Sorry a correction regarding creating incrementing ID in Pyspark
>>> df = spark.range(1,5)
>>> from pyspark.sql.window import Window as W
>>> from pyspark.sql import functions as F
>>> df = df.withColumn("idx", F.monotonically_increasing_id())
>>> Wspec = W.orderBy("idx")
>>> df.withColumn("idx", F.row_number().over(windowSpec)).show()
2021-07-13 22:04:15,001 WARN window.WindowExec: No Partition Defined for
Window operation! Moving all data to a single partition, this can cause
serious performance degradation.
+---+---+
| id|idx|
+---+---+
| 1| 1|
| 2| 2|
| 3| 3|
| 4| 4|
+---+---+
HTH
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On Tue, 13 Jul 2021 at 21:32, Mich Talebzadeh <mich.talebza...@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 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 Tue, 13 Jul 2021 at 19:53, Felix Kizhakkel Jose <
> felixkizhakkelj...@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
>>
>
