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[SPARK-23598][SQL] Make methods in BufferedRowIterator public to avoid runtime error for a large query #20779

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[SPARK-23598][SQL] Make methods in BufferedRowIterator public to avoid runtime error for a large query #20779

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e206fff
initial commit
kiszk Mar 9, 2018
dccae53
add test case
kiszk Mar 9, 2018
6e45791
address review comments
kiszk Mar 10, 2018
603ce0f
address review comment
kiszk Mar 10, 2018
8fb5df0
address review comment
kiszk Mar 13, 2018
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8 changes: 4 additions & 4 deletions sql/core/src/main/java/org/apache/spark/sql/execution/BufferedRowIterator.java
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Expand Up @@ -65,7 +65,7 @@ public long durationMs() {
/**
* Append a row to currentRows.
*/
protected void append(InternalRow row) {
public void append(InternalRow row) {

@viirya viirya Mar 13, 2018

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nit: Although we added the test, should we also add a short sentence that said this is public so inner classes can also access it?

currentRows.add(row);
}

Expand All @@ -75,7 +75,7 @@ protected void append(InternalRow row) {
* If it returns true, the caller should exit the loop that [[InputAdapter]] generates.
* This interface is mainly used to limit the number of input rows.
*/
protected boolean stopEarly() {
public boolean stopEarly() {
return false;
}

Expand All @@ -84,14 +84,14 @@ protected boolean stopEarly() {
*
* If it returns true, the caller should exit the loop (return from processNext()).
*/
protected boolean shouldStop() {
public boolean shouldStop() {
return !currentRows.isEmpty();
}

/**
* Increase the peak execution memory for current task.
*/
protected void incPeakExecutionMemory(long size) {
public void incPeakExecutionMemory(long size) {
TaskContext.get().taskMetrics().incPeakExecutionMemory(size);
}

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45 changes: 45 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/WholeStageCodegenSuite.scala
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Expand Up @@ -32,6 +32,8 @@ import org.apache.spark.sql.types.{IntegerType, StringType, StructType}

class WholeStageCodegenSuite extends QueryTest with SharedSQLContext {

import testImplicits._

test("range/filter should be combined") {
val df = spark.range(10).filter("id = 1").selectExpr("id + 1")
val plan = df.queryExecution.executedPlan
Expand Down Expand Up @@ -307,4 +309,47 @@ class WholeStageCodegenSuite extends QueryTest with SharedSQLContext {
// a different query can result in codegen cache miss, that's by design
}
}

test("SPARK-23598: Avoid compilation error with a lot of aggregation operations") {

@dvogelbacher dvogelbacher Mar 9, 2018

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It's not a compilation but a runtime error. maybe better say: codegen working for lots of aggregation operations without runtime errors

withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
val df = Seq((8, "bat"), (15, "mouse"), (5, "horse")).toDF("age", "name")
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))

@mgaido91 mgaido91 Mar 9, 2018

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can we do this with a loop?

.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).groupBy("name").agg(avg("age").alias("age"))
.groupBy("name").agg(avg("age").alias("age")).limit(1)
assert(df.collect() === Array(Row("bat", 8.0)))
}
}
}