[jira] [Updated] (SPARK-21797) spark cannot read partitioned data in S3 that are partly in glacier
[
https://issues.apache.org/jira/browse/SPARK-21797?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Steve Loughran updated SPARK-21797:
---
Environment: Amazon EMR
> 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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[jira] [Updated] (SPARK-21797) spark cannot read partitioned data in S3 that are partly in glacier
[
https://issues.apache.org/jira/browse/SPARK-21797?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Boris Clémençon updated SPARK-21797:
-
Priority: Major (was: Critical)
> 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
>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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[jira] [Updated] (SPARK-21797) spark cannot read partitioned data in S3 that are partly in glacier
[
https://issues.apache.org/jira/browse/SPARK-21797?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Boris Clémençon updated SPARK-21797:
-
Description:
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?
was:
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 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?
> 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
>Reporter: Boris Clémençon
>Priority: Critical
> 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?
--
This message was sent by Atlassian JIRA
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[jira] [Updated] (SPARK-21797) spark cannot read partitioned data in S3 that are partly in glacier
[
https://issues.apache.org/jira/browse/SPARK-21797?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Boris Clémençon updated SPARK-21797:
-
Description:
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 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?
was:
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 the 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 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?
> 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
>Reporter: Boris Clémençon
>Priority: Critical
> 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 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?
--
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