Hi Jack,
I don't get the difference from the "MiniBatch Aggregation" if
compared with the "Local-Global Aggregation". On the web page [1] it
says that I have to enable the TWO_PHASE parameter. So I compared the
query plan from both, with and without the TWO_PHASE parameter. And
they are the same. So, I conclude that the mini-batch already is a
TWO_PHASE strategy since it is already pre-aggregating locally. Is it
correct?
Here are both query plans:
Thanks, Felipe
Table API: mini-batch.enable : true
Table API: distinct-agg.split.enabled : false
Table API: parallelism : 4
Table API: mini-batch.latency : 1 s
Table API: mini_batch.size : 1000
Table API: mini_batch.two_phase : false
{
"nodes" : [ {
"id" : 1,
"type" : "Source: source",
"pact" : "Data Source",
"contents" : "Source: source",
"parallelism" : 4
}, {
"id" : 2,
"type" : "tokenizer",
"pact" : "Operator",
"contents" : "tokenizer",
"parallelism" : 4,
"predecessors" : [ {
"id" : 1,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 3,
"type" : "SourceConversion(table=[Unregistered_DataStream_2],
fields=[dayOfTheYear, driverId, endLat, endLon, endTime, isStart,
passengerCnt, rideId, startLat, startLon, startTime, taxiId])",
"pact" : "Operator",
"contents" : "SourceConversion(table=[Unregistered_DataStream_2],
fields=[dayOfTheYear, driverId, endLat, endLon, endTime, isStart,
passengerCnt, rideId, startLat, startLon, startTime, taxiId])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 2,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 4,
"type" : "MiniBatchAssigner(interval=[1000ms], mode=[ProcTime])",
"pact" : "Operator",
"contents" : "MiniBatchAssigner(interval=[1000ms], mode=[ProcTime])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 3,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 5,
"type" : "LocalGroupAggregate(groupBy=[taxiId], select=[taxiId,
COUNT(passengerCnt) AS count$0])",
"pact" : "Operator",
"contents" : "LocalGroupAggregate(groupBy=[taxiId],
select=[taxiId, COUNT(passengerCnt) AS count$0])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 4,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 7,
"type" : "GlobalGroupAggregate(groupBy=[taxiId], select=[taxiId,
COUNT(count$0) AS EXPR$0])",
"pact" : "Operator",
"contents" : "GlobalGroupAggregate(groupBy=[taxiId],
select=[taxiId, COUNT(count$0) AS EXPR$0])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 5,
"ship_strategy" : "HASH",
"side" : "second"
} ]
}, {
"id" : 8,
"type" : "SinkConversionToTuple2",
"pact" : "Operator",
"contents" : "SinkConversionToTuple2",
"parallelism" : 4,
"predecessors" : [ {
"id" : 7,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 9,
"type" : "flat-output",
"pact" : "Operator",
"contents" : "flat-output",
"parallelism" : 4,
"predecessors" : [ {
"id" : 8,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 10,
"type" : "Sink: sink",
"pact" : "Data Sink",
"contents" : "Sink: sink",
"parallelism" : 4,
"predecessors" : [ {
"id" : 9,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
} ]
}
Table API: mini-batch.enable : true
Table API: distinct-agg.split.enabled : false
Table API: parallelism : 4
Table API: mini-batch.latency : 1 s
Table API: mini_batch.size : 1000
Table API: mini_batch.two_phase : true
{
"nodes" : [ {
"id" : 1,
"type" : "Source: source",
"pact" : "Data Source",
"contents" : "Source: source",
"parallelism" : 4
}, {
"id" : 2,
"type" : "tokenizer",
"pact" : "Operator",
"contents" : "tokenizer",
"parallelism" : 4,
"predecessors" : [ {
"id" : 1,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 3,
"type" : "SourceConversion(table=[Unregistered_DataStream_2],
fields=[dayOfTheYear, driverId, endLat, endLon, endTime, isStart,
passengerCnt, rideId, startLat, startLon, startTime, taxiId])",
"pact" : "Operator",
"contents" : "SourceConversion(table=[Unregistered_DataStream_2],
fields=[dayOfTheYear, driverId, endLat, endLon, endTime, isStart,
passengerCnt, rideId, startLat, startLon, startTime, taxiId])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 2,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 4,
"type" : "MiniBatchAssigner(interval=[1000ms], mode=[ProcTime])",
"pact" : "Operator",
"contents" : "MiniBatchAssigner(interval=[1000ms], mode=[ProcTime])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 3,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 5,
"type" : "LocalGroupAggregate(groupBy=[taxiId], select=[taxiId,
COUNT(passengerCnt) AS count$0])",
"pact" : "Operator",
"contents" : "LocalGroupAggregate(groupBy=[taxiId],
select=[taxiId, COUNT(passengerCnt) AS count$0])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 4,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 7,
"type" : "GlobalGroupAggregate(groupBy=[taxiId], select=[taxiId,
COUNT(count$0) AS EXPR$0])",
"pact" : "Operator",
"contents" : "GlobalGroupAggregate(groupBy=[taxiId],
select=[taxiId, COUNT(count$0) AS EXPR$0])",
"parallelism" : 4,
"predecessors" : [ {
"id" : 5,
"ship_strategy" : "HASH",
"side" : "second"
} ]
}, {
"id" : 8,
"type" : "SinkConversionToTuple2",
"pact" : "Operator",
"contents" : "SinkConversionToTuple2",
"parallelism" : 4,
"predecessors" : [ {
"id" : 7,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 9,
"type" : "flat-output",
"pact" : "Operator",
"contents" : "flat-output",
"parallelism" : 4,
"predecessors" : [ {
"id" : 8,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
}, {
"id" : 10,
"type" : "Sink: sink",
"pact" : "Data Sink",
"contents" : "Sink: sink",
"parallelism" : 4,
"predecessors" : [ {
"id" : 9,
"ship_strategy" : "FORWARD",
"side" : "second"
} ]
} ]
}
[1]
https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/tuning/streaming_aggregation_optimization.html
--
-- Felipe Gutierrez
-- skype: felipe.o.gutierrez
-- https://felipeogutierrez.blogspot.com
On Tue, Nov 10, 2020 at 6:25 PM Felipe Gutierrez
<[email protected]> wrote:
>
> I see, thanks Timo
>
> --
> -- Felipe Gutierrez
> -- skype: felipe.o.gutierrez
> -- https://felipeogutierrez.blogspot.com
>
> On Tue, Nov 10, 2020 at 3:22 PM Timo Walther <[email protected]> wrote:
> >
> > Hi Felipe,
> >
> > with non-deterministic Jark meant that you never know if the mini batch
> > timer (every 3 s) or the mini batch threshold (e.g. 3 rows) fires the
> > execution. This depends how fast records arrive at the operator.
> >
> > In general, processing time can be considered non-deterministic, because
> > 100ms must not be 100ms. This depends on the CPU load and other tasks
> > such garbage collection etc. Only event-time (and thus event time
> > windows) that work on the timestamp in the data instead of machine time
> > is determistic,
> >
> > Regards,
> > Timo
> >
> >
> > On 10.11.20 12:02, Felipe Gutierrez wrote:
> > > Hi Jark,
> > >
> > > thanks for your reply. Indeed, I forgot to write DISTINCT on the query
> > > and now the query plan is splitting into two hash partition phases.
> > >
> > > what do you mean by deterministic time? Why only the window aggregate
> > > is deterministic? If I implement the ProcessingTimeCallback [1]
> > > interface is it deterministic?
> > >
> > > [1]
> > > https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/streaming/runtime/tasks/ProcessingTimeCallback.html
> > > Thanks
> > >
> > > --
> > > -- Felipe Gutierrez
> > > -- skype: felipe.o.gutierrez
> > > -- https://felipeogutierrez.blogspot.com
> > >
> > > On Tue, Nov 10, 2020 at 7:55 AM Jark Wu <[email protected]> wrote:
> > >>
> > >> Hi Felipe,
> > >>
> > >> The "Split Distinct Aggregation", i.e. the
> > >> "table.optimizer.distinct-agg.split.enabled" option,
> > >> only works for distinct aggregations (e.g. COUNT(DISTINCT ...)).
> > >>
> > >> However, the query in your example is using COUNT(driverId).
> > >> You can update it to COUNT(DISTINCT driverId), and it should show two
> > >> hash phases.
> > >>
> > >> Regarding "MiniBatch Aggregation", it is not the same as a
> > >> processing-time window aggregation.
> > >>
> > >> 1) MiniBatch is just an optimization on unbounded aggregation, it
> > >> buffers some input records in memory
> > >> and processes them together to reduce the state accessing. But
> > >> processing-time window is still a per-record
> > >> state accessing style. Besides, the local aggregation also applies
> > >> mini-batch, it only sends the accumulator
> > >> of current this mini-batch to the downstream global aggregation, and
> > >> this improves performance a lot.
> > >> 2) The size of MiniBach is not deterministic. It may be triggered by the
> > >> number of records or a timeout.
> > >> But a window aggregate is triggered by a deterministic time.
> > >>
> > >>
> > >> Best,
> > >> Jark
> > >>
> > >>
> > >> On Mon, 9 Nov 2020 at 21:45, Felipe Gutierrez
> > >> <[email protected]> wrote:
> > >>>
> > >>> I realized that I forgot the image. Now it is attached.
> > >>> --
> > >>> -- Felipe Gutierrez
> > >>> -- skype: felipe.o.gutierrez
> > >>> -- https://felipeogutierrez.blogspot.com
> > >>>
> > >>> On Mon, Nov 9, 2020 at 1:41 PM Felipe Gutierrez
> > >>> <[email protected]> wrote:
> > >>>>
> > >>>> Hi community,
> > >>>>
> > >>>> I am testing the "Split Distinct Aggregation" [1] consuming the taxi
> > >>>> ride data set. My sql query from the table environment is the one
> > >>>> below:
> > >>>>
> > >>>> Table tableCountDistinct = tableEnv.sqlQuery("SELECT startDate,
> > >>>> COUNT(driverId) FROM TaxiRide GROUP BY startDate");
> > >>>>
> > >>>> and I am enableing:
> > >>>> configuration.setString("table.exec.mini-batch.enabled", "true");
> > >>>> configuration.setString("table.exec.mini-batch.allow-latency", "3 s");
> > >>>> configuration.setString("table.exec.mini-batch.size", "5000");
> > >>>> configuration.setString("table.optimizer.agg-phase-strategy",
> > >>>> "TWO_PHASE");
> > >>>> and finally
> > >>>> configuration.setString("table.optimizer.distinct-agg.split.enabled",
> > >>>> "true");
> > >>>>
> > >>>> I was expecting that the query plan at the WEB UI show to me two hash
> > >>>> phases as it is present here on the image [1]. Instead, it is showing
> > >>>> to me the same plan with one hash phase as I was deploying only one
> > >>>> Local aggregate and one Global aggregate (of course, taking the
> > >>>> parallel instances into consideration). Please see the query execution
> > >>>> plan image attached.
> > >>>>
> > >>>> Is there something that I am missing when I config the Table API?
> > >>>> By the way, I am a bit confused with the "MiniBatch Aggregation" [2].
> > >>>> Is the "MiniBatch Aggregation" aggregating as a processing time window
> > >>>> on the operator after the hash phase? If it is, isn't it the same as a
> > >>>> window aggregation instead of an unbounded window as the example
> > >>>> presents?
> > >>>>
> > >>>> Thanks!
> > >>>> Felipe
> > >>>>
> > >>>> [1]
> > >>>> https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/tuning/streaming_aggregation_optimization.html#split-distinct-aggregation
> > >>>> [2]
> > >>>> https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/tuning/streaming_aggregation_optimization.html#minibatch-aggregation
> > >>>> --
> > >>>> -- Felipe Gutierrez
> > >>>> -- skype: felipe.o.gutierrez
> > >>>> -- https://felipeogutierrez.blogspot.com
> > >
> >
