如何计算中位数火花sqlContext数据类型为双列 [英] How to calculate Median in spark sqlContext for column of data type double
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问题描述
我已经给出了示例表。我想从每个组的源列值列中的值。哪里
源列是字符串数据类型
值列是双数据类型的
I have given the sample table. I want to get the median from "value" column for each group "source" column. Where source column is of String DataType value column is of double DataType
scala> sqlContext.sql("SELECT * from tTab order by source").show
+---------------+-----+
| Source|value|
+---------------+-----+
|131.183.222.110| 1.0|
| 131.183.222.85| 1.0|
| 131.183.222.85| 0.0|
| 131.183.222.85| 0.5|
| 131.183.222.85| 1.0|
| 131.183.222.85| 1.0|
| 43.230.146.7| 0.0|
| 43.230.146.7| 1.0|
| 43.230.146.7| 1.0|
| 43.230.146.8| 1.0|
| 43.230.146.8| 1.0|
+---------------+-----+
scala> tTab.printSchema
root
|-- Source: string (nullable = true)
|-- value: double (nullable = true)
预期的答案：
+---------------+-----+
| Source|value|
+---------------+-----+
|131.183.222.110| 1.0|
| 131.183.222.85| 1.0|
| 43.230.146.7| 1.0|
| 43.230.146.8| 1.0|
+---------------+-----+
如果值栏中将是诠释，下面的查询工作。由于价值是数据类型为双，这是给我的错误：
If "value" column would have been Int, below query is working. Since "value" is of data type as double, it is giving me error:
sqlContext.sql("SELECT source , percentile(value,0.5) OVER (PARTITION BY source) AS Median from tTab ").show
错误：
org.apache.hadoop.hive.ql.exec.NoMatchingMethodException: No matching method for class org.apache.hadoop.hive.ql.udf.UDAFPercentile with (double, double). Possible choices: _FUNC_(bigint, array<double>) _FUNC_(bigint, double)
at org.apache.hadoop.hive.ql.exec.FunctionRegistry.getMethodInternal(FunctionRegistry.java:1164)
at org.apache.hadoop.hive.ql.exec.DefaultUDAFEvaluatorResolver.getEvaluatorClass(DefaultUDAFEvaluatorResolver.java:83)
at org.apache.hadoop.hive.ql.udf.generic.GenericUDAFBridge.getEvaluator(GenericUDAFBridge.java:56)
at org.apache.hadoop.hive.ql.udf.generic.AbstractGenericUDAFResolver.getEvaluator(AbstractGenericUDAFResolver.java:47)
at org.apache.spark.sql.hive.HiveWindowFunction.evaluator$lzycompute(hiveUDFs.scala:351)
at org.apache.spark.sql.hive.HiveWindowFunction.evaluator(hiveUDFs.scala:349)
at org.apache.spark.sql.hive.HiveWindowFunction.returnInspector$lzycompute(hiveUDFs.scala:357)
at org.apache.spark.sql.hive.HiveWindowFunction.returnInspector(hiveUDFs.scala:356)
at org.apache.spark.sql.hive.HiveWindowFunction.dataType(hiveUDFs.scala:362)
at org.apache.spark.sql.catalyst.expressions.WindowExpression.dataType(windowExpressions.scala:313)
at org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:140)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$$anonfun$35$$anonfun$apply$15.applyOrElse(Analyzer.scala:856)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$$anonfun$35$$anonfun$apply$15.applyOrElse(Analyzer.scala:852)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:227)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:227)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:226)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$$anonfun$35.apply(Analyzer.scala:852)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$$anonfun$35.apply(Analyzer.scala:863)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$.org$apache$spark$sql$catalyst$analysis$Analyzer$ExtractWindowExpressions$$addWindow(Analyzer.scala:849)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$$anonfun$apply$16.applyOrElse(Analyzer.scala:957)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$$anonfun$apply$16.applyOrElse(Analyzer.scala:913)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:227)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:227)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:226)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$.apply(Analyzer.scala:913)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions$.apply(Analyzer.scala:745)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)
at scala.collection.immutable.List.foreach(List.scala:318)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)
at org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:916)
at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:916)
at org.apache.spark.sql.SQLContext$QueryExecution.assertAnalyzed(SQLContext.scala:914)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:132)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:51)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:725)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:20)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:25)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:27)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:29)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:31)
at $iwC$$iwC$$iwC.<init>(<console>:33)
at $iwC$$iwC.<init>(<console>:35)
at $iwC.<init>(<console>:37)
at <init>(<console>:39)
at .<init>(<console>:43)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1340)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:670)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:997)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:672)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
感谢你了！
推荐答案
有关非整数值，你应该使用 percentile_approx UDF：
For non integral values you should use percentile_approx UDF:
import org.apache.spark.mllib.random.RandomRDDs
val df = RandomRDDs.normalRDD(sc, 1000, 10, 1).map(Tuple1(_)).toDF("x")
df.registerTempTable("df")
sqlContext.sql("SELECT percentile_approx(x, 0.5) FROM df").show
// +--------------------+
// | _c0|
// +--------------------+
// |0.035379710486199915|
// +--------------------+
在一个侧面不应该使用 GROUP BY 不是 PARTITION BY 。后者则用于窗口功能，并且比你预期不同的效果。
On a side not you should use GROUP BY not PARTITION BY. Latter one is used for window functions and has different effect than you expect.
SELECT source, percentile_approx(value, 0.5) FROM df GROUP BY source
又见如何使用星火
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