如何优化 Spark 以将大量数据写入 S3

Question

I do a fair amount of ETL using Apache Spark on EMR.

I'm fairly comfortable with most of the tuning necessary to get good performance, but I have one job that I can't seem to figure out.

Basically, I'm taking about 1 TB of parquet data - spread across tens of thousands of files in S3 - and adding a few columns and writing it out partitioned by one of the date attributes of the data - again, parquet formatted in S3.

I run like this:

spark-submit --conf spark.dynamicAllocation.enabled=true  --num-executors 1149 --conf spark.driver.memoryOverhead=5120 --conf  spark.executor.memoryOverhead=5120 --conf  spark.driver.maxResultSize=2g --conf spark.sql.shuffle.partitions=1600 --conf spark.default.parallelism=1600 --executor-memory 19G --driver-memory 19G --executor-cores 3 --driver-cores 3 --class com.my.class path.to.jar <program args>

The size of the cluster is dynamically determined based on the size of the input data set, and the num-executors, spark.sql.shuffle.partitions, and spark.default.parallelism arguments are calculated based on the size of the cluster.

The code roughly does this:

va df = (read from s3 and add a few columns like timestamp and source file name)

val dfPartitioned = df.coalesce(numPartitions)

val sqlDFProdDedup = spark.sql(s""" (query to dedup against prod data """);

sqlDFProdDedup.repartition($"partition_column")
  .write.partitionBy("partition_column")
  .mode(SaveMode.Append).parquet(outputPath)

When I look at the ganglia chart, I get a huge resource spike while the de-dup logic runs and some data shuffles, but then the actual writing of the data only uses a tiny fraction of the resources and runs for several hours.

I don't think the primary issue is partition skew, because the data should be fairly distributed across all the partitions.

The partition column is essentially a day of the month, so each job typically only has 5-20 partitions, depending on the span of the input data set. Each partition typically has about 100 GB of data across 10-20 parquet files.

I'm setting spark.sql.files.maxRecordsPerFile to manage the size of those output files.

So, my big question is: how can I improve the performance here?

Simply adding resources doesn't seem to help much.

I've tried making the executors larger (to reduce shuffling) and also to increase the number of CPUs per executor, but that doesn't seem to matter.

Thanks in advance!

Answer 1

Zack, I have a similar use case with 'n' times more files to process on a daily basis. I am going to assume that you are using the code above as is and trying to improve the performance of the overall job. Here are couple of my observations:

1. Not sure what the coalesce(numPartitions) number actually is and why its being used before de-duplication process. Your spark-submit shows you are creating 1600 partitions and thats good enough to start with.

2. If you are going to repartition before write then the coalesce above may not be beneficial at all as re-partition will shuffle data.

3. Since you claim writing 10-20 parquet files it means you are only using 10-20 cores in writing in the last part of your job which is the main reason its slow. Based on 100 GB estimate the parquet file ranges from approx 5GB to 10 GB, which is really huge and I doubt one will be able to open them on their local laptop or EC2 machine unless they use EMR or similar (with huge executor memory if reading whole file or spill to disk) because the memory requirement will be too high. I will recommend creating parquet files of around 1GB to avoid any of those issues.

Also if you create 1GB parquet file, you will likely speed up the process 5 to 10 times as you will be using more executors/cores to write them in parallel. You can actually run an experiment by simply writing the dataframe with default partitions.

Which brings me to the point that you really don't need to use re-partition as you want to write.partitionBy("partition_date") call. Your repartition() call is actually forcing the dataframe to only have max 30-31 partitions depending upon the number of days in that month which is what is driving the number of files being written. The write.partitionBy("partition_date") is actually writing the data in S3 partition and if your dataframe has say 90 partitions it will write 3 times faster (3 *30). df.repartition() is forcing it to slow it down. Do you really need to have 5GB or larger files?

4. Another major point is that Spark lazy evaluation is sometimes too smart. In your case it will most likely only use the number of executors for the whole program based on the repartition(number) . Instead you should try, df.cache() -> df.count() and then df.write() . What this does is that it forces spark to use all available executor cores. I am assuming you are reading files in parallel. In your current implementation you are likely using 20-30 cores. One point of caution, as you are using r4/r5 machines, feel free to up your executor memory to 48G with 8 cores. I have found 8cores to be faster for my task instead of standard 5 cores recommendation.

5. Another pointer is to try ParallelGC instead of G1GC. For the use case like this when you are reading 1000x of files, I have noticed it performs better or not any worse than G1Gc. Please give it a try.

In my workload, I use coalesce(n) based approach where 'n' gives me a 1GB parquet file. I read files in parallel using ALL the cores available on the cluster. Only during the write part my cores are idle but there's not much you can do to avoid that.

I am not sure how spark.sql.files.maxRecordsPerFile works in conjunction with coalesce() or repartition() but I have found 1GB seems acceptable with pandas, Redshift spectrum, Athena etc.

Hope it helps. Charu

Answer 2

Here are some optimizations for faster running.

(1) File committer - this is how Spark will read the part files out to the S3 bucket. Each operation is distinct and will be based upon

spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version 2

Description

This will write the files directly to part files instead or initially loading them to temp files and copying them over to their end-state part files.

(2) For file size you can derive it based upon getting the average number of bytes per record. Below I am figuring out the number of bytes per record to figure the number of records for 1024 MBs. I would try it first with 1024MBs per partition, then move upwards.

import org.apache.spark.util.SizeEstimator

val numberBytes : Long = SizeEstimator.estimate(inputDF.rdd)
val reduceBytesTo1024MB = numberBytes/123217728
val numberRecords = inputDF.count
val recordsFor1024MB = (numberRecords/reduceBytesTo1024MB).toInt + 1 

(3) [I haven't tried this] EMR Committer - if you are using EMR 5.19 or higher, since you are outputting Parquet. You can set the Parquet optimized writer to TRUE.

spark.sql.parquet.fs.optimized.committer.optimization-enabled true