Apache spark 使用pyspark建立与钻孔的连接

我正在尝试使用pysparkshell中的drill-to-connect将maprdb中的数据提取到数据帧中

以下是我在pyspark shell中所做的操作：

 `dataframe_mysql = sqlContext.read.format("jdbc").option("url", "jdbc:drill:zk=localhost:5181/drill/demo_mapr_com-drillbits;schema=dfs;").option("driver","org.apache.drill.jdbc.Driver").option("dbtable","select * from dfs.`/DDDE/jsondb/ruleengine/testtransactions").option("user","root").option("password","mapr").load()`

不幸的是，我得到了以下错误

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/mapr/spark/spark-1.6.3-bin-hadoop2.6/python/pyspark/sql/readwriter.py", line 139, in load
    return self._df(self._jreader.load())
  File "/opt/mapr/spark/spark-1.6.3-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in __call__
  File "/opt/mapr/spark/spark-1.6.3-bin-hadoop2.6/python/pyspark/sql/utils.py", line 45, in deco
    return f(*a, **kw)
  File "/opt/mapr/spark/spark-1.6.3-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line 308, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o88.load.
: java.lang.ClassNotFoundException: org.apache.drill.jdbc.Driver
        at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
        at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
        at java.security.AccessController.doPrivileged(Native Method)
        at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
        at org.apache.spark.sql.execution.datasources.jdbc.DriverRegistry$.register(DriverRegistry.scala:38)
        at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:45)
        at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:45)
        at scala.Option.foreach(Option.scala:236)
        at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.createConnectionFactory(JdbcUtils.scala:45)
        at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:120)
        at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation.<init>(JDBCRelation.scala:91)
        at org.apache.spark.sql.execution.datasources.jdbc.DefaultSource.createRelation(DefaultSource.scala:57)
        at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:158)
        at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:119)
        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 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

---

Drill JDBC驱动程序JAR文件必须存在于客户机上，以便您可以为要使用的应用程序或第三方工具配置驱动程序。您可以按如下方式获取驱动程序：

将

drill jdbc all

JAR文件从以下drill安装目录复制到您的工作目录，然后按照以下步骤启动脚本：

./bin/spark-submit --jars drill-jdbc-all-<version>.jar your_spark_script.py

如果找不到JAR，请在钻孔目录安装中：

$> tree jars/jdbc-driver/
jars/jdbc-driver/
 └── drill-jdbc-all-1.10.0.jar # this is it 

---

使用Drill ODBC驱动程序jar文件的另一种方法是使用如下所述的Drill RESTful API：

集群中是否存在Drill jdbc驱动程序？@eliasah我不知道如何检查。顺便说一句，请检查编辑。我现在在shell中运行命令，而不是在python脚本中。谢谢。我在

drill-1.10.0/jars

中安装了很多jar。我没有找到

drill-jdbc-all-jar

，但我有

drill-jdbc-1.10.0.jar

。这是你要我抄的吗？再次谢谢。这种联系建立得很好。但是我的Select查询有一些问题。谢谢，当然可以。最后一件事。在

选项（“dbtable”，“select*from dfs.

/DDDE/jsondb/ruleengine/testtransactions”）中必须提供什么？这似乎是一个错误。让我们来看看。

pyspark --jars drill-jdbc-all-<version>.jar

$> tree jars/jdbc-driver/
jars/jdbc-driver/
 └── drill-jdbc-all-1.10.0.jar # this is it