Spark 将每个操作执行两次

我创建了一个简单的 Java 应用程序，它使用 Apache Spark 从 Cassandra 检索数据，对其进行一些转换并将其保存在另一个 Cassandra 表中。

我正在使用 Apache Spark 1.4.1，它配置为独立集群模式，具有单个主服务器和从服务器，位于我的计算机上。

DataFrame customers = sqlContext.cassandraSql("SELECT email, first_name, last_name FROM customer " +
    "WHERE CAST(store_id as string) = '" + storeId + "'");

DataFrame customersWhoOrderedTheProduct = sqlContext.cassandraSql("SELECT email FROM customer_bought_product " +
    "WHERE CAST(store_id as string) = '" + storeId + "' AND product_id = " + productId + "");

// We need only the customers who did not order the product
// We cache the DataFrame because we use it twice.
DataFrame customersWhoHaventOrderedTheProduct = customers
    .join(customersWhoOrderedTheProduct
    .select(customersWhoOrderedTheProduct.col("email")), customers.col("email").equalTo(customersWhoOrderedTheProduct.col("email")), "leftouter")
    .where(customersWhoOrderedTheProduct.col("email").isNull())
    .drop(customersWhoOrderedTheProduct.col("email"))
    .cache();

int numberOfCustomers = (int) customersWhoHaventOrderedTheProduct.count();

Date reportTime = new Date();

// Prepare the Broadcast values. They are used in the map below.
Broadcast<String> bStoreId = sparkContext.broadcast(storeId, classTag(String.class));
Broadcast<String> bReportName = sparkContext.broadcast(MessageBrokerQueue.report_did_not_buy_product.toString(), classTag(String.class));
Broadcast<java.sql.Timestamp> bReportTime = sparkContext.broadcast(new java.sql.Timestamp(reportTime.getTime()), classTag(java.sql.Timestamp.class));
Broadcast<Integer> bNumberOfCustomers = sparkContext.broadcast(numberOfCustomers, classTag(Integer.class));

// Map the customers to a custom class, thus adding new properties.
DataFrame storeCustomerReport = sqlContext.createDataFrame(customersWhoHaventOrderedTheProduct.toJavaRDD()
    .map(row -> new StoreCustomerReport(bStoreId.value(), bReportName.getValue(), bReportTime.getValue(), bNumberOfCustomers.getValue(), row.getString(0), row.getString(1), row.getString(2))), StoreCustomerReport.class);


// Save the DataFrame to cassandra
storeCustomerReport.write().mode(SaveMode.Append)
    .option("keyspace", "my_keyspace")
    .option("table", "my_report")
    .format("org.apache.spark.sql.cassandra")
    .save();

正如你所看到的，我cache the customersWhoHaventOrderedTheProductDataFrame，之后我执行count并打电话toJavaRDD.

By my calculations these actions should be executed only once. But when I go in the Spark UI for the current job I see the following stages:

正如您所看到的，每个动作都会执行两次。

难道我做错了什么？有什么设置我错过了吗？

任何想法都将不胜感激。

EDIT:

我打电话后System.out.println(storeCustomerReport.toJavaRDD().toDebugString());

这是调试字符串：

(200) MapPartitionsRDD[43] at toJavaRDD at DidNotBuyProductReport.java:93 []
  |   MapPartitionsRDD[42] at createDataFrame at DidNotBuyProductReport.java:89 []
  |   MapPartitionsRDD[41] at map at DidNotBuyProductReport.java:90 []
  |   MapPartitionsRDD[40] at toJavaRDD at DidNotBuyProductReport.java:89 []
  |   MapPartitionsRDD[39] at toJavaRDD at DidNotBuyProductReport.java:89 []
  |   MapPartitionsRDD[38] at toJavaRDD at DidNotBuyProductReport.java:89 []
  |   ZippedPartitionsRDD2[37] at toJavaRDD at DidNotBuyProductReport.java:89 []
  |   MapPartitionsRDD[31] at toJavaRDD at DidNotBuyProductReport.java:89 []
  |   ShuffledRDD[30] at toJavaRDD at DidNotBuyProductReport.java:89 []
  +-(2) MapPartitionsRDD[29] at toJavaRDD at DidNotBuyProductReport.java:89 []
     |  MapPartitionsRDD[28] at toJavaRDD at DidNotBuyProductReport.java:89 []
     |  MapPartitionsRDD[27] at toJavaRDD at DidNotBuyProductReport.java:89 []
     |  MapPartitionsRDD[3] at cache at DidNotBuyProductReport.java:76 []
     |  CassandraTableScanRDD[2] at RDD at CassandraRDD.scala:15 []
  |   MapPartitionsRDD[36] at toJavaRDD at DidNotBuyProductReport.java:89 []
  |   ShuffledRDD[35] at toJavaRDD at DidNotBuyProductReport.java:89 []
  +-(2) MapPartitionsRDD[34] at toJavaRDD at DidNotBuyProductReport.java:89 []
     |  MapPartitionsRDD[33] at toJavaRDD at DidNotBuyProductReport.java:89 []
     |  MapPartitionsRDD[32] at toJavaRDD at DidNotBuyProductReport.java:89 []
     |  MapPartitionsRDD[5] at cache at DidNotBuyProductReport.java:76 []
     |  CassandraTableScanRDD[4] at RDD at CassandraRDD.scala:15 []

EDIT 2:

因此，经过一些研究并结合试验和错误，我设法优化了这项工作。

我创建了一个 RDDcustomersWhoHaventOrderedTheProduct我在调用之前缓存它count()行动。 （我将缓存从DataFrame to the RDD).

之后我用这个RDD来创建storeCustomerReport DataFrame.

JavaRDD<Row> customersWhoHaventOrderedTheProductRdd = customersWhoHaventOrderedTheProduct.javaRDD().cache();

现在各个阶段如下所示：

正如你所看到的两个count and cache现在已经消失了，但仍然有两个“javaRDD”操作。我不知道他们从哪里来，正如我所说的toJavaRDD我的代码中只出现过一次。

看起来您正在下面的代码段中应用两个操作

// Map the customers to a custom class, thus adding new properties.
DataFrame storeCustomerReport = sqlContext.createDataFrame(customersWhoHaventOrderedTheProduct.toJavaRDD()
    .map(row -> new StoreCustomerReport(bStoreId.value(), bReportName.getValue(), bReportTime.getValue(), bNumberOfCustomers.getValue(), row.getString(0), row.getString(1), row.getString(2))), StoreCustomerReport.class);


// Save the DataFrame to cassandra
storeCustomerReport.write().mode(SaveMode.Append)
    .option("keyspace", "my_keyspace")

One at sqlContext.createDataFrame()另一个在storeCustomerReport.write()而这两者都需要customersWhoHaventOrderedTheProduct.toJavaRDD().

持久化 生成的 RDD 应该可以解决这个问题。

JavaRDD cachedRdd = customersWhoHaventOrderedTheProduct.toJavaRDD().persist(StorageLevel.DISK_AND_MEMORY) //Or any other storage level