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From Aljoscha Krettek <aljos...@apache.org>
Subject Re: Streaming use case: Row enrichment
Date Thu, 15 Jun 2017 12:30:25 GMT
Ok, just trying to make sure I understand everything: You have this:

1. A bunch of data in HDFS that you want to enrich
2. An external service (Solr/ES) that you query for enriching the data rows stored in 1.
3. You need to store the enriched rows in HDFS again

I think you could just do this (roughly):

StreamExecutionEnvironment env = …;

DataStream<Row> input = env.readFile(new RowCsvInputFormat(…), “<hdfs path>”);

DataStream<Row> enriched = input.flatMap(new MyEnricherThatCallsES());
// or
DataStream<Row> enriched = AsyncDataStream.unorderedWait(input, …) // yes, the interface
for this is a bit strange

BucketingSink sink = new BucketingSink(“<hdfs sink path>”);
// this is responsible for putting files into buckets, so that you don’t have to many small
HDFS files
sink.setBucketer(new MyBucketer());

In this case, the file source will close once all files are read and the job will finish.
If you don’t want this you can also use a different readFile() method where you can specify
 that you want to continue monitoring the directory for new files.


> On 6. Jun 2017, at 17:38, Flavio Pompermaier <pompermaier@okkam.it> wrote:
> 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
> 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 <mailto:aljoscha@apache.org>>
> 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 <mailto:pompermaier@okkam.it>>
>> 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:
>> 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:
>> 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)?
>> how to control the number dynamically? is it possible to change the parallelism once
the job has started?
>> 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 queries into a queue/topic and wait for the result. The problem with
this approach is:
>> 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).
>> 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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