnormal for spark streaming?
>>> Date: Thu, 22 Jan 2015 12:32:05 +
>>>
>>>
>>> Hi Gerard,
>>> Thanks for the response.
>>>
>>> The messages get desrialised from msgpack format, and one of the strings
>>> is desrialised to json. Certain fields are checked to decide if further
>>> processing is required. If so, it goes through a series of in mem filters
>>> to check if more processing is required. If so, only then does the "heavy"
>>> work start. That consists of a few db queries, and potential updates to the
>>> db + message on message queue. The majority of messages don't need
>>> processing. The messages needing processing at peak are about three every
>>> other second.
>>>
>>> One possible things that might be happening is the session
>>> initialisation and prepared statement initialisation for each partition. I
>>> can resort to some tricks, but I think I'll try increasing batch interval
>>> to 15 seconds. I'll report back with findings.
>>>
>>> Thanks,
>>> Ashic.
>>>
>>> --
>>> From: gerard.m...@gmail.com
>>> Date: Thu, 22 Jan 2015 12:30:08 +0100
>>> Subject: Re: Are these numbers abnormal for spark streaming?
>>> To: tathagata.das1...@gmail.com
>>> CC: as...@live.com; t...@databricks.com; user@spark.apache.org
>>>
>>> and post the code (if possible).
>>> In
Re: Are these numbers abnormal for spark streaming?
So the system has gone from 7msg in 4.961 secs (median) to 106msgs in 4,761 seconds. I think there's evidence that setup costs are quite high in this case and increasing the batch interval is helping. On Thu, Jan 22, 2015 at 4:12 PM, Sudipta Banerjee < asudipta.baner...@gmail.com> wrote: > Hi Ashic Mahtab, > > The Cassandra and the Zookeeper are they installed as a part of Yarn > architecture or are they installed in a separate layer with Apache Spark . > > Thanks and Regards, > Sudipta > > On Thu, Jan 22, 2015 at 8:13 PM, Ashic Mahtab wrote: > >> Hi Guys, >> So I changed the interval to 15 seconds. There's obviously a lot more >> messages per batch, but (I think) it looks a lot healthier. Can you see any >> major warning signs? I think that with 2 second intervals, the setup / >> teardown per partition was what was causing the delays. >> >> Streaming >> >>- *Started at: *Thu Jan 22 13:23:12 GMT 2015 >>- *Time since start: *1 hour 17 minutes 16 seconds >>- *Network receivers: *2 >>- *Batch interval: *15 seconds >>- *Processed batches: *309 >>- *Waiting batches: *0 >> >> >> >> Statistics over last 100 processed batchesReceiver Statistics >> >>- Receiver >> >> >>- Status >> >> >>- Location >> >> >>- Records in last batch >>- [2015/01/22 14:40:29] >> >> >>- Minimum rate >>- [records/sec] >> >> >>- Median rate >>- [records/sec] >> >> >>- Maximum rate >>- [records/sec] >> >> >>- Last Error >> >> RmqReceiver-0ACTIVEVDCAPP53.foo.local2.6 K29106295-RmqReceiver-1ACTIVE >> VDCAPP50.bar.local2.6 K29107291- >> Batch Processing Statistics >> >>MetricLast batchMinimum25th percentileMedian75th >> percentileMaximumProcessing >>Time4 seconds 812 ms4 seconds 698 ms4 seconds 738 ms4 seconds 761 ms4 >>seconds 788 ms5 seconds 802 msScheduling Delay2 ms0 ms3 ms3 ms4 ms9 >> msTotal >>Delay4 seconds 814 ms4 seconds 701 ms4 seconds 739 ms4 seconds 764 ms4 >>seconds 792 ms5 seconds 809 ms >> >> >> Regards, >> Ashic. >> -- >> From: as...@live.com >> To: gerard.m...@gmail.com >> CC: user@spark.apache.org >> Subject: RE: Are these numbers abnormal for spark streaming? >> Date: Thu, 22 Jan 2015 12:32:05 + >> >> >> Hi Gerard, >> Thanks for the response. >> >> The messages get desrialised from msgpack format, and one of the strings >> is desrialised to json. Certain fields are checked to decide if further >> processing is required. If so, it goes through a series of in mem filters >> to check if more processing is required. If so, only then does the "heavy" >> work start. That consists of a few db queries, and potential updates to the >> db + message on message queue. The majority of messages don't need >> processing. The messages needing processing at peak are about three every >> other second. >> >> One possible things that might be happening is the session initialisation >> and prepared statement initialisation for each partition. I can resort to >> some tricks, but I think I'll try increasing batch interval to 15 seconds. >> I'll report back with findings. >> >> Thanks, >> Ashic. >> >> -- >> From: gerard.m...@gmail.com >> Date: Thu, 22 Jan 2015 12:30:08 +0100 >> Subject: Re: Are these numbers abnormal for spark streaming? >> To: tathagata.das1...@gmail.com >> CC: as...@live.com; t...@databricks.com; user@spark.apache.org >> >> and post the code (if possible). >> In a nutshell, your processing time > batch interval, resulting in an >> ever-increasing delay that will end up in a crash. >> 3 secs to process 14 messages looks like a lot. Curious what the job >> logic is. >> >> -kr, Gerard. >> >> On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das < >> tathagata.das1...@gmail.com> wrote: >> >> This is not normal. Its a huge scheduling delay!! Can you tell me more >> about the application? >> - cluser setup, number of receivers, whats the computation, etc. >> >> On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote: >> >> Hate to do this...but...erm...bump? Would really appreciate input from >> others using Streaming. Or at least some docs that would tell me if these >> are expected or not. >> >>
RE: Are these numbers abnormal for spark streaming?
Hi Sudipta, Standalone spark master. Separate Zookeeper cluster. 4 worker nodes with cassandra + spark on each. No hadoop / hdfs / yarn. Regards, Ashic. Date: Thu, 22 Jan 2015 20:42:43 +0530 Subject: Re: Are these numbers abnormal for spark streaming? From: asudipta.baner...@gmail.com To: as...@live.com CC: gerard.m...@gmail.com; user@spark.apache.org; tathagata.das1...@gmail.com Hi Ashic Mahtab, The Cassandra and the Zookeeper are they installed as a part of Yarn architecture or are they installed in a separate layer with Apache Spark . Thanks and Regards, Sudipta On Thu, Jan 22, 2015 at 8:13 PM, Ashic Mahtab wrote: Hi Guys, So I changed the interval to 15 seconds. There's obviously a lot more messages per batch, but (I think) it looks a lot healthier. Can you see any major warning signs? I think that with 2 second intervals, the setup / teardown per partition was what was causing the delays. StreamingStarted at: Thu Jan 22 13:23:12 GMT 2015Time since start: 1 hour 17 minutes 16 secondsNetwork receivers: 2Batch interval: 15 secondsProcessed batches: 309Waiting batches: 0 Statistics over last 100 processed batchesReceiver StatisticsReceiverStatusLocationRecords in last batch[2015/01/22 14:40:29]Minimum rate[records/sec]Median rate[records/sec]Maximum rate[records/sec]Last ErrorRmqReceiver-0ACTIVEVDCAPP53.foo.local2.6 K29106295-RmqReceiver-1ACTIVEVDCAPP50.bar.local2.6 K29107291-Batch Processing StatisticsMetricLast batchMinimum25th percentileMedian75th percentileMaximumProcessing Time4 seconds 812 ms4 seconds 698 ms4 seconds 738 ms4 seconds 761 ms4 seconds 788 ms5 seconds 802 msScheduling Delay2 ms0 ms3 ms3 ms4 ms9 msTotal Delay4 seconds 814 ms4 seconds 701 ms4 seconds 739 ms4 seconds 764 ms4 seconds 792 ms5 seconds 809 ms Regards, Ashic. From: as...@live.com To: gerard.m...@gmail.com CC: user@spark.apache.org Subject: RE: Are these numbers abnormal for spark streaming? Date: Thu, 22 Jan 2015 12:32:05 + Hi Gerard, Thanks for the response. The messages get desrialised from msgpack format, and one of the strings is desrialised to json. Certain fields are checked to decide if further processing is required. If so, it goes through a series of in mem filters to check if more processing is required. If so, only then does the "heavy" work start. That consists of a few db queries, and potential updates to the db + message on message queue. The majority of messages don't need processing. The messages needing processing at peak are about three every other second. One possible things that might be happening is the session initialisation and prepared statement initialisation for each partition. I can resort to some tricks, but I think I'll try increasing batch interval to 15 seconds. I'll report back with findings. Thanks, Ashic. From: gerard.m...@gmail.com Date: Thu, 22 Jan 2015 12:30:08 +0100 Subject: Re: Are these numbers abnormal for spark streaming? To: tathagata.das1...@gmail.com CC: as...@live.com; t...@databricks.com; user@spark.apache.org and post the code (if possible).In a nutshell, your processing time > batch interval, resulting in an ever-increasing delay that will end up in a crash. 3 secs to process 14 messages looks like a lot. Curious what the job logic is. -kr, Gerard. On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das wrote: This is not normal. Its a huge scheduling delay!! Can you tell me more about the application?- cluser setup, number of receivers, whats the computation, etc. On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote: Hate to do this...but...erm...bump? Would really appreciate input from others using Streaming. Or at least some docs that would tell me if these are expected or not. From: as...@live.com To: user@spark.apache.org Subject: Are these numbers abnormal for spark streaming? Date: Wed, 21 Jan 2015 11:26:31 + Hi Guys, I've got Spark Streaming set up for a low data rate system (using spark's features for analysis, rather than high throughput). Messages are coming in throughout the day, at around 1-20 per second (finger in the air estimate...not analysed yet). In the spark streaming UI for the application, I'm getting the following after 17 hours. StreamingStarted at: Tue Jan 20 16:58:43 GMT 2015Time since start: 18 hours 24 minutes 34 secondsNetwork receivers: 2Batch interval: 2 secondsProcessed batches: 16482Waiting batches: 1 Statistics over last 100 processed batchesReceiver StatisticsReceiverStatusLocationRecords in last batch[2015/01/21 11:23:18]Minimum rate[records/sec]Median rate[records/sec]Maximum rate[records/sec]Last ErrorRmqReceiver-0ACTIVEF 144727-RmqReceiver-1ACTIVEBR 124726-Batch Processing StatisticsMetricLast batchMinimum25th percentileMedian75th percentileMaximumProcessing Time3 seconds 994 ms157 ms4 seconds 16 ms4 seconds 961 ms5 seconds 3 ms5 seconds 171 msScheduling Delay9 hours 15 minutes 4 seconds9 hours 10 minutes 54
Re: Are these numbers abnormal for spark streaming?
Hi Ashic Mahtab, The Cassandra and the Zookeeper are they installed as a part of Yarn architecture or are they installed in a separate layer with Apache Spark . Thanks and Regards, Sudipta On Thu, Jan 22, 2015 at 8:13 PM, Ashic Mahtab wrote: > Hi Guys, > So I changed the interval to 15 seconds. There's obviously a lot more > messages per batch, but (I think) it looks a lot healthier. Can you see any > major warning signs? I think that with 2 second intervals, the setup / > teardown per partition was what was causing the delays. > > Streaming > >- *Started at: *Thu Jan 22 13:23:12 GMT 2015 >- *Time since start: *1 hour 17 minutes 16 seconds >- *Network receivers: *2 >- *Batch interval: *15 seconds >- *Processed batches: *309 >- *Waiting batches: *0 > > > > Statistics over last 100 processed batchesReceiver Statistics > >- Receiver > > >- Status > > >- Location > > >- Records in last batch >- [2015/01/22 14:40:29] > > >- Minimum rate >- [records/sec] > > >- Median rate >- [records/sec] > > >- Maximum rate >- [records/sec] > > >- Last Error > > RmqReceiver-0ACTIVEVDCAPP53.foo.local2.6 K29106295-RmqReceiver-1ACTIVE > VDCAPP50.bar.local2.6 K29107291- > Batch Processing Statistics > >MetricLast batchMinimum25th percentileMedian75th > percentileMaximumProcessing >Time4 seconds 812 ms4 seconds 698 ms4 seconds 738 ms4 seconds 761 ms4 >seconds 788 ms5 seconds 802 msScheduling Delay2 ms0 ms3 ms3 ms4 ms9 msTotal >Delay4 seconds 814 ms4 seconds 701 ms4 seconds 739 ms4 seconds 764 ms4 >seconds 792 ms5 seconds 809 ms > > > Regards, > Ashic. > -- > From: as...@live.com > To: gerard.m...@gmail.com > CC: user@spark.apache.org > Subject: RE: Are these numbers abnormal for spark streaming? > Date: Thu, 22 Jan 2015 12:32:05 + > > > Hi Gerard, > Thanks for the response. > > The messages get desrialised from msgpack format, and one of the strings > is desrialised to json. Certain fields are checked to decide if further > processing is required. If so, it goes through a series of in mem filters > to check if more processing is required. If so, only then does the "heavy" > work start. That consists of a few db queries, and potential updates to the > db + message on message queue. The majority of messages don't need > processing. The messages needing processing at peak are about three every > other second. > > One possible things that might be happening is the session initialisation > and prepared statement initialisation for each partition. I can resort to > some tricks, but I think I'll try increasing batch interval to 15 seconds. > I'll report back with findings. > > Thanks, > Ashic. > > -- > From: gerard.m...@gmail.com > Date: Thu, 22 Jan 2015 12:30:08 +0100 > Subject: Re: Are these numbers abnormal for spark streaming? > To: tathagata.das1...@gmail.com > CC: as...@live.com; t...@databricks.com; user@spark.apache.org > > and post the code (if possible). > In a nutshell, your processing time > batch interval, resulting in an > ever-increasing delay that will end up in a crash. > 3 secs to process 14 messages looks like a lot. Curious what the job logic > is. > > -kr, Gerard. > > On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das < > tathagata.das1...@gmail.com> wrote: > > This is not normal. Its a huge scheduling delay!! Can you tell me more > about the application? > - cluser setup, number of receivers, whats the computation, etc. > > On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote: > > Hate to do this...but...erm...bump? Would really appreciate input from > others using Streaming. Or at least some docs that would tell me if these > are expected or not. > > -- > From: as...@live.com > To: user@spark.apache.org > Subject: Are these numbers abnormal for spark streaming? > Date: Wed, 21 Jan 2015 11:26:31 + > > > Hi Guys, > I've got Spark Streaming set up for a low data rate system (using spark's > features for analysis, rather than high throughput). Messages are coming in > throughout the day, at around 1-20 per second (finger in the air > estimate...not analysed yet). In the spark streaming UI for the > application, I'm getting the following after 17 hours. > > Streaming > >- *Started at: *Tue Jan 20 16:58:43 GMT 2015 >- *Time since start: *18 hours 24 minutes 34 seconds >- *Network receivers: *2 >- *Batch interval: *2 seconds >- *Processed batches: *16482
RE: Are these numbers abnormal for spark streaming?
Hi Guys, So I changed the interval to 15 seconds. There's obviously a lot more messages per batch, but (I think) it looks a lot healthier. Can you see any major warning signs? I think that with 2 second intervals, the setup / teardown per partition was what was causing the delays. StreamingStarted at: Thu Jan 22 13:23:12 GMT 2015Time since start: 1 hour 17 minutes 16 secondsNetwork receivers: 2Batch interval: 15 secondsProcessed batches: 309Waiting batches: 0 Statistics over last 100 processed batchesReceiver StatisticsReceiverStatusLocationRecords in last batch[2015/01/22 14:40:29]Minimum rate[records/sec]Median rate[records/sec]Maximum rate[records/sec]Last ErrorRmqReceiver-0ACTIVEVDCAPP53.foo.local2.6 K29106295-RmqReceiver-1ACTIVEVDCAPP50.bar.local2.6 K29107291-Batch Processing StatisticsMetricLast batchMinimum25th percentileMedian75th percentileMaximumProcessing Time4 seconds 812 ms4 seconds 698 ms4 seconds 738 ms4 seconds 761 ms4 seconds 788 ms5 seconds 802 msScheduling Delay2 ms0 ms3 ms3 ms4 ms9 msTotal Delay4 seconds 814 ms4 seconds 701 ms4 seconds 739 ms4 seconds 764 ms4 seconds 792 ms5 seconds 809 ms Regards, Ashic. From: as...@live.com To: gerard.m...@gmail.com CC: user@spark.apache.org Subject: RE: Are these numbers abnormal for spark streaming? Date: Thu, 22 Jan 2015 12:32:05 + Hi Gerard, Thanks for the response. The messages get desrialised from msgpack format, and one of the strings is desrialised to json. Certain fields are checked to decide if further processing is required. If so, it goes through a series of in mem filters to check if more processing is required. If so, only then does the "heavy" work start. That consists of a few db queries, and potential updates to the db + message on message queue. The majority of messages don't need processing. The messages needing processing at peak are about three every other second. One possible things that might be happening is the session initialisation and prepared statement initialisation for each partition. I can resort to some tricks, but I think I'll try increasing batch interval to 15 seconds. I'll report back with findings. Thanks, Ashic. From: gerard.m...@gmail.com Date: Thu, 22 Jan 2015 12:30:08 +0100 Subject: Re: Are these numbers abnormal for spark streaming? To: tathagata.das1...@gmail.com CC: as...@live.com; t...@databricks.com; user@spark.apache.org and post the code (if possible).In a nutshell, your processing time > batch interval, resulting in an ever-increasing delay that will end up in a crash. 3 secs to process 14 messages looks like a lot. Curious what the job logic is. -kr, Gerard. On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das wrote: This is not normal. Its a huge scheduling delay!! Can you tell me more about the application?- cluser setup, number of receivers, whats the computation, etc. On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote: Hate to do this...but...erm...bump? Would really appreciate input from others using Streaming. Or at least some docs that would tell me if these are expected or not. From: as...@live.com To: user@spark.apache.org Subject: Are these numbers abnormal for spark streaming? Date: Wed, 21 Jan 2015 11:26:31 + Hi Guys, I've got Spark Streaming set up for a low data rate system (using spark's features for analysis, rather than high throughput). Messages are coming in throughout the day, at around 1-20 per second (finger in the air estimate...not analysed yet). In the spark streaming UI for the application, I'm getting the following after 17 hours. StreamingStarted at: Tue Jan 20 16:58:43 GMT 2015Time since start: 18 hours 24 minutes 34 secondsNetwork receivers: 2Batch interval: 2 secondsProcessed batches: 16482Waiting batches: 1 Statistics over last 100 processed batchesReceiver StatisticsReceiverStatusLocationRecords in last batch[2015/01/21 11:23:18]Minimum rate[records/sec]Median rate[records/sec]Maximum rate[records/sec]Last ErrorRmqReceiver-0ACTIVEF 144727-RmqReceiver-1ACTIVEBR 124726-Batch Processing StatisticsMetricLast batchMinimum25th percentileMedian75th percentileMaximumProcessing Time3 seconds 994 ms157 ms4 seconds 16 ms4 seconds 961 ms5 seconds 3 ms5 seconds 171 msScheduling Delay9 hours 15 minutes 4 seconds9 hours 10 minutes 54 seconds9 hours 11 minutes 56 seconds9 hours 12 minutes 57 seconds9 hours 14 minutes 5 seconds9 hours 15 minutes 4 secondsTotal Delay9 hours 15 minutes 8 seconds9 hours 10 minutes 58 seconds9 hours 12 minutes9 hours 13 minutes 2 seconds9 hours 14 minutes 10 seconds9 hours 15 minutes 8 seconds Are these "normal". I was wondering what the scheduling delay and total delay terms are, and if it's normal for them to be 9 hours. I've got a standalone spark master and 4 spark nodes. The streaming app has been given 4 cores, and it's using 1 core per worker node. The streaming app is submitted from a 5th
RE: Are these numbers abnormal for spark streaming?
Hi Gerard, Thanks for the response. The messages get desrialised from msgpack format, and one of the strings is desrialised to json. Certain fields are checked to decide if further processing is required. If so, it goes through a series of in mem filters to check if more processing is required. If so, only then does the "heavy" work start. That consists of a few db queries, and potential updates to the db + message on message queue. The majority of messages don't need processing. The messages needing processing at peak are about three every other second. One possible things that might be happening is the session initialisation and prepared statement initialisation for each partition. I can resort to some tricks, but I think I'll try increasing batch interval to 15 seconds. I'll report back with findings. Thanks, Ashic. From: gerard.m...@gmail.com Date: Thu, 22 Jan 2015 12:30:08 +0100 Subject: Re: Are these numbers abnormal for spark streaming? To: tathagata.das1...@gmail.com CC: as...@live.com; t...@databricks.com; user@spark.apache.org and post the code (if possible).In a nutshell, your processing time > batch interval, resulting in an ever-increasing delay that will end up in a crash. 3 secs to process 14 messages looks like a lot. Curious what the job logic is. -kr, Gerard. On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das wrote: This is not normal. Its a huge scheduling delay!! Can you tell me more about the application?- cluser setup, number of receivers, whats the computation, etc. On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote: Hate to do this...but...erm...bump? Would really appreciate input from others using Streaming. Or at least some docs that would tell me if these are expected or not. From: as...@live.com To: user@spark.apache.org Subject: Are these numbers abnormal for spark streaming? Date: Wed, 21 Jan 2015 11:26:31 + Hi Guys, I've got Spark Streaming set up for a low data rate system (using spark's features for analysis, rather than high throughput). Messages are coming in throughout the day, at around 1-20 per second (finger in the air estimate...not analysed yet). In the spark streaming UI for the application, I'm getting the following after 17 hours. StreamingStarted at: Tue Jan 20 16:58:43 GMT 2015Time since start: 18 hours 24 minutes 34 secondsNetwork receivers: 2Batch interval: 2 secondsProcessed batches: 16482Waiting batches: 1 Statistics over last 100 processed batchesReceiver StatisticsReceiverStatusLocationRecords in last batch[2015/01/21 11:23:18]Minimum rate[records/sec]Median rate[records/sec]Maximum rate[records/sec]Last ErrorRmqReceiver-0ACTIVEF 144727-RmqReceiver-1ACTIVEBR 124726-Batch Processing StatisticsMetricLast batchMinimum25th percentileMedian75th percentileMaximumProcessing Time3 seconds 994 ms157 ms4 seconds 16 ms4 seconds 961 ms5 seconds 3 ms5 seconds 171 msScheduling Delay9 hours 15 minutes 4 seconds9 hours 10 minutes 54 seconds9 hours 11 minutes 56 seconds9 hours 12 minutes 57 seconds9 hours 14 minutes 5 seconds9 hours 15 minutes 4 secondsTotal Delay9 hours 15 minutes 8 seconds9 hours 10 minutes 58 seconds9 hours 12 minutes9 hours 13 minutes 2 seconds9 hours 14 minutes 10 seconds9 hours 15 minutes 8 seconds Are these "normal". I was wondering what the scheduling delay and total delay terms are, and if it's normal for them to be 9 hours. I've got a standalone spark master and 4 spark nodes. The streaming app has been given 4 cores, and it's using 1 core per worker node. The streaming app is submitted from a 5th machine, and that machine has nothing but the driver running. The worker nodes are running alongside Cassandra (and reading and writing to it). Any insights would be appreciated. Regards, Ashic.
RE: Are these numbers abnormal for spark streaming?
Hi TD,
Here's some information:
1. Cluster has one standalone master, 4 workers. Workers are co-hosted with
Apache Cassandra. Master is set up with external Zookeeper.
2. Each machine has 2 cores and 4GB of ram. This is for testing. All machines
are vmware vms. Spark has 2GB dedicated to it on each node.
3. In addition to the streaming details, the master details as of now are given
below. Only the streaming app is running.
4. I'm listening to two rabbitmq queues using a rabbitmq receiver (code:
https://gist.github.com/ashic/b5edc7cfdc85aa60b066 ). Notifier code is here
https://gist.github.com/ashic/9abd352c691eafc8c9f3
5. The receivers are initialised with the following code:
val ssc = new StreamingContext(sc, Seconds(2))
val messages1 = ssc.receiverStream(new RmqReceiver("abc", "abc", "/",
"vdclog03", "abc_input", None))
val messages2 = ssc.receiverStream(new RmqReceiver("abc", "abc", "/",
"vdclog04", "abc_input", None))
val messages = messages1.union(messages2)
val notifier = new RabbitMQEventNotifier("vdclog03", "abc",
"abc_output_events", "abc", "abc", "/")
6. Usage:
messages.map(x => ScalaMessagePack.read[RadioMessage](x))
.flatMap(InputMessageParser.parse(_).getEvents())
.foreachRDD(x => {
x.foreachPartition(x => {
cassandraConnector.withSessionDo(session =>{
val graphStorage = new CassandraGraphStorage(session)
val notificationStorage = new CassandraNotificationStorage(session)
val savingNotifier = new SavingNotifier(notifier, notificationStorage)
x.foreach(eventWrapper => eventWrapper.event match {
//do some queries.
// save some stuff if needed to cassandra
//raise a message to a separate queue with a msg => Unit()
operation.
7. The algorithm is simple: listen to messages from two separate rmq queues.
union them. for each message, check message properties.
if needed, query cassandra for additional details (graph search..but done in
0.5-3 seconds...and rare..shouldn't overwhelm with low input rate).
If needed, save some info back into cassandra (1-2ms), and raise an event to
the notifier.
I'm probably missing something basic, just wondering what. It has been running
fine for about 42 hours now, but the numbers are a tad worrying.
Cheers,
Ashic.
Workers: 4Cores: 8 Total, 4 UsedMemory: 8.0 GB Total, 2000.0 MB
UsedApplications: 1 Running, 0 CompletedDrivers: 0 Running, 0 CompletedStatus:
ALIVEWorkersIdAddressStateCoresMemoryworker-20141208131918-VDCAPP50.AAA.local-44476VDCAPP50.AAA.local:44476ALIVE2
(1 Used)2.0 GB (500.0 MB
Used)worker-20141208132012-VDCAPP52.AAA.local-34349VDCAPP52.AAA.local:34349ALIVE2
(1 Used)2.0 GB (500.0 MB
Used)worker-20141208132136-VDCAPP53.AAA.local-54000VDCAPP53.AAA.local:54000ALIVE2
(1 Used)2.0 GB (500.0 MB
Used)worker-2014121627-VDCAPP49.AAA.local-57899VDCAPP49.AAA.local:57899ALIVE2
(1 Used)2.0 GB (500.0 MB Used)Running ApplicationsIDNameCoresMemory per
NodeSubmitted TimeUserStateDurationapp-20150120165844-0005App1
4500.0 MB2015/01/20 16:58:44rootWAITING42.4 h
From: tathagata.das1...@gmail.com
Date: Thu, 22 Jan 2015 03:15:58 -0800
Subject: Re: Are these numbers abnormal for spark streaming?
To: as...@live.com; t...@databricks.com
CC: user@spark.apache.org
This is not normal. Its a huge scheduling delay!! Can you tell me more about
the application?- cluser setup, number of receivers, whats the computation, etc.
On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote:
Hate to do this...but...erm...bump? Would really appreciate input from others
using Streaming. Or at least some docs that would tell me if these are expected
or not.
From: as...@live.com
To: user@spark.apache.org
Subject: Are these numbers abnormal for spark streaming?
Date: Wed, 21 Jan 2015 11:26:31 +
Hi Guys,
I've got Spark Streaming set up for a low data rate system (using spark's
features for analysis, rather than high throughput). Messages are coming in
throughout the day, at around 1-20 per second (finger in the air estimate...not
analysed yet). In the spark streaming UI for the application, I'm getting the
following after 17 hours.
StreamingStarted at: Tue Jan 20 16:58:43 GMT 2015Time since start: 18 hours 24
minutes 34 secondsNetwork receivers: 2Batch interval: 2 secondsProcessed
batches: 16482Waiting batches: 1
Statistics over last 100 processed batchesReceiver
StatisticsReceiverStatusLocationRecords in last batch[2015/01/21
11:23:18]Minimum rate[records/sec]Median rate[records/sec]Maximum
rate[records/sec]Last ErrorRmqReceiver-0ACTIVEF
144727-RmqReceiver-1ACTIVEBR
124726-Batch Processing StatisticsMetricLast batchMinimum25th
percentileMedian75th percentileMaximumProcessing Time3 seconds 994 ms157 ms4
seconds
Re: Are these numbers abnormal for spark streaming?
and post the code (if possible). In a nutshell, your processing time > batch interval, resulting in an ever-increasing delay that will end up in a crash. 3 secs to process 14 messages looks like a lot. Curious what the job logic is. -kr, Gerard. On Thu, Jan 22, 2015 at 12:15 PM, Tathagata Das wrote: > This is not normal. Its a huge scheduling delay!! Can you tell me more > about the application? > - cluser setup, number of receivers, whats the computation, etc. > > On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote: > >> Hate to do this...but...erm...bump? Would really appreciate input from >> others using Streaming. Or at least some docs that would tell me if these >> are expected or not. >> >> -- >> From: as...@live.com >> To: user@spark.apache.org >> Subject: Are these numbers abnormal for spark streaming? >> Date: Wed, 21 Jan 2015 11:26:31 + >> >> >> Hi Guys, >> I've got Spark Streaming set up for a low data rate system (using spark's >> features for analysis, rather than high throughput). Messages are coming in >> throughout the day, at around 1-20 per second (finger in the air >> estimate...not analysed yet). In the spark streaming UI for the >> application, I'm getting the following after 17 hours. >> >> Streaming >> >>- *Started at: *Tue Jan 20 16:58:43 GMT 2015 >>- *Time since start: *18 hours 24 minutes 34 seconds >>- *Network receivers: *2 >>- *Batch interval: *2 seconds >>- *Processed batches: *16482 >>- *Waiting batches: *1 >> >> >> >> Statistics over last 100 processed batchesReceiver Statistics >> >>- Receiver >> >> >>- Status >> >> >>- Location >> >> >>- Records in last batch >>- [2015/01/21 11:23:18] >> >> >>- Minimum rate >>- [records/sec] >> >> >>- Median rate >>- [records/sec] >> >> >>- Maximum rate >>- [records/sec] >> >> >>- Last Error >> >> RmqReceiver-0ACTIVEF >> 144727-RmqReceiver-1ACTIVEBR >> 124726- >> Batch Processing Statistics >> >>MetricLast batchMinimum25th percentileMedian75th >> percentileMaximumProcessing >>Time3 seconds 994 ms157 ms4 seconds 16 ms4 seconds 961 ms5 seconds 3 >>ms5 seconds 171 msScheduling Delay9 hours 15 minutes 4 seconds9 hours >>10 minutes 54 seconds9 hours 11 minutes 56 seconds9 hours 12 minutes >>57 seconds9 hours 14 minutes 5 seconds9 hours 15 minutes 4 secondsTotal >>Delay9 hours 15 minutes 8 seconds9 hours 10 minutes 58 seconds9 hours >>12 minutes9 hours 13 minutes 2 seconds9 hours 14 minutes 10 seconds9 >>hours 15 minutes 8 seconds >> >> >> Are these "normal". I was wondering what the scheduling delay and total >> delay terms are, and if it's normal for them to be 9 hours. >> >> I've got a standalone spark master and 4 spark nodes. The streaming app >> has been given 4 cores, and it's using 1 core per worker node. The >> streaming app is submitted from a 5th machine, and that machine has nothing >> but the driver running. The worker nodes are running alongside Cassandra >> (and reading and writing to it). >> >> Any insights would be appreciated. >> >> Regards, >> Ashic. >> > >
Re: Are these numbers abnormal for spark streaming?
This is not normal. Its a huge scheduling delay!! Can you tell me more about the application? - cluser setup, number of receivers, whats the computation, etc. On Thu, Jan 22, 2015 at 3:11 AM, Ashic Mahtab wrote: > Hate to do this...but...erm...bump? Would really appreciate input from > others using Streaming. Or at least some docs that would tell me if these > are expected or not. > > -- > From: as...@live.com > To: user@spark.apache.org > Subject: Are these numbers abnormal for spark streaming? > Date: Wed, 21 Jan 2015 11:26:31 + > > > Hi Guys, > I've got Spark Streaming set up for a low data rate system (using spark's > features for analysis, rather than high throughput). Messages are coming in > throughout the day, at around 1-20 per second (finger in the air > estimate...not analysed yet). In the spark streaming UI for the > application, I'm getting the following after 17 hours. > > Streaming > >- *Started at: *Tue Jan 20 16:58:43 GMT 2015 >- *Time since start: *18 hours 24 minutes 34 seconds >- *Network receivers: *2 >- *Batch interval: *2 seconds >- *Processed batches: *16482 >- *Waiting batches: *1 > > > > Statistics over last 100 processed batchesReceiver Statistics > >- Receiver > > >- Status > > >- Location > > >- Records in last batch >- [2015/01/21 11:23:18] > > >- Minimum rate >- [records/sec] > > >- Median rate >- [records/sec] > > >- Maximum rate >- [records/sec] > > >- Last Error > > RmqReceiver-0ACTIVEF > 144727-RmqReceiver-1ACTIVEBR > 124726- > Batch Processing Statistics > >MetricLast batchMinimum25th percentileMedian75th > percentileMaximumProcessing >Time3 seconds 994 ms157 ms4 seconds 16 ms4 seconds 961 ms5 seconds 3 ms5 >seconds 171 msScheduling Delay9 hours 15 minutes 4 seconds9 hours 10 >minutes 54 seconds9 hours 11 minutes 56 seconds9 hours 12 minutes 57 >seconds9 hours 14 minutes 5 seconds9 hours 15 minutes 4 secondsTotal >Delay9 hours 15 minutes 8 seconds9 hours 10 minutes 58 seconds9 hours >12 minutes9 hours 13 minutes 2 seconds9 hours 14 minutes 10 seconds9 >hours 15 minutes 8 seconds > > > Are these "normal". I was wondering what the scheduling delay and total > delay terms are, and if it's normal for them to be 9 hours. > > I've got a standalone spark master and 4 spark nodes. The streaming app > has been given 4 cores, and it's using 1 core per worker node. The > streaming app is submitted from a 5th machine, and that machine has nothing > but the driver running. The worker nodes are running alongside Cassandra > (and reading and writing to it). > > Any insights would be appreciated. > > Regards, > Ashic. >
RE: Are these numbers abnormal for spark streaming?
Hate to do this...but...erm...bump? Would really appreciate input from others using Streaming. Or at least some docs that would tell me if these are expected or not. From: as...@live.com To: user@spark.apache.org Subject: Are these numbers abnormal for spark streaming? Date: Wed, 21 Jan 2015 11:26:31 + Hi Guys, I've got Spark Streaming set up for a low data rate system (using spark's features for analysis, rather than high throughput). Messages are coming in throughout the day, at around 1-20 per second (finger in the air estimate...not analysed yet). In the spark streaming UI for the application, I'm getting the following after 17 hours. StreamingStarted at: Tue Jan 20 16:58:43 GMT 2015Time since start: 18 hours 24 minutes 34 secondsNetwork receivers: 2Batch interval: 2 secondsProcessed batches: 16482Waiting batches: 1 Statistics over last 100 processed batchesReceiver StatisticsReceiverStatusLocationRecords in last batch[2015/01/21 11:23:18]Minimum rate[records/sec]Median rate[records/sec]Maximum rate[records/sec]Last ErrorRmqReceiver-0ACTIVEF 144727-RmqReceiver-1ACTIVEBR 124726-Batch Processing StatisticsMetricLast batchMinimum25th percentileMedian75th percentileMaximumProcessing Time3 seconds 994 ms157 ms4 seconds 16 ms4 seconds 961 ms5 seconds 3 ms5 seconds 171 msScheduling Delay9 hours 15 minutes 4 seconds9 hours 10 minutes 54 seconds9 hours 11 minutes 56 seconds9 hours 12 minutes 57 seconds9 hours 14 minutes 5 seconds9 hours 15 minutes 4 secondsTotal Delay9 hours 15 minutes 8 seconds9 hours 10 minutes 58 seconds9 hours 12 minutes9 hours 13 minutes 2 seconds9 hours 14 minutes 10 seconds9 hours 15 minutes 8 seconds Are these "normal". I was wondering what the scheduling delay and total delay terms are, and if it's normal for them to be 9 hours. I've got a standalone spark master and 4 spark nodes. The streaming app has been given 4 cores, and it's using 1 core per worker node. The streaming app is submitted from a 5th machine, and that machine has nothing but the driver running. The worker nodes are running alongside Cassandra (and reading and writing to it). Any insights would be appreciated. Regards, Ashic.
