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Emergent Distributed Data Storage


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This was presented at NHN on Jan. 27, 2009. …

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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  • 1. Emergent Distributed Data Storages for Big Data, Storage, and Analysis Woohyun Kim The creator of open source “Coord” ( 2010-01-27
  • 2. Contents The Advent of Big Data MapReduce Debates • Noah’s Ark Problem • MapReduce is just A Major Step Backwards!!! • Key Issues with ‚Big Data‛ • RDB experts Jump the MR Shark • How to deal with ‚Big Data‛ • DBs are hammers; MR is a screwdriver • MR is a Step Backwards, but some Steps Forward Hadoop Revolution A Hybrid of MapReduce and RDBMS • Integrate MapReduce into RDBMS • Best Practice in Hadoop • In-Database MapReduce vs. File-only MapReduce • Hadoop is changing the Game • Big Data goes well with Hadoop Non-Relational Data Storages • Case Study: Parallel Join • Throw ‘Relational’ Away, and Take ‘Schema-Free’ • Case Study: Further Study in Parallel Join • A Comparison of Non-Relational Data Storages • Case Study: Improvements in Parallel Join • Emergent Document-oriented Storages • Document-oriented vs. RDBMS
  • 3. The Advent of Big Data
  • 4. 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
  • 5. 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
  • 6. How to deal with ‚Big Data‛ Struggling to STORE and ANALYZE ‚Big Data‛
  • 7. Appendix: What is ETL? ETL(Extract, Transform, and Load) • 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 • Talend Open Studio • Pentaho Data Integration (Kettle) • RapidMiner • Jitterbit 2.0, • Apatar • Clover.ETL • Scriptelle
  • 8. Hadoop Revolution
  • 9. Best Practice in Hadoop • Software Stack in Google/Hadoop • Cookbook for ‚Big Data‛ • Structured Data Storage for ‚Big Data‛ Row key Column key Row Structured Data Time Column Column stamp Family Family
  • 10. 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 Map Reduce Map Reduce Map Group-By/ Aggregate/ Filter Aggregator
  • 11. Hadoop is changing the Game • Hadoop, DW, and BI
  • 12. Big Data goes well with Hadoop • Parallelize Relational Algebra Operations using MapReduce
  • 13. Case Study: Parallel Join • A Parallel Join Example using MapReduce
  • 14. 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)
  • 15. 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
  • 16. MapReduce Debates
  • 17. 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
  • 18. 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
  • 19. 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
  • 20. 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
  • 21. What the !@# MapReduce?
  • 22. RDB experts Jump the MR Shark Greg 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
  • 23. DBs are hammers; MR is a screwdriver Mark 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
  • 24. MR is a Step Backwards, but some Steps Forward 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
  • 25.
  • 26. 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
  • 27. A Hybrid of MapReduce and RDBMS
  • 28. Integrate MapReduce into RDBMS RDBMS MapReduce Data size Gigabytes Petabytes Updates Read and write(Mutable) Write once, read many times(Immutable) Latency Low High Access Interactive(point query) and batch Batch(ad-hoc query in brute-force) Structure Fixed schema Semi-structured schema Language SQL Procedural (Java, C++, etc) Integrity High Low Scaling Nonlinear Linear HadoopDB Greenplum Aster Data
  • 29. In-Database MapReduce vs. File-only MapReduce • In-Database MapReduce • Greenplum, Aster Data, HadoopDB • File-only MapReduce • Pig, Hive, Cloudbase 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)
  • 30. Non-Relational Data Storages
  • 31. 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
  • 32. A Comparison of Non-Relational Data Storages Name 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 Zvents, Baidu, y Hypertable C++ Replication, partitioning Custom on-disk Thrift, other Bigtable A es Neptune Java Replication, partitioning Custom on-disk Custom API, Thrift, Rest Bigtable A NHN, some partitioned, replicated, read-rep Pluggable: BerkleyDB, Mys Structured / Voldemort Java Java API A Linkedin, no air ql blob / text partitioned, replicated, immutab Custom on-disk (append o Ringo Erlang HTTP blob B Nokia, no le nly log) 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 Pluggable: BerkleyDB, Cust Document or Third rail, unsur ThruDB C++ Replication Thrift C+ om, Mysql, S3 iented e Document or CouchDB Erlang Replication, partitioning? Custom on-disk HTTP, json A Apache, yes iented (json) Bigtable me Cassandra Java Replication, partitioning Custom on-disk Thrift F Facebook, no ets Dynamo Pluggable: in-memory, Luc Coord C++ Replication?, partitioning Custom API, Thrift text / blob A NHN, some ene, BerkelyDB, Mysql Stolen from Anti-RDBMS - A list of distributed key-value stores by Richard Jones Bigtable DHT HBase Cassandra Dynamo Hypertable Voldemort Dynomite KAI SimpleDB Chordless CouchDB Tokyo Cabinet MongoDB MemcacheDB ThruDB Scalaris Document-oriented Key-Value On-going classification by Woohyun Kim
  • 33. 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
  • 34. Document-oriented vs. RDBMS CouchDB MongoDB MySQL Data Model Document-Oriented (JSON) Document-Oriented (BSON) Relational string, int, double, boolean, date, bytea Data Types ? Link rray, object, array, others Large Objects (Files) Yes (attachments) Yes (GridFS) no??? Master-master (with developer sup Replication Master-slave Master-slave plied conflict resolution) Object(row) Storage One large repository Collection based Table based Map/reduce of javascript functions Dynamic; object-based query language Query Method Dynamic; SQL to lazily build an index per query Secondary Indexes Yes Yes Yes Atomicity Single document Single document Yes – advanced Interface REST Native drivers Native drivers Server-side batch dat ? Yes, via javascript Yes (SQL) a manipulation Written in Erlang C++ C Concurrency Control MVCC Update in Place Update in Place
  • 35. Thank you.
  • 36. 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
  • 37. 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 App take write read 2m-1 0 node 1 node 2 node 3 node n