如何从spark连接到远程配置单元服务器

Question

我正在本地运行spark并希望访问位于远程Hadoop集群中的Hive表.

我可以通过SPARK_HOME下的直线访问蜂巢表

[ml@master spark-2.0.0]$./bin/beeline 
Beeline version 1.2.1.spark2 by Apache Hive
beeline> !connect jdbc:hive2://remote_hive:10000
Connecting to jdbc:hive2://remote_hive:10000
Enter username for jdbc:hive2://remote_hive:10000: root
Enter password for jdbc:hive2://remote_hive:10000: ******
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/ml/spark/spark-2.0.0/jars/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
16/10/12 19:06:39 INFO jdbc.Utils: Supplied authorities: remote_hive:10000
16/10/12 19:06:39 INFO jdbc.Utils: Resolved authority: remote_hive:10000
16/10/12 19:06:39 INFO jdbc.HiveConnection: Will try to open client transport with JDBC Uri: jdbc:hive2://remote_hive:10000
Connected to: Apache Hive (version 1.2.1000.2.4.2.0-258)
Driver: Hive JDBC (version 1.2.1.spark2)
Transaction isolation: TRANSACTION_REPEATABLE_READ
0: jdbc:hive2://remote_hive:10000>

如何从spark以编程方式访问远程配置单元表？

Answer 1

JDBC不是必需的

Spark直接连接到Hive Metastore,而不是通过HiveServer2.要配置这个,

1. 穿上hive-site.xml你的classpath,并指定hive.metastore.uri你的hive Metastore托管的地方.另请参阅如何在SparkSQL中以编程方式连接到Hive Metastore？

2. 导入org.apache.spark.sql.hive.HiveContext,因为它可以在Hive表上执行SQL查询.

3. 限定 val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)

4. 验证sqlContext.sql("show tables")它是否有效

Hive表上的SparkSQL

结论:如果你必须采用jdbc方式

看看远程连接apache spark和apache hive.

请注意,直线也通过jdbc连接.从你的日志中它自己很明显.

[ml @ master spark-2.0.0] $./ bin/beeline Beeline版本1.2.1.spark2由Apache Hive beeline>!connect jdbc:hive2:// remote_hive:10000

连接到jdbc:hive2:// remote_hive:10000

所以请看看这篇有趣的文章

• 方法1:使用JDBC将表拉入Spark
• 方法2:将Spark JdbcRDD与HiveServer2 JDBC驱动程序一起使用
• 方法3:在客户端获取数据集,然后手动创建RDD

目前HiveServer2驱动程序不允许我们使用"Sparkling"方法1和2,我们只能依赖方法3

下面是可以实现的示例代码片段

通过HiveServer2 JDBC连接将数据从一个Hadoop集群(也称为"远程")加载到另一个集群(我的Spark居住的地方也称为"国内").

import java.sql.Timestamp
import scala.collection.mutable.MutableList

case class StatsRec (
  first_name: String,
  last_name: String,
  action_dtm: Timestamp,
  size: Long,
  size_p: Long,
  size_d: Long
)

val conn: Connection = DriverManager.getConnection(url, user, password)
val res: ResultSet = conn.createStatement
                   .executeQuery("SELECT * FROM stats_201512301914")
val fetchedRes = MutableList[StatsRec]()
while(res.next()) {
  var rec = StatsRec(res.getString("first_name"), 
     res.getString("last_name"), 
     Timestamp.valueOf(res.getString("action_dtm")), 
     res.getLong("size"), 
     res.getLong("size_p"), 
     res.getLong("size_d"))
  fetchedRes += rec
}
conn.close()
val rddStatsDelta = sc.parallelize(fetchedRes)
rddStatsDelta.cache()




 // Basically we are done. To check loaded data:

println(rddStatsDelta.count)
rddStatsDelta.collect.take(10).foreach(println)