Hi to all,
in my use case I have to ingest data from a rest service, where I periodically poll the data (of course a queue would be a better choice but this doesn't depend on me). So I wrote a RichSourceFunction that starts a thread that poll for new data. However, I'd like to restart from the last "from" value (in the case the job is stopped). My initial thought was to write somewhere the last used date and, on job restart, read that date (from a file for example). However, Flink stateful source should be a better choice here...am I wrong? So I made my source function implementing ListCheckpointed<String>: @Override public List<String> snapshotState(long checkpointId, long timestamp) throws Exception { return Collections.singletonList(pollingThread.getDateFromAsString()); } @Override public void restoreState(List<String> state) throws Exception { for (String dateFrom : state) { startDateStr = dateFrom; } } @Override public void run(SourceContext<MyEvent> ctx) throws Exception { final Object lock = ctx.getCheckpointLock(); Client httpClient = getHttpClient(); try { pollingThread = new MyPollingThread.Builder(baseUrl, httpClient)// .setStartDate(startDateStr, datePatternStr)// .build(); // start the polling thread new Thread(pr).start(); .... (etc) } Is this the correct approach or did I misunderstood how stateful source functions work? Best, Flavio |
This looks fine to me.
What exactly were you worried about? On 19/06/2019 12:33, Flavio Pompermaier wrote: > Hi to all, > in my use case I have to ingest data from a rest service, where I > periodically poll the data (of course a queue would be a better choice > but this doesn't depend on me). > > So I wrote a RichSourceFunction that starts a thread that poll for new > data. > However, I'd like to restart from the last "from" value (in the case > the job is stopped). > > My initial thought was to write somewhere the last used date and, on > job restart, read that date (from a file for example). However, Flink > stateful source should be a better choice here...am I wrong? So I > made my source function implementing ListCheckpointed<String>: > > @Override > public List<String> snapshotState(long checkpointId, long timestamp) > throws Exception { > return Collections.singletonList(pollingThread.getDateFromAsString()); > } > @Override > public void restoreState(List<String> state) throws Exception { > for (String dateFrom : state) { > startDateStr = dateFrom; > } > } > > @Override > public void run(SourceContext<MyEvent> ctx) throws Exception { > final Object lock = ctx.getCheckpointLock(); > Client httpClient = getHttpClient(); > try { > pollingThread = new MyPollingThread.Builder(baseUrl, > httpClient)// > .setStartDate(startDateStr, datePatternStr)// > .build(); > // start the polling thread > new Thread(pr).start(); > .... (etc) > } > > Is this the correct approach or did I misunderstood how stateful > source functions work? > > Best, > Flavio |
It's not clear to me why the source checkpoint returns a list of object...when it could be useful to use a list instead of a single value? The documentation says The returned list should contain one entry for redistributable unit of state" but this is not very clear to me.. Best, Flavio On Wed, Jun 19, 2019 at 12:40 PM Chesnay Schepler <[hidden email]> wrote: This looks fine to me. |
It returns a list of states so that
state can be re-distributed if the parallelism changes.
If you hard-code the interface to return a single value then you're implicitly locking the parallelism. When you reduce the parallelism you'd no longer be able to restore all state, since you have less instances than stored state. On 19/06/2019 14:19, Flavio Pompermaier wrote:
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My sourcefunction is intrinsically single-thread. Is there a way to force this aspect? I can't find a real difference between a RichParallelSourceFunction and a RichSourceFunction. Is this last (RichSourceFunction) implicitly using parallelism = 1? On Wed, Jun 19, 2019 at 2:25 PM Chesnay Schepler <[hidden email]> wrote:
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A (Rich)SourceFunction that does not
implement RichParallelSourceFunction is always run with a
parallelism of 1.
On 19/06/2019 14:36, Flavio Pompermaier wrote:
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Ok great! Thanks everybody for the support On Wed, Jun 19, 2019 at 3:05 PM Chesnay Schepler <[hidden email]> wrote:
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In reply to this post by Chesnay Schepler
Hi Chesnay,
RichSourceFunction says "Base class for implementing a parallel data source…” and also talks about (in a similar, but not identical way as RichParallelSourceFunction) use of getRuntimeContext() to determine the sub-task index. But you’d always want to extend RichParallelSourceFunction to create a parallel data source, yes? Seems confusing. Thanks, — Ken
-------------------------- Ken Krugler custom big data solutions & training Hadoop, Cascading, Cassandra & Solr |
Small correction to what I said:
Sources have to implement ParallelSourceFunction in order to be
run with a higher parallelism.
The javadocs for the RichSourceFunction
are somewhat incorrect, but in a sense also correct.
This is because you can have a
RichSourceFunction that also implements ParallelSourceFunction,
which would then be functionally equivalent to
RichParallelSourceFunction.
Ultimately there's little difference
between a RichSourceFunction and a RichParallelSourceFunction;
it's just that the latter also implements ParallelSourceFunction.
ParallelSourceFunction also is really
just an interface for tagging; there's nothing functional in
there.
So whenever you look at the javadocs for a method you end up in the
RichSourceFunction interface; so there's some value in ignoring this
slight difference for practical purposes.But to wrap up, generally speaking,
yes, you'd always want to extend RichParallelSourceFunction for a
parallel data source; not out of necessity, but simplicity.
On 07/06/2020 17:43, Ken Krugler wrote:
Hi Chesnay,
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