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From Sebastian Piu <sebastian....@gmail.com>
Subject Re: [Spark Structured Streaming] retry/replay failed messages
Date Fri, 09 Jul 2021 21:36:38 GMT
So in payment systems you have something similar I think

You have an authorisation, then the actual transaction and maybe a refund
some time in the future. You want to proceed with a transaction only if
you've seen the auth but in an eventually consistent system this might not
always happen.

You are asking in the case of receiving the transaction before the auth how
to retry later?

Right now you are discarding those transactions that didn't match so you
instead would need to persist them somewhere and either reinject them into
the job that does lookup (say after x minutes)

Is this what you are looking for?

On Fri, 9 Jul 2021, 9:44 pm Bruno Oliveira, <bruno.ariev@gmail.com> wrote:

> I'm terribly sorry, Mich. That was my mistake.
> The timestamps are not the same (I copy&pasted without realizing that, I'm
> really sorry for the confusion)
>
> Please assume NONE of the following transactions are in the database yet
>
> *transactions-created:*
> { "transaction_id": 1, "amount":  1000, "timestamp": "2020-04-04 11:01:00"
> }
> { "transaction_id": 2, "amount":  2000, "timestamp": "2020-04-04
> 08:02:00" }
>
> *transactions-processed: *
> { "transaction_id": 1, "timestamp": "2020-04-04 11:03:00" }     // so it's
> processed 2 minutes after it was created
> { "transaction_id": 2, "timestamp": "2020-04-04 12:02:00" }     // so it's
> processed 4 hours after it was created
> { "transaction_id": 3, "timestamp": "2020-04-04 13:03:00" }    // cannot
> be persisted into the DB yet, because this "transaction_id 3" with the
> status "CREATED" does NOT exist in the DB
>
>
> *(...) Transactions-created are created at the same time (the same
>> timestamp) but you have NOT received them and they don't yet exist in your
>> DB (...)*
>
> - Not at the same timestamp, that was my mistake.
> - Imagine two transactions with the same ID (neither of them are in any
> Kafka topic yet),
>
>    - One with the status CREATED, and another with the status PROCESSED,
>    - The one with the status PROCESSED will ALWAYS have a higher/greater
>    timestamp than the one with the status CREATED
>    - Now for whatever reason, this happens:
>       - Step a) some producer *fails* to push the *created* one to the
>       topic  *transactions-created, it will RETRY, and will eventually
>       succeed, but that can take minutes, or hours*
>       - Step b) however, the producer *succeeds* in pushing the*
>       'processed' *one to the topic *transactions-processed *
>
>
> *(...) because presumably your relational database is too slow to ingest
>> them? (...)*
>
>
> - it's not like the DB was slow, it was because the message for
> transaction_id 3 didn't arrive at the *topic-created *yet, due to some
> error/failure in Step A, for example
>
>
> * you do a query in Postgres for say transaction_id 3 but they don't exist
>> yet? When are they expected to arrive?*
>
>
> - That's correct. It could take minutes, maybe hours. But it is guaranteed
> that at some point, in the future, they will arrive. I just have to keep
> trying until it works, this transaction_id 3 with the status CREATED
> arrives at the database
>
>
> Huge apologies for the confusion... Is it a bit more clear now?
>
> *PS:* This is a simplified scenario, in practise, there is yet another
> topic for "transactions-refunded". But which cannot be sinked to the DB,
> unless the same transaction_id with the status "PROCESSED" is there. (but
> again, there can only be a transaction_id PROCESSED, if the same
> transaction_id with CREATED exists in the DB)
>
>
> On Fri, Jul 9, 2021 at 4:51 PM Mich Talebzadeh <mich.talebzadeh@gmail.com>
> wrote:
>
>> One second
>>
>> The topic called transactions_processed is streaming through Spark.
>> Transactions-created are created at the same time (the same timestamp) but
>> you have NOT received them and they don't yet exist in your DB,
>> because presumably your relational database is too slow to ingest them? you
>> do a query in Postgres for say transaction_id 3 but they don't exist yet?
>> When are they expected to arrive?
>>
>>
>>
>>
>>    view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Fri, 9 Jul 2021 at 19:12, Bruno Oliveira <bruno.ariev@gmail.com>
>> wrote:
>>
>>> Thanks for the quick reply!
>>>
>>> I'm not sure I got the idea correctly... but from what I'm underding,
>>> wouldn't that actually end the same way?
>>> Because, this is the current scenario:
>>>
>>> *transactions-processed: *
>>> { "transaction_id": 1, "timestamp": "2020-04-04 11:01:00" }
>>> { "transaction_id": 2, "timestamp": "2020-04-04 12:02:00" }
>>> { "transaction_id": 3, "timestamp": "2020-04-04 13:03:00" }
>>> { "transaction_id": 4, "timestamp": "2020-04-04 14:04:00" }
>>>
>>> *transactions-created:*
>>> { "transaction_id": 1, "amount":  1000, "timestamp": "2020-04-04
>>> 11:01:00" }
>>> { "transaction_id": 2, "amount":  2000, "timestamp": "2020-04-04
>>> 12:02:00" }
>>>
>>> - So, when I fetch ALL messages from both topics, there are still 2x
>>> transactions (id: "*3*" and "*4*") which do *not* exist in the topic
>>> "transaction-created" yet (and they aren't in Postgres either)
>>> - But since they were pulled by "Structured Streaming" already, they'll
>>> be kinda marked as "processed" by Spark Structure Streaming checkpoint
>>> anyway.
>>>
>>> And therefore, I can't replay/reprocess them again...
>>>
>>> Is my understanding correct? Am I missing something here?
>>>
>>> On Fri, Jul 9, 2021 at 2:02 PM Mich Talebzadeh <
>>> mich.talebzadeh@gmail.com> wrote:
>>>
>>>> Thanks for the details.
>>>>
>>>> Can you read these in the same app. For example. This is PySpark but it
>>>> serves the purpose.
>>>>
>>>> Read topic "newtopic" in micro batch and the other topic "md" in
>>>> another microbatch
>>>>
>>>>         try:
>>>>             # process topic --> newtopic
>>>>             streamingNewtopic = self.spark \
>>>>                 .readStream \
>>>>                 .format("kafka") \
>>>>                 .option("kafka.bootstrap.servers",
>>>> config['MDVariables']['bootstrapServers'],) \
>>>>                 .option("schema.registry.url",
>>>> config['MDVariables']['schemaRegistryURL']) \
>>>>                 .option("group.id", config['common']['newtopic']) \
>>>>                 .option("zookeeper.connection.timeout.ms",
>>>> config['MDVariables']['zookeeperConnectionTimeoutMs']) \
>>>>                 .option("rebalance.backoff.ms",
>>>> config['MDVariables']['rebalanceBackoffMS']) \
>>>>                 .option("zookeeper.session.timeout.ms",
>>>> config['MDVariables']['zookeeperSessionTimeOutMs']) \
>>>>                 .option("auto.commit.interval.ms",
>>>> config['MDVariables']['autoCommitIntervalMS']) \
>>>>                 *.option("subscribe",
>>>> config['MDVariables']['newtopic']) \*
>>>>                 .option("failOnDataLoss", "false") \
>>>>                 .option("includeHeaders", "true") \
>>>>                 .option("startingOffsets", "latest") \
>>>>                 .load() \
>>>>                 .select(from_json(col("value").cast("string"),
>>>> newtopicSchema).alias("newtopic_value"))
>>>>
>>>>             # construct a streaming dataframe streamingDataFrame that
>>>> subscribes to topic config['MDVariables']['topic']) -> md (market data)
>>>>             streamingDataFrame = self.spark \
>>>>                 .readStream \
>>>>                 .format("kafka") \
>>>>                 .option("kafka.bootstrap.servers",
>>>> config['MDVariables']['bootstrapServers'],) \
>>>>                 .option("schema.registry.url",
>>>> config['MDVariables']['schemaRegistryURL']) \
>>>>                 .option("group.id", config['common']['appName']) \
>>>>                 .option("zookeeper.connection.timeout.ms",
>>>> config['MDVariables']['zookeeperConnectionTimeoutMs']) \
>>>>                 .option("rebalance.backoff.ms",
>>>> config['MDVariables']['rebalanceBackoffMS']) \
>>>>                 .option("zookeeper.session.timeout.ms",
>>>> config['MDVariables']['zookeeperSessionTimeOutMs']) \
>>>>                 .option("auto.commit.interval.ms",
>>>> config['MDVariables']['autoCommitIntervalMS']) \
>>>>                 *.option("subscribe", config['MDVariables']['topic'])
>>>> \*
>>>>                 .option("failOnDataLoss", "false") \
>>>>                 .option("includeHeaders", "true") \
>>>>                 .option("startingOffsets", "latest") \
>>>>                 .load() \
>>>>                 .select(from_json(col("value").cast("string"),
>>>> schema).alias("parsed_value"))
>>>>
>>>>
>>>>             streamingNewtopic.printSchema()
>>>>
>>>>             # Now do a writeStream and call the relevant functions to
>>>> process dataframes
>>>>
>>>>             newtopicResult = streamingNewtopic.select( \
>>>>                      col("newtopic_value.uuid").alias("uuid") \
>>>>                    ,
>>>> col("newtopic_value.timeissued").alias("timeissued") \
>>>>                    , col("newtopic_value.queue").alias("queue") \
>>>>                    , col("newtopic_value.status").alias("status")). \
>>>>                      writeStream. \
>>>>                      outputMode('append'). \
>>>>                      option("truncate", "false"). \
>>>>   *                   foreachBatch(sendToControl). \*
>>>>                      trigger(processingTime='2 seconds'). \
>>>>                      queryName(config['MDVariables']['newtopic']). \
>>>>                      start()
>>>>
>>>>             result = streamingDataFrame.select( \
>>>>                      col("parsed_value.rowkey").alias("rowkey") \
>>>>                    , col("parsed_value.ticker").alias("ticker") \
>>>>                    , col("parsed_value.timeissued").alias("timeissued")
>>>> \
>>>>                    , col("parsed_value.price").alias("price")). \
>>>>                      writeStream. \
>>>>                      outputMode('append'). \
>>>>                      option("truncate", "false"). \
>>>>                      *foreachBatch(sendToSink). \*
>>>>                      trigger(processingTime='30 seconds'). \
>>>>                      option('checkpointLocation', checkpoint_path). \
>>>>                      queryName(config['MDVariables']['topic']). \
>>>>                      start()
>>>>             print(result)
>>>>
>>>>         except Exception as e:
>>>>                 print(f"""{e}, quitting""")
>>>>                 sys.exit(1)
>>>>
>>>> Inside that function say *sendToSink *you can get the df and batchId
>>>>
>>>> def sendToSink(df, batchId):
>>>>     if(len(df.take(1))) > 0:
>>>>         print(f"""md batchId is {batchId}""")
>>>>         df.show(100,False)
>>>>         df. persist()
>>>>         # write to BigQuery batch table
>>>>         s.writeTableToBQ(df, "append",
>>>> config['MDVariables']['targetDataset'],config['MDVariables']['targetTable'])
>>>>         df.unpersist()
>>>>         print(f"""wrote to DB""")
>>>>     else:
>>>>         print("DataFrame md is empty")
>>>>
>>>> And you have created DF from the other topic newtopic
>>>>
>>>> def sendToControl(dfnewtopic, batchId):
>>>>     if(len(dfnewtopic.take(1))) > 0:
>>>>         ......
>>>>
>>>> Now you have  two dataframe* df* and *dfnewtopic* in the same session.
>>>> Will you be able to join these two dataframes through common key value?
>>>>
>>>> HTH
>>>>
>>>>
>>>>
>>>>    view my Linkedin profile
>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>>
>>>>
>>>>
>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>>> any loss, damage or destruction of data or any other property which may
>>>> arise from relying on this email's technical content is explicitly
>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>> arising from such loss, damage or destruction.
>>>>
>>>>
>>>>
>>>>
>>>> On Fri, 9 Jul 2021 at 17:41, Bruno Oliveira <bruno.ariev@gmail.com>
>>>> wrote:
>>>>
>>>>> Hello! Sure thing!
>>>>>
>>>>> I'm reading them *separately*, both are apps written with Scala +
>>>>> Spark Structured Streaming.
>>>>>
>>>>> I feel like I missed some details on my original thread (sorry it was
>>>>> past 4 AM) and it was getting frustrating
>>>>> Please let me try to clarify some points:
>>>>>
>>>>> *Transactions Created Consumer*
>>>>> -----------------------------------
>>>>> | Kafka trx-created-topic   |   <--- (Scala + SparkStructured
>>>>> Streaming) ConsumerApp --->  Sinks to ---> Postgres DB Table
>>>>> (Transactions)
>>>>> -----------------------------------
>>>>>
>>>>> *Transactions Processed Consumer*
>>>>> -------------------------------------
>>>>> | Kafka trx-processed-topic |  <---   1) (Scala + SparkStructured
>>>>> Streaming) AnotherConsumerApp fetches a Dataset (let's call it "a")
>>>>> -------------------------------------           2) Selects the Ids
>>>>> -------------------------------------
>>>>> |   Postgres / Trx table         |. <--- 3) Fetches the rows w/ the
>>>>> matching ids that have status 'created (let's call it "b")
>>>>> -------------------------------------         4)  Performs an
>>>>> intersection between "a" and "b" resulting in a "b_that_needs_sinking"
(but
>>>>> now there's some "b_leftovers" that were out of the intersection)
>>>>>                                                      5)  Sinks
>>>>> "b_that_needs_sinking" to DB, but that leaves the "b_leftovers" as
>>>>> unprocessed (not persisted)
>>>>>                                                      6) However, those
>>>>> "b_leftovers" would, ultimately, be processed at some point (even if
it
>>>>> takes like 1-3 days) - when their corresponding transaction_id are
>>>>>                                                          pushed to the
>>>>> "trx-created-topic" Kafka topic, and are then processed by that first
>>>>> consumer
>>>>>
>>>>> So, what I'm trying to accomplish is find a way to reprocess those
>>>>> "b_leftovers" *without *having to restart the app
>>>>> Does that make sense?
>>>>>
>>>>> PS: It doesn't necessarily have to be real streaming, if
>>>>> micro-batching (legacy Spark Streaming) would allow such a thing, it
would
>>>>> technically work (although I keep hearing it's not advisable)
>>>>>
>>>>> Thank you so much!
>>>>>
>>>>> Kind regards
>>>>>
>>>>> On Fri, Jul 9, 2021 at 12:13 PM Mich Talebzadeh <
>>>>> mich.talebzadeh@gmail.com> wrote:
>>>>>
>>>>>> Can you please clarify if you are reading these two topics separately
>>>>>> or within the same scala or python script in Spark Structured Streaming?
>>>>>>
>>>>>> HTH
>>>>>>
>>>>>>
>>>>>>    view my Linkedin profile
>>>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>>>>
>>>>>>
>>>>>>
>>>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility
>>>>>> for any loss, damage or destruction of data or any other property
which may
>>>>>> arise from relying on this email's technical content is explicitly
>>>>>> disclaimed. The author will in no case be liable for any monetary
damages
>>>>>> arising from such loss, damage or destruction.
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Fri, 9 Jul 2021 at 13:44, Bruno Oliveira <bruno.ariev@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hello guys,
>>>>>>>
>>>>>>> I've been struggling with this for some days now, without success,
>>>>>>> so I would highly appreciate any enlightenment. The simplified
scenario is
>>>>>>> the following:
>>>>>>>
>>>>>>>    - I've got 2 topics in Kafka (it's already like that in
>>>>>>>    production, can't change it)
>>>>>>>       - transactions-created,
>>>>>>>       - transaction-processed
>>>>>>>    - Even though the schema is not exactly the same, they all
share
>>>>>>>    a correlation_id, which is their "transaction_id"
>>>>>>>
>>>>>>> So, long story short, I've got 2 consumers, one for each topic,
and
>>>>>>> all I wanna do is sink them in a chain order. I'm writing them
w/ Spark
>>>>>>> Structured Streaming, btw
>>>>>>>
>>>>>>> So far so good, the caveat here is:
>>>>>>>
>>>>>>> - I cannot write a given "*processed" *transaction unless there
is
>>>>>>> an entry of that same transaction with the status "*created*".
>>>>>>>
>>>>>>> - There is *no* guarantee that any transactions in the topic
>>>>>>> "transaction-*processed*" have a match (same transaction_id)
in the
>>>>>>> "transaction-*created*" at the moment the messages are fetched.
>>>>>>>
>>>>>>> So the workflow so far is:
>>>>>>> - Msgs from the "transaction-created" just get synced to postgres,
>>>>>>> no questions asked
>>>>>>>
>>>>>>> - As for the "transaction-processed", it goes as follows:
>>>>>>>
>>>>>>>    - a) Messages are fetched from the Kafka topic
>>>>>>>    - b) Select the transaction_id of those...
>>>>>>>    - c) Fetch all the rows w/ the corresponding id from a Postgres
>>>>>>>    table AND that have the status "CREATED"
>>>>>>>    - d) Then, a pretty much do a intersection between the two
>>>>>>>    datasets, and sink only on "processed" ones that have with
step c
>>>>>>>    - e) Persist the resulting dataset
>>>>>>>
>>>>>>> But the rows (from the 'processed') that were not part of the
>>>>>>> intersection get lost afterwards...
>>>>>>>
>>>>>>> So my question is:
>>>>>>> - Is there ANY way to reprocess/replay them at all WITHOUT
>>>>>>> restarting the app?
>>>>>>> - For this scenario, should I fall back to Spark Streaming, instead
>>>>>>> of Structured Streaming?
>>>>>>>
>>>>>>> PS: I was playing around with Spark Streaming (legacy) and managed
>>>>>>> to commit only the ones in the microbatches that were fully successful
>>>>>>> (still failed to find a way to "poll" for the uncommitted ones
without
>>>>>>> restarting, though).
>>>>>>>
>>>>>>> Thank you very much in advance!
>>>>>>>
>>>>>>>

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