MapReduce Job Processing

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1 April 17, 2012

2 Background: Hadoop Distributed File System (HDFS) Hadoop requires a Distributed File System (DFS), we utilize the Hadoop Distributed File System (HDFS).

3 Background: Hadoop Distributed File System (HDFS) Hadoop requires a Distributed File System (DFS), we utilize the Hadoop Distributed File System (HDFS). NameNode R Record ID User ID Object ID DataNodes

4 Background: Hadoop Distributed File System (HDFS) Hadoop requires a Distributed File System (DFS), we utilize the Hadoop Distributed File System (HDFS). NameNode R s DataChunks (Splits) 64MB 64MB 64MB 64MB DataNodes

5 Background: Hadoop Distributed File System (HDFS) Hadoop requires a Distributed File System (DFS), we utilize the Hadoop Distributed File System (HDFS). NameNode R s DataChunks (Splits) 64MB 64MB 64MB 64MB DataNodes

6 Background: Hadoop Core Hadoop Core consists of one master and several TaskTrackers.

7 Background: Hadoop Core Hadoop Core consists of one master and several TaskTrackers. We assume one TaskTracker per physical machine.

8 Background: Hadoop Core Hadoop Core consists of one master and several TaskTrackers. We assume one TaskTracker per physical machine. TaskTrackers

9 Background: Hadoop Core Hadoop Core consists of one master and several TaskTrackers. We assume one TaskTracker per physical machine. Job Scheduling Task Scheduling Reduce Task Scheduling TaskTrackers

10 Background: Hadoop Core Hadoop Core consists of one master and several TaskTrackers. We assume one TaskTracker per physical machine. Job Scheduling Task Scheduling Reduce Task Scheduling s Reducers TaskTrackers

11 Background: Hadoop Cluster In a Hadoop cluster one machine typically runs both the NameNode and tasks and is called the master.

12 Background: Hadoop Cluster In a Hadoop cluster one machine typically runs both the NameNode and tasks and is called the master. The other machines run DataNode and TaskTracker tasks and are called slaves.

13 Background: Hadoop Cluster In a Hadoop cluster one machine typically runs both the NameNode and tasks and is called the master. The other machines run DataNode and TaskTracker tasks and are called slaves. NameNode + DataNodes + TaskTrackers

14 Background: Hadoop Cluster In a Hadoop cluster one machine typically runs both the NameNode and tasks and is called the master. The other machines run DataNode and TaskTracker tasks and are called slaves. NameNode + Master Slaves DataNodes + TaskTrackers

15 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Map Phase Next we look at an overview of a typical MapReduce Job.

16 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Map Phase Job specific variables are first placed in the Job Configuration which is sent to each Task by the.

17 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Map Phase Large data such as files or libraries are then put in the Distributed Cache which is copied to each TaskTracker by the.

18 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Map Phase The next assigns each InputSplit to a task on a TaskTracker, we assume m s and m InputSplits.

19 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Map Phase Each maps a (k 1, v 1 ) pair to an intermediate (k 2, v 2 ) pair and partitions by k 2, i.e. hash(k 2 ) = p i for i [1, r], r = reducers.

20 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Map Phase An optional is executed over (k 2, list(v 2 )).

21 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Map Phase The aggregates v 2 for a k 2 and a (k 2, v 2 ) is written to a partition on disk.

22 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Reducer Reducer Map Phase Shuffle/Sort Phase The assigns two TaskTrackers to run the Reducers, each Reducer copies and sorts it s inputs from corresponding partitions.

23 Background: MapReduce Job Overview Job Configuration Distributed Cache s reduce communication overhead! split 1 split 2 split 3 split 4 Reducer Reducer Map Phase Shuffle/Sort Phase The assigns two TaskTrackers to run the Reducers, each Reducer copies and sorts it s inputs from corresponding partitions.

24 Background: MapReduce Job Overview Job Configuration Distributed Cache split 1 split 2 split 3 split 4 Reducer Reducer (k3, v3) (k3, v3) o1 o2 Map Phase Shuffle/Sort Phase Reduce Phase Each Reducer reduces a (k 2, list(v 2 )) to a single (k 3, v 3 ) and writes the results to a DFS file, o i for i [1, r].

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