GitHub user yhuai opened a pull request:
https://github.com/apache/spark/pull/14064
[SPARK-15968][SQL] Nonempty partitioned metastore tables are not cached
This PR backports your fix (https://github.com/apache/spark/pull/13818) to
branch 2.0.
This PR addresses
[SPARK-15968](https://issues.apache.org/jira/browse/SPARK-15968).
## What changes were proposed in this pull request?
The `getCached` method of
[HiveMetastoreCatalog](https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala)
computes `pathsInMetastore` from the metastore relation's catalog table. This
only returns the table base path, which is incomplete/inaccurate for a nonempty
partitioned table. As a result, cached lookups on nonempty partitioned tables
always miss.
Rather than get `pathsInMetastore` from
metastoreRelation.catalogTable.storage.locationUri.toSeq
I modified the `getCached` method to take a `pathsInMetastore` argument.
Calls to this method pass in the paths computed from calls to the Hive
metastore. This is how `getCached` was implemented in Spark 1.5:
https://github.com/apache/spark/blob/e0c3212a9b42e3e704b070da4ac25b68c584427f/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala#L444.
I also added a call in `InsertIntoHiveTable.scala` to invalidate the table
from the SQL session catalog.
## How was this patch tested?
I've added a new unit test to `parquetSuites.scala`:
SPARK-15968: nonempty partitioned metastore Parquet table lookup should
use cached relation
Note that the only difference between this new test and the one above it in
the file is that the new test populates its partitioned table with a single
value, while the existing test leaves the table empty. This reveals a subtle,
unexpected hole in test coverage present before this patch.
Note I also modified a different but related unit test in
`parquetSuites.scala`:
SPARK-15248: explicitly added partitions should be readable
This unit test asserts that Spark SQL should return data from a table
partition which has been placed there outside a metastore query immediately
after it is added. I changed the test so that, instead of adding the data as a
parquet file saved in the partition's location, the data is added through a SQL
`INSERT` query. I made this change because I could find no way to efficiently
support partitioned table caching without failing that test.
In addition to my primary motivation, I can offer a few reasons I believe
this is an acceptable weakening of that test. First, it still validates a fix
for [SPARK-15248](https://issues.apache.org/jira/browse/SPARK-15248), the issue
for which it was written. Second, the assertion made is stronger than that
required for non-partitioned tables. If you write data to the storage location
of a non-partitioned metastore table without using a proper SQL DML query, a
subsequent call to show that data will not return it. I believe this is an
intentional limitation put in place to make table caching feasible, but I'm
only speculating.
Building a large `HadoopFsRelation` requires `stat`-ing all of its data
files. In our environment, where we have tables with 10's of thousands of
partitions, the difference between using a cached relation versus a new one is
a matter of seconds versus minutes. Caching partitioned table metadata vastly
improves the usability of Spark SQL for these cases.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/yhuai/spark spark-15968-branch-2.0
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/14064.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #14064
----
commit 02aa39cf56d37f800803c5d8f0e92510d21e8af3
Author: Michael Allman <[email protected]>
Date: 2016-07-05T01:49:25Z
[SPARK-15968][SQL] Nonempty partitioned metastore tables are not cached
(Please note this is a revision of PR #13686, which has been closed in
favor of this PR.)
This PR addresses
[SPARK-15968](https://issues.apache.org/jira/browse/SPARK-15968).
## What changes were proposed in this pull request?
The `getCached` method of
[HiveMetastoreCatalog](https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala)
computes `pathsInMetastore` from the metastore relation's catalog table. This
only returns the table base path, which is incomplete/inaccurate for a nonempty
partitioned table. As a result, cached lookups on nonempty partitioned tables
always miss.
Rather than get `pathsInMetastore` from
metastoreRelation.catalogTable.storage.locationUri.toSeq
I modified the `getCached` method to take a `pathsInMetastore` argument.
Calls to this method pass in the paths computed from calls to the Hive
metastore. This is how `getCached` was implemented in Spark 1.5:
https://github.com/apache/spark/blob/e0c3212a9b42e3e704b070da4ac25b68c584427f/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala#L444.
I also added a call in `InsertIntoHiveTable.scala` to invalidate the table
from the SQL session catalog.
## How was this patch tested?
I've added a new unit test to `parquetSuites.scala`:
SPARK-15968: nonempty partitioned metastore Parquet table lookup should
use cached relation
Note that the only difference between this new test and the one above it in
the file is that the new test populates its partitioned table with a single
value, while the existing test leaves the table empty. This reveals a subtle,
unexpected hole in test coverage present before this patch.
Note I also modified a different but related unit test in
`parquetSuites.scala`:
SPARK-15248: explicitly added partitions should be readable
This unit test asserts that Spark SQL should return data from a table
partition which has been placed there outside a metastore query immediately
after it is added. I changed the test so that, instead of adding the data as a
parquet file saved in the partition's location, the data is added through a SQL
`INSERT` query. I made this change because I could find no way to efficiently
support partitioned table caching without failing that test.
In addition to my primary motivation, I can offer a few reasons I believe
this is an acceptable weakening of that test. First, it still validates a fix
for [SPARK-15248](https://issues.apache.org/jira/browse/SPARK-15248), the issue
for which it was written. Second, the assertion made is stronger than that
required for non-partitioned tables. If you write data to the storage location
of a non-partitioned metastore table without using a proper SQL DML query, a
subsequent call to show that data will not return it. I believe this is an
intentional limitation put in place to make table caching feasible, but I'm
only speculating.
Building a large `HadoopFsRelation` requires `stat`-ing all of its data
files. In our environment, where we have tables with 10's of thousands of
partitions, the difference between using a cached relation versus a new one is
a matter of seconds versus minutes. Caching partitioned table metadata vastly
improves the usability of Spark SQL for these cases.
Thanks.
Author: Michael Allman <[email protected]>
Closes #13818 from mallman/spark-15968.
commit d53afa6b22325fe5d9ce0d526714f56347ccc054
Author: Reynold Xin <[email protected]>
Date: 2016-07-05T19:06:41Z
[HOTFIX] Fix build break.
----
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
