Thanks Arvid for the explanation.
Assume, we have 5 assets.
Asset 1, 2 sending current timestamp May 17th 00:00
Asset 3 went down on May 15th 00:00 and it restarted on May 17th 00:00 and it
started sending data from May 15th 00:00
Asset 4 went down on May 16th 00:00 and it is still down
Asset 5 went down on May 14th 00:00 and it restarted on May 17th 01:00 and it
started sending data from May 14th 00:00
In the above scenario,
* Due to the uncertainty on the asset connectivity, I can’t predict out of
orderness duration.
* Not all the asset will communicate all the time.
I have a kafka topic as source and sink to flink job and I have event time
window of 1min.
Query:
1. As I have 5 assets, if I set parallelism of 5 in window operator level,
will it not have any issues in watermark progression when asset 4 is not
communicating? Assume, I use key By asset id(1 to 5)
2. Assume window operator chose the watermark as May 15th 00:00 of asset 3
as it is a minimum event time across the sub task of window, if asset 5 sends
the data for May14th 00:00, will asset 5 data not be dropped considering it as
a late date?
Regards,
Gnana
From: Arvid Heise <ar...@ververica.com>
Date: Monday, 18 May 2020 at 4:59 PM
To: Gnanasoundari Soundarajan <gnanasoundari.soundara...@man-es.com>
Cc: Alexander Fedulov <alexan...@ververica.com>, "user@flink.apache.org"
<user@flink.apache.org>
Subject: Re: Watermarks and parallelism
Hi Gnanasoundari,
Your use case is very typical and pretty much the main motivation for event
time and watermarks. It's supported out of the box. I recommend reading again
the first resource of Alex.
To make it clear, let's have a small example:
Source 1 -\
+--> Window --> Sink
Source 2 -/
Consider source 1 being 1s ahead of source 2. Then the watermarks are also one
1s ahead. Now at the window level, the watermark will only advance at the
minimum! That's why no data is lost in your case.
In particular, consider the following events that pop up once per second that
just need to be summed up per minute.
Source1: (00:01, s1_event1), ..., (00:59, s1_event59), (01:00, s1_event60),
(01:01, s1_event61), ...
Source2: (00:00, s2_event1), ..., (00:58, s2_event59), (00:59, s2_event60),
(01:00, s2_event61), ...
For simplicity, assume that watermark = event timestamp.
Then consider a window [00:00, 00:59], this window will only close off, perform
the aggregation, and fire the result, if the watermark from both sources
reached 01:00 (so when the event with that timestamp occurs).
It will contain 59 events from Source1 and 60 events from Source2.
In particular, when event s1_event60 arrives at 01:00, it carries over to the
next window [01:00, 01:59], while the previous window is still open for events
from Source2. Only after receiving s2_event61 at 01:00, the first window will
result in an output event.
Of course that also means that data from quick sources need to live as long in
the main memory (or actually state backend) as it takes for the slowest source
to catch up.
On Fri, May 15, 2020 at 7:16 PM Gnanasoundari Soundarajan
<gnanasoundari.soundara...@man-es.com<mailto:gnanasoundari.soundara...@man-es.com>>
wrote:
Thanks Alexander for your detailed response.
I have a requirement that each asset will communicate different event time due
to connectivity issues. If I have 50 asset and each communicates with different
event time, I should not lose the data because of lateness.
To handle this, I have tried with keyBy operator to route the data by asset
context and try to maintain watermark per asset (key) using keyedProcess
function by registering eventtime timer for each asset (key). When I have
tried this option, I observed that eventtime timer is not triggered
keyedProcess function and hence data didn’t flow downstream.
I am curious to know that whether will it be a feasible requirement to achieve
it in flink using event time?
Regards,
Gnana
From: Alexander Fedulov
<alexan...@ververica.com<mailto:alexan...@ververica.com>>
Date: Thursday, 14 May 2020 at 9:25 PM
To: Gnanasoundari Soundarajan
<gnanasoundari.soundara...@man-es.com<mailto:gnanasoundari.soundara...@man-es.com>>
Cc: "user@flink.apache.org<mailto:user@flink.apache.org>"
<user@flink.apache.org<mailto:user@flink.apache.org>>
Subject: Re: Watermarks and parallelism
Hi Gnana,
1. No, watermarks are generated independently per subtask. I think this section
of the docs might make things more clear -
[1]<https://ci.apache.org/projects/flink/flink-docs-master/concepts/timely-stream-processing.html#watermarks-in-parallel-streams>
.
2. The same watermark from the input of the keyBy will be dispatched to all of
the instances of the downstream keyed operator. That said, there is no global
coordination between the subtasks. The same watermark can arrive at the
downstream subtask at a different time, depending on how much time they'd spend
on the input channels. Notice also that watermarks are managed on the subtask
level, not at the level of the individual keys.
3. I am not quite sure I get what you mean by this one and what exactly you try
to achieve. I assume you want to basically have parallel windows that are
scoped to all of the items coming from a corresponding subtask of the previous
non-keyed operator. As Flink windows can be executed in parallel only on keyed
streams, you could do a little trick - use `reinterpredAsKeyedStream`
[2]<https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/experimental.html#reinterpreting-a-pre-partitioned-data-stream-as-keyed-stream>.
This will make it possible to basically have a "passthrough" partitioning,
without an actual data shuffle. Another alternative would be to implement your
Map function as a RichMapFunction, which gives you the access to the runtime
context. From there:
1) use `getRuntimeContext().getIndexOfThisSubtask();` to retrieve the ID of the
current subtask
2) enrich your events with a new field, containing the subtask ID
3) use this ID as the key in your keyBy operator
The problem is that both of those approaches will be non-deterministic in terms
of state recovery when, for instance, you would like to scale out your job to a
higher degree of parallelism. You'd need to decide if this is relevant for your
use case.
[1]
https://ci.apache.org/projects/flink/flink-docs-master/concepts/timely-stream-processing.html#watermarks-in-parallel-streams
[2]
https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/experimental.html#reinterpreting-a-pre-partitioned-data-stream-as-keyed-stream
Best,
--
Alexander Fedulov | Solutions Architect
+49 1514 6265796
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On Thu, May 14, 2020 at 6:14 AM Gnanasoundari Soundarajan
<gnanasoundari.soundara...@man-es.com<mailto:gnanasoundari.soundara...@man-es.com>>
wrote:
Hi all,
I have below queries in flink. Could anyone help me to understand?
Query:
1 Is watermark maintained globally at the operator level?
2 When we have a keyByOperator with parallelism >1, is there a single watermark
maintained across all the parallel subtasks or for each of the parallel subtasks
3. Assuming I have a keybyoperator with parallelism > 1, is it possible to feed
data to this operator from only one stream from the previous parameter (say map
(1) always goes to window (1)
Regards,
Gnana
--
Arvid Heise | Senior Java Developer
[Image removed by sender.]<https://www.ververica.com/>
Follow us @VervericaData
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Join Flink Forward<https://flink-forward.org/> - The Apache Flink Conference
Stream Processing | Event Driven | Real Time
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Ververica GmbH | Invalidenstrasse 115, 10115 Berlin, Germany
--
Ververica GmbH
Registered at Amtsgericht Charlottenburg: HRB 158244 B
Managing Directors: Timothy Alexander Steinert, Yip Park Tung Jason, Ji (Toni)
Cheng
