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This was presented at NHN on Jan. 27, 2009. It introduces Big Data, its storages, and its analyses. Especially, it covers MapReduce debates and hybrid systems of RDBMS and MapReduce. In addition, in terms of Schema-Free, various non-relational data storages are explained.
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http://www.coordguru.com
Woohyun Kim
The creator of open source “Coord”
(http://www.coordguru.com)
2010-01-27
Emergent Distributed Data Storagesfor Big Data, Storage, and Analysis
http://www.coordguru.com
ContentsThe Advent of Big Data
• Noah’s Ark Problem
• Key Issues with ‚Big Data‛
• How to deal with ‚Big Data‛
MapReduce Debates• MapReduce is just A Major Step Backwards!!!
• RDB experts Jump the MR Shark
• DBs are hammers; MR is a screwdriver
• MR is a Step Backwards, but some Steps Forward
Hadoop Revolution• Best Practice in Hadoop
• Hadoop is changing the Game
• Big Data goes well with Hadoop
• Case Study: Parallel Join
• Case Study: Further Study in Parallel Join
• Case Study: Improvements in Parallel Join
A Hybrid of MapReduce and RDBMS • Integrate MapReduce into RDBMS
• In-Database MapReduce vs. File-only MapReduce
Non-Relational Data Storages• Throw ‘Relational’ Away, and Take ‘Schema-Free’
• A Comparison of Non-Relational Data Storages
• Emergent Document-oriented Storages
• Document-oriented vs. RDBMS
http://www.coordguru.com
The Advent of Big Data
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Noah’s Ark Problem• Did Noah take dinosaurs on the Ark?
• The Ark was a very large ship designed especially for its important purpose
• It was so large and complex that it took Noah 120 years to build
• How to put such a big thing• Diet?• Split?
• Differentiate• Put• Integrate
• Scale Up?• Scale Out?
• ‚Big Data‛ problem is just like that
http://www.coordguru.com
Key Issues with ‚Big Data‛• Lookup
• Metadata server -> centralized or distributed -> partitioned replicas to avoid a single of the failure
• Partition• Data locality -> network bandwidth reduction -> putting the computation near the data
• Replication• Hardwar Failure -> Data Loss -> Availability from redundant copies of the data
• Load-balanced Parallel Processing• Corrupt Data or Remote process failure -> speculative execution or rescheduling
• Ad-hoc Analysis• Some partitioned data may need to be combined with another data
http://www.coordguru.com
Struggling to STORE and ANALYZE ‚Big Data‛
How to deal with ‚Big Data‛
http://www.coordguru.com
Appendix: What is ETL?
ETL(Extract, Transform, and Load)
• Talend Open Studio• Pentaho Data Integration (Kettle)• RapidMiner• Jitterbit 2.0, • Apatar• Clover.ETL• Scriptelle
• A process in database usage and especially in data warehousing that involves:
• Extracting data from outside sources(such as different data
organization/format, non-relational database structures)
• Transforming it to fit operational needs (which can include quality levels)
• Selection, translation, encoding, calculation, filtering, sorting, joining,
aggregation, transposing or pivoting, splitting, disaggregation
• Loading it into the end target (database or data warehouse)
ETL Open Sources
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Hadoop Revolution
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Row
Row key Column key
Column
Family
Column
Family
Time
stamp
Best Practice in Hadoop• Software Stack in Google/Hadoop • Cookbook for ‚Big Data‛
StructuredData
• Structured Data Storage for ‚Big Data‛
http://www.coordguru.com
Appendix: What is MapReduce?
Map• Read a set of ‚records’ from an input file, which acts as filtering or transformations
• Output a set of (key, data) pair, which partitions them into R disjoint buckets by
the key
Reduce• Read a set of (key, a list of data) pairs from R disjoint buckets
• Each R from map’s outputs is shuffled, and aggregated into its corresponding
reduce with being ordering by the key
• Output a new set of records
MapReduce
ReduceMap
Map
Group-By/Filter
Aggregate/Aggregator
http://www.coordguru.com
Hadoop is changing the Game
• Hadoop, DW, and BI
http://www.coordguru.com
Big Data goes well with Hadoop
• Parallelize Relational Algebra Operations using MapReduce
http://www.coordguru.com
Case Study: Parallel Join
• A Parallel Join Example using MapReduce
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Case Study: Further Study in Parallel Join
Problems
• Need to sort
• Move the partitioned data across the network
• Due to shuffling, must send the whole data
• Skewed by popular keys
• All records for a particular key are sent to the same reducer
• Overhead by tagging
Alternatives• Map-side Join
• Mapper-only job to avoid sort and to reduce data movement across the
network
• Semi-Join
• Shrink data size through semi-join(by preprocessing)
http://www.coordguru.com
Case Study: Improvements in Parallel Join
Map-Side Join• Replicate a relatively smaller input source to the cluster
• Put the replicated dataset into a local hash table
• Join – a relatively larger input source with each local hash table
• Mapper: do Mapper-side Join
Semi-Join• Extract – unique IDs referenced in a larger input source(A)
• Mapper: extract Movie IDs from Ratings records
• Reducer: accumulate all unique Movie IDs
• Filter – the other larger input source(B) with the referenced unique IDs
• Mapper: filter the referenced Movie IDs from full Movie dataset
• Join - a larger input source(A) with the filtered datasets
• Mapper: do Mapper-side Join• Ratings records & the filtered movie IDs dataset
http://www.coordguru.com
MapReduce Debates
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MapReduce is just A Major Step Backwards!!!Dewitt and StoneBraker in January 17, 2008
• A giant step backward in the programming paradigm for large-scale data intensive applications
• Schema are good• Type check in runtime, so no garbage
• Separation of the schema from the application is good• Schema is stored in catalogs, so can be queried(in SQL)
• High-level access languages are good• Present what you want rather than an algorithm for how to get it
• No schema??!• At least one data field by specifying the key as input• For Bigtable/Hbase, different tuples within the same table can
actually have different schemas• Even there is no support for logical schema changes such as
views
http://www.coordguru.com
MapReduce is just A Major Step Backwards!!! (cont’d)Dewitt and StoneBraker in January 17, 2008
• A sub-optimal implementation, in that it uses brute force instead of indexing
• Indexing• All modern DBMSs use hash or B-tree indexes to accelerate access to data• In addition, there is a query optimizer to decide whether to use an index or
perform a brute-force sequential search• However, MapReduce has no indexes, so processes only in brute force fashion
• Automatic parallel execution• In the 1980s, DBMS research community explored it such as Gamma, Bubba,
Grace, even commercial Teradata
• Skew• The distribution of records with the same key causes is skewed in the map
phase, so it causes some reduce to take much longer than others
• Intermediate data pulling• In the reduce phase, two or more reduce attempt to read input files form the
same map node simultaneously
http://www.coordguru.com
MapReduce is just A Major Step Backwards!!! (cont’d)Dewitt and StoneBraker in January 17, 2008
• Not novel at all – it represents a specific implementation of well known techniques developed nearly 25 years ago
• Partitioning for join• Application of Hash to Data Base Machine and its Architecture, 1983
• Joins in parallel on a shared-nothing• Multiprocessor Hash-based Join Algorithms, 1985• The Case for Shared-Nothing, 1986
• Aggregates in parallel• The Gamma Database Machine Project, 1990• Parallel Database System: The Future of High Performance Database Systems,
1992• Adaptive Parallel Aggregation Algorithms, 1995
• Teradata has been selling a commercial DBMS utilizing all of these techniques for more than 20 years
• PostgreSQL supported user-defined functions and user-defined aggregates in the mid 1980s
http://www.coordguru.com
MapReduce is just A Major Step Backwards!!! (cont’d)Dewitt and StoneBraker in January 17, 2008
• Missing most of the features that are routinely included in current DBMS• MapReduce provides only a sliver of the functionality found in modern DBMSs
• Bulk loader – transform input data in files into a desired format and load it into a DBMS• Indexing – hash or B-Tree indexes• Updates – change the data in the data base• Transactions – support parallel update and recovery from failures during update• integrity constraints – help keep garbage out of the data base• referential integrity – again, help keep garbage out of the data base• Views – so the schema can change without having to rewrite the application program
• Incompatible with all of the tools DBMS users have come to depend on• MapReduce cannot use the tools available in a modern SQL DBMS, and has none of
its own• Report writers(Crystal reports)• Prepare reports for human visualization• business intelligence tools(Business Objects or Cognos)• Enable ad-hoc querying of large data warehouses• data mining tools(Oracle Data Mining or IBM DB2 Intelligent Miner)• Allow a user to discover structure in large data sets• replication tools(Golden Gate)• Allow a user to replicate data from on DBMS to another• database design tools(Embarcadero)• Assist the user in constructing a data base
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What the !@# MapReduce?
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RDB experts Jump the MR SharkGreg Jorgensen in January 17, 2008
• Arg1: MapReduce is a step backwards in database access• MapReduce is not a database, a data storage, or management system• MapReduce is an algorithmic technique for the distributed processing of large
amounts of data
• Arg2: MapReduce is a poor implementation• MapReduce is one way to generate indexes from a large volume of data, but it’s not
a data storage and retrieval system
• Arg3: MapReduce is not novel• Hashing, parallel processing, data partitioning, and user-defined functions are all old
hat in the RDBMS world, but so what?• The big innovation MapReduce enables is distributing data processing across a
network of cheap and possibly unreliable computers
• Arg4: MapReduce is missing features• Arg5: MapReduce is incompatible with the DBMS tools
• The ability to process a huge volume of data quickly such as web crawling and log analysis is more important than guaranteeing 100% data integrity and completeness
http://www.coordguru.com
DBs are hammers; MR is a screwdriverMark C. Chu-Carroll
• RDBs don’t parallelize very well• How many RDBs do you know that can efficiently split a
task among 1,000 cheap computers?
• RDBs don’t handle non-tabular data well• RDBs are notorious for doing a poor job on recursive data
structures
• MapReduce isn’t intended to replace relational databases
• It’s intended to provide a lightweight way of programming things so that they can run fast by running in parallel on a lot of machines
http://www.coordguru.com
Eugene Shekita
• Arg1: Data Models, Schemas, and Query Languages• Semi-structured data model and high level of parallel data flow query language is
built on top of MapReduce• Pig, Hive, Jaql, Cascading, Cloudbase
• Hadoop will eventually have a real data model, schema, catalogs, and query language
• Moreover, Pig, Jaql, and Cascading are some steps forward• Support semi-structured data• Support more high level-like parallel data flow languages than declarative query
languages• Greenplum and Aster Data support MapReduce, but look more limited than Pig, Jaql,
Cascading• The calls to MapReduce functions wrapped in SQL queries will make it difficult
to work with semi-structured data and program multi-step dataflows
• Arg3: Novelty• Teradata was doing parallel group-by 20 years ago• UDAs and UDFs appeared in PostgreSQL in the mid 80s• And yet, MapReduce is much more flexible, and fault-tolerant
• Support semi-structured data types, customizable partitioning
MR is a Step Backwards, but some Steps Forward
http://www.coordguru.com
http://www.coordguru.com
Lessons Learned from the Debates
Who Moved My Cheese?• Speed
• The seek times of physical storage is not keeping pace with improvements
in network speeds
• Scale
• The difficulty of scaling the RDBMS out efficiently
• Clustering beyond a handful of servers is notoriously hard
• Integration
• Today’s data processing tasks increasingly have to access and combine
data from many different non-relational sources, often over a network
• Volume
• Data volumes have grown from tens of gigabytes in the 1990s to
hundreds of terabytes and often petabytes in recent years
Stolen from 10 Ways To complement the Enterprise RDBMS using Hadoop
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A Hybrid of MapReduce and RDBMS
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Integrate MapReduce into RDBMS
HadoopDB Greenplum Aster Data
RDBMS MapReduceData size Gigabytes PetabytesUpdates Read and write(Mutable) Write once, read many times(Immutable)Latency Low HighAccess Interactive(point query) and batch Batch(ad-hoc query in brute-force)
Structure Fixed schema Semi-structured schemaLanguage SQL Procedural (Java, C++, etc)Integrity High LowScaling Nonlinear Linear
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In-Database MapReduce vs. File-only MapReduce
In-Database MapReduce File-Only MapReduce
Target User Analyst, DBA, Data Miner Computer Science Engineer
Scale & Performance High High
Hardware Costs Low Low
Analytical Insights High High
Failover & Recovery High High
Use: Ad-Hoc Queries Easy (seamless) Harder (custom)
Use: UI, Client Tools BI Tool (GUI), SQL (CLI) Developer Tool (Java)
Use: Ecosystem High (JDBC, ODBC) Lower (custom)
Protect: Data Integrity High (ACID, schema) Lower (no transaction guarantees)
Protect: Security High (roles, privileges) Lower (custom)
Protect: Backup & DR High (database backup/DR) Lower (custom)
Performance: Mixed Workloads High (workload/QoS mgmt) Lower (limited concurrency)
Performance: Network Bottleneck No (optimized partitioning) Higher (network inefficient)
Operational Cost Low (1 DBA) Higher (several engineers)
• In-Database MapReduce
• Greenplum, Aster Data, HadoopDB
• File-only MapReduce
• Pig, Hive, Cloudbase
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Non-Relational Data Storages
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Throw ‘Relational’ Away, and Take ‘Schema-Free’
The new face of data• Scale out, not up• Online load balancing, cluster growth• Flexible schema
• Some data have sparse attributes, do not need ‘relational’ property• Document/Term vector, User/Item matrix, Log-structured data
• Key-oriented queries• Some data are stored and retrieved mainly by primary key, without complex joins
• Trade-off of Consistency, Availability, and Partition Tolerance
Two of Feasible Approaches• Bigtable
• How can we build a distributed DB on top of GFS?
• B+ Tree style Lookup, Synchronized consistency• Memtable/Commit Log/Immutable SSTable/Indexes, Compaction
• Dynamo• How can we build a distributed hash table appropriate for the data center?
• DHT style Lookup, Eventually consistency
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A Comparison of Non-Relational Data StoragesName Language Fault-tolerance Persistence Client Protocol Data model Docs Community
Hbase Java Replication, partitioning Custom on-disk Custom API, Thrift, Rest Bigtable A Apache, yes
Hypertable C++ Replication, partitioning Custom on-disk Thrift, other Bigtable AZvents, Baidu, yes
Neptune Java Replication, partitioning Custom on-disk Custom API, Thrift, Rest Bigtable A NHN, some
Voldemort Javapartitioned, replicated, read-repair
Pluggable: BerkleyDB, Mysql
Java APIStructured / blob / text
A Linkedin, no
Ringo Erlangpartitioned, replicated, immutable
Custom on-disk (append only log)
HTTP blob B Nokia, no
Scalaris Erlang partitioned, replicated, paxos In-memory only Erlang, Java, HTTP blob B OnScale, no
Kai Erlang partitioned, replicated? On-disk Dets file Memcached blob C no
Dynomite Erlang partitioned, replicated Pluggable: couch, dets Custom ascii, Thrift blob D+ Powerset, no
MemcacheDB C replication BerkleyDB Memcached blob B some
ThruDB C++ ReplicationPluggable: BerkleyDB, Custom, Mysql, S3
ThriftDocument oriented
C+Third rail, unsure
CouchDB Erlang Replication, partitioning? Custom on-disk HTTP, jsonDocument oriented (json)
A Apache, yes
Cassandra Java Replication, partitioning Custom on-disk ThriftBigtable meets Dynamo
F Facebook, no
Coord C++ Replication?, partitioningPluggable: in-memory, Lucene, BerkelyDB, Mysql
Custom API, Thrift text / blob A NHN, some
HBaseHypertable
Bigtable
Dynamo
Dynomite
Voldemort
DHT
KAI
CouchDB
ThruDB
MongoDB
Document-oriented
SimpleDB
Scalaris
Tokyo Cabinet
Key-Value
Chordless
MemcacheDB
Cassandra
Stolen from Anti-RDBMS - A list of distributed key-value stores by Richard Jones
On-going classification by Woohyun Kim
http://www.coordguru.com
Emergent Document-oriented Storages
Why Document-oriented?• All fields become optional
• All relationships become Many-to-Many
• Chatter always expands
Key Features• Schema-Free
• Straightforward Data Model
• Full Text Indexing
• RESTful HTTP/JSON API
http://www.coordguru.com
Document-oriented vs. RDBMSCouchDB MongoDB MySQL
Data Model Document-Oriented (JSON) Document-Oriented (BSON) Relational
Data Types ? string, int, double, boolean, date, bytearray, object, array, others
Link
Large Objects (Files) Yes (attachments) Yes (GridFS) no???
Replication Master-master (with developer supplied conflict resolution)
Master-slave Master-slave
Object(row) Storage One large repository Collection based Table based
Query Method Map/reduce of javascript functions to lazily build an index per query
Dynamic; object-based query languageDynamic; SQL
Secondary Indexes Yes Yes Yes
Atomicity Single document Single document Yes – advanced
Interface REST Native drivers Native drivers
Server-side batch data manipulation
? Yes, via javascript Yes (SQL)
Written in Erlang C++ C
Concurrency Control MVCC Update in Place Update in Place
http://www.coordguru.com
Thank you.
http://www.coordguru.com
Appendix: What is Coord?
Architectural Comparison• dust: a distributed file system based on DHT
• coord spaces: a resource sharable store system based on SBA
• coord mapreduce: a simplified large-scale data processing framework
• warp: a scalable remote/parallel execution system
• graph: a large-scale distributed graph search system
http://www.coordguru.com
Appendix: Coord Internals A space-based architecture built on distributed hash tables
SBA(Space-based Architecture) processes communicate with others thru. only spaces
DHT(Distributed Hash Tables) data identified by hash functions are placed on numerically near nodes
A computing platform to project a single address space on distributed memories As if users worked in a single computing environment
node 1 node 2 node 3 node n
02m-1
App
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