Mixing
Spark S3 Eventual Consistency Issues
I have several Spark jobs that write data to and read data from S3. Occasionally (about once per week for approximately 3 hours), the Spark jobs will fail with the following exception:
org.apache.spark.sql.AnalysisException: Path does not exist.
I've uncovered that this is likely due to the consistency model in S3, where list operations are eventually consistent. S3 Guard claims to solve this issue, but I'm in a Spark environment that doesn't support that utility.
Has anyone else run into this issue and figured out a reasonable approach for dealing with it?
apache-spark amazon-s3
asked Nov 19 '18 at 13:51
Weston Sankey
1749
add a comment |
I have several Spark jobs that write data to and read data from S3. Occasionally (about once per week for approximately 3 hours), the Spark jobs will fail with the following exception:
org.apache.spark.sql.AnalysisException: Path does not exist.
I've uncovered that this is likely due to the consistency model in S3, where list operations are eventually consistent. S3 Guard claims to solve this issue, but I'm in a Spark environment that doesn't support that utility.
Has anyone else run into this issue and figured out a reasonable approach for dealing with it?
apache-spark amazon-s3
asked Nov 19 '18 at 13:51
Weston Sankey
1749
add a comment |
I have several Spark jobs that write data to and read data from S3. Occasionally (about once per week for approximately 3 hours), the Spark jobs will fail with the following exception:
org.apache.spark.sql.AnalysisException: Path does not exist.
I've uncovered that this is likely due to the consistency model in S3, where list operations are eventually consistent. S3 Guard claims to solve this issue, but I'm in a Spark environment that doesn't support that utility.
Has anyone else run into this issue and figured out a reasonable approach for dealing with it?
apache-spark amazon-s3
asked Nov 19 '18 at 13:51
Weston Sankey
1749
I have several Spark jobs that write data to and read data from S3. Occasionally (about once per week for approximately 3 hours), the Spark jobs will fail with the following exception:
org.apache.spark.sql.AnalysisException: Path does not exist.
I've uncovered that this is likely due to the consistency model in S3, where list operations are eventually consistent. S3 Guard claims to solve this issue, but I'm in a Spark environment that doesn't support that utility.
Has anyone else run into this issue and figured out a reasonable approach for dealing with it?
apache-spark amazon-s3
apache-spark amazon-s3
asked Nov 19 '18 at 13:51
Weston Sankey
1749
asked Nov 19 '18 at 13:51
Weston Sankey
1749
asked Nov 19 '18 at 13:51
Weston Sankey
1749
asked Nov 19 '18 at 13:51
Weston Sankey
1749
asked Nov 19 '18 at 13:51
Weston Sankey
1749
1749
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
- If you are using AWS EMR, they offer consistent EMR.
- if you are using Databricks: they offer a consistency mechanism in their transactional IO
- Both HDP and CDH ship with S3Guard
- if you are running your own home-rolled spark stack, , move to Hadoop 2.9+ to get S3Guard, even better: Hadoop 3.1 for the zero-rename S3A committer.
Otherwise: don't use S3 as your direct destination of work.
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
I'm using Databricks and have found that they don't have a reliable consistency mechanism when using a mounted S3 bucket.
– Weston Sankey
Nov 19 '18 at 17:58
I believe their transactional IO does; it's what I was thinking of. Added a link to it
– Steve Loughran
Nov 20 '18 at 15:02
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
- If you are using AWS EMR, they offer consistent EMR.
- if you are using Databricks: they offer a consistency mechanism in their transactional IO
- Both HDP and CDH ship with S3Guard
- if you are running your own home-rolled spark stack, , move to Hadoop 2.9+ to get S3Guard, even better: Hadoop 3.1 for the zero-rename S3A committer.
Otherwise: don't use S3 as your direct destination of work.
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
I'm using Databricks and have found that they don't have a reliable consistency mechanism when using a mounted S3 bucket.
– Weston Sankey
Nov 19 '18 at 17:58
I believe their transactional IO does; it's what I was thinking of. Added a link to it
– Steve Loughran
Nov 20 '18 at 15:02
add a comment |
- If you are using AWS EMR, they offer consistent EMR.
- if you are using Databricks: they offer a consistency mechanism in their transactional IO
- Both HDP and CDH ship with S3Guard
- if you are running your own home-rolled spark stack, , move to Hadoop 2.9+ to get S3Guard, even better: Hadoop 3.1 for the zero-rename S3A committer.
Otherwise: don't use S3 as your direct destination of work.
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
I'm using Databricks and have found that they don't have a reliable consistency mechanism when using a mounted S3 bucket.
– Weston Sankey
Nov 19 '18 at 17:58
I believe their transactional IO does; it's what I was thinking of. Added a link to it
– Steve Loughran
Nov 20 '18 at 15:02
add a comment |
- If you are using AWS EMR, they offer consistent EMR.
- if you are using Databricks: they offer a consistency mechanism in their transactional IO
- Both HDP and CDH ship with S3Guard
- if you are running your own home-rolled spark stack, , move to Hadoop 2.9+ to get S3Guard, even better: Hadoop 3.1 for the zero-rename S3A committer.
Otherwise: don't use S3 as your direct destination of work.
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
- If you are using AWS EMR, they offer consistent EMR.
- if you are using Databricks: they offer a consistency mechanism in their transactional IO
- Both HDP and CDH ship with S3Guard
- if you are running your own home-rolled spark stack, , move to Hadoop 2.9+ to get S3Guard, even better: Hadoop 3.1 for the zero-rename S3A committer.
Otherwise: don't use S3 as your direct destination of work.
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
edited Nov 20 '18 at 15:01
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
answered Nov 19 '18 at 16:48
Steve Loughran
5,10211417
5,10211417
I'm using Databricks and have found that they don't have a reliable consistency mechanism when using a mounted S3 bucket.
– Weston Sankey
Nov 19 '18 at 17:58
I believe their transactional IO does; it's what I was thinking of. Added a link to it
– Steve Loughran
Nov 20 '18 at 15:02
add a comment |
I'm using Databricks and have found that they don't have a reliable consistency mechanism when using a mounted S3 bucket.
– Weston Sankey
Nov 19 '18 at 17:58
I believe their transactional IO does; it's what I was thinking of. Added a link to it
– Steve Loughran
Nov 20 '18 at 15:02
I'm using Databricks and have found that they don't have a reliable consistency mechanism when using a mounted S3 bucket.
– Weston Sankey
Nov 19 '18 at 17:58
I'm using Databricks and have found that they don't have a reliable consistency mechanism when using a mounted S3 bucket.
– Weston Sankey
Nov 19 '18 at 17:58
I believe their transactional IO does; it's what I was thinking of. Added a link to it
– Steve Loughran
Nov 20 '18 at 15:02
I believe their transactional IO does; it's what I was thinking of. Added a link to it
– Steve Loughran
Nov 20 '18 at 15:02
add a comment |
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