从 scala 中的 hive 表创建数据框时无法提供模式名称作为输入

Question

我正在尝试从 clickstream_db 架构中存在的现有配置单元表创建数据框。

val ganulardataframe=hc.table("clickstream_db.granulartable");

它给出了一个错误：

org.apache.spark.sql.catalyst.analysis.NoSuchTableException
        at org.apache.spark.sql.hive.client.ClientInterface$$anonfun$getTable$1.apply(ClientInterface.scala:112)
        at org.apache.spark.sql.hive.client.ClientInterface$$anonfun$getTable$1.apply(ClientInterface.scala:112)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.sql.hive.client.ClientInterface$class.getTable(ClientInterface.scala:112)
        at org.apache.spark.sql.hive.client.ClientWrapper.getTable(ClientWrapper.scala:61)
        at org.apache.spark.sql.hive.HiveMetastoreCatalog.lookupRelation(HiveMetastoreCatalog.scala:227)
        at org.apache.spark.sql.hive.HiveContext$$anon$2.org$apache$spark$sql$catalyst$analysis$OverrideCatalog$$super$lookupRelation(HiveContext.scala:373)
        at org.apache.spark.sql.catalyst.analysis.OverrideCatalog$$anonfun$lookupRelation$3.apply(Catalog.scala:165)
        at org.apache.spark.sql.catalyst.analysis.OverrideCatalog$$anonfun$lookupRelation$3.apply(Catalog.scala:165)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.sql.catalyst.analysis.OverrideCatalog$class.lookupRelation(Catalog.scala:165)
        at org.apache.spark.sql.hive.HiveContext$$anon$2.lookupRelation(HiveContext.scala:373)
        at org.apache.spark.sql.SQLContext.table(SQLContext.scala:765)
        at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:23)
        at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:28)
        at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:30)
        at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:32)
        at $iwC$$iwC$$iwC$$iwC.<init>(<console>:34)
        at $iwC$$iwC$$iwC.<init>(<console>:36)
        at $iwC$$iwC.<init>(<console>:38)
        at $iwC.<init>(<console>:40)
        at <init>(<console>:42)
        at .<init>(<console>:46)
        at .<clinit>(<console>)
        at .<init>(<console>:7)
        at .<clinit>(<console>)
        at $print(<console>)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:497)
        at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
        at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
        at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
        at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
        at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
        at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
        at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
        at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
        at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
        at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
        at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$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$$process(SparkILoop.scala:945)
        at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
        at org.apache.spark.repl.Main$.main(Main.scala:31)
        at org.apache.spark.repl.Main.main(Main.scala)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:497)
        at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:665)
        at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:170)
        at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:193)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:112)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

如果我将granulartable 作为输入，它将在default 架构中查找此表。

val ganulardataframe=hc.table("granulartable");

我能想到的一个解决方案是在默认数据库中创建同一个表并从中创建数据框。

有没有办法为“表”函数提供模式名称？

谢谢。

Answer 1

试试这个，应该可以的

val l=hc.sql("select * from clickstream_db.granulartable");

使用sql 代替table 并使用sql 查询获取数据。

Answer 2

hc.sql("use clickstream_db");

默认情况下，hive 将使用默认数据库。我们应该将其更改为您要使用的特定数据库。

val ganulardataframe=hc.table("granulartable");