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https://issues.apache.org/jira/browse/SPARK-21797?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16141483#comment-16141483
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Steve Loughran commented on SPARK-21797:
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bq. According to our test, it is 20% slower maximum to read parquet data from
S3 than HDFS. Do you agree?
You are testing the amazon EMR client. try with the Hadoop 2.8 JARs and the s3a
client, enable columnar store optimised seek with
spark.hadoop.fs.s3a.experimental.fadvise=random & see how things compare then.
You will still be at a disadvantage with any directory scanning/walking which
can take seconds rather than millis, and seeks are still expensive as you have
to issue new HTTP requests with different content ranges. And of course, AWS
throttles your VMs and shard-specific access to subtrees of a single bucket.
HDFS locally still wins
> spark cannot read partitioned data in S3 that are partly in glacier
> -------------------------------------------------------------------
>
> Key: SPARK-21797
> URL: https://issues.apache.org/jira/browse/SPARK-21797
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.2.0
> Environment: Amazon EMR
> Reporter: Boris Clémençon
> Labels: glacier, partitions, read, s3
>
> I have a dataset in parquet in S3 partitioned by date (dt) with oldest date
> stored in AWS Glacier to save some money. For instance, we have...
> {noformat}
> s3://my-bucket/my-dataset/dt=2017-07-01/ [in glacier]
> ...
> s3://my-bucket/my-dataset/dt=2017-07-09/ [in glacier]
> s3://my-bucket/my-dataset/dt=2017-07-10/ [not in glacier]
> ...
> s3://my-bucket/my-dataset/dt=2017-07-24/ [not in glacier]
> {noformat}
> I want to read this dataset, but only a subset of date that are not yet in
> glacier, eg:
> {code:java}
> val from = "2017-07-15"
> val to = "2017-08-24"
> val path = "s3://my-bucket/my-dataset/"
> val X = spark.read.parquet(path).where(col("dt").between(from, to))
> {code}
> Unfortunately, I have the exception
> {noformat}
> java.io.IOException:
> com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception:
> The operation is not valid for the object's storage class (Service: Amazon
> S3; Status Code: 403; Error Code: InvalidObjectState; Request ID:
> C444D508B6042138)
> {noformat}
> I seems that spark does not like partitioned dataset when some partitions are
> in Glacier. I could always read specifically each date, add the column with
> current date and reduce(_ union _) at the end, but not pretty and it should
> not be necessary.
> Is there any tip to read available data in the datastore even with old data
> in glacier?
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