Andrew Olson created SPARK-36576:
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Summary: Improve range split calculation for Kafka Source
minPartitions option
Key: SPARK-36576
URL: https://issues.apache.org/jira/browse/SPARK-36576
Project: Spark
Issue Type: Improvement
Components: Structured Streaming
Affects Versions: 3.1.2
Reporter: Andrew Olson
While the
[documentation|https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html]
does contain a clear disclaimer,
{quote}Please note that this configuration is like a {{hint}}: the number of
Spark tasks will be *approximately* {{minPartitions}}. It can be less or more
depending on rounding errors or Kafka partitions that didn't receive any new
data.
{quote}
there are cases where the calculated Kafka partition range splits can differ
greatly from expectations. For evenly distributed data and most
{{minPartitions}} values this would not be a major or commonly encountered
concern. However when the distribution of data across partitions is very
heavily skewed, somewhat surprising range split calculations can result.
For example, given the following input data:
* 1 partition containing 10,000 messages
* 1,000 partitions each containing 1 message
Spark processing code loading from this collection of 1,001 partitions may
decide that it would like each task to read no more than 1,000 messages.
Consequently, it could specify a {{minPartitions}} value of 1,010 - expecting
the single large partition to be split into 10 equal chunks, along with the
1,000 small partitions each having their own task. That is far from what
actually occurs. The {{KafkaOffsetRangeCalculator}} algorithm ends up splitting
the large partition into 918 chunks of 10 or 11 messages, two orders of
magnitude from the desired maximum message count per task and nearly double the
number of Spark tasks hinted in the configuration.
Proposing that range the calculation logic be modified to exclude small (i.e.
un-split) partitions from the overall proportional distribution math, in order
to more reasonably divide the large partitions when they are accompanied by
many small partitions, and to provide optimal behavior for cases where a
{{minPartitions}} value is deliberately computed based on the volume of data
being read.
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