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Daniel Abadi HadoopWorld 2010
 

Daniel Abadi HadoopWorld 2010

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Daniel Abadi's HadoopWorld 2010 Slides

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    Daniel Abadi HadoopWorld 2010 Daniel Abadi HadoopWorld 2010 Presentation Transcript

    • MapReduce and Parallel Database Systems: Complementary or Competitive Technology? Daniel Abadi Yale University October 12 th , 2010
    • Brief History of MapReduce
      • Pre-2004: used at Google for many data processing apps, including Web indexing
      • 2004: paper in academic conference not written in traditional academic style
      • 2004-2006: Implemented in Nutch
      • 2006-2008: Split off into Hadoop; significant usage at Yahoo; buzz increases
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    • Controversy
      • Vast majority of the outrage was about the comparison of the systems
      • BUT:
        • The line between MapReduce and Hadoop (which comes with HDFS) was blurring
        • Hadoop can be used as an alternative to traditional DW implementations built using DBMS software
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    • SIGMOD 2009 Paper
      • Benchmarked Hadoop vs. 2 parallel database systems
        • Compared across a variety of dimensions including performance and ease of use
        • Measured differences in load and query time for some common data processing tasks
        • Used Web analytics benchmark whose goal was to be representative of tasks that:
          • Both should excel at
          • Hadoop should excel at
          • Databases should excel at
    • Hardware Setup
      • 100 node cluster
      • Each node
        • 2.4 GHz Code 2 Duo Processors
        • 4 GB RAM
        • 2 250 GB SATA HDs (74 MB/Sec sequential I/O)
      • Dual GigE switches, each with 50 nodes
        • 128 Gbit/sec fabric
      • Connected by a 64 Gbit/sec ring
    • Join Task
    • UDF Task DBMS clearly doesn’t scale
      • Calculate PageRank over a set of HTML documents
      • Performed via a UDF
    • Benchmark Conclusions
      • Hadoop has many advantages
        • Load time much faster
        • Significantly easier to install, use
        • Better parallelization of UDFs
      • Hadoop is consistently less efficient for structured, relational data
        • Reasons both fundamental and non-fundamental
        • Needs better support for compression and direct operation on compressed data
        • Needs better support for indexing
        • Needs better support for co-partitioning of datasets
    • Overall Conclusion
      • MapReduce/Hadoop and parallel databases are clearly complementary
      • Use MapReduce if you want to do:
        • ETL
        • Unstructured data processing
        • Deep analysis that is hard to express in SQL
      • Use parallel databases for:
        • Traditional data warehousing / data marts
        • Structured data processing expressible in SQL
      • Cloudera agrees!
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    • We’re all in agreement, right?
    • But Wait!
      • Hadoop can do everything a parallel database can do
      • Hadoop has (something resembling) a SQL interface (Hive)
      • Many of Hadoop’s performance deficiencies not fundamental
        • Result of initial design for unstructured data
        • Over 20 research papers in the last two years on improving Hadoop performance for DBMS workloads
      • Hadoop is free and open source
        • (Oracle, IBM/Netezza, Microsoft, Teradata, Vertica, Greenplum, and Aster Data are all proprietary)
    • People are using Hadoop as a DW
      • Facebook has 12PB data warehouse in Hadoop/Hive
        • Adding 10TB per day
      • Yahoo’s warehouse is the same order of magnitude
        • Recently switched to Hadoop
    • Fault Tolerance and Cluster Heterogeneity Results Database systems restart entire query upon a single node failure, and do not adapt if a node is running slowly
    • So …
      • Hadoop can do everything that parallel databases can do, but:
        • Has better fault tolerance
        • Adjusts better to runtime performance fluctuations
        • Is more open / cheaper
        • Has at least as good scalability (if not better)
      • If only we could fix those performance problems on structured data
        • HadoopDB!
    • HadoopDB
      • Use Hadoop to coordinate execution of multiple independent (typically single node, open source) database systems
        • Flexible query interface (accepts both SQL and MapReduce)
        • Open source (built using open source components)
    • HadoopDB Architecture
    • TPC-H Benchmark Results
    • Fault Tolerance and Cluster Heterogeneity Results
    • HadoopDB: Current Status
      • Initial open source release over a year ago
        • A bunch of new code since then, but not yet put up online
        • This new code is available by request
      • Expect the next release to be in mid-2011
      • Money available for people who want to help with development (e-mail justin.borgman@yale.edu)
    • Invisible Loading
      • Data starts in HDFS
      • Data is immediately available for processing (immediate gratification paradigm)
      • Each MapReduce job causes data movement from HDFS to database systems
      • Data is incrementally loaded, sorted, and indexed
      • Query performance improves “invisibly”
    • Conclusions
      • MapReduce and parallel databases are definitely complimentary
      • MapReduce and parallel databases are definitely competitive
      • HadoopDB is awesome