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...
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]
I want to read this dataset, but only the a subset of date that are not yet in glacier, eg:
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))
Unfortunately, I have the exception
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)
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 it is ugly like hell and it should not be necessary.
Is there any tip to read available data in the datastore even with old data in glacier?
Error you are getting not related to Apache spark , you are getting exception because of Glacier service in short S3 objects in the Glacier storage class are not accessible in the same way as normal objects, they need to be retrieved from Glacier before they can be read.
Apache Spark cannot handle directly glacier storage TABLE/PARTITION mapped to an S3 .
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)
When S3 moves any objects from S3 storage classes
It is still an S3 object, but has the GLACIER storage class.
When you need to access one of these objects, you initiate a restore,
which temporary copy into S3 .
Move data into S3 bucket read into Apache Spark will resolve your issue.
- https://aws.amazon.com/s3/storage-classes/
Note : Apache Spark , AWS athena etc cannot read object directly from glacier if you try will get 403 error.
If you archive objects using the Glacier storage option, you must
inspect the storage class of an object before you attempt to retrieve
it. The customary GET request will work as expected if the object is
stored in S3 Standard or Reduced Redundancy (RRS) storage. It will
fail (with a 403 error) if the object is archived in Glacier. In this
case, you must use the RESTORE operation (described below) to make
your data available in S3.
- https://aws.amazon.com/blogs/aws/archive-s3-to-glacier/
403 error is due to the fact you can not read object that is archieve in Glacier, source
Reading Files from Glacier
If you want to read files from Glacier, you need to restore them to s3 before using them in Apache Spark, a copy will be available on s3 for the time mentioned during restore command, for details see here, you can use S3 console, cli or any language to do that too
Discarding some Glacier files that you do not want to restore
Let's say you do not want to restore all the files from Glacier and discard them during processing, from Spark 2.1.1, 2.2.0 you can ignore those files (with IO/Runtime Exception), by setting spark.sql.files.ignoreCorruptFiles to true source
The S3 connectors from Amazon (s3://) and the ASF (s3a://) don't work with Glacier. Certainly nobody tests s3a against glacier. and if there were problems, you'd be left to fix them yourself. Just copy the data into s3 or onto local HDFS and then work with it there
If you define your table through Hive, and use the Hive metastore catalog to query it, it won't try to go onto the non selected partitions.
Take a look at the spark.sql.hive.metastorePartitionPruning setting
