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From Flavio Pompermaier <pomperma...@okkam.it>
Subject Re: Streaming use case: Row enrichment
Date Tue, 06 Jun 2017 15:38:51 GMT
Hi Aljosha,
thanks for getting back to me on this! I'll try to simplify the thread
starting from what we want to achieve.

At the moment we execute some queries to a db and we store the data into
Parquet directories (one for each query).
Let's say we then create a DataStream<Row> from each dir, what we would
like to achieve is to perform some sort of throttling of the queries to
perfrom to this external service (in order to not overload it with too many
queries...but we also need to run as much queries as possible in order to
execute this process in a reasonable time).

The current batch process has the downside that you must know at priori the
right parallelism of the job while the streaming process should be able to
rescale when needed [1] so it should be easier to tune the job parallelism
without loosing all the already performed queries [2]. Moreover, it the job
crash you loose all the work done up to that moment because there's no
checkpointing...
My initial idea was to read from HDFS and put the data into Kafka to be
able to change the number of consumers at runtime (accordingly to the
maxmimum parallelism we can achieve with the external service) but maybe
this could be done in a easier way (we've started using streaming from a
few time so we can see things more complicated than they are).

Moreover, as the last step, we need to know when all the data has been
enriched so we can stop this first streaming job and we can start with the
next one (that cannot run if the acquisition job is still in progress
because it can break referential integrity). Is there any example of such a
use case?

[1] at the moment manually..maybe automatically in the future, right?
[2] with the batch job if we want to change the parallelism we need to stop
it and relaunch it, loosing all the already enriched data because there's
no checkpointing there

On Tue, Jun 6, 2017 at 4:46 PM, Aljoscha Krettek <aljoscha@apache.org>
wrote:

> Hi Flavio,
>
> I’ll try and answer your questions:
>
> Regarding 1. Why do you need to first read the data from HDFS into Kafka
> (or another queue)? Using StreamExecutionEnvironment.readFile(FileInputFormat,
> String, FileProcessingMode, long) you can monitor a directory in HDFS and
> process the files that are there and any newly arriving files. For batching
> your output, you could look into the BucketingSink which will write to
> files in HDFS (or some other DFS) and start new files (buckets) based on
> some criteria, for example number of processed elements or time.
>
> Regarding 2. I didn’t completely understand this part. Could you maybe
> elaborate a bit, please?
>
> Regarding 3. Yes, I think you can. You would use this to fire of your
> queries to solr/ES.
>
> Best,
> Aljoscha
>
> On 11. May 2017, at 15:06, Flavio Pompermaier <pompermaier@okkam.it>
> wrote:
>
> Hi to all,
> we have a particular use case where we have a tabular dataset on HDFS
> (e.g. a CSV) that we want to enrich filling some cells with the content
> returned by a query to a reverse index (e.g. solr/elasticsearch).
> Since we want to be able to make this process resilient and scalable we
> thought that Flink streaming could be a good fit since we can control the
> "pressure" on the index by adding/removing consumers dynamically and there
> is automatic error recovery.
>
> Right now we developed 2 different solutions to the problem:
>
>    1. *move the dataset from HDFS to a queue/topic* (like Kafka or
>    RabbitMQ) and then let the queue consumers do the real job (pull Rows from
>    the queue, enrich and then persist the enriched Rows). The questions here
>    are:
>       1. how to properly manage writing to HDFS ? if we read a set of
>       rows, we enrich them and we need to write the result back to HDFS, is it
>       possible to automatically compact files in order to avoid the "too many
>       small files" problem on HDFS? How to avoid file name collision (put each
>       batch of rows to a different file)?
>       2. how to control the number dynamically? is it possible to change
>       the parallelism once the job has started?
>       2. in order to avoid useless data transfer from HDFS to a
>    queue/topic (since we don't need all the Row fields to create the
>    query..usually only 2/5 fields are needed) we can create a Flink job that
>    put the q*ueries into a queue/topic *and wait for the result. The
>    problem with this approach is:
>       1. how to correlate queries with their responses? creating a unique
>       response queue/topic implies that all consumers reads all messages (and
>       discard those that are not directed to them) while creating a queue/topic
>       for each sub-task could be expansive (in terms of resources and
>       managment..but we don't have any evidence/experience of this..it's just a
>       possible problem).
>    3. Maybe we can exploit *Flink async/IO *somehow...? But how?
>
>
> Any suggestion/drawbacks on the 2 approaches?
>
> Thanks in advance,
> Flavio
>
>
>

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