spark-instrumented-optimizer/sql/hive
Max Gekk e79c1cde1b [SPARK-34138][SQL] Keep dependants cached while refreshing v1 tables
### What changes were proposed in this pull request?
This PR changes cache refreshing of v1 tables in v1 commands. In particular, v1 table dependents are not removed from the cache after this PR. Comparing to current implementation, we just clear cached data of all dependents and keep them in the cache. So, the next actions will fill in the cached data of the original v1 table and its dependents. In more details:
1. Modified the `CatalogImpl.refreshTable()` method to use `recacheByPlan()` instead of `lookupCachedData()`, `uncacheQuery()` and `cacheQuery()`. Users can call this method via public API like `spark.catalog.refreshTable()`.
2. Rewritten the part in `CatalogImpl.refreshTable()` which was responsible for table meta-data refreshing because this code stopped to work properly after removing of the second `sparkSession.table(tableIdent)`.
3. Added new private method `invalidateCachedTable()` to `SessionCatalog`. Comparing to the existing `SessionCatalog.refreshTable`, it invalidates the relation cache only. If we called `SessionCatalog.refreshTable` from `CatalogImpl.refreshTable()`, we would refresh temporary and global temporary views twice (that could lead to refreshing file index twice).

### Why are the changes needed?
1. This should improve user experience with table/view caching. For example, let's imagine that an user has cached v1 table and cached view based on the table. And the user passed the table to external library which drops/renames/adds partitions in the v1 table. Unfortunately, the user gets the view uncached after that even he/she hasn't uncached the view explicitly.
2. To improve code maintenance.
3. To reduce the amount of calls to Hive external catalog.
4. Also this should speed up table recaching.
5. To have the same behavior as for v2 tables supported by https://github.com/apache/spark/pull/31172

### Does this PR introduce _any_ user-facing change?
From the view of the correctness of query results, there are no behavior changes but the changes might influence on consuming memory and query execution time. For example:

Before:
```scala
scala> sql("CREATE TABLE tbl (c int)")
scala> sql("CACHE TABLE tbl")
scala> sql("CREATE VIEW v AS SELECT * FROM tbl")
scala> sql("CACHE TABLE v")

scala> spark.catalog.isCached("v")
res6: Boolean = true
scala> spark.catalog.refreshTable("tbl")

scala> spark.catalog.isCached("v")
res8: Boolean = false
```

After:
```scala
scala> spark.catalog.refreshTable("tbl")

scala> spark.catalog.isCached("v")
res8: Boolean = true
```

### How was this patch tested?
1. Added new unit tests that create a view, a temporary view and a global temporary view on top of v1/v2 tables, and refresh the base table via `ALTER TABLE .. ADD/DROP/RENAME PARTITION`.
2. By running the unified test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableAddPartitionSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
# build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenamePartitionSuite"
```

Closes #31206 from MaxGekk/refreshTable-recache-by-plan.

Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2021-01-21 13:03:24 +00:00
..
benchmarks [SPARK-20202][BUILD][SQL] Remove references to org.spark-project.hive (Hive 1.2.1) 2020-10-05 15:29:56 -07:00
compatibility/src/test/scala/org/apache/spark/sql/hive/execution [SPARK-33428][SQL] Conv UDF use BigInt to avoid Long value overflow 2020-12-14 14:32:08 +00:00
src [SPARK-34138][SQL] Keep dependants cached while refreshing v1 tables 2021-01-21 13:03:24 +00:00
pom.xml [SPARK-27733][CORE] Upgrade Avro to version 1.10.1 2021-01-20 15:42:27 -08:00