The Performance of MapReduce: An In-depth Study

Transcript

1. Dawei Jiang, Beng Chin Ooi, Lei Shi, Sai Wu, School of Computing, NUS Presented by Tang Kai
2.  Introduction  Factors affecting Performance of MR  Pruning search space  Implementation  Benchmark
3.  MapReduce-based systems are increasingly being used. ◦ Simple yet impressive interface  Map() Reduce() ◦ Flexible  Storage system independence ◦ Scalable ◦ Fine-grain fault tolerance
4.  Previous study ◦ Fundamental difference  Schema support  Data access  Fault tolerance ◦ Benchmark  Parallel DB >> MR-based
5.  Is it not possible to have a flexible, scalable and efficient MapReduce-based systems?  Works ◦ Identify several performance bottlenecks ◦ manage bottlenecks and tune performance  well-known engineering and database techniques  Conclusion ◦ 2.5x-3.5x
6.  Introduction  Factors affecting Performance of MR  Pruning search space  Implementation  Benchmark
7.  7 steps of a MapReduce job 1) Map 2) Parse 3) Process 4) Sort 5) Shuffle 6) Merge 7) Reduce
8.  I/O mode  Indexing  Parsing  Sorting
9.  Direct I/O ◦ read data from the disk directly ◦ Local  Streaming I/O ◦ streaming data from the storage system by an inter-process communication scheme,  such as TCP/IP or JDBC. ◦ Local and remote  Direct I/O > Streaming I/O by 10%-15%
10.  Input of a MapReduce job ◦ a set of files stored in a distributed file system, i.e. HDFS  Ranged-indexes ◦ input HDFS files are not sorted but each data chunk in the files are indexed by keys  Block-level indexes ◦ tables stored in database servers  Database indexed tables Boost selection task 2x-10x depending on the selectivity
11.  Raw data -> <k,v> pair  Immutable decoding ◦ Read-only records (set once)  Mutable decoding  Mutable decoder is 10x faster. ◦ boost selection task 2x overall
12.  Map-side sorting affects performance of aggregation ◦ Cost of key comparison is non-trivial.  Example ◦ SourceIP in UserVisits Table ◦ Sort intermediate records. ◦ sourceIP variable-length string  String compare (byte-to-byte)  Fingerprint compare (integer)  Fingerprint-based is 4x-5x faster. ◦ 20%-25% overall
13.  Why ◦ 4 factors  Resulting in large search space (2*2*3*2) ◦ Budget limit on Amazon EC2  Greedy
14.  Greedy Stategy I/O mode Parser Different sort schemes In various architecture Direct I/O Stream I/O Hadoop Writable Google’s ProtocolBuffer Berkeley DB 3 datasets 4 queries Bench mark
15.  Introduction  Factors affecting Performance of MR  Pruning search space  Implementation  Benchmark
16.  Hadoop 0.19.2 as code base  Direct I/O ◦ Modification of data node implementation  Text decoder ◦ Immutable same as Dewitt ◦ Mutable by ourselves  Binary decoder ◦ Hadoop  Immutable Writable decoder  Mutable using hadoop API by ourselves ◦ Google Protocol buffer  Build-in compiler->mutable  Immutable by ourselves ◦ Berkeley DB  BDB binding API (mutable)
17.  Amazon EC2 (Elastic computing cloud) ◦ 7.5GB memory ◦ 2 virtual cores ◦ 64-bits Fedora 8  Tuning EC2 disk I/O by shifting peak time.  Hadoop Setting ◦ Block size of HDFS: 512MB ◦ Heap size of JVM: 1024MB
18.  Introduction  Factors affecting Performance of MR  Pruning search space  Implementation  Benchmark
19.  Results for different I/O mode ◦ Single node ◦ No-op job w/ map w/o reduce
20.  Results for record parsing ◦ Run in Java process instead of MapReduce job ◦ Time start after loading into memory  Mutable > Immutable ◦ Mutable text> mutable binary
21.  In between hadoop-based system ◦ Cache factor  In between hadoop-based and Parallel DB ◦ Close
22.  Selection task -> scan -> Index  Caching  Indexing
23.  Parsing: 2x faster  Sorting: 20%-25% faster ◦ Not significant in small size aggregation task
24.  On decoding scheme  Comparison of tuned MR-based & Parallel DB
25.  Cons ◦ Need to be committed/forked to Hadoop source code tree ◦ A complete framework is needed instead of miscellaneous patches. ◦ Various API support: CLI, Web rather than Java.  Future work ◦ Provide query parser, optimizer etc to build a complete solution ◦ Elastic power-aware data intensive Cloud  http://www.comp.nus.edu.sg/~epic/download/MapRe duceBenchmark.tar.gz Tenzing: A SQL Implemetation On The MapReduce Framework