MapReduce Online. Tyson Condie, Neil Conway, Peter Alvaro, Joseph Hellerstein, Khaled Elmeleegy, Russell Sears. Neeraj Ganapathy

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1 MapReduce Online Tyson Condie, Neil Conway, Peter Alvaro, Joseph Hellerstein, Khaled Elmeleegy, Russell Sears Neeraj Ganapathy

2 Outline Hadoop Architecture Pipelined MapReduce Online Aggregation Continuous MapReduce Jobs Performance Analysis Future Work

3 Hadoop Architecture Hadoop MapReduce User defined Map, Reduce functions Job Tracker accepts jobs from clients Job is divided into smaller tasks and assigned to slave nodes Hadoop Distributed File System(HDFS) Files are stored in fixed size blocks(64mb default) Stores input to Map and output from Reduce Output of Map and Reduce tasks are written to local file before it can be consumed Simple fault tolerance mechanism

4 Dataflow in Hadoop Submit job map schedule reduce map reduce

5 Dataflow Map phase Read Input File Block 1 map reduce HDFS Block 2 map reduce

6 Dataflow Commit phase Finished Finished + Location map Local FS reduce map Local FS reduce

7 Dataflow Shuffle phase map Local FS reduce HTTP GET map Local FS reduce

8 Dataflow Sort, Reduce phase reduce Write Final Answer HDFS reduce

9 Pipelined MapReduce Advantages of Pipelining Online aggregation is possible Support for continuous queries Better performance

10 Hadoop Online Prototype(HOP) Naïve pipelining JobTracker assigns map and reduce tasks to available TaskTracker slots Reduce task opens TCP socket with the Map tasks Data is pushed from Mapper to Reducer Disadvantages Prevents use of combiners Map function can block on network I/O Additional sorting work for the Reducer

11 HOP Refinements Using two threads for executing the Map function and sending the data Wait for in-memory buffer to grow to a threshold size Adaptive flow control mechanism Task scheduling

12 HOP Fault Tolerance Bookkeeping in reduce tasks to recover from map failures Map tasks retain their output for the complete job duration to recover from reduce failures Checkpoint concept

13 Dataflow - HOP Schedule Schedule + Location map reduce Pipeline request map reduce

14 Outline Hadoop Architecture Pipelined MapReduce Online Aggregation Continuous MapReduce Jobs Performance Analysis Future Work

15 Online Aggregation Traditional MapReduce provide poor interface for interactive data analysis Most users prefer a quick and dirty approximation Pipelining allows the system to give approximate early results and refine them

16 Single-Job Online Aggregation Reduce function is periodically invoked on available data User specifies how often snapshots should be computed(e.g. 25%,50%,75% of input) Upon completion of sufficient percentage of input, reduce tasks write snapshot into HDFS Applications poll these files find approximate answers However, accuracy cannot be estimated

17 Single-Job Online Aggregation Read Input File Block 1 map reduce HDFS Block 2 map reduce HDFS Write Snapshot Answer

18 Multi-Job Online Aggregation Snapshots are sent periodically to consumer jobs Requires co-scheduling a sequence of jobs Disadvantage Redundancy in computation by second job as snapshots of the first job are produced

19 Inter-Job Online Aggregation reduce reduce Job 1 Reducers Write Answer HDFS map map Job 2 Mappers

20 Example Top K most-frequent-words in 5.5GB Wikipedia articles (implemented as 2 MR jobs) 60 node EC2 cluster

21 Outline Hadoop Architecture Pipelined MapReduce Online Aggregation Continuous MapReduce Jobs Performance Analysis Future Work

22 Continuous MapReduce Jobs MapReduce is used to analyze streams of constantly arriving data(e.g. logs, network traffic etc.) Traditional batch processing approach introduces latency Solution Run MapReduce jobs continuously and analyze data as it arrives

23 Fault Tolerance Complete job duration data cannot be stored in this case Reducer requires only suffix of map output stream Map-side spill files are assigned IDs to recover from failures

24 Outline Hadoop Architecture Pipelined MapReduce Online Aggregation Continuous MapReduce Jobs Performance Analysis Future Work

25 Performance Analysis

26 Performance Analysis

27 Performance Analysis

28 Big question Why don t we use HOP instead of traditional Hadoop?

29 Disadvantages of HOP Manual configuration is required which is error prone Intermediate results may be inaccurate HOP might not be very efficient when there is a huge difference between the number of nodes and map tasks

30 Outline Hadoop Architecture Pipelined MapReduce Online Aggregation Continuous MapReduce Jobs Performance Analysis Future Work

31 Future Work Elastic scale up/down of map and reduce tasks Develop full-fledged stream processing system Scheduling tasks More interactive applications

32 Queries? Thank You

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