One alternative I can think of is that you publish your orphaned
transactions to another topic from the main Spark job
You create a new DF based on orphaned transactions
result = orphanedDF \
......
.writeStream \
.outputMode('complete') \
.format("kafka") \
.option("kafka.bootstrap.servers",
config['MDVariables']['bootstrapServers'],) \
.option("topic", "orphaned") \
.option('checkpointLocation', checkpoint_path) \
.queryName("orphanedTransactions") \
.start()
And consume it somewhere else
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
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The author will in no case be liable for any monetary damages arising from
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On Sat, 10 Jul 2021 at 00:36, Bruno Oliveira <bruno.ar...@gmail.com> wrote:
> I mean... I guess?
>
> But I don't really have Airflow here, and I didn't really wanted to fall
> back to a "batch"-kinda approach with Airflow
>
> I'd rather use a Dead Letter Queue approach instead (like I mentioned
> another topic for the failed ones, which is later consumed and pumps
> the messages back to the original topic),
> or something with Spark+Delta Lake instead...
>
> I was just hoping I could somewhat just retry/replay these "orphaned"
> transactions somewhat easier...
>
> *Question) *Those features of "Stateful Streaming" or "Continuous
> Processing" mode wouldn't help solve my case, would they?
>
> On Fri, Jul 9, 2021 at 8:19 PM Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>> Well this is a matter of using journal entries.
>>
>> What you can do is that those "orphaned" transactions that you cannot
>> pair through transaction_id can be written to a journal table in your
>> Postgres DB. Then you can pair them with the entries in the relevant
>> Postgres table. If the essence is not time critical this can be done
>> through a scheduling job every x minutes through airflow or something
>> similar on the database alone.
>>
>> 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 23:53, Bruno Oliveira <bruno.ar...@gmail.com>
>> wrote:
>>
>>> That is exactly the case, Sebastian!
>>>
>>> - In practise, that "created means "*authorized*", but I still cannot
>>> deduct anything from the customer balance
>>> - the "processed" means I can safely deduct the transaction_amount from
>>> the customer balance,
>>> - and the "refunded" means I must give the transaction amount back to
>>> the customer balance
>>>
>>> So, technically, we cannot process something that is not "AUTHORIZED"
>>> (created) yet, nor can we process a refund for a transaction that has NOT
>>> been PROCESSED yet.
>>>
>>>
>>> *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.*
>>>
>>>
>>> That's absolutely the case! So, yes, That's correct.
>>>
>>> *You are asking in the case of receiving the transaction before the auth
>>>> how to retry later? *
>>>
>>>
>>> Yeah! I'm struggling for days on how to solve with Spark Structured
>>> Streaming...
>>>
>>> *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) *
>>>
>>>
>>>
>>> *Right now, the best I could think of is: *
>>>
>>> - Say, I'm reading the messages w/ transaction_id [1, 2, 3] from
>>> Kafka (topic "transactions-processed")
>>> - Then I'm querying the database for these IDs that have the status
>>> "CREATED" (or "AUTHORIZED" to be more accurate), and it returns the
>>> transactions for IDs [1, 2]
>>> - So, while it'll work for the ones with ID [1. 2] , I would have to
>>> put that transaction_id 3 in another topic, say, "
>>> *transaction-processed-retry*"
>>> - And write yet another consumer, to fetch the messages from that
>>> "*transaction-processed-retry"
>>> *and put them back to the original topic (transactions-processed)
>>> - And do something similar for the transactions-refunded
>>>
>>> *Q1) *I think this approach may work, but I can't stop thinking I'm
>>> overengineering this, and was wondering if there isn't a better approach...
>>> ?
>>>
>>> *Is this what you are looking for?*
>>>
>>>
>>> Yes, that's exactly it.
>>>
>>>
>>> *Q2)* I know that, under the hood, Structured Streaming is actually
>>> using the micro-batch engine,
>>> if I switched to *Continuous Processing*, would it make any
>>> difference? Would it allow me any "retry" mechanism out of the box?
>>>
>>> *Q3)* I stumbled upon a *Stateful Streaming* (
>>> https://databricks.com/session/deep-dive-into-stateful-stream-processing-in-structured-streaming)
>>> , but I have never ever used it before,
>>> would that actually do something for my case (retrying/replaying
>>> a given message) ?
>>>
>>>
>>> Thank you very VERY in advance!
>>> Best regards
>>>
>>>
>>> On Fri, Jul 9, 2021 at 6:36 PM Sebastian Piu <sebastian....@gmail.com>
>>> wrote:
>>>
>>>> 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.ar...@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.talebza...@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.ar...@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.talebza...@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.ar...@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.talebza...@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.ar...@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!
>>>>>>>>>>>
>>>>>>>>>>>
