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[GitHub] [spark] imback82 opened a new pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
imback82 opened a new pull request #30815:
URL: https://github.com/apache/spark/pull/30815
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### What changes were proposed in this pull request?
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This PR proposes to update `CACHE TABLE` to use a `LogicalPlan` when caching a query to avoid creating a `DataFrame` as suggested here: https://github.com/apache/spark/pull/30743#discussion_r543123190
For reference, `UNCACHE TABLE` also uses `LogicalPlan`: https://github.com/apache/spark/blob/0c129001201ccb63ae96f576b6f354da84024fb3/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/CacheTableExec.scala#L91-L98
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To avoid creating an unnecessary dataframe and make it consistent with `uncacheQuery` used in `UNCACHE TABLE`.
### Does this PR introduce _any_ user-facing change?
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No, just internal changes.
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Existing tests since this is an internal refactoring change.
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[GitHub] [spark] SparkQA commented on pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/spark/pull/30815#issuecomment-747193234
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[GitHub] [spark] sunchao commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
sunchao commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r545302857
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,34 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQuery(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQuery(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String]): Unit = {
+ cacheQuery(spark, planToCache, tableName, MEMORY_AND_DISK)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ */
+ def cacheQuery(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String],
+ storageLevel: StorageLevel): Unit = {
Review comment:
Ah got it. Thanks.
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[GitHub] [spark] cloud-fan closed pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
cloud-fan closed pull request #30815:
URL: https://github.com/apache/spark/pull/30815
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[GitHub] [spark] cloud-fan commented on pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on pull request #30815:
URL: https://github.com/apache/spark/pull/30815#issuecomment-747860684
thanks, merging to master!
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[GitHub] [spark] dongjoon-hyun commented on pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
dongjoon-hyun commented on pull request #30815:
URL: https://github.com/apache/spark/pull/30815#issuecomment-747590478
cc @sunchao
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* This patch **fails from timeout after a configured wait of `500m`**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] [spark] imback82 commented on pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
imback82 commented on pull request #30815:
URL: https://github.com/apache/spark/pull/30815#issuecomment-747207300
cc @cloud-fan, thanks!
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[GitHub] [spark] imback82 commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
imback82 commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r544818506
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,24 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQueryWithLogicalPlan(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQueryWithLogicalPlan(
Review comment:
Unfortunately, we cannot because Scala doesn't allow overloading methods with default arguments.
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[GitHub] [spark] imback82 commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
imback82 commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r545359286
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,34 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQuery(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQuery(
Review comment:
thanks, updated.
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[GitHub] [spark] cloud-fan commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r545302003
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,34 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQuery(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQuery(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String]): Unit = {
+ cacheQuery(spark, planToCache, tableName, MEMORY_AND_DISK)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ */
+ def cacheQuery(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String],
+ storageLevel: StorageLevel): Unit = {
Review comment:
The scala compiler will complain if we do that. See https://github.com/apache/spark/pull/30815#discussion_r544815956
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[GitHub] [spark] sunchao commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
sunchao commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r545298884
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,34 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQuery(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQuery(
Review comment:
+1 on this! I think we may replace one more usage in `DataSourceV2Strategy.invalidateCache` as well.
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,34 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQuery(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQuery(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String]): Unit = {
+ cacheQuery(spark, planToCache, tableName, MEMORY_AND_DISK)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ */
+ def cacheQuery(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String],
+ storageLevel: StorageLevel): Unit = {
Review comment:
perhaps we can just keep a single method with default value of `tableName` being `None` and `storageLevel` being `MEMORY_AND_DISK`?
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[GitHub] [spark] imback82 commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
imback82 commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r544822268
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,24 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQueryWithLogicalPlan(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQueryWithLogicalPlan(
Review comment:
Ah, I think we can do the folllowing:
```scala
def cacheQuery(
spark: SparkSession,
planToCache: LogicalPlan,
tableName: Option[String]): Unit = {
cacheQuery(spark, planToCache, tableName, MEMORY_AND_DISK)
}
def cacheQuery(
spark: SparkSession,
planToCache: LogicalPlan,
tableName: Option[String],
storageLevel: StorageLevel)
```
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Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/spark/pull/30815#issuecomment-747796225
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* This patch passes all tests.
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[GitHub] [spark] cloud-fan commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r544822162
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,24 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQueryWithLogicalPlan(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQueryWithLogicalPlan(
Review comment:
Ah, got it. Then how about `cacheLogicalPlan` to be simpler?
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[GitHub] [spark] cloud-fan commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r544815956
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,24 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQueryWithLogicalPlan(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQueryWithLogicalPlan(
Review comment:
shall we just call it `cacheQuery` to be consistent with `uncacheQuery`?
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[GitHub] [spark] cloud-fan commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r544822490
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,24 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQueryWithLogicalPlan(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQueryWithLogicalPlan(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String] = None,
Review comment:
does this method need default parameter values?
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Posted by GitBox <gi...@apache.org>.
cloud-fan commented on pull request #30815:
URL: https://github.com/apache/spark/pull/30815#issuecomment-747484985
retest this please
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[GitHub] [spark] AmplabJenkins commented on pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
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[GitHub] [spark] imback82 commented on a change in pull request #30815: [SPARK-33817][SQL] CACHE TABLE uses a logical plan when caching a query to avoid creating a dataframe
Posted by GitBox <gi...@apache.org>.
imback82 commented on a change in pull request #30815:
URL: https://github.com/apache/spark/pull/30815#discussion_r544823257
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
##########
@@ -88,12 +88,24 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
query: Dataset[_],
tableName: Option[String] = None,
storageLevel: StorageLevel = MEMORY_AND_DISK): Unit = {
- val planToCache = query.logicalPlan
+ cacheQueryWithLogicalPlan(query.sparkSession, query.logicalPlan, tableName, storageLevel)
+ }
+
+ /**
+ * Caches the data produced by the given [[LogicalPlan]].
+ * Unlike `RDD.cache()`, the default storage level is set to be `MEMORY_AND_DISK` because
+ * recomputing the in-memory columnar representation of the underlying table is expensive.
+ */
+ def cacheQueryWithLogicalPlan(
+ spark: SparkSession,
+ planToCache: LogicalPlan,
+ tableName: Option[String] = None,
Review comment:
I think https://github.com/apache/spark/pull/30815/files#r544822268 should work. Let me update this PR. Thanks!
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