Scala Spark SQL+Cassandra:性能不好

我刚刚开始使用Spark SQL+Cassandra，可能遗漏了一些重要的内容，但一个简单的查询需要约45秒。我正在使用cassanda spark连接器库，并运行本地web服务器，该服务器也托管spark。所以我的设置大致如下：

12:48:50 INFO  org.apache.spark.SparkContext - Starting job: collect at V1Servlet.scala:1146
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Got job 1 (collect at V1Servlet.scala:1146) with 1 output partitions (allowLocal=false)
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Final stage: ResultStage 1(collect at V1Servlet.scala:1146)
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Parents of final stage: List()
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Missing parents: List()
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Submitting ResultStage 1 (MapPartitionsRDD[29] at collect at V1Servlet.scala:1146), which has no missing parents
12:48:50 INFO  org.apache.spark.storage.MemoryStore - ensureFreeSpace(18696) called with curMem=26661, maxMem=825564856
12:48:50 INFO  org.apache.spark.storage.MemoryStore - Block broadcast_1 stored as values in memory (estimated size 18.3 KB, free 787.3 MB)
12:48:50 INFO  org.apache.spark.storage.MemoryStore - ensureFreeSpace(8345) called with curMem=45357, maxMem=825564856
12:48:50 INFO  org.apache.spark.storage.MemoryStore - Block broadcast_1_piece0 stored as bytes in memory (estimated size 8.1 KB, free 787.3 MB)
12:48:50 INFO  o.a.spark.storage.BlockManagerInfo - Added broadcast_1_piece0 in memory on localhost:56289 (size: 8.1 KB, free: 787.3 MB)
12:48:50 INFO  org.apache.spark.SparkContext - Created broadcast 1 from broadcast at DAGScheduler.scala:874
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD[29] at collect at V1Servlet.scala:1146)
12:48:50 INFO  o.a.s.scheduler.TaskSchedulerImpl - Adding task set 1.0 with 1 tasks
12:48:50 INFO  o.a.spark.scheduler.TaskSetManager - Starting task 0.0 in stage 1.0 (TID 1, localhost, NODE_LOCAL, 59413 bytes)
12:48:50 INFO  org.apache.spark.executor.Executor - Running task 0.0 in stage 1.0 (TID 1)
12:48:50 INFO  com.datastax.driver.core.Cluster - New Cassandra host localhost/127.0.0.1:9042 added
12:48:50 INFO  c.d.s.c.cql.CassandraConnector - Connected to Cassandra cluster: Super Cluster
12:49:11 INFO  o.a.spark.storage.BlockManagerInfo - Removed broadcast_0_piece0 on localhost:56289 in memory (size: 8.0 KB, free: 787.3 MB)
12:49:35 INFO  org.apache.spark.executor.Executor - Finished task 0.0 in stage 1.0 (TID 1). 6124 bytes result sent to driver
12:49:35 INFO  o.a.spark.scheduler.TaskSetManager - Finished task 0.0 in stage 1.0 (TID 1) in 45199 ms on localhost (1/1)
12:49:35 INFO  o.a.s.scheduler.TaskSchedulerImpl - Removed TaskSet 1.0, whose tasks have all completed, from pool 
12:49:35 INFO  o.a.spark.scheduler.DAGScheduler - ResultStage 1 (collect at V1Servlet.scala:1146) finished in 45.199 s

在sbt中：

    "org.apache.spark" %% "spark-core" % "1.4.1" excludeAll(ExclusionRule(organization = "org.slf4j")),
    "org.apache.spark" %% "spark-sql" % "1.4.1" excludeAll(ExclusionRule(organization = "org.slf4j")),
    "com.datastax.spark" %% "spark-cassandra-connector" % "1.4.0-M3" excludeAll(ExclusionRule(organization = "org.slf4j"))

在代码中，我有一个托管SparkContext和CassandraSqlContext的单例。然后从servlet调用它。以下是单例代码的外观：

object SparkModel {

  val conf =
    new SparkConf()
      .setAppName("core")
      .setMaster("local")
      .set("spark.cassandra.connection.host", "127.0.0.1")

  val sc = new SparkContext(conf)
  val sqlC = new CassandraSQLContext(sc)
  sqlC.setKeyspace("core")

  val df: DataFrame = sqlC.cassandraSql(
    "SELECT email, target_entity_id, target_entity_type " +
    "FROM tracking_events " +
    "LEFT JOIN customers " +
    "WHERE entity_type = 'User' AND entity_id = customer_id")
}

下面是我如何使用它：

get("/spark") {
  SparkModel.df.collect().map(r => TrackingEvent(r.getString(0), r.getString(1), r.getString(2))).toList
}

Cassandra、Spark和web应用程序在我的Macbook Pro虚拟机的同一台主机上运行，规格相当。Cassandra查询本身需要10-20毫秒

当我第一次调用这个端点时，返回结果需要70-80秒。后续查询大约需要45秒。后续操作的日志如下所示：

12:48:50 INFO  org.apache.spark.SparkContext - Starting job: collect at V1Servlet.scala:1146
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Got job 1 (collect at V1Servlet.scala:1146) with 1 output partitions (allowLocal=false)
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Final stage: ResultStage 1(collect at V1Servlet.scala:1146)
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Parents of final stage: List()
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Missing parents: List()
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Submitting ResultStage 1 (MapPartitionsRDD[29] at collect at V1Servlet.scala:1146), which has no missing parents
12:48:50 INFO  org.apache.spark.storage.MemoryStore - ensureFreeSpace(18696) called with curMem=26661, maxMem=825564856
12:48:50 INFO  org.apache.spark.storage.MemoryStore - Block broadcast_1 stored as values in memory (estimated size 18.3 KB, free 787.3 MB)
12:48:50 INFO  org.apache.spark.storage.MemoryStore - ensureFreeSpace(8345) called with curMem=45357, maxMem=825564856
12:48:50 INFO  org.apache.spark.storage.MemoryStore - Block broadcast_1_piece0 stored as bytes in memory (estimated size 8.1 KB, free 787.3 MB)
12:48:50 INFO  o.a.spark.storage.BlockManagerInfo - Added broadcast_1_piece0 in memory on localhost:56289 (size: 8.1 KB, free: 787.3 MB)
12:48:50 INFO  org.apache.spark.SparkContext - Created broadcast 1 from broadcast at DAGScheduler.scala:874
12:48:50 INFO  o.a.spark.scheduler.DAGScheduler - Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD[29] at collect at V1Servlet.scala:1146)
12:48:50 INFO  o.a.s.scheduler.TaskSchedulerImpl - Adding task set 1.0 with 1 tasks
12:48:50 INFO  o.a.spark.scheduler.TaskSetManager - Starting task 0.0 in stage 1.0 (TID 1, localhost, NODE_LOCAL, 59413 bytes)
12:48:50 INFO  org.apache.spark.executor.Executor - Running task 0.0 in stage 1.0 (TID 1)
12:48:50 INFO  com.datastax.driver.core.Cluster - New Cassandra host localhost/127.0.0.1:9042 added
12:48:50 INFO  c.d.s.c.cql.CassandraConnector - Connected to Cassandra cluster: Super Cluster
12:49:11 INFO  o.a.spark.storage.BlockManagerInfo - Removed broadcast_0_piece0 on localhost:56289 in memory (size: 8.0 KB, free: 787.3 MB)
12:49:35 INFO  org.apache.spark.executor.Executor - Finished task 0.0 in stage 1.0 (TID 1). 6124 bytes result sent to driver
12:49:35 INFO  o.a.spark.scheduler.TaskSetManager - Finished task 0.0 in stage 1.0 (TID 1) in 45199 ms on localhost (1/1)
12:49:35 INFO  o.a.s.scheduler.TaskSchedulerImpl - Removed TaskSet 1.0, whose tasks have all completed, from pool 
12:49:35 INFO  o.a.spark.scheduler.DAGScheduler - ResultStage 1 (collect at V1Servlet.scala:1146) finished in 45.199 s

从日志中可以看到，最长的暂停时间在这3行之间21+24秒：

12:48:50 INFO  c.d.s.c.cql.CassandraConnector - Connected to Cassandra cluster: Super Cluster
12:49:11 INFO  o.a.spark.storage.BlockManagerInfo - Removed broadcast_0_piece0 on localhost:56289 in memory (size: 8.0 KB, free: 787.3 MB)
12:49:35 INFO  org.apache.spark.executor.Executor - Finished task 0.0 in stage 1.0 (TID 1). 6124 bytes result sent to driver

显然，我做错了什么。那是什么？我该如何改进这一点

编辑：重要补充：表的大小很小，约200个条目用于跟踪_事件，约20个条目用于客户，因此将它们全部读取到内存中不会占用太多时间。这是一个本地Cassandra安装，不涉及集群，不涉及网络

  "SELECT email, target_entity_id, target_entity_type " +
    "FROM tracking_events " +
    "LEFT JOIN customers " +
    "WHERE entity_type = 'User' AND entity_id = customer_id")

此查询将从tracking_events和customers表中读取所有数据。我会将性能与只在两个表上执行SELECT COUNT*进行比较。如果有显著的不同，那么可能会有问题，但我猜这只是将两个表完全读入内存所需的时间

有几个旋钮用于调整读取方式，由于默认值面向更大的数据集，您可能需要更改这些旋钮

spark.cassandra.input.split.size_in_mb  approx amount of data to be fetched into a Spark partition  64 MB
spark.cassandra.input.fetch.size_in_rows    number of CQL rows fetched per driver request   1000

我会确保您生成的任务数量至少与您的核心数量相同，这样您就可以充分利用您的所有资源。为此，请收缩input.split.size

---

fetch size控制一个executor core一次分页的行数，因此在某些使用情况下，增加该值可以提高速度。

优秀答案Russ！我注意到了同样的性能问题，但假设这是因为我的Spark cluster在本地VM上运行。出于某种原因，我现在无法启动Cassandra实例，但重要的是，这两个表很小。跟踪活动有200个条目，客户只有20个左右。因为数据加载的原因，它不会花那么长时间。你为什么不检查一下UI，它应该会准确地为你划分时间。我不知道怎么做。我不运行独立的Spark，只是作为对我的web应用程序的依赖。我试着转到localhost:4040，如文档所述，但那里什么都没有。Spark并不是真正为实时查询而设计的，它更像是一个批分析框架，也许你想寻找类似Solr？/ElasticSerach的东西？