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?