Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/10835#discussion_r50788584
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/columnar/PartitionBatchPruningSuite.scala
---
@@ -32,30 +39,41 @@ class PartitionBatchPruningSuite extends SparkFunSuite
with SharedSQLContext {
super.beforeAll()
// Make a table with 5 partitions, 2 batches per partition, 10
elements per batch
sqlContext.setConf(SQLConf.COLUMN_BATCH_SIZE, 10)
-
- val pruningData = sparkContext.makeRDD((1 to 100).map { key =>
- val string = if (((key - 1) / 10) % 2 == 0) null else key.toString
- TestData(key, string)
- }, 5).toDF()
- pruningData.registerTempTable("pruningData")
-
// Enable in-memory partition pruning
sqlContext.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
// Enable in-memory table scan accumulators
sqlContext.setConf("spark.sql.inMemoryTableScanStatistics.enable",
"true")
- sqlContext.cacheTable("pruningData")
}
override protected def afterAll(): Unit = {
try {
sqlContext.setConf(SQLConf.COLUMN_BATCH_SIZE,
originalColumnBatchSize)
sqlContext.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING,
originalInMemoryPartitionPruning)
- sqlContext.uncacheTable("pruningData")
} finally {
super.afterAll()
}
}
+ override protected def beforeEach(): Unit = {
+ super.beforeEach()
+ // This creates accumulators, which get cleaned up after every single
test,
--- End diff --
no, just here
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