HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads (VLDB '09)

2012-11-20 16:20

Yale DB

# 1. Problem

Large analytical data management (OLAP) with commodity clusters

# 2. Challenges

  • Performance and efficiency
    • hadoop
      • is not for structured data analysis
      • scale well
      • open source, without cost
  • scalability, fault-tolerance, and flexibility
    • Previous parallel db fit into tens of nodes, not thousand of nodes.
    • not scale well for failures, heterogeneous machines, performance untested

# 3. Solutions

  • MapReduce (Hadoop) + parallel db (or single-node DBs) = HadoopDB

    • Translation layer: Hive
    • Communication layer: Hadoop
    • Database layer: PostgreSQL (or MySQL, … JDBC)
  • Components

    1. Database Connector

      • Interface among dbs on nodes
      • extends InputFormat class -> InputFormat Implementation lib
      • JDBC-complaint
    2. Catalog

      • metainformation as XML = connection para + metadata
    3. Data Loader

      • Global Hasher: MR, repartition raw data from HDFS to NO. of nodes
      • Local Hasher: copy from HDFS, repartition the partition into chunks
      • Better load balance than Hadoop
    4. SQL - MR - SQL (SMS) Planner

      • extend Hive for
      • open and low cost
      • each table stored separately in HDFS, low performance in multi-table trans. intercept normal Hive flow in
      1. update MetaStore before query execution
      2. between query plan generation and MR jobs retrieve fields, determine partition keys

      traverse DAG bottom-up

      only support filter, select, aggregation

  • Extend performance [23] with fault tolerance and heterogeneous node exp

    TODO: modify the current task scheduler, connect not straggler node but the replicas.

    Exp on EC2, Hadoop, HadoopDB, Vertica, DBMS-X

# 4. Conclusion

  • HadoopDB’s performance < parallel db for
    • PostgreSQL (not column-store, not compression)
    • Hadoop and Hive are young
  • performance, heterogeneous environ., fault tolerance, flexibility

[23] A Comparison of Approaches to Large Scale Data Analysis

[6] Scope [11] Hive [24] C-store [4] What is the right way to measure scale?

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