python - Pyspark sparkSql 问题

Question

我正在使用 cloudera vm 10.0，spark 版本为 1.6。

登录 pyspark 控制台后，我正在尝试以下语句从配置单元中获取数据

sqlContext.sql("select * from /user/hive/warehouse/default.party").show()

我收到下面给出的错误。

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/spark/python/pyspark/sql/context.py", line 580, in sql
    return DataFrame(self._ssql_ctx.sql(sqlQuery), self)
  File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in __call__
  File "/usr/lib/spark/python/pyspark/sql/utils.py", line 45, in deco
    return f(*a, **kw)
  File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line 308, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o18.sql.
: java.lang.RuntimeException: [1.15] failure: ``('' expected but `/' found


select * from /user/hive/warehouse/default.party
              ^
    at scala.sys.package$.error(package.scala:27)
    at org.apache.spark.sql.catalyst.AbstractSparkSQLParser.parse(AbstractSparkSQLParser.scala:36)
    at org.apache.spark.sql.catalyst.DefaultParserDialect.parse(ParserDialect.scala:67)
    at org.apache.spark.sql.SQLContext$$anonfun$2.apply(SQLContext.scala:211)
    at org.apache.spark.sql.SQLContext$$anonfun$2.apply(SQLContext.scala:211)
    at org.apache.spark.sql.execution.SparkSQLParser$$anonfun$org$apache$spark$sql$execution$SparkSQLParser$$others$1.apply(SparkSQLParser.scala:114)
    at org.apache.spark.sql.execution.SparkSQLParser$$anonfun$org$apache$spark$sql$execution$SparkSQLParser$$others$1.apply(SparkSQLParser.scala:113)
    at scala.util.parsing.combinator.Parsers$Success.map(Parsers.scala:136)
    at scala.util.parsing.combinator.Parsers$Success.map(Parsers.scala:135)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:242)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:242)
    at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:222)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1$$anonfun$apply$2.apply(Parsers.scala:254)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1$$anonfun$apply$2.apply(Parsers.scala:254)
    at scala.util.parsing.combinator.Parsers$Failure.append(Parsers.scala:202)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1.apply(Parsers.scala:254)
    at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1.apply(Parsers.scala:254)
    at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:222)
    at scala.util.parsing.combinator.Parsers$$anon$2$$anonfun$apply$14.apply(Parsers.scala:891)
    at scala.util.parsing.combinator.Parsers$$anon$2$$anonfun$apply$14.apply(Parsers.scala:891)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
    at scala.util.parsing.combinator.Parsers$$anon$2.apply(Parsers.scala:890)
    at scala.util.parsing.combinator.PackratParsers$$anon$1.apply(PackratParsers.scala:110)
    at org.apache.spark.sql.catalyst.AbstractSparkSQLParser.parse(AbstractSparkSQLParser.scala:34)
    at org.apache.spark.sql.SQLContext$$anonfun$1.apply(SQLContext.scala:208)
    at org.apache.spark.sql.SQLContext$$anonfun$1.apply(SQLContext.scala:208)
    at org.apache.spark.sql.execution.datasources.DDLParser.parse(DDLParser.scala:43)
    at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:231)
    at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
    at sun.reflect.GeneratedMethodAccessor3.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)

请帮我解决这个障碍

Answer 1

为了查询 Hive 表，您需要先将其注册为临时表。

from pyspark.sql import HiveContext
sqlContext = HiveContext(sc)
party = sqlContext.table("default.party")
party.registerTempTable("party_temp_in_spark")
sqlContext.sql("select * from party_temp_in_spark").show()

希望对您有所帮助!