[SPARK-26203][SQL][TEST] Benchmark performance of In and InSet expressions #23291

aokolnychyi · 2018-12-11T19:09:12Z

What changes were proposed in this pull request?

This PR contains benchmarks for In and InSet expressions. They cover literals of different data types and will help us to decide where to integrate the switch-based logic for bytes/shorts/ints.

As discussed in PR-23171, one potential approach is to convert In to InSet if all elements are literals independently of data types and the number of elements. According to the results of this PR, we might want to keep the threshold for the number of elements. The if-else approach approach might be faster for some data types on a small number of elements (structs? arrays? small decimals?).

byte / short / int / long

Unless the number of items is really big, InSet is slower than In because of autoboxing .

Interestingly, In scales worse on bytes/shorts than on ints/longs. For example, InSet starts to match the performance on around 50 bytes/shorts while this does not happen on the same number of ints/longs. This is a bit strange as shorts/bytes (e.g., (byte) 1, (short) 2) are represented as ints in the bytecode.

float / double

Use cases on floats/doubles also suffer from autoboxing. Therefore, In outperforms InSet on 10 elements.

Similarly to shorts/bytes, In scales worse on floats/doubles than on ints/longs because the equality condition is more complicated (e.g., java.lang.Float.isNaN(filter_valueArg_0) && java.lang.Float.isNaN(9.0F)) || filter_valueArg_0 == 9.0F).

decimal

The reason why we have separate benchmarks for small and large decimals is that Spark might use longs to represent decimals in some cases.

If this optimization happens, then equals will be nothing else as comparing longs. If this does not happen, Spark will create an instance of scala.BigDecimal and use it for comparisons. The latter is more expensive.

Decimal$hashCode will always use scala.BigDecimal$hashCode even if the number is small enough to fit into a long variable. As a consequence, we see that use cases on small decimals are faster with In as they are using long comparisons under the hood. Large decimal values are always faster with InSet.

string

UTF8String$equals is not cheap. Therefore, In does not really outperform InSet as in previous use cases.

timestamp / date

Under the hood, timestamp/date values will be represented as long/int values. So, In allows us to avoid autoboxing.

array

Arrays are working as expected. In is faster on 5 elements while InSet is faster on 15 elements. The benchmarks are using UnsafeArrayData.

struct

InSet is always faster than In for structs. These benchmarks use GenericInternalRow.

dongjoon-hyun · 2018-12-11T19:13:45Z

Thank you for making the benchmark, @aokolnychyi . I'll take a look.

SparkQA · 2018-12-11T19:23:08Z

Test build #99987 has finished for PR 23291 at commit 6a5e992.

This patch fails to generate documentation.
This patch merges cleanly.
This patch adds no public classes.

dongjoon-hyun · 2018-12-11T19:35:06Z

sql/core/src/test/scala/org/apache/spark/sql/InSetBenchmark.scala

+    df.createOrReplaceTempView("t")
+
+    def testClosure(): Unit = {
+      val df = spark.sql(s"SELECT * FROM t WHERE id IN (${values mkString ","})")


values mkString "," -> values.mkString(",")

Yep, will update that.

dongjoon-hyun · 2018-12-11T19:35:57Z

sql/core/src/test/scala/org/apache/spark/sql/InSetBenchmark.scala

+
+  override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
+    val largeNumRows = 10000000
+    val smallNumRows = 1000000


Is smallNumRows used simply in order to reduce the benchmark time?

Yes, exactly, smallNumRows is used with data types that take longer to process.

dongjoon-hyun · 2018-12-11T19:40:06Z

sql/core/src/test/scala/org/apache/spark/sql/InSetBenchmark.scala

+
+      intBenchmark(numItems = 10, largeNumRows, minNumIters).run()
+      intBenchmark(numItems = 50, largeNumRows, minNumIters).run()
+      intBenchmark(numItems = 250, largeNumRows, minNumIters).run()


Ur, can we use a consistent set of numItems if there is no other reason?
Here, we already used three different sets; byte -> (10,50), short -> (10, 100), int -> (10, 50, 250).

What about (10,50) for bytes/shorts and (10, 50, 250) for ints/longs?

Since the default value of OPTIMIZER_INSET_CONVERSION_THRESHOLD is 10, what about (10, 25, 50, 100, 200) for all types? We had better collect more data points (if possible) because this is a benchmark and not a part of UTs.

If there are no objections w.r.t. to increasing the execution time, it would make sense to have more data points, I agree.

What if we follow the pattern (10, 25, 50, 100, 200) but stop as soon as InSet starts to outperform In? For example, InSet starts to match the performance of In on 50 bytes. If we run the test on 100 bytes, InSet will be faster than In. So, I propose to drop the case with 200 elements as the outcome is clear and it will just increase the benchmarking time.

Non-determinism is not good for benchmark because everything(Spark/JVM/HW) will change. BTW, at the final step, I will run this benchmark on Amazon EC2 machine, too.

stop as soon as InSet starts to outperform In

dongjoon-hyun · 2018-12-11T19:49:46Z

sql/core/src/test/scala/org/apache/spark/sql/InSetBenchmark.scala

+    val name = s"$numItems ints"
+    val values = 1 to numItems
+    val df = spark.range(1, numRows).select($"id".cast(IntegerType))
+    benchmark(name, df, values, numRows, minNumIters)


nit. Shall we invoke run here intead of intBenchmark(...).run()? It seems to be repeated multiple times.

- benchmark(name, df, values, numRows, minNumIters) + benchmark(name, df, values, numRows, minNumIters).run()

We can do this. Shall we rename intBenchmark to runIntBenchmark then? There is no consistency in existing benchmarks, unfortunately.

+1 for renaming. Yep. We overlooked the naming consistency in the previous benchmarks.

dongjoon-hyun · 2018-12-11T19:50:28Z

sql/core/src/test/scala/org/apache/spark/sql/InSetBenchmark.scala

+
+  import spark.implicits._
+
+  def byteBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {


def -> private def

SparkQA · 2018-12-11T23:35:56Z

Test build #99993 has finished for PR 23291 at commit 987bea4.

This patch fails to generate documentation.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2018-12-12T09:13:56Z

Test build #100016 has finished for PR 23291 at commit 1cbf93d.

This patch fails to generate documentation.
This patch merges cleanly.
This patch adds no public classes.

… docs

SparkQA · 2018-12-12T14:18:59Z

Test build #100019 has finished for PR 23291 at commit d9b46f3.

This patch fails SparkR unit tests.
This patch merges cleanly.
This patch adds no public classes.

aokolnychyi · 2018-12-12T14:23:52Z

I believe the last failure is not related to this PR as other PRs failed with the same error.

dongjoon-hyun · 2018-12-12T15:07:52Z

Right. Currently, we are retrying this in Jenkins, #23294 (comment) .

dongjoon-hyun · 2018-12-12T15:08:12Z

Retest this please.

SparkQA · 2018-12-12T18:45:17Z

Test build #100027 has finished for PR 23291 at commit d9b46f3.

This patch fails SparkR unit tests.
This patch merges cleanly.
This patch adds no public classes.

dongjoon-hyun · 2018-12-12T18:47:35Z

In this case, let's ignore the irrelevant SparkR failure for a while.

dongjoon-hyun · 2018-12-13T15:38:44Z

Retest this please

SparkQA · 2018-12-13T15:44:19Z

Test build #100099 has started for PR 23291 at commit d9b46f3.

aokolnychyi · 2018-12-13T22:38:19Z

Retest this please

SparkQA · 2018-12-13T22:41:51Z

Test build #100113 has started for PR 23291 at commit d9b46f3.

aokolnychyi · 2018-12-14T13:42:29Z

Retest this please

SparkQA · 2018-12-14T17:19:01Z

Test build #100153 has finished for PR 23291 at commit d9b46f3.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

aokolnychyi · 2018-12-18T09:12:20Z

@dongjoon-hyun could you, please, do another review round?

dongjoon-hyun · 2018-12-19T06:06:22Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+      runByteBenchmark(numItems = 10, largeNumRows, minNumIters)
+      runByteBenchmark(numItems = 25, largeNumRows, minNumIters)
+      runByteBenchmark(numItems = 50, largeNumRows, minNumIters)
+      runByteBenchmark(numItems = 100, largeNumRows, minNumIters)


It seems that we missed 200 only here.

I skipped this intentionally to avoid hitting overflow for byte values. We can change the way we generate bytes: (Byte.MinValue to Byte.MinValue + numItems).map(v => s"${v}Y"). It just looks a bit more involved and I wanted to keep it consistent with other cases. I do not mind changing, though. Let me know your opinion.

Oh, I got it. And, +1 for the new way starting from MinValue.

dongjoon-hyun · 2018-12-19T06:22:09Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+
+  private def runDoubleBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Unit = {
+    val name = s"$numItems doubles"
+    val values = 1.0 to numItems by 1.0


FractionalProxy.to is deprecated in Scala 2.12 and is already removed at Scala 2.13. Shall we avoid this syntax?

https://github.com/scala/scala/blob/2.12.x/src/library/scala/runtime/ScalaNumberProxy.scala#L73

dongjoon-hyun · 2018-12-19T06:36:49Z

sql/core/benchmarks/InExpressionBenchmark-results.txt

+200 shorts:                              Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
+------------------------------------------------------------------------------------------------
+In expression                                  259 /  265         38.6          25.9       1.0X
+InSet expression                               134 /  139         74.5          13.4       1.9X


It's good to compare In and InSet, but it seems to be difficult to see the trend. Let me think about this.

dongjoon-hyun · 2018-12-19T06:46:27Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+
+  private def runByteBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Unit = {
+    val name = s"$numItems bytes"
+    val values = (1 to numItems).map(v => s"CAST($v AS tinyint)")


Like line 64, shall we use literal? Y is the postfix for tinyint.

dongjoon-hyun · 2018-12-19T06:46:47Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+
+  private def runShortBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Unit = {
+    val name = s"$numItems shorts"
+    val values = (1 to numItems).map(v => s"CAST($v AS smallint)")


ditto. S is the postfix for small int literal.

dongjoon-hyun · 2018-12-19T06:48:44Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+    val name = s"$numItems doubles"
+    val values = 1.0 to numItems by 1.0
+    val df = spark.range(1, numRows).select($"id".cast(DoubleType))
+    runBenchmark(name, df, values, numRows, minNumIters)


Also, this will generate decimal. Please note that 1.0 is decimal(2,1). For double literal, please use 1.0D.

dongjoon-hyun

Hi, @aokolnychyi . I left a few comments and runDoubleBenchmark must be fixed.

dongjoon-hyun · 2018-12-19T06:54:01Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+      runShortBenchmark(numItems = 25, largeNumRows, minNumIters)
+      runShortBenchmark(numItems = 50, largeNumRows, minNumIters)
+      runShortBenchmark(numItems = 100, largeNumRows, minNumIters)
+      runShortBenchmark(numItems = 200, largeNumRows, minNumIters)


And, shorter is better.

Seq(5, 10, 25, 50, 100, 200).foreach { numItems => runShortBenchmark(numItems, largeNumRows, minNumIters) }

What about this?

override def runBenchmarkSuite(mainArgs: Array[String]): Unit = { val largeNumRows = 10000000 val smallNumRows = 1000000 val minNumIters = 5 runBenchmark("In Expression Benchmark") { Seq(5, 10, 25, 50, 100, 200).foreach { numItems => runByteBenchmark(numItems, largeNumRows, minNumIters) runShortBenchmark(numItems, largeNumRows, minNumIters) runIntBenchmark(numItems, largeNumRows, minNumIters) runLongBenchmark(numItems, largeNumRows, minNumIters) runFloatBenchmark(numItems, largeNumRows, minNumIters) runDoubleBenchmark(numItems, largeNumRows, minNumIters) runSmallDecimalBenchmark(numItems, smallNumRows, minNumIters) runLargeDecimalBenchmark(numItems, smallNumRows, minNumIters) runStringBenchmark(numItems, smallNumRows, minNumIters) runTimestampBenchmark(numItems, largeNumRows, minNumIters) runDateBenchmark(numItems, largeNumRows, minNumIters) runArrayBenchmark(numItems, smallNumRows, minNumIters) runStructBenchmark(numItems, smallNumRows, minNumIters) } } }

It'll be difficult to see the result. You need to go back and forward to see the single type trends.

SparkQA · 2018-12-22T15:32:23Z

Test build #100395 has finished for PR 23291 at commit 9842635.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

aokolnychyi · 2019-01-11T18:32:57Z

@dongjoon-hyun could you, please, do another pass?

dbtsai · 2019-01-11T21:17:52Z

LGTM for me. Waiting for @dongjoon-hyun for another pass.

dongjoon-hyun · 2019-01-12T05:03:44Z

Retest this please.

dongjoon-hyun · 2019-01-12T05:45:01Z

Hi, @aokolnychyi . I rebased this PR and refresh the result on EC2 instance. I made a PR to you. Could you review and merge aokolnychyi#1 ? For the other things, it looks good to me.

SparkQA · 2019-01-12T08:05:02Z

Test build #101116 has finished for PR 23291 at commit 9842635.

This patch fails due to an unknown error code, -9.
This patch merges cleanly.
This patch adds no public classes.

viirya · 2019-01-12T09:17:31Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions.{array, struct}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{ByteType, DateType, DecimalType, DoubleType, FloatType, IntegerType, ShortType, StringType, TimestampType}


Just org.apache.spark.sql.types._?

viirya · 2019-01-12T09:23:57Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+        runLargeDecimalBenchmark(numItems, smallNumRows, minNumIters)
+      }
+      numItemsSeq.foreach { numItems =>
+        runStringBenchmark(numItems, smallNumRows, minNumIters)


Why some benchmarks use large num rows, others use small num rows?

This is done to shorten the execution time for benchmarks that take longer. The ratio stays identical.

viirya · 2019-01-12T09:59:10Z

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/InExpressionBenchmark.scala

+  private def runStructBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Unit = {
+    val name = s"$numItems structs"
+    val values = (1 to numItems).map(i => s"struct($i)")
+    val df = spark.range(0, numRows).select(struct($"id".as("col1")).as("id"))


The range of numItems (max: 200) is relatively smaller than numRows (max: 10000000). So most of time, In/InSet expressions can't find the value in given list. I think it is naturally bad for In expression's performance since it compares all values in the list.

Is this a fair performance comparison between In and InSet expressions?

I think we are interested in the worst case performance. I believe this benchmark is close to a real use case as most of the time we search for a relatively small set of values in a huge distributed data set.

Can you add this description in the beginning comment of benchmark code? It is better to explain this is for worse case performance comparisons.

I am still not sure here. The main idea behind converting In to InSet is to avoid iterating through all elements. This benchmark mostly tests when this optimization works and when it makes things even worse. I tend to think that having In expression on a real data set that exits after a few branches is rather an edge case. I can be convinced but if there is no strong community's opinion on this, I would keep it as it is.

@viirya what about having this?

/** * A benchmark that compares the performance of different ways to evaluate SQL IN expressions. * * Specifically, this class compares the if-based approach, which might iterate through all items * inside the IN value list, to other options with better worst-case time complexities (e.g., sets). * * To run this benchmark: * {{{ * 1. without sbt: bin/spark-submit --class <this class> <spark sql test jar> * 2. build/sbt "sql/test:runMain <this class>" * 3. generate result: SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain <this class>" * Results will be written to "benchmarks/InExpressionBenchmark-results.txt". * }}} */

LGTM, thanks @aokolnychyi

EC2 result on Jan. 11th

aokolnychyi · 2019-01-14T08:42:05Z

@dongjoon-hyun thanks for the time to run this on EC2.

SparkQA · 2019-01-14T12:33:30Z

Test build #101173 has finished for PR 23291 at commit 79ade5d.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2019-01-14T12:35:44Z

Test build #101171 has finished for PR 23291 at commit b7e190a.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

dongjoon-hyun

Thank you, @aokolnychyi .
+1, LGTM. Merged to master.

SparkQA · 2019-01-15T19:44:37Z

Test build #101266 has finished for PR 23291 at commit 713f3db.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

…sions ## What changes were proposed in this pull request? This PR contains benchmarks for `In` and `InSet` expressions. They cover literals of different data types and will help us to decide where to integrate the switch-based logic for bytes/shorts/ints. As discussed in [PR-23171](apache#23171), one potential approach is to convert `In` to `InSet` if all elements are literals independently of data types and the number of elements. According to the results of this PR, we might want to keep the threshold for the number of elements. The if-else approach approach might be faster for some data types on a small number of elements (structs? arrays? small decimals?). ### byte / short / int / long Unless the number of items is really big, `InSet` is slower than `In` because of autoboxing . Interestingly, `In` scales worse on bytes/shorts than on ints/longs. For example, `InSet` starts to match the performance on around 50 bytes/shorts while this does not happen on the same number of ints/longs. This is a bit strange as shorts/bytes (e.g., `(byte) 1`, `(short) 2`) are represented as ints in the bytecode. ### float / double Use cases on floats/doubles also suffer from autoboxing. Therefore, `In` outperforms `InSet` on 10 elements. Similarly to shorts/bytes, `In` scales worse on floats/doubles than on ints/longs because the equality condition is more complicated (e.g., `java.lang.Float.isNaN(filter_valueArg_0) && java.lang.Float.isNaN(9.0F)) || filter_valueArg_0 == 9.0F`). ### decimal The reason why we have separate benchmarks for small and large decimals is that Spark might use longs to represent decimals in some cases. If this optimization happens, then `equals` will be nothing else as comparing longs. If this does not happen, Spark will create an instance of `scala.BigDecimal` and use it for comparisons. The latter is more expensive. `Decimal$hashCode` will always use `scala.BigDecimal$hashCode` even if the number is small enough to fit into a long variable. As a consequence, we see that use cases on small decimals are faster with `In` as they are using long comparisons under the hood. Large decimal values are always faster with `InSet`. ### string `UTF8String$equals` is not cheap. Therefore, `In` does not really outperform `InSet` as in previous use cases. ### timestamp / date Under the hood, timestamp/date values will be represented as long/int values. So, `In` allows us to avoid autoboxing. ### array Arrays are working as expected. `In` is faster on 5 elements while `InSet` is faster on 15 elements. The benchmarks are using `UnsafeArrayData`. ### struct `InSet` is always faster than `In` for structs. These benchmarks use `GenericInternalRow`. Closes apache#23291 from aokolnychyi/spark-26203. Lead-authored-by: Anton Okolnychyi <aokolnychyi@apple.com> Co-authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

aokolnychyi mentioned this pull request Dec 11, 2018

[SPARK-26205][SQL] Optimize InSet Expression for bytes, shorts, ints, dates #23171

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[SPARK-26203][SQL] Benchmark performance of In and InSet expressions

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dongjoon-hyun changed the title ~~[SPARK-26203][SQL] Benchmark performance of In and InSet expressions~~ [SPARK-26203][SQL][TEST] Benchmark performance of In and InSet expressions Dec 19, 2018

[SPARK-26203][SQl] Second review round

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Merge pull request #1 from dongjoon-hyun/PR-23291-2

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		import spark.implicits._

		def byteBenchmark(numItems: Int, numRows: Long, minNumIters: Int): Benchmark = {

[SPARK-26203][SQL][TEST] Benchmark performance of In and InSet expressions #23291

[SPARK-26203][SQL][TEST] Benchmark performance of In and InSet expressions #23291

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What changes were proposed in this pull request?

byte / short / int / long

float / double

decimal

string

timestamp / date

array

struct

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