spark+phoenix

 

phoenix作为查询引擎，为了提高查询效率，为phoenix表创建了二级索引，而数据是sparkstreaming通过hbase api直接向hbase插数据。那么问题来了，对于phoenix的二级索引，直接插入底层hbase的源表，不会引起二级索引的更新，从而导致phoenix索引数据和hbase源表数据不一致。而对于spark+phoenix的写入方式，官方有文档说明，但是有版本限制，以下是官方原文：

• • To ensure that all requisite Phoenix / HBase platform dependencies are available on the classpath for the Spark executors and drivers, set both ‘spark.executor.extraClassPath’ and ‘spark.driver.extraClassPath’ in spark-defaults.conf to include the ‘phoenix-<version>-client.jar’

  • Note that for Phoenix versions 4.7 and 4.8 you must use the ‘phoenix-<version>-client-spark.jar’. As of Phoenix 4.10, the ‘phoenix-<version>-client.jar’ is compiled against Spark 2.x. If compability with Spark 1.x if needed, you must compile Phoenix with the spark16 maven profile.

所以只能考虑用jdbc的方式做。

我使用的版本信息：

• spark：2.2.1
• phoenix：4.13.2

jar包引入：

•  <dependency>
              <groupId>org.apache.phoenix</groupId>
              <artifactId>phoenix-core</artifactId>
              <version>4.13.1-HBase-1.2</version>
          </dependency>
          <dependency>
              <groupId>org.apache.phoenix</groupId>
              <artifactId>phoenix-spark</artifactId>
              <version>4.13.1-HBase-1.2</version>
          </dependency>

phoenixUtil类:

• public class PhoenixUtil {
  
      private static LinkedList<Connection> connectionQueue;
  
      static {
          try {
              Class.forName("org.apache.phoenix.jdbc.PhoenixDriver");
          } catch (ClassNotFoundException e) {
              e.printStackTrace();
          }
      }
  
      public synchronized static Connection getConnection() throws SQLException {
          try {
              if (connectionQueue == null){
                  connectionQueue = new LinkedList<Connection>();
                  for (int i = 0;i < 3;i++){
                      Connection conn = DriverManager.getConnection("jdbc:phoenix:hostname:2181");
  
                      connectionQueue.push(conn);
                  }
              }
          }catch (Exception e1){
              e1.printStackTrace();
          }
          return connectionQueue.poll();
      }
  
      public static void returnConnection(Connection conn){
          connectionQueue.push(conn);
      }

在sparkstreaming中引入phoenixUtil类（由于业务关系，这里使用的是statement）：

saveLines.foreachRDD(rdd -> { rdd.foreachPartition(p -> { Connection conn = PhoenixUtil.getConnection(); Statement stmt = conn.createStatement(); conn.setAutoCommit(false); //业务逻辑 //sql } stmt.addBatch(sql); } stmt.executeBatch(); conn.commit(); stmt.close(); PhoenixUtil.returnConnection(conn); ZkKafkaUtil.updateOffset(offsetRanges, GROUP_ID, TOPIC); }); });

最后，如果大家有更好的方式处理这个问题，欢迎指教。