Github user clockfly commented on a diff in the pull request:
https://github.com/apache/spark/pull/14868#discussion_r76695365
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/ApproximatePercentileQuerySuite.scala
---
@@ -0,0 +1,226 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import
org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile.DEFAULT_PERCENTILE_ACCURACY
+import
org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile.PercentileDigest
+import org.apache.spark.sql.test.SharedSQLContext
+
+class ApproximatePercentileQuerySuite extends QueryTest with
SharedSQLContext {
+ import testImplicits._
+
+ private val table = "percentile_test"
+
+ test("percentile_approx, single percentile value") {
+ withTempView(table) {
+ (1 to 1000).toDF("col").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(
+ s"""
+ |SELECT
+ | percentile_approx(col, 0.25),
+ | percentile_approx(col, 0.5),
+ | percentile_approx(col, 0.75d),
+ | percentile_approx(col, 0.0),
+ | percentile_approx(col, 1.0),
+ | percentile_approx(col, 0),
+ | percentile_approx(col, 1)
+ |FROM $table
+ """.stripMargin),
+ Row(250D, 500D, 750D, 1D, 1000D, 1D, 1000D)
+ )
+ }
+ }
+
+ test("percentile_approx, array of percentile value") {
+ withTempView(table) {
+ (1 to 1000).toDF("col").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(
+ s"""SELECT
+ | percentile_approx(col, array(0.25, 0.5, 0.75D)),
+ | count(col),
+ | percentile_approx(col, array(0.0, 1.0)),
+ | sum(col)
+ |FROM $table
+ """.stripMargin),
+ Row(Seq(250D, 500D, 750D), 1000, Seq(1D, 1000D), 500500)
+ )
+ }
+ }
+
+ test("percentile_approx, with different accuracies") {
+
+ withTempView(table) {
+ (1 to 1000).toDF("col").createOrReplaceTempView(table)
+
+ // With different accuracies
+ val expectedPercentile = 250D
+ val accuracies = Array(1, 10, 100, 1000, 10000)
+ val errors = accuracies.map { accuracy =>
+ val df = spark.sql(s"SELECT percentile_approx(col, 0.25,
$accuracy) FROM $table")
+ val approximatePercentile = df.collect().head.getDouble(0)
+ val error = Math.abs(approximatePercentile - expectedPercentile)
+ error
+ }
+
+ // The larger accuracy value we use, the smaller error we get
+ assert(errors.sorted.sameElements(errors.reverse))
+ }
+ }
+
+ test("percentile_approx, supports constant folding for parameter
accuracy and percentages") {
+ withTempView(table) {
+ (1 to 1000).toDF("col").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(s"SELECT percentile_approx(col, array(0.25 + 0.25D), 200
+ 800D) FROM $table"),
+ Row(Seq(500D))
+ )
+ }
+ }
+
+ test("percentile_approx(), aggregation on empty input table, no group
by") {
+ withTempView(table) {
+ Seq.empty[Int].toDF("col").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(s"SELECT sum(col), percentile_approx(col, 0.5) FROM
$table"),
+ Row(null, null)
+ )
+ }
+ }
+
+ test("percentile_approx(), aggregation on empty input table, with group
by") {
+ withTempView(table) {
+ Seq.empty[Int].toDF("col").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(s"SELECT sum(col), percentile_approx(col, 0.5) FROM
$table GROUP BY col"),
+ Seq.empty[Row]
+ )
+ }
+ }
+
+ test("percentile_approx(null), aggregation with group by") {
+ withTempView(table) {
+ (1 to 1000).map(x => (x % 3, x)).toDF("key",
"value").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(
+ s"""SELECT
+ | key,
+ | percentile_approx(null, 0.5)
+ |FROM $table
+ |GROUP BY key
+ """.stripMargin),
+ Seq(
+ Row(0, null),
+ Row(1, null),
+ Row(2, null))
+ )
+ }
+ }
+
+ test("percentile_approx(null), aggregation without group by") {
+ withTempView(table) {
+ (1 to 1000).map(x => (x % 3, x)).toDF("key",
"value").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(
+ s"""SELECT
+ | percentile_approx(null, 0.5),
+ | sum(null),
+ | percentile_approx(null, 0.5)
+ |FROM $table
+ """.stripMargin),
+ Row(null, null, null)
+ )
+ }
+ }
+
+ test("percentile_approx(col, ...), input rows contains null, with out
group by") {
+ withTempView(table) {
+ (1 to 1000).map(new Integer(_)).flatMap(Seq(null: Integer,
_)).toDF("col")
+ .createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(
+ s"""SELECT
+ | percentile_approx(col, 0.5),
+ | sum(null),
+ | percentile_approx(col, 0.5)
+ |FROM $table
+ """.stripMargin),
+ Row(500D, null, 500D))
+ }
+ }
+
+ test("percentile_approx(col, ...), input rows contains null, with group
by") {
+ withTempView(table) {
+ val rand = new java.util.Random()
+ (1 to 1000)
+ .map(new Integer(_))
+ .map(v => (new Integer(v % 2), v))
+ // Add some nulls
+ .flatMap(Seq(_, (null: Integer, null: Integer)))
+ .toDF("key", "value").createOrReplaceTempView(table)
+ checkAnswer(
+ spark.sql(
+ s"""SELECT
+ | percentile_approx(value, 0.5),
+ | sum(value),
+ | percentile_approx(value, 0.5)
+ |FROM $table
+ |GROUP BY key
+ """.stripMargin),
+ Seq(
+ Row(499.0D, 250000, 499.0D),
+ Row(500.0D, 250500, 500.0D),
+ Row(null, null, null))
+ )
+ }
+ }
+
+ test("percentile_approx(col, ...) works in window function") {
--- End diff --
TODO: The implementation of QuantileSummaries is not very clear when
handling boundary (for example, add one record, and do immediate compression,
and make a percentile query). We need to do a double check to make
QuantileSummaries is correctly implemented.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
