An An Architectural Hybrid of MapReduce & DBMS Technologies for Analytical Workloads

1. HadoopDB
An An Architectural Hybrid of MapReduce
& DBMS Technologies for Analytical
Workloads
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Tilani Gunawardena

2. Road Map
• Motivation
• Introduction
• Desired Properties
• Background & Shortfalls
• HadoopDB
• Benchmarks
• Fault Tolerance
• Conclusion
• Related Work
• References
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3. Introduction
• Analyzing massive structured data on 1000s of shared-nothing
nodes
• Shared nothing architecture:
• A collection of independent,possibly virtual matchines eact with
local disk and local main memory connected together on a high-
speed network
• Approachs:
• Parallel databases
• Map/Reduce systems
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4. Desired Properties
• Performance
• A primary characteristic that commercial database systems use to
distinguish themselves
• A Fault tolerance
• Heterogeneus environments
• Increasing number of nodes
• Difficult homogeneous
• Flexible query interface
• Usually JDBC or ODBC
• UDF mechanism
• Desirable SQL and no SQL interfaces
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5. Background-PDBMS
• Standard relational tables and SQL
• Indexing, compression,caching, I/O sharing
• Tables partitioned over nodes
• Transparent to the user
• Meet performance
• Needed highly skilled DBA
• Flexible query interfaces
• UDFs varies accros implementations
• Fault tolerance
• Not score so well
• Assumption: failures are rare
• Assumption: dozens of nodes in clusters
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6. Background-MapReduce
• Satisfies fault tolerance
• Works on heterogeneus environment
• Drawback: performance
• No enhacing performance techniques
• Interfaces
• Write M/R jobs in multiple languages
• SQL not supported directly ( excluding eg: Hive )
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7. • MapReduce (Hadoop) MapReduce is a programming model
which specifies:
• A map function that processes a key/value pair to generate a set
of intermediate key/value pairs,
• A reduce function that merges all intermediate values associated
with the same intermediate key.
• Hadoop
• Is a MapReduce implementation for processing large data sets
over 1000s of nodes.
• Maps and Reduces run independently of each other over blocks
of data distributed across a cluster
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8. Background-MapReduce
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9. Differences between Parallel
Databases and MapReduce?
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10. 10

11. HadoopDB
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12. HadoopDB
• Hadoop as communication layer above multiple nodes running
single-node DBMS instances
• Full open-source solution :
• PostgreSQL as DB layer
• Hadoop as communication layer
• Hive as translation layer
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13. HadoopDB
RDBMS Hadoop
• Careful layout of data • Job scheduling
• Indexing • Task coordination
• Sorting • Parallellization
• Query optimization
• compression
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14. Ideas
• Main goal: achieve the properties described before
• Connect multiple single-datanode systems
• Hadoop as the task coordination & network communication layer
• Queries parallelized across the nodes using MapReduce framework
• Fault tolerant and work in heterogeneus nodes
• Parallel databases performance
• Query processing in database engine
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15. Architecture Background
• Data Storage layer (HDFS)
• Block structured file system managed by central NameNode
• Files broken in blocks and ditributed
• Data processing layer (Map/Reduce framework)
• Master/slave architecture
• Job and Task trackers
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16. HadoopDB Components
• Database Connector
• Catalog
• Data Loader
• Planner (SMS)
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17. Database Connector
• Interface between DBMS and TaskTacker
• Responsabilities
• Connect to the database
• Execute the SQL query
• Return the results as key-value pairs
• Achieved goal
• Datasources are similar to datablocks in HDFS
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18. Catalog
• Maintain information about database
• Database location, driver class
• Darasets in cluster, replica or partitioning
• Catalog stored as xml file in HDFS
• Plan to deploy as separated service
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19. Data Loader
• Responsabilities:
• Globally partition the data on given key
• Break single node data into chunks
• Bulk-loading chunks in single-node databases
• Two main components:
• Global hasher
• Map/Reduce job read from HDS and repartition
• Local Hasher
• Copies from HDFS to local file system
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20. SMS Planner
• Extends Hive
• Steps
• Parser transforms query to (AST)abstract syntax tree
• Get table schema information from catalog
• Logical plan generator creates query plan
• Optimizer breaks up plan to Map or Reduce phases
• Executable plan generated for one or more MapReduce jobs
• SMS tries to push maximum work to database layer
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21. 21

22. Benchmarking
• Environment
• Amazon EC2 “large” instances
• Each instance
• 7,5 GB memory,2 virtual cores,850 GB storage,64 bits Linux Fedora 8
• Systems
• Hadoop
• 256MB data blocks,1024 MB heap size, 200Mb sort buffer
• HadoopDB
• Similar to Hadoop conf,PostgreSQL 8.2.5,No compress data
• Vertica
• Used a cloud edition
• All data is compressed
• DBMS-X
• Comercial parallel row
• Run on EC2 (not cloud edition available) 22

23. Benchmarking
• Used data
• Http log files, html pages, ranking
• Sizes (per node):
• 155 millions user visits (~ 20Gigabytes)
• 18 millions ranking (~1Gigabyte)
• Stored as plain text in HDFS
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24. Evaluating HadoopDB
• Compare HadoopDB to
• 1 Hadoop
• 2 Parallel databases (Vertica, DBMS-X)
• Features:
• 1 Performance:
• We expected HadoopDB to approach the performance of parallel
databases
• 2 Scalability:
• We expected HadoopDB to scale as well as Hadoop We ran the Pavlo
et al. SIGMOD’09 benchmark on Amazon EC2 clusters of 10, 50, 100
nodes.
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25. Benchmark tasks
• Data loading
• Grep task
• Selection task
• Aggregation task
• Join task
• UDF Aggregation task
• Fault tolerance and Heterogeneous environment
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26. Data Load
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27. Queries Result
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28. • load -data loads are slower than Hadoop, but faster than
parallel databases
• runtime -
• Structured data-HadoopDB is faster than Hadoop but slower than
parallel databases(HadoopDB’s performance is close to parallel
databases)
• Unstructured data- HadoopDB’s performance matches Hadoop
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29. Scalability:Setup
• Simple aggregation task - full table scan
• Data replicated across 10 nodes
• Fault-tolerance: Kill a node halfway
• Fluctuation-tolerance: Slow down a node for the entire
experiment
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30. Scalability:Results
• HadoopDB and Hadoop take advantage of runtime acheduling
by splitting data into chunks
• Parallel databases restart entire query on node failure or wait
for the slowest node
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31. To Summarize
• HadoopDB - a hybrid of DBMS and MapReduce
• HadoopDB is close in performance to parallel databases
• HadoopDB is able to operate in truly heterogeneous
environment and has the fault tolerance of Hadoop
environment
• Is free and open-source
http://hadoopdb.sourceforge.net
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32. Related Work
• Pig Project at yahoo
• SCOPE project at Microsoft
• Hive project
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33. Future Work
• Integration with other open source databases
• Full automation of the loading and replication process
• Dynamically adjusting fault-tolerance levels based on failure
rate
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34. Thank You!
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