Sungho Ham created SPARK-19036:
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Summary: Merging dealyed micro batches for parallelism
Key: SPARK-19036
URL: https://issues.apache.org/jira/browse/SPARK-19036
Project: Spark
Issue Type: New Feature
Reporter: Sungho Ham
Priority: Minor
Efficiency of parallel execution get worsen when data is not evenly distributed
by both time and message. Theses skews make streaming batch delayed despite
sufficient resources.
Merging small-sized delayed batches could help increase efficiency of micro
batch.
Here is an example.
t messages
---------------------------------
4 1 <-- current time
3 1
2 1
1 1000 <-- processing
After long-running batch (t=1), three batches has only one message. These
batches cannot utilize parallelism of Spark. By merging stream RDDs from t=1 to
t=4 into one RDD, Spark can process them 3 times faster.
If processing time of each message is highly skewed also, not only utilizing
parallelism matters. Suppose batches from t=2 to t=4 consist of one BIG message
and ten small messages. Then, merging three RDDs still could considerably
improve efficiency.
t messages
---------------------------------
4 1+10 <-- current time
3 1+10
2 1+10
1 1000 <-- processing
There could two parameters to describe merging behavior.
- delay_time_limit: when to start merge
- merge_record_limit: when to stop merge
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