Re: Spark Streaming timing considerations
Hi TD,
Thanks for the help.
The only problem left here is that the dstreamTime contains some extra
information which seems date i.e. 1405944367000 ms whereas my application
timestamps are just in sec which I converted
to ms. e.g. 2300, 2400, 2500 etc. So the filter doesn't take effect.
I was thinking to add that extra info to my Time(4000). But I am not really
sure what it is?
val keyAndValues = eegStreams.map(x= {
val token = x.split( )
((token(0).toDouble *
1000).toLong,token(1).toDouble)
})
val transformed =
keyAndValues.window(Seconds(8),Seconds(4)).transform((windowedRDD, dstreamTime)
= {
val currentAppTimeWindowStart = dstreamTime - Time(4000) //
define the window over the timestamp that you want to process
val currentAppTimeWindowEnd = dstreamTime
val filteredRDD = windowedRDD.filter(r = Duration(r._1)
currentAppTimeWindowStart Time(r._1) = currentAppTimeWindowEnd)
filteredRDD
})
The sample input is as under
AppTimestamp Datapoints
0 -145.934066
0.003906 0.19536
0.007812 0.19536
0.011719 0.19536
0.015625 0.19536
0.019531 0.976801
0.023438 0.586081
0.027344 -1.758242
0.03125 -1.367521
0.035156 2.930403
0.039062 4.102564
0.042969 3.711844
0.046875 2.148962
0.050781 -4.102564
0.054688 -1.758242
0.058594 3.711844
0.0625 9.181929
0.066406 11.135531
0.070312 4.884005
0.074219 0.976801
0.078125 4.493284
0.082031 11.135531
0.085938 12.698413
0.089844 15.824176
0.09375 21.684982
0.097656 22.466422
0.101562 18.949939
0.105469 14.652015
0.109375 11.135531
0.113281 1.758242
0.117188 -6.056166
0.121094 -0.976801
0.125 0.19536
0.128906 -6.837607
0.132812 -8.400488
0.136719 -14.261294
0.140625 -24.810745
0.144531 -25.592186
0.148438 -19.73138
0.152344 -18.559219
0.15625 -25.201465
Regards,
Laeeq
On Thursday, July 17, 2014 8:58 PM, Tathagata Das tathagata.das1...@gmail.com
wrote:
You have to define what is the range records that needs to be filtered out in
every windowed RDD, right? For example, when the DStream.window has data from
from times 0 - 8 seconds by DStream time, you only want to filter out data that
falls into say 4 - 8 seconds by application time. This latter is the
application-level time window that you need to define in the transform
function. What may help is that there is another version of transform which
allows you to get the current DStream time (that is, it will give the value
8) from which you can calculate the app-time-window 4 - 8.
val transformed = keyAndValues.window(Seconds(8),
Seconds(4)).transform((windowedRDD: RDD[...], dstreamTime: Time) = {
val currentAppTimeWindowStart = dstreamTime - appTimeWindowSize
// define the window over the timestamp that you want to process
val currentAppTimeWindowEnd = dstreamTime
val filteredRDD = windowedRDD.filter(r = r._1 = currentAppTimeWindowEnd
r._1 currentAppTimeWindowStart) // filter and retain only the records
that fall in the current app-time window
return filteredRDD
})
Hope this helps!
TD
On Thu, Jul 17, 2014 at 5:54 AM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi TD,
I have been able to filter the first WindowedRDD, but I am not sure how to
make a generic filter. The larger window is 8 seconds and want to fetch 4
second based on application-time-stamp. I have seen an earlier post which
suggest timeStampBasedwindow but I am not sure how to make
timestampBasedwindow in the following example.
val transformed = keyAndValues.window(Seconds(8),
Seconds(4)).transform(windowedRDD = {
//val timeStampBasedWindow = ??? // define the window over
the timestamp that you want to process
val filteredRDD = windowedRDD.filter(_._1 4) // filter and retain only
the records that fall in the timestamp-based window
return filteredRDD
})
Consider the input tuples as (1,23),(1.2,34) . . . . . (3.8,54)(4,413) . . .
whereas key is the timestamp.
Regards,
Laeeq
On Saturday, July 12, 2014 8:29 PM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi,
Thanks I will try to implement it.
Regards,
Laeeq
On Saturday, July 12, 2014 4:37 AM, Tathagata Das
tathagata.das1...@gmail.com wrote:
This is not in the current streaming API.
Queue stream is useful for testing with generated RDDs, but not for actual
data. For actual data stream, the slack time can be implemented by doing
DStream.window on a larger window that take slack time in consideration, and
then the required application-time-based-window of data filtered out. For
example, if you want a slack time of 1 minute and batches of 10 seconds, then
do a window operation of 70 seconds, then in each RDD filter out the records
with the desired application time and process them.
TD
On Fri, Jul 11, 2014 at 7:44 AM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi,
In the
Re: Spark Streaming timing considerations
That is just standard Unix time. 1405944367000 = Sun, 09 Aug 46522 05:56:40 GMT On Mon, Jul 21, 2014 at 5:43 PM, Laeeq Ahmed laeeqsp...@yahoo.com wrote: Hi TD, Thanks for the help. The only problem left here is that the dstreamTime contains some extra information which seems date i.e. 1405944367000 ms whereas my application timestamps are just in sec which I converted to ms. e.g. 2300, 2400, 2500 etc. So the filter doesn't take effect. I was thinking to add that extra info to my Time(4000). But I am not really sure what it is?
Re: Spark Streaming timing considerations
Uh, right. I mean: 1405944367 = Mon, 21 Jul 2014 12:06:07 GMT On Mon, Jul 21, 2014 at 5:47 PM, Sean Owen so...@cloudera.com wrote: That is just standard Unix time. 1405944367000 = Sun, 09 Aug 46522 05:56:40 GMT On Mon, Jul 21, 2014 at 5:43 PM, Laeeq Ahmed laeeqsp...@yahoo.com wrote: Hi TD, Thanks for the help. The only problem left here is that the dstreamTime contains some extra information which seems date i.e. 1405944367000 ms whereas my application timestamps are just in sec which I converted to ms. e.g. 2300, 2400, 2500 etc. So the filter doesn't take effect. I was thinking to add that extra info to my Time(4000). But I am not really sure what it is?
Re: Spark Streaming timing considerations
You will have to use some function that converts the dstreamTime (ms since epoch, same format as returned by System.currentTimeMillis), and your application-level time. TD On Mon, Jul 21, 2014 at 9:47 AM, Sean Owen so...@cloudera.com wrote: Uh, right. I mean: 1405944367 = Mon, 21 Jul 2014 12:06:07 GMT On Mon, Jul 21, 2014 at 5:47 PM, Sean Owen so...@cloudera.com wrote: That is just standard Unix time. 1405944367000 = Sun, 09 Aug 46522 05:56:40 GMT On Mon, Jul 21, 2014 at 5:43 PM, Laeeq Ahmed laeeqsp...@yahoo.com wrote: Hi TD, Thanks for the help. The only problem left here is that the dstreamTime contains some extra information which seems date i.e. 1405944367000 ms whereas my application timestamps are just in sec which I converted to ms. e.g. 2300, 2400, 2500 etc. So the filter doesn't take effect. I was thinking to add that extra info to my Time(4000). But I am not really sure what it is?
Re: Spark Streaming timing considerations
Hi TD,
I have been able to filter the first WindowedRDD, but I am not sure how to make
a generic filter. The larger window is 8 seconds and want to fetch 4 second
based on application-time-stamp. I have seen an earlier post which suggest
timeStampBasedwindow but I am not sure how to make timestampBasedwindow in the
following example.
val transformed = keyAndValues.window(Seconds(8),
Seconds(4)).transform(windowedRDD = {
//val timeStampBasedWindow = ??? // define the window over
the timestamp that you want to process
val filteredRDD = windowedRDD.filter(_._1 4) // filter and retain only
the records that fall in the timestamp-based window
return filteredRDD
})
Consider the input tuples as (1,23),(1.2,34) . . . . . (3.8,54)(4,413) . . .
whereas key is the timestamp.
Regards,
Laeeq
On Saturday, July 12, 2014 8:29 PM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi,
Thanks I will try to implement it.
Regards,
Laeeq
On Saturday, July 12, 2014 4:37 AM, Tathagata Das
tathagata.das1...@gmail.com wrote:
This is not in the current streaming API.
Queue stream is useful for testing with generated RDDs, but not for actual
data. For actual data stream, the slack time can be implemented by doing
DStream.window on a larger window that take slack time in consideration, and
then the required application-time-based-window of data filtered out. For
example, if you want a slack time of 1 minute and batches of 10 seconds, then
do a window operation of 70 seconds, then in each RDD filter out the records
with the desired application time and process them.
TD
On Fri, Jul 11, 2014 at 7:44 AM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi,
In the spark streaming paper, slack time has been suggested for delaying the
batch creation in case of external timestamps. I don't see any such option in
streamingcontext. Is it available in the API?
Also going through the previous posts, queueStream has been suggested for
this. I looked into to queueStream example.
// Create and push some RDDs into Queue
for (i - 1 to 30) {
rddQueue += ssc.sparkContext.makeRDD(1 to 10)
Thread.sleep(1000)
}
The only thing I am unsure is how to make batches(basic RDD) out of stream
coming on a port.
Regards,
Laeeq
Re: Spark Streaming timing considerations
You have to define what is the range records that needs to be filtered out
in every windowed RDD, right? For example, when the DStream.window has data
from from times 0 - 8 seconds by DStream time, you only want to filter out
data that falls into say 4 - 8 seconds by application time. This latter is
the application-level time window that you need to define in the transform
function. What may help is that there is another version of transform which
allows you to get the current DStream time (that is, it will give the value
8) from which you can calculate the app-time-window 4 - 8.
val transformed = keyAndValues.window(Seconds(8),
Seconds(4)).transform((windowedRDD:
RDD[...], dstreamTime: Time) = {
val currentAppTimeWindowStart = dstreamTime - appTimeWindowSize
// define the window over the timestamp that you want to process
val currentAppTimeWindowEnd = dstreamTime
val filteredRDD = windowedRDD.filter(r = r._1 = currentAppTimeWindowEnd
r._1 currentAppTimeWindowStart) // filter and retain only the
records that fall in the current app-time window
return filteredRDD
})
Hope this helps!
TD
On Thu, Jul 17, 2014 at 5:54 AM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi TD,
I have been able to filter the first WindowedRDD, but I am not sure how
to make a generic filter. The larger window is 8 seconds and want to
fetch 4 second based on application-time-stamp. I have seen an earlier post
which suggest timeStampBasedwindow but I am not sure how to make
timestampBasedwindow in the following example.
val transformed = keyAndValues.window(Seconds(8),
Seconds(4)).transform(windowedRDD = {
//val timeStampBasedWindow = ???// define the window
over the timestamp that you want to process
val filteredRDD = windowedRDD.filter(_._1 4) // filter and retain
only the records that fall in the timestamp-based window
return filteredRDD
})
Consider the input tuples as (1,23),(1.2,34) . . . . . (3.8,54)(4,413) . .
. whereas key is the timestamp.
Regards,
Laeeq
On Saturday, July 12, 2014 8:29 PM, Laeeq Ahmed laeeqsp...@yahoo.com
wrote:
Hi,
Thanks I will try to implement it.
Regards,
Laeeq
On Saturday, July 12, 2014 4:37 AM, Tathagata Das
tathagata.das1...@gmail.com wrote:
This is not in the current streaming API.
Queue stream is useful for testing with generated RDDs, but not for actual
data. For actual data stream, the slack time can be implemented by doing
DStream.window on a larger window that take slack time in consideration,
and then the required application-time-based-window of data filtered out.
For example, if you want a slack time of 1 minute and batches of 10
seconds, then do a window operation of 70 seconds, then in each RDD filter
out the records with the desired application time and process them.
TD
On Fri, Jul 11, 2014 at 7:44 AM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi,
In the spark streaming paper, slack time has been suggested for delaying
the batch creation in case of external timestamps. I don't see any such
option in streamingcontext. Is it available in the API?
Also going through the previous posts, queueStream has been suggested for
this. I looked into to queueStream example.
// Create and push some RDDs into Queue
for (i - 1 to 30) {
rddQueue += ssc.sparkContext.makeRDD(1 to 10)
Thread.sleep(1000)
}
The only thing I am unsure is how to make batches(basic RDD) out of stream
coming on a port.
Regards,
Laeeq
Spark Streaming timing considerations
Hi,
In the spark streaming paper, slack time has been suggested for delaying the
batch creation in case of external timestamps. I don't see any such option in
streamingcontext. Is it available in the API?
Also going through the previous posts, queueStream has been suggested for this.
I looked into to queueStream example.
// Create and push some RDDs into Queue
for (i - 1 to 30) {
rddQueue += ssc.sparkContext.makeRDD(1 to 10)
Thread.sleep(1000)
}
The only thing I am unsure is how to make batches(basic RDD) out of stream
coming on a port.
Regards,
Laeeq
Re: Spark Streaming timing considerations
This is not in the current streaming API.
Queue stream is useful for testing with generated RDDs, but not for actual
data. For actual data stream, the slack time can be implemented by doing
DStream.window on a larger window that take slack time in consideration,
and then the required application-time-based-window of data filtered out.
For example, if you want a slack time of 1 minute and batches of 10
seconds, then do a window operation of 70 seconds, then in each RDD filter
out the records with the desired application time and process them.
TD
On Fri, Jul 11, 2014 at 7:44 AM, Laeeq Ahmed laeeqsp...@yahoo.com wrote:
Hi,
In the spark streaming paper, slack time has been suggested for delaying
the batch creation in case of external timestamps. I don't see any such
option in streamingcontext. Is it available in the API?
Also going through the previous posts, queueStream has been suggested for
this. I looked into to queueStream example.
// Create and push some RDDs into Queue
for (i - 1 to 30) {
rddQueue += ssc.sparkContext.makeRDD(1 to 10)
Thread.sleep(1000)
}
The only thing I am unsure is how to make batches(basic RDD) out of stream
coming on a port.
Regards,
Laeeq
