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
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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
>
