Apache Spark 需要 5 到 6 分钟来简单计算来自 Cassandra 的 10 亿行 [英] Apache Spark taking 5 to 6 minutes for simple count of 1 billon rows from Cassandra

问题描述

我使用的是 Spark Cassandra 连接器.从 Cassandra 表中获取数据需要 5-6 分钟.在 Spark 中，我在日志中看到了许多任务和 Executor.原因可能是 Spark 把流程分成了很多任务！

I am using the Spark Cassandra connector. It take 5-6 minutes for fetch data from Cassandra table. In Spark I have seen many tasks and Executor in log. The reason might be that Spark divided the process in many tasks!

下面是我的代码示例:

public static void main(String[] args) {

    SparkConf conf = new SparkConf(true).setMaster("local[4]")
            .setAppName("App_Name")
            .set("spark.cassandra.connection.host", "127.0.0.1");

    JavaSparkContext sc = new JavaSparkContext(conf);

    JavaRDD<Demo_Bean> empRDD = javaFunctions(sc).cassandraTable("dev",
            "demo");
    System.out.println("Row Count"+empRDD.count());
}

推荐答案

在 Google 上搜索后，我喜欢最新的 spark-cassandra-connector 中的问题.参数 spark.cassandra.input.split.size_in_mb 默认值为 64 MB，在代码中被解释为 64 字节.所以尝试spark.cassandra.input.split.size_in_mb = 64 * 1024 * 1024 = 67108864

After searching on Google i fond the issue in the latest spark-cassandra-connector. The parameter spark.cassandra.input.split.size_in_mb Default value is 64 MB which is being interpreted as 64 bytes in the code. So try with spark.cassandra.input.split.size_in_mb = 64 * 1024 * 1024 = 67108864

Hear 是一个例子:

Hear is an example :

public static void main(String[] args) {

    SparkConf conf = new SparkConf(true).setMaster("local[4]")
            .setAppName("App_Name")
            .set("spark.cassandra.connection.host", "127.0.0.1")
            .set("spark.cassandra.input.split.size_in_mb","67108864");


    JavaSparkContext sc = new JavaSparkContext(conf);

    JavaRDD<Demo_Bean> empRDD = javaFunctions(sc).cassandraTable("dev",
            "demo");
    System.out.println("Row Count"+empRDD.count());
}

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