Re: TextIO().Read pipeline implementation question
You have a great fundamental point Lukasz :))But I dont have any other choice but running it in Beam Flink & Spark for bench-marking purposes.Similar research has been done on Storm & Streams & we want to compare Flink & Spark engines under Beam.THIS PROBLEM has been used in the previous work. So ultimately we need to compare apples & apples.That's why :)I wrote a little DataProvider sending the records according to the delayed logic I have below.I send them to KafkaIO(). Have not tested it yet. Kinda late wanna go home. Will start it from home this eve.Maybe the "delay" will make a magic difference...will see... But, without these delays, in the past I always got redundant processing under KafkaIO() as I have posted my questions on that issue in this forum before.Thats why I turned to TextIO() which works fine. However according to everyone suggestions here, I cannt build the Beam Pipeline & include this delay+read step.So am back to KafkaIO()...perhaps with more questions in the near future...Thanks for your help.CheersAmir- From: Lukasz Cwik <lc...@google.com> To: user@beam.incubator.apache.org Cc: amir bahmanyari <amirto...@yahoo.com> Sent: Wednesday, August 24, 2016 4:13 PM Subject: Re: TextIO().Read pipeline implementation question Amir, it seems like your attempting to build a network simulation (https://en.wikipedia.org/wiki/Network_simulation). Are you sure Apache Beam is the right tool for this? On Wed, Aug 24, 2016 at 3:54 PM, Thomas Groh <tg...@google.com> wrote: The Beam model generally is agnostic to the rate at which elements are produced and consumed. Instead, it uses the concept of a watermark to provide a completion metric, and element timestamps to record when an event happened (which is independent of when the event was processed). Your pipeline should be correct regardless of the input rate by using the (data-based) timestamp of arriving elements instead of the time they arrived in the Pipeline. This allows you to describe the output of your Pipeline in terms of the input records (which have associated timestamps) rather than the rate at which input arrived. You can assign timestamps to an existing PCollection using the 'WithTimestamps' PTransform, or create a new PCollection where elements have associated timestamps using the 'Create.timestamped()' PTransform. Some sources will also output elements with a Timestamp already associated with the element (e.g. KafkaIO or PubSubIO). If the sole desire is to rate limit your input, using CountingInput.unbounded(). withRate(Duration) will output elements at a continuous rate to your downstream PCollection. This will output elements over time in such a way that the desired rate is reached. On Wed, Aug 24, 2016 at 3:34 PM, amir bahmanyari <amirto...@yahoo.com> wrote: Thanks for your response Ben.The sleep+read is a part of the problem solution requirements. I know what you mean by why not process them immediately. The problem solution intentionally slows down processing to simulate the traffic in expressway(s).The assumption is that each car in emits a "record" every 30 seconds. Making the story short, at runtime, the behavior I provided below is expected to be implemented to accurately provide a simulated solution.So lets say I want to inject a Sleep(random-seconds) in the pipeline superficially before actually ParDo gets into the action. What are the options to do that?And using TextIO(), how can I buffer the read records by TextIO() while Sleep() is in progress?Thanks for your valuable time. From: Ben Chambers <bchamb...@apache.org> To: user@beam.incubator.apache.org ; amir bahmanyari <amirto...@yahoo.com> Sent: Wednesday, August 24, 2016 3:24 PM Subject: Re: TextIO().Read pipeline implementation question I think the most important question is why do you want to slow down the reads like that? If this is for testing purposes, there may be other options, such as test specific sources. At a high level, the process you describes sounds somewhat like an Unbounded Source, or perhaps an application of the not-yet-built Splittable DoFn (https://s.apache.org/splittab le-do-fn). Even in those cases, "reading 100 records and then sleeping" is normally undesirable because it limits the throughput of the pipeline. If there were 1000 records waiting to be processed, why not process them? In general, a given step doesn't "submit elements to the next step". It just outputs the elements. This is important since there may be two steps that read from that PCollection, meaning thaht there isn't a single ParDo to submit the elements to. -- Ben On Wed, Aug 24, 2016 at 3:12 PM amir bahmanyari <amirto...@yahoo.com> wrote: Hi Dan,Thanks so much for your response.Lets focus on your "The other side" section below.I provided the target process I am trying to implement in
Re: TextIO().Read pipeline implementation question
Amir, it seems like your attempting to build a network simulation ( https://en.wikipedia.org/wiki/Network_simulation). Are you sure Apache Beam is the right tool for this? On Wed, Aug 24, 2016 at 3:54 PM, Thomas Groh <tg...@google.com> wrote: > The Beam model generally is agnostic to the rate at which elements are > produced and consumed. Instead, it uses the concept of a watermark to > provide a completion metric, and element timestamps to record when an event > happened (which is independent of when the event was processed). Your > pipeline should be correct regardless of the input rate by using the > (data-based) timestamp of arriving elements instead of the time they > arrived in the Pipeline. This allows you to describe the output of your > Pipeline in terms of the input records (which have associated timestamps) > rather than the rate at which input arrived. You can assign timestamps to > an existing PCollection using the 'WithTimestamps' PTransform, or create a > new PCollection where elements have associated timestamps using the > 'Create.timestamped()' PTransform. Some sources will also output elements > with a Timestamp already associated with the element (e.g. KafkaIO or > PubSubIO). > > If the sole desire is to rate limit your input, using > CountingInput.unbounded().withRate(Duration) will output elements at a > continuous rate to your downstream PCollection. This will output elements > over time in such a way that the desired rate is reached. > > On Wed, Aug 24, 2016 at 3:34 PM, amir bahmanyari <amirto...@yahoo.com> > wrote: > >> Thanks for your response Ben. >> The sleep+read is a part of the problem solution requirements. I know >> what you mean by why not process them immediately. >> The problem solution intentionally slows down processing to simulate the >> traffic in expressway(s). >> The assumption is that each car in emits a "record" every 30 seconds. >> Making the story short, at runtime, the behavior I provided below is >> expected to be implemented to accurately provide a simulated solution. >> So lets say I want to inject a Sleep(random-seconds) in the pipeline >> superficially before actually ParDo gets into the action. >> What are the options to do that? >> And using TextIO(), how can I buffer the read records by TextIO() while >> Sleep() is in progress? >> Thanks for your valuable time. >> >> >> -- >> *From:* Ben Chambers <bchamb...@apache.org> >> *To:* user@beam.incubator.apache.org; amir bahmanyari < >> amirto...@yahoo.com> >> *Sent:* Wednesday, August 24, 2016 3:24 PM >> >> *Subject:* Re: TextIO().Read pipeline implementation question >> >> I think the most important question is why do you want to slow down the >> reads like that? If this is for testing purposes, there may be other >> options, such as test specific sources. >> >> At a high level, the process you describes sounds somewhat like an >> Unbounded Source, or perhaps an application of the not-yet-built Splittable >> DoFn (https://s.apache.org/splittable-do-fn). >> >> Even in those cases, "reading 100 records and then sleeping" is normally >> undesirable because it limits the throughput of the pipeline. If there were >> 1000 records waiting to be processed, why not process them? >> >> In general, a given step doesn't "submit elements to the next step". It >> just outputs the elements. This is important since there may be two steps >> that read from that PCollection, meaning thaht there isn't a single ParDo >> to submit the elements to. >> >> -- Ben >> >> On Wed, Aug 24, 2016 at 3:12 PM amir bahmanyari <amirto...@yahoo.com> >> wrote: >> >> Hi Dan, >> Thanks so much for your response. >> Lets focus on your "The other side" section below. >> I provided the target process I am trying to implement in my first email >> below. >> According to your "runners do not expose hooks to control how often they >> read records." looks like I am out of luck to achieve that on random >> basis. >> So, am trying to articulate an equivalent read/process as close as >> possible to what I want. >> From the "- Wake-up" step in my algorithm, I should be able to read >> records but no more than 100. >> Lets say I sleep for 150 milliseconds, - Wake-up, and read 100 records >> all at once, and submit it to ParDo DoFn to process one by one. >> How would that pipeline implementation look like? >> Is there an example that shows implementation how to "sleep 150 ms" in >> pipeline,
Re: TextIO().Read pipeline implementation question
The Beam model generally is agnostic to the rate at which elements are produced and consumed. Instead, it uses the concept of a watermark to provide a completion metric, and element timestamps to record when an event happened (which is independent of when the event was processed). Your pipeline should be correct regardless of the input rate by using the (data-based) timestamp of arriving elements instead of the time they arrived in the Pipeline. This allows you to describe the output of your Pipeline in terms of the input records (which have associated timestamps) rather than the rate at which input arrived. You can assign timestamps to an existing PCollection using the 'WithTimestamps' PTransform, or create a new PCollection where elements have associated timestamps using the 'Create.timestamped()' PTransform. Some sources will also output elements with a Timestamp already associated with the element (e.g. KafkaIO or PubSubIO). If the sole desire is to rate limit your input, using CountingInput.unbounded().withRate(Duration) will output elements at a continuous rate to your downstream PCollection. This will output elements over time in such a way that the desired rate is reached. On Wed, Aug 24, 2016 at 3:34 PM, amir bahmanyari <amirto...@yahoo.com> wrote: > Thanks for your response Ben. > The sleep+read is a part of the problem solution requirements. I know what > you mean by why not process them immediately. > The problem solution intentionally slows down processing to simulate the > traffic in expressway(s). > The assumption is that each car in emits a "record" every 30 seconds. > Making the story short, at runtime, the behavior I provided below is > expected to be implemented to accurately provide a simulated solution. > So lets say I want to inject a Sleep(random-seconds) in the pipeline > superficially before actually ParDo gets into the action. > What are the options to do that? > And using TextIO(), how can I buffer the read records by TextIO() while > Sleep() is in progress? > Thanks for your valuable time. > > > -- > *From:* Ben Chambers <bchamb...@apache.org> > *To:* user@beam.incubator.apache.org; amir bahmanyari <amirto...@yahoo.com> > > *Sent:* Wednesday, August 24, 2016 3:24 PM > > *Subject:* Re: TextIO().Read pipeline implementation question > > I think the most important question is why do you want to slow down the > reads like that? If this is for testing purposes, there may be other > options, such as test specific sources. > > At a high level, the process you describes sounds somewhat like an > Unbounded Source, or perhaps an application of the not-yet-built Splittable > DoFn (https://s.apache.org/splittable-do-fn). > > Even in those cases, "reading 100 records and then sleeping" is normally > undesirable because it limits the throughput of the pipeline. If there were > 1000 records waiting to be processed, why not process them? > > In general, a given step doesn't "submit elements to the next step". It > just outputs the elements. This is important since there may be two steps > that read from that PCollection, meaning thaht there isn't a single ParDo > to submit the elements to. > > -- Ben > > On Wed, Aug 24, 2016 at 3:12 PM amir bahmanyari <amirto...@yahoo.com> > wrote: > > Hi Dan, > Thanks so much for your response. > Lets focus on your "The other side" section below. > I provided the target process I am trying to implement in my first email > below. > According to your "runners do not expose hooks to control how often they > read records." looks like I am out of luck to achieve that on random > basis. > So, am trying to articulate an equivalent read/process as close as > possible to what I want. > From the "- Wake-up" step in my algorithm, I should be able to read > records but no more than 100. > Lets say I sleep for 150 milliseconds, - Wake-up, and read 100 records all > at once, and submit it to ParDo DoFn to process one by one. > How would that pipeline implementation look like? > Is there an example that shows implementation how to "sleep 150 ms" in > pipeline, then reading n number of records i.e.100 at once, and then submit > them to ParDo to process one by one pls? > I have tried so many ways to implement it but keep getting weird > compilation errors... > I appreciate your help. > Amir- > > -- > *From:* Dan Halperin <dhalp...@google.com> > *To:* user@beam.incubator.apache.org; amir bahmanyari <amirto...@yahoo.com> > > *Sent:* Wednesday, August 24, 2016 1:42 PM > > *Subject:* Re: TextIO().Read pipeline implementation question > Hi Amir, > > It is very hard to respond withou
Re: TextIO().Read pipeline implementation question
I think the most important question is why do you want to slow down the
reads like that? If this is for testing purposes, there may be other
options, such as test specific sources.
At a high level, the process you describes sounds somewhat like an
Unbounded Source, or perhaps an application of the not-yet-built Splittable
DoFn (https://s.apache.org/splittable-do-fn).
Even in those cases, "reading 100 records and then sleeping" is normally
undesirable because it limits the throughput of the pipeline. If there were
1000 records waiting to be processed, why not process them?
In general, a given step doesn't "submit elements to the next step". It
just outputs the elements. This is important since there may be two steps
that read from that PCollection, meaning thaht there isn't a single ParDo
to submit the elements to.
-- Ben
On Wed, Aug 24, 2016 at 3:12 PM amir bahmanyari <amirto...@yahoo.com> wrote:
> Hi Dan,
> Thanks so much for your response.
> Lets focus on your "The other side" section below.
> I provided the target process I am trying to implement in my first email
> below.
> According to your "runners do not expose hooks to control how often they
> read records." looks like I am out of luck to achieve that on random
> basis.
> So, am trying to articulate an equivalent read/process as close as
> possible to what I want.
> From the "- Wake-up" step in my algorithm, I should be able to read
> records but no more than 100.
> Lets say I sleep for 150 milliseconds, - Wake-up, and read 100 records all
> at once, and submit it to ParDo DoFn to process one by one.
> How would that pipeline implementation look like?
> Is there an example that shows implementation how to "sleep 150 ms" in
> pipeline, then reading n number of records i.e.100 at once, and then submit
> them to ParDo to process one by one pls?
> I have tried so many ways to implement it but keep getting weird
> compilation errors...
> I appreciate your help.
> Amir-
>
> --
> *From:* Dan Halperin <dhalp...@google.com>
> *To:* user@beam.incubator.apache.org; amir bahmanyari <amirto...@yahoo.com>
>
> *Sent:* Wednesday, August 24, 2016 1:42 PM
>
> *Subject:* Re: TextIO().Read pipeline implementation question
> Hi Amir,
>
> It is very hard to respond without sufficient details to reproduce. Can
> you please send a full pipeline that we can test with test data (e.g., the
> LICENSE file), including pipeline options (which runner, etc.)?
>
> The other side -- in general, runners do not expose hooks to control how
> often they read records. If you have something like TextIO.Read |
> ParDo.of(sleep for 1s) you will get 1s sleep per record, but you cannot
> control how this is interleaved with reading. A runner is free to read all
> the records before sleeping, read one record and sleep in a loop, and
> everything in between.
>
> Thanks,
> Dan
> On Tue, Aug 23, 2016 at 5:07 PM, amir bahmanyari <amirto...@yahoo.com>
> wrote:
>
> So here is what happened as a result of inserting Window of random seconds
> buffering in my TextIO().Read & DoFn<>:
> the number of records processed got doubled :-((
> Why is that? Could someone shed light on this pls, I appreciate it very
> much.
> Thanks.
> Amir-
>
> ----------
> *From:* amir bahmanyari <amirto...@yahoo.com>
> *To:* "user@beam.incubator.apache. org <user@beam.incubator.apache.org>"
> <user@beam.incubator.apache.
> org <user@beam.incubator.apache.org>>
> *Sent:* Tuesday, August 23, 2016 4:40 PM
> *Subject:* Re: TextIO().Read pipeline implementation question
>
> Would this implementation work?
> I am thinking to buffer records within a window of random seconds, process
> DoFn
> them as per each record, and repeat another random window seconds length:
>
> p.apply(TextIO.Read.from("/ tmp/LRData.dat")).*apply(
> Window.into( FixedWindows.of(Duration.
> standardSeconds((int)(((15-5) * r.nextDouble()) + 5)*
>
> .apply("PseduLRDoFn", ParDo.of(new DoFn<String, String>() {
>
> Thanks for your help.
> Amir-
>
>
> --
> *From:* amir bahmanyari <amirto...@yahoo.com>
> *To:* "user@beam.incubator.apache. org <user@beam.incubator.apache.org>"
> <user@beam.incubator.apache.
> org <user@beam.incubator.apache.org>>
> *Sent:* Tuesday, August 23, 2016 3:51 PM
> *Subject:* TextIO().Read pipeline implementation question
>
> Hi Colleagues,
> I have no problem reading through TextIO() & processing, all by default
> behavior.
>
> p.apply(TextIO.Read.from("/ tmp/LRData.dat"))
>
Re: TextIO().Read pipeline implementation question
Hi Amir,
It is very hard to respond without sufficient details to reproduce. Can you
please send a full pipeline that we can test with test data (e.g., the
LICENSE file), including pipeline options (which runner, etc.)?
The other side -- in general, runners do not expose hooks to control how
often they read records. If you have something like TextIO.Read |
ParDo.of(sleep for 1s) you will get 1s sleep per record, but you cannot
control how this is interleaved with reading. A runner is free to read all
the records before sleeping, read one record and sleep in a loop, and
everything in between.
Thanks,
Dan
On Tue, Aug 23, 2016 at 5:07 PM, amir bahmanyari <amirto...@yahoo.com>
wrote:
> So here is what happened as a result of inserting Window of random seconds
> buffering in my TextIO().Read & DoFn<>:
> the number of records processed got doubled :-((
> Why is that? Could someone shed light on this pls, I appreciate it very
> much.
> Thanks.
> Amir-
>
> --
> *From:* amir bahmanyari <amirto...@yahoo.com>
> *To:* "user@beam.incubator.apache.org" <user@beam.incubator.apache.org>
> *Sent:* Tuesday, August 23, 2016 4:40 PM
> *Subject:* Re: TextIO().Read pipeline implementation question
>
> Would this implementation work?
> I am thinking to buffer records within a window of random seconds, process
> DoFn
> them as per each record, and repeat another random window seconds length:
>
> p.apply(TextIO.Read.from("/tmp/LRData.dat")).*apply(Window.into(FixedWindows.of(Duration.standardSeconds((int)(((15-5)
> * r.nextDouble()) + 5)*
> .apply("PseduLRDoFn", ParDo.of(new DoFn<String, String>() {
>
> Thanks for your help.
> Amir-
>
>
> --
> *From:* amir bahmanyari <amirto...@yahoo.com>
> *To:* "user@beam.incubator.apache.org" <user@beam.incubator.apache.org>
> *Sent:* Tuesday, August 23, 2016 3:51 PM
> *Subject:* TextIO().Read pipeline implementation question
>
> Hi Colleagues,
> I have no problem reading through TextIO() & processing, all by default
> behavior.
> p.apply(TextIO.Read.from("/tmp/LRData.dat"))
> .apply("PseduLRDoFn", ParDo.of(new DoFn<String, String>() {
>
> I want to change this logic like the following:
>
> - Start executing TextIo().Read but before reading anything yet
> - Sleep for a random no of seconds between 5 & 15
> - Wake-up
> - Read the records from the file (for the time-stamps) while TextIo().Read
> was sleep
> - Process records
> - Back to putting TextIo() to sleep for a random no of seconds between 5
> & 15 and continue til end of the file is reached
>
> I appreciate your suggestions and/or if you can point me to an example.
> Cheers+thanks
> Amir-
>
>
>
>
>
Re: TextIO().Read pipeline implementation question
So here is what happened as a result of inserting Window of random seconds
buffering in my TextIO().Read & DoFn<>:the number of records processed got
doubled :-((Why is that? Could someone shed light on this pls, I appreciate it
very much.Thanks.Amir-
From: amir bahmanyari <amirto...@yahoo.com>
To: "user@beam.incubator.apache.org" <user@beam.incubator.apache.org>
Sent: Tuesday, August 23, 2016 4:40 PM
Subject: Re: TextIO().Read pipeline implementation question
Would this implementation work?I am thinking to buffer records within a window
of random seconds, process DoFn them as per each record, and repeat another
random window seconds length:
p.apply(TextIO.Read.from("/tmp/LRData.dat")).apply(Window.into(FixedWindows.of(Duration.standardSeconds((int)(((15-5)
* r.nextDouble()) + 5) .apply("PseduLRDoFn", ParDo.of(new DoFn<String,
String>() {
Thanks for your help.Amir-
From: amir bahmanyari <amirto...@yahoo.com>
To: "user@beam.incubator.apache.org" <user@beam.incubator.apache.org>
Sent: Tuesday, August 23, 2016 3:51 PM
Subject: TextIO().Read pipeline implementation question
Hi Colleagues,I have no problem reading through TextIO() & processing, all by
default behavior.p.apply(TextIO.Read.from("/tmp/LRData.dat"))
.apply("PseduLRDoFn", ParDo.of(new DoFn<String, String>() {
I want to change this logic like the following:
- Start executing TextIo().Read but before reading anything yet- Sleep for a
random no of seconds between 5 & 15- Wake-up- Read the records from the file
(for the time-stamps) while TextIo().Read was sleep- Process records- Back to
putting TextIo() to sleep for a random no of seconds between 5 & 15 and
continue til end of the file is reached
I appreciate your suggestions and/or if you can point me to an
example.Cheers+thanksAmir-
Re: TextIO().Read pipeline implementation question
Would this implementation work?I am thinking to buffer records within a window
of random seconds, process DoFn them as per each record, and repeat another
random window seconds length:
p.apply(TextIO.Read.from("/tmp/LRData.dat")).apply(Window.into(FixedWindows.of(Duration.standardSeconds((int)(((15-5)
* r.nextDouble()) + 5) .apply("PseduLRDoFn", ParDo.of(new DoFn() {
Thanks for your help.Amir-
From: amir bahmanyari
To: "user@beam.incubator.apache.org"
Sent: Tuesday, August 23, 2016 3:51 PM
Subject: TextIO().Read pipeline implementation question
Hi Colleagues,I have no problem reading through TextIO() & processing, all by
default behavior.p.apply(TextIO.Read.from("/tmp/LRData.dat"))
.apply("PseduLRDoFn", ParDo.of(new DoFn() {
I want to change this logic like the following:
- Start executing TextIo().Read but before reading anything yet- Sleep for a
random no of seconds between 5 & 15- Wake-up- Read the records from the file
(for the time-stamps) while TextIo().Read was sleep- Process records- Back to
putting TextIo() to sleep for a random no of seconds between 5 & 15 and
continue til end of the file is reached
I appreciate your suggestions and/or if you can point me to an
example.Cheers+thanksAmir-
