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From Jungtaek Lim <kabhwan.opensou...@gmail.com>
Subject Re: How to generate unique incrementing identifier in a structured streaming dataframe
Date Tue, 13 Jul 2021 23:58:57 GMT
Theoretically, the composed value of batchId +
monotonically_increasing_id() would achieve the goal. The major downside is
that you'll need to deal with "deduplication" of output based on batchID
as monotonically_increasing_id() is indeterministic. You need to ensure
there's NO overlap on output against multiple reattempts for the same batch
ID.

Btw, even just assume you dealt with auto increasing ID on write, how do
you read files and apply range pruning by auto increasing ID? Is the
approach scalable and efficient? You probably couldn't avoid reading
unnecessary files unless you build an explicit metadata regarding files
like the map file name to the range of ID and also craft a custom reader to
leverage the information.


On Wed, Jul 14, 2021 at 6:00 AM Sebastian Piu <sebastian.piu@gmail.com>
wrote:

> If you want them to survive across jobs you can use snowflake IDs or
> similar ideas depending on your use case
>
> On Tue, 13 Jul 2021, 9:33 pm Mich Talebzadeh, <mich.talebzadeh@gmail.com>
> wrote:
>
>> Meaning as a monolithically incrementing ID as in Oracle sequence for
>> each record read from Kafka. adding that to your dataframe?
>>
>> If you do Structured Structured Streaming in microbatch mode, you will
>> get what is known as BatchId
>>
>>            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']). \
>>
>> That function sendToSink will introduce two variables 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")
>>
>> That value batchId can be used for each Batch.
>>
>>
>> Otherwise you can do this
>>
>>
>> startval = 1
>> df = df.withColumn('id', monotonicallyIncreasingId + startval)
>>
>> 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
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>> 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 Tue, 13 Jul 2021 at 19:53, Felix Kizhakkel Jose <
>> felixkizhakkeljose@gmail.com> wrote:
>>
>>> Hello,
>>>
>>> I am using Spark Structured Streaming to sink data from Kafka to AWS S3.
>>> I am wondering if its possible for me to introduce a uniquely incrementing
>>> identifier for each record as we do in RDBMS (incrementing long id)?
>>> This would greatly benefit to range prune while reading based on this ID.
>>>
>>> Any thoughts? I have looked at monotonically_incrementing_id but seems
>>> like its not deterministic and it wont ensure new records gets next id from
>>> the latest id what  is already present in the storage (S3)
>>>
>>> Regards,
>>> Felix K Jose
>>>
>>

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