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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2020/06/17 08:36:07 UTC
[GitHub] [spark] xuanyuanking commented on a change in pull request #28725: [WIP][SPARK-31905][SS] Add compatibility tests for streaming state store format
xuanyuanking commented on a change in pull request #28725:
URL: https://github.com/apache/spark/pull/28725#discussion_r441377492
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File path: sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingStateStoreFormatCompatibilitySuite.scala
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@@ -0,0 +1,248 @@
+/*
+ * 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.streaming
+
+import java.io.File
+
+import org.apache.commons.io.FileUtils
+
+import org.apache.spark.SparkException
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.streaming.InternalOutputModes.Complete
+import org.apache.spark.sql.execution.streaming.MemoryStream
+import org.apache.spark.sql.functions._
+import org.apache.spark.util.Utils
+
+/**
+ * An integrated test for streaming state store format compatibility.
+ * For each PR breaks this test, we need to pay attention to the underlying unsafe row format
+ * changing. All the checkpoint dirs were generated based on Spark version 2.4.5. If we accept the
+ * changes, it means the checkpoint for Structured Streaming will become non-reusable. Please add
+ * a new test for the issue, just like the test suite "SPARK-28067 changed the sum decimal unsafe
+ * row format".
+ */
+class StreamingStateStoreFormatCompatibilitySuite extends StreamTest {
+ import testImplicits._
+
+ private def prepareCheckpointDir(testName: String): File = {
+ val resourceUri = this.getClass.getResource("/structured-streaming/" +
+ s"checkpoint-version-2.4.5-for-compatibility-test-${testName}").toURI
+ val checkpointDir = Utils.createTempDir().getCanonicalFile
+ FileUtils.copyDirectory(new File(resourceUri), checkpointDir)
+ checkpointDir
+ }
+
+ test("common functions") {
+ val inputData = MemoryStream[Int]
+ val aggregated =
+ inputData.toDF().toDF("value")
+ .selectExpr(
+ "value",
+ "value % 5 AS id",
+ "CAST(value AS STRING) as str",
+ "CAST(value AS FLOAT) as f",
+ "CAST(value AS DOUBLE) as d",
+ "CAST(value AS DECIMAL) as dec",
+ "value % 3 AS mod",
+ "named_struct('key', CAST(value AS STRING), 'value', value) AS s")
+ .groupBy($"id")
+ .agg(
+ avg($"value").as("avg_v"),
+ avg($"f").as("avg_f"),
+ avg($"d").as("avg_d"),
+ avg($"dec").as("avg_dec"),
+ count($"value").as("cnt"),
+ first($"value").as("first_v"),
+ first($"s").as("first_s"),
+ last($"value").as("last_v"),
+ last($"s").as("last_s"),
+ min(struct("value", "str")).as("min_struct"),
+ max($"value").as("max_v"),
+ sum($"value").as("sum_v"),
+ sum($"f").as("sum_f"),
+ sum($"d").as("sum_d"),
+ // The test for sum decimal broke by SPARK-28067, use separated test for it
+ // sum($"dec").as("sum_dec"),
+ collect_list($"value").as("col_list"),
+ collect_set($"mod").as("col_set"))
+ .select("id", "avg_v", "avg_f", "avg_d", "avg_dec", "cnt", "first_v", "first_s.value",
+ "last_v", "last_s.value", "min_struct.value", "max_v", "sum_v", "sum_f", "sum_d",
+ "col_list", "col_set")
+
+ val checkpointDir = prepareCheckpointDir("common-functions")
+ inputData.addData(0 to 9: _*)
+
+ testStream(aggregated, Complete)(
+ StartStream(checkpointLocation = checkpointDir.getAbsolutePath),
+ /*
+ Note: The checkpoint was generated using the following input in Spark version 2.4.5
+ AddData(inputData, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9),
+ CheckAnswer(
+ Row(0, 2.5, 2.5F, 2.5, 2.5000, 2, 0, 0, 5, 5, 0, 5, 5, 5.0, 5.0, Seq(0, 5),
+ Seq(0, 2)),
+ Row(1, 3.5, 3.5F, 3.5, 3.5000, 2, 1, 1, 6, 6, 1, 6, 7, 7.0, 7.0, Seq(1, 6),
+ Seq(0, 1)),
+ Row(2, 4.5, 4.5F, 4.5, 4.5000, 2, 2, 2, 7, 7, 2, 7, 9, 9.0, 9.0, Seq(2, 7),
+ Seq(1, 2)),
+ Row(3, 5.5, 5.5F, 5.5, 5.5000, 2, 3, 3, 8, 8, 3, 8, 11, 11.0, 11.0, Seq(3, 8),
+ Seq(0, 2)),
+ Row(4, 6.5, 6.5F, 6.5, 6.5000, 2, 4, 4, 9, 9, 4, 9, 13, 13.0, 13.0, Seq(4, 9),
+ Seq(0, 1)))
+ */
+ AddData(inputData, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19),
+ CheckAnswer(
+ Row(0, 7.5, 7.5, 7.5, 7.5000, 4, 0, 0, 15, 15, 0, 15, 30, 30.0, 30.0,
+ Seq(0, 5, 10, 15), Seq(0, 1, 2)),
+ Row(1, 8.5, 8.5, 8.5, 8.5000, 4, 1, 1, 16, 16, 1, 16, 34, 34.0, 34.0,
+ Seq(1, 6, 11, 16), Seq(0, 1, 2)),
+ Row(2, 9.5, 9.5, 9.5, 9.5000, 4, 2, 2, 17, 17, 2, 17, 38, 38.0, 38.0,
+ Seq(2, 7, 12, 17), Seq(0, 1, 2)),
+ Row(3, 10.5, 10.5, 10.5, 10.5000, 4, 3, 3, 18, 18, 3, 18, 42, 42.0, 42.0,
+ Seq(3, 8, 13, 18), Seq(0, 1, 2)),
+ Row(4, 11.5, 11.5, 11.5, 11.5000, 4, 4, 4, 19, 19, 4, 19, 46, 46.0, 46.0,
+ Seq(4, 9, 14, 19), Seq(0, 1, 2)))
+ )
+ }
+
+ test("statistical functions") {
+ val inputData = MemoryStream[Long]
+ val aggregated =
+ inputData.toDF().toDF("value")
+ .selectExpr(
+ "value",
+ "value % 5 AS id",
+ "CAST(value AS STRING) as str",
+ "CAST(value AS FLOAT) as f",
+ "CAST(value AS DOUBLE) as d",
+ "CAST(value AS DECIMAL) as dec",
+ "value % 3 AS mod")
+ .groupBy($"id")
+ .agg(
+ kurtosis($"d").as("kts"),
+ skewness($"d").as("skew"),
+ approx_count_distinct($"mod").as("approx_cnt"),
+ approx_count_distinct($"f").as("approx_cnt_f"),
+ approx_count_distinct($"d").as("approx_cnt_d"),
+ approx_count_distinct($"dec").as("approx_cnt_dec"),
+ approx_count_distinct($"str").as("approx_cnt_str"),
+ stddev_pop($"d").as("stddev_pop"),
+ stddev_samp($"d").as("stddev_samp"),
+ var_pop($"d").as("var_pop"),
+ var_samp($"d").as("var_samp"),
+ covar_pop($"value", $"mod").as("covar_pop"),
+ covar_samp($"value", $"mod").as("covar_samp"),
+ corr($"value", $"mod").as("corr"))
+ .select("id", "kts", "skew", "approx_cnt", "approx_cnt_f", "approx_cnt_d",
+ "approx_cnt_dec", "approx_cnt_str", "stddev_pop", "stddev_samp", "var_pop", "var_samp",
+ "covar_pop", "covar_samp", "corr")
+
+ val checkpointDir = prepareCheckpointDir("statistical-functions")
+ inputData.addData(0L to 9L: _*)
+
+ testStream(aggregated, Complete)(
+ StartStream(checkpointLocation = checkpointDir.getAbsolutePath),
+ /*
+ Note: The checkpoint was generated using the following input in Spark version 2.4.5
+ AddData(inputData, 0L to 9L: _*),
+ CheckAnswer(
+ Row(0, -2.0, 0.0, 2, 2, 2, 2, 2, 2.5, 3.5355339059327378, 6.25, 12.5, 2.5, 5.0, 1.0),
+ Row(1, -2.0, 0.0, 2, 2, 2, 2, 2, 2.5, 3.5355339059327378, 6.25, 12.5, -1.25, -2.5, -1.0),
+ Row(2, -2.0, 0.0, 2, 2, 2, 2, 2, 2.5, 3.5355339059327378, 6.25, 12.5, -1.25, -2.5, -1.0),
+ Row(3, -2.0, 0.0, 2, 2, 2, 2, 2, 2.5, 3.5355339059327378, 6.25, 12.5, 2.5, 5.0, 1.0),
+ Row(4, -2.0, 0.0, 2, 2, 2, 2, 2, 2.5, 3.5355339059327378, 6.25, 12.5, -1.25, -2.5, -1.0))
+ */
+
+ AddData(inputData, 10L to 19L: _*),
+ CheckAnswer(
+ Row(0, -1.36, 0.0, 3, 4, 4, 4, 4, 5.5901699437494745, 6.454972243679028, 31.25,
+ 41.666666666666664, -0.625, -0.8333333333333334, -0.13483997249264842),
+ Row(1, -1.36, 0.0, 3, 4, 4, 4, 4, 5.5901699437494745, 6.454972243679028, 31.25,
+ 41.666666666666664, 1.25, 1.6666666666666667, 0.31622776601683794),
+ Row(2, -1.36, 0.0, 3, 4, 4, 4, 4, 5.5901699437494745, 6.454972243679028, 31.25,
+ 41.666666666666664, -0.625, -0.8333333333333334, -0.13483997249264842),
+ Row(3, -1.36, 0.0, 3, 4, 4, 4, 4, 5.5901699437494745, 6.454972243679028, 31.25,
+ 41.666666666666664, -0.625, -0.8333333333333334, -0.13483997249264842),
+ Row(4, -1.36, 0.0, 3, 4, 4, 4, 4, 5.5901699437494745, 6.454972243679028, 31.25,
+ 41.666666666666664, 1.25, 1.6666666666666667, 0.31622776601683794))
+ )
+ }
+
+ test("deduplicate with all columns") {
Review comment:
cc @maropu @dongjoon-hyun
This new integrated test will return the wrong answer without #28830.
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