Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/15090#discussion_r79335325
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsColumnSuite.scala
---
@@ -0,0 +1,228 @@
+/*
+ * 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.hive
+
+import java.sql.{Date, Timestamp}
+
+import org.apache.spark.sql.{AnalysisException, Row}
+import org.apache.spark.sql.catalyst.plans.logical.BasicColStats
+import org.apache.spark.sql.execution.command.AnalyzeColumnCommand
+import org.apache.spark.sql.types._
+
+class StatisticsColumnSuite extends StatisticsTest {
+
+ test("parse analyze column commands") {
+ val table = "table"
+ assertAnalyzeCommand(
+ s"ANALYZE TABLE $table COMPUTE STATISTICS FOR COLUMNS key, value",
+ classOf[AnalyzeColumnCommand])
+
+ val noColumnError = intercept[AnalysisException] {
+ sql(s"ANALYZE TABLE $table COMPUTE STATISTICS FOR COLUMNS")
+ }
+ assert(noColumnError.message == "Need to specify the columns to
analyze. Usage: " +
+ "ANALYZE TABLE tbl COMPUTE STATISTICS FOR COLUMNS key, value")
+
+ withTable(table) {
+ sql(s"CREATE TABLE $table (key INT, value STRING)")
+ val invalidColError = intercept[AnalysisException] {
+ sql(s"ANALYZE TABLE $table COMPUTE STATISTICS FOR COLUMNS k")
+ }
+ assert(invalidColError.message == s"Invalid column name: k")
+
+ val duplicateColError = intercept[AnalysisException] {
+ sql(s"ANALYZE TABLE $table COMPUTE STATISTICS FOR COLUMNS key,
value, key")
+ }
+ assert(duplicateColError.message == s"Duplicate column name: key")
+
+ withSQLConf("spark.sql.caseSensitive" -> "true") {
+ val invalidErr = intercept[AnalysisException] {
+ sql(s"ANALYZE TABLE $table COMPUTE STATISTICS FOR COLUMNS keY")
+ }
+ assert(invalidErr.message == s"Invalid column name: keY")
+ }
+
+ withSQLConf("spark.sql.caseSensitive" -> "false") {
+ val duplicateErr = intercept[AnalysisException] {
+ sql(s"ANALYZE TABLE $table COMPUTE STATISTICS FOR COLUMNS key,
value, vaLue")
+ }
+ assert(duplicateErr.message == s"Duplicate column name: vaLue")
+ }
+ }
+ }
+
+ test("basic statistics for integral type columns") {
+ val rdd = sparkContext.parallelize(Seq("1", null, "2", "3", null)).map
{ i =>
+ if (i != null) Row(i.toByte, i.toShort, i.toInt, i.toLong) else
Row(i, i, i, i)
--- End diff --
@wzhfy I guess he understood `"1", null, "2", "3", null` are the actual
values for rows. Could we maybe make this easier to read? How about the codes
below?
```scala
val values = (0 to 5).map { i =>
if (i % 2 == 0) None else Some(i)
}
val data = values.map { i =>
(i.map(_.toByte), i.map(_.toShort), i.map(_.toInt), i.map(_.toLong))
}
val df = data.toDF("c1", "c2", "c3", "c4")
val statsSeq = df.schema.map { f =>
val basicStats = BasicColStats(
dataType = f.dataType,
numNulls = values.count(_.isDefined),
max = values.filter(_.isDefined).max,
min = values.filter(_.isDefined).min,
ndv = Some(values.distinct.length.toLong))
(f.name, basicStats)
}
checkColStats(df, statsSeq)
```
with importing `import testImplicits._` right below
`StatisticsColumnSuite` and then changing `checkColStats`
as below:
```scala
def checkColStats(
df: DataFrame,
expectedColStatsSeq: Seq[(String, BasicColStats)]): Unit = {
val table = "tbl"
withTable(table) {
df.write.format("json").saveAsTable(table)
val columns = expectedColStatsSeq.map(_._1).mkString(", ")
sql(s"ANALYZE TABLE $table COMPUTE STATISTICS FOR COLUMNS $columns")
val readback = sql(s"SELECT * FROM $table")
val stats = readback.queryExecution.analyzed.collect {
case rel: LogicalRelation =>
expectedColStatsSeq.foreach { expected =>
assert(rel.catalogTable.get.stats.get.basicColStats.contains(expected._1))
checkColStats(colStats =
rel.catalogTable.get.stats.get.basicColStats(expected._1),
expectedColStats = expected._2)
}
}
assert(stats.size == 1)
}
}
```
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