Hey, Thanks for the responses, guys!
On Thu, Jul 6, 2017 at 7:08 AM, Steve Loughran <ste...@hortonworks.com> wrote: > > On 5 Jul 2017, at 14:40, Vadim Semenov <vadim.seme...@datadoghq.com> > wrote: > > Are you sure that you use S3A? > Because EMR says that they do not support S3A > > https://aws.amazon.com/premiumsupport/knowledge-center/emr-file-system-s3/ > > Amazon EMR does not currently support use of the Apache Hadoop S3A file > system. > > Oof. I figured they didn't offer technical support for S3A, but didn't know that there was something saying EMR does not support use of S3A. My impression was that many people were using it and it's the recommended S3 library in Hadoop 2.7+ <https://wiki.apache.org/hadoop/AmazonS3> from Hadoop's point of view. We're using it rather than S3N because we use encrypted buckets, and I don't think S3N supports picking up credentials from a machine role. Also, it was a bit distressing that it's unmaintained and has open bugs. We're S3A rather than EMRFS because we have a setup where we submit work to a cluster via spark-submit run outside the cluster master node with --master yarn. When you do this, the Hadoop configuration accessible to spark-submit overrides that of the EMR cluster itself. If you use a configuration that uses EMRFS and any of the resources (like the JAR) you give to spark-submit are on S3, spark-submit will instantiate the EMRFS FileSystem impl, which is currently only available on the cluster, and fail. That said, we could work around this by resetting the configuration in code. > > I think that the HEAD requests come from the `createBucketIfNotExists` in > the AWS S3 library that checks if the bucket exists every time you do a PUT > request, i.e. creates a HEAD request. > > You can disable that by setting `fs.s3.buckets.create.enabled` to `false` > http://docs.aws.amazon.com/emr/latest/ManagementGuide/emr- > plan-upload-s3.html > > Oh, interesting. We are definitely seeing a ton of HEAD requests, which might be that. It looks like the `fs.s3.buckets.create.enabled` is an EMRFS option, though, not one common to the Hadoop S3 FileSystem implementations. Does that sound right? > > > > > Yeah, I'd like to see the stack traces before blaming S3A and the ASF > codebase > (Sorry, to be clear -- I'm not trying to blame S3A. I figured someone else might've hit this and bet we had just misconfigured something or were doing this the wrong way.) > > One thing I do know is that the shipping S3A client doesn't have any > explicit handling of 503/retry events. I know that: > https://issues.apache.org/jira/browse/HADOOP-14531 > > There is some retry logic in bits of the AWS SDK related to file upload: > that may log and retry, but in all the operations listing files, getting > their details, etc: no resilience to throttling. > > If it is surfacing against s3a, there isn't anything which can immediately > be done to fix it, other than "spread your data around more buckets". Do > attach the stack trace you get under https://issues.apache.or > g/jira/browse/HADOOP-14381 though: I'm about half-way through the > resilience code (& fault injection needed to test it). The more where I can > see problems arise, the more confident I can be that those codepaths will > be resilient. > Will do! We did end up finding that some of our jobs were sharding data way too finely, ending up with 5-10k+ tiny Parquet shards per table. This happened when we unioned many Spark DataFrames together without doing a repartition or coalesce afterwards. After throwing in a repartition (to additionally balance the output shards) we haven't seen the error, again, but our graphs of S3 HEAD requests are still rather alarmingly high. > > > On Thu, Jun 29, 2017 at 4:56 PM, Everett Anderson < > ever...@nuna.com.invalid> wrote: > >> Hi, >> >> We're using Spark 2.0.2 + Hadoop 2.7.3 on AWS EMR with S3A for direct I/O >> from/to S3 from our Spark jobs. We set >> mapreduce.fileoutputcommitter.algorithm.version=2 >> and are using encrypted S3 buckets. >> >> This has been working fine for us, but perhaps as we've been running more >> jobs in parallel, we've started getting errors like >> >> Status Code: 503, AWS Service: Amazon S3, AWS Request ID: ..., AWS Error >> Code: SlowDown, AWS Error Message: Please reduce your request rate., S3 >> Extended Request ID: ... >> >> We enabled CloudWatch S3 request metrics for one of our buckets and I was >> a little alarmed to see spikes of over 800k S3 requests over a minute or >> so, with the bulk of them HEAD requests. >> >> We read and write Parquet files, and most tables have around 50 >> shards/parts, though some have up to 200. I imagine there's additional >> parallelism when reading a shard in Parquet, though. >> >> Has anyone else encountered this? How did you solve it? >> >> I'd sure prefer to avoid copying all our data in and out of HDFS for each >> job, if possible. >> >> Thanks! >> >> > >
