Spark AQE Post-Shuffle partitions coalesce don't work as expected, and even make data skew in some partitions. Why?

Question

I use global sort on my spark DF, and when I enable AQE and post-shuffle coalesce, my partitions after sort operation become even worse distributed than before.

    "spark.sql.adaptive.enabled" -> "true",
    "spark.sql.adaptive.coalescePartitions.enabled" -> "true",
    "spark.sql.adaptive.advisoryPartitionSizeInBytes" -> "256mb",
    "spark.sql.adaptive.coalescePartitions.minPartitionNum" -> "1",
    "spark.sql.adaptive.coalescePartitions.initialPartitionNum" -> "20000"

My query, on high level, looks:

.readFromKafka
.deserializeJsonToRow
.cache
.sort(const_part, column which can cause skew, some salt columns)
.writeToS3

1. column which can cause skew -> yes my data is not well distributed, that's why I use salts.
2. I read data from Kafka, so I use Kafka partition + offset columns as salt.
3. Why Sort which uses reaprtitoinByRange under the hood doesn't help me and I want to enable AQE? -> Right now I see that my Kafka message can have a too big a difference in size. So I see that my partitions after range repartition have near the same amount of records, but still very uneven in bytes.
4. Why I think AQE must help me? -> I want to create many small ranges which even with my data skew will not be more than ~50mb, so post shuffle coalesce will be able to coalesce them to target size(256mb). In my case spikes, up to 320mb are ok.

My first assumption was, that even with a small range has a too big a spike. But I check and confirm that repartition by a range gives me good distribution in records, but bad is size. I have nearly 200 partitions with near the same amount of records and size differences of up to 9x times, from ~100Mb to ~900mb. But with AEQ and repartition to 18000 small ranges, the smallest partition was 18mib and the biggest 1.8Gib. This state of things is much worse than without AEQ. It's important to highlight that I use metrics from Spark UI -> Details for Stage tab to identify partitions size in bytes, and I have my own logs for records.

So I start to debug the issue, but AQE don't have enough logs on the input and output of ShufflePartitionsUtil.coalescePartitions. That why I rewrite my query to repartitionByRange.sortWithingPartitoins. And fork Physical Plan optimization with additional logging. My logs show me, that my initial idea was right.

• Input partitions after map and write shuffle stage was split to be small enough
• Coalesce algorithm collects them to a correct number well distributed in bytes partition.

Input shuffleId:2 partitions:17999
Max partition size :27362117
Min partition size :8758435

And

Number of shuffle stages to coalesce 1
Reduce number of partitions from 17999 to 188
Output partition  maxsize :312832323
Output partition min size :103832323

Min size is so different, because of the size of the last partition, it's expected. TRACE log level shows that 99% of partitions is near 290mib.

• But why spark UI show so different results? ->

• May spark UI be wrong? ->

• Maybe, but except task size, the duration of a task is also too big, which makes me think spark UI is ok.

• So my assumption is that the issue is with MapOutputStatistics in my stage. But does it always broken or only in my case? ->

• Only in my case? -> I made a few checks to confirm it.

• • I read the same dataset from s3(parquet files with block size 120mb)-> and AQE work as expected. post shuffle coalesce return to me 188, well distributed by size, partitions. it's important to notice that data on s3 not well distributed, but spark during reading split it to 259 near 120mb size partitions, most of all because of parquet block size 120mb.
• • I read the same dataset from Kafka, but exclude the column with a skew from the partition function -> and AQE work as expected. post shuffle coalesce return to me 203, well distributed by size, partitions.
• • I try to disable cache -> this does not have any result. I use the cache, only to avoid double reading from kafka. Because repartition by a range use sampling.
• • I try to disable AQE and write 18000 partitions to s3 -> result was expected and the same as what my log on coalescing input show: 17999 files, smallest near 8mib and biggest 56mib.

• All these checks make me think that MapOutputStatistics is wrong only for my case. May be an issue that how to relate to Kafka source or that my Kafka input data is very uneven distributed.

Questions:

• So do anyone have an idea what I do wrong?
• And what I can do with input data to make post shuffle coalesce work in my case?
• If you think that I am right, please put a comment about it.

P.S. I also want to mention that my input Kafka data frame is 2160, not even distributed partitions -> some partitions can be 2x time bigger then others. Read from Kafka topic with 720 partitions and minPartitions option * 3.

Answer 1

Here is the answer: https://www.mail-archive.com/[email protected]/msg26851.html

The worst case of enabling AQE in cached data is not losing the opportunity of using/reusing the cache, but rather just an extra shuffle if the outputPartitioning happens to match without AQE and not match after AQE. The chance of this happening is rather low.