Hi Tzahi, that is a huge cost. So that I can understand the question before answering it: 1. what is the SPARK version that you are using? 2. what is the SQL code that you are using to read and write?
There are several other questions that are pertinent, but the above will be a great starting point. Regards, Gourav Sengupta On Tue, Apr 6, 2021 at 7:46 PM Tzahi File <tzahi.f...@ironsrc.com> wrote: > Hi All, > > We have a spark cluster on aws ec2 that has 60 X i3.4xlarge. > > The spark job running on that cluster reads from an S3 bucket and writes > to that bucket. > > the bucket and the ec2 run in the same region. > > As part of our efforts to reduce the runtime of our spark jobs we found > there's serious latency when reading from S3. > > When the job: > > - reads the parquet files from S3 and also writes to S3, it takes 22 > min > - reads the parquet files from S3 and writes to its local hdfs, it > takes the same amount of time (±22 min) > - reads the parquet files from S3 (they were copied into the hdfs > before) and writes to its local hdfs, the job took 7 min > > the spark job has the following S3-related configuration: > > - spark.hadoop.fs.s3a.connection.establish.timeout=5000 > - spark.hadoop.fs.s3a.connection.maximum=200 > > when reading from S3 we tried to increase the > spark.hadoop.fs.s3a.connection.maximum config param from 200 to 400 or 900 > but it didn't reduce the S3 latency. > > Do you have any idea for the cause of the read latency from S3? > > I saw this post > <https://aws.amazon.com/premiumsupport/knowledge-center/s3-transfer-data-bucket-instance/> > to > improve the transfer speed, is something here relevant? > > > Thanks, > Tzahi >
