Hi Sachit,
What do you mean by "spark is running only 1 executor with 1 task" ?
Did you submit the spark application with multiple executors but only 1 is
being used and rest are idle ?
If that's the case, then it might happen due to spark.locality.wait setting
which is by default set to 3s. This will enable spark to wait for 3s for
the tasks to finish on the executor before submitting the next batch on
another executors. This happens due to spark's preference for cached kafka
consumers.
And regarding having 1 task doing all the processing. Please check if your
kafka topic has only 1 partition. Spark draws the parallelism from the
number of partitions in the kafka topic. Once you have loaded the data from
partitions, you can choose to repartition the batch so it is processed by
multiple tasks.
On Mon, Mar 8, 2021 at 10:57 PM Sachit Murarka <connectsac...@gmail.com>
wrote:
> Hi All,
>
> I am using Spark 3.0.1 Structuring streaming with Pyspark.
>
> The problem is spark is running only 1 executor with 1 task. Following is
> the summary of what I am doing.
>
> Can anyone help on why my executor is 1 only?
>
> def process_events(event):
> fetch_actual_data()
> #many more steps
>
> def fetch_actual_data():
> #applying operation on actual data
>
> df = spark.readStream.format("kafka") \
> .option("kafka.bootstrap.servers", KAFKA_URL) \
> .option("subscribe", KAFKA_TOPICS) \
> .option("startingOffsets",
> START_OFFSET).load() .selectExpr("CAST(value AS STRING)")
>
>
> query =
> df.writeStream.foreach(process_events).option("checkpointLocation",
> "/opt/checkpoint").trigger(processingTime="30 seconds").start()
>
>
>
> Kind Regards,
> Sachit Murarka
>
--
Regards
Kapil Garg
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