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![Document-oriented
FirstName="Jonathan",
Address="15 Wanamassa Point
Road",
K Children=[
E {Name:"Michael",Age:10},
Y {Name:"Jennifer", Age:8},
{Name:"Samantha", Age:5},
{Name:"Elena", Age:2}
]](https://image.slidesharecdn.com/nosqlsolutions-121207231634-phpapp01/85/No-sql-solutions-10-320.jpg)
Uploaded byByeongweon Moon
No sql solutions - 공개용
This document compares NoSQL solutions like Redis, Couchbase, MongoDB, and Membase. It discusses their data models, features, and how they differ from relational databases. Key-value, column-oriented, and document-oriented databases are covered. Specific products like Membase, Redis, MongoDB, and CouchDB are also summarized, including their data models, replication methods, and typical uses in applications.
No sql solutions - 공개용
- 1. NoSQL Solutions Byeongweon Moon tasyblue@gmail.com 2012.01.26
- 2. Redis, Couchbase, MongoDB,Membase
- 3. Memory Base Cachevs. Database
- 5. NoSQL • Collections (versustables) • Documents (versus rows) • Loosely defined fields (versus columns) • Scale out (versus scale up) • Denormalization (versus normalization)
- 6. Data Model • Relational • Key-value • Column-oriented • Document-oriented
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- 10. Document-oriented FirstName="Jonathan", Address="15 Wanamassa Point Road", K Children=[ E {Name:"Michael",Age:10}, Y {Name:"Jennifer", Age:8}, {Name:"Samantha", Age:5}, {Name:"Elena", Age:2} ]
- 11. Memory Base Cache • Weak Persistence • Weak Consistency • Strong Performance • Low Latency
- 12. Membase • Based onMemcached • Written by C++(Memcached), Erlang(Membase) • Distributed, in-memory key-value database management system • Optimized for storing data behind web applications
- 13. Membase (cont’) • Persistence – Asynchronously writes data to disk after acknowledging write to client – Guarantees data consistency • Replication and failover (server failures recoverable in under 100ms) • Scalability and performance – Distributed object store – Dynamic cluster resizing and rebalancing – Guaranteed data consistency – High sustained throughput – Low, predictable latency
- 14. Redis • In-memory, key-valuedata store • Written by ANSI C, support various client
- 15. Redis (cont’) • VariousData Models – List, Set, Sorted Set, Hash – Support atomic operation about data types • Persistence – Data is held in memory but written to disk asynchronously • Replication – Master-Slave replication • Performance – Non-blocking I/O. Single threaded • Publish/Subscribe
- 16. Membase vs. Redis Membase Redis String Set, List, Sorted List, Hash.. Master-Master Master-Slave Storing, inc/dec API Various operations includes pop, push, extract … Web management UI Console management tool
- 17. How to use… •Normally use … $key = md5('SELECT * FROM rest_of_sql_statement_goes_here'); if ($memcache->get($key)) { return $memcache->get($key); } else { $result = $query_results_mangled_into_most_likely_an_array $memcache->set($key, $result, TRUE, 86400); return $result; }
- 18. How to use… (cont’) • Structured Data (array, struct…) – Serialize KEY VALUE user:$user_id name:문병원|call:하겐다즈|… – Normalization KEY VALUE user:$user_id:name 문병원 user:$user_id:call 하겐다즈
- 19. Application Design using Membase • Cache result other than SQL data! • Use a cache hierarchy • Update membase as your data updates • Race conditions and stale data • Pre warm your cache • Storing lists with keys • Batch your requests with get_multi From memcached FAQ
- 20. Database • Persistence • Reliable • Scalable • Distributed • Clustered
- 21. MongoDB • Document-oriented Storage • High Write Performance • Full index support • Master/Slave Replication • Support Map/Reduce • Auto-Sharding • Querying • GridFS • Written in C++
- 22. CouchDB • Document-oriented Storage •High Read Performance • ACID Semantics • Map/Reduce View and Indexes • Distributed Architecture with Replication • REST API • Eventual Consistency • Written in Erlang
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- 28. CouchDB MongoDB MySQL Document-Oriented (JS Document-Oriented Data Model Relational ON) (BSON) string, int, doubl string, number, boole e, boolean, date, Various Types L Data Types an, array, object byte array, object ink , array, others Large Object Yes (attachments) Yes (GridFS) BlobZ s (Files) Horizontal p artitioning CouchDB Lounge Auto-sharding Partitioning scheme Master-slave, m Master-master (with d Master-slave and r ulti-master, an Replication eveloper supplied con eplica sets d circular repl flict resolution) ication Object(row) One large repository Collection-based Table-based Storage
- 29. Map/reduce of ja vascript functio Dynamic; object- Query Method ns to lazily bui based query lang Dynamic; SQL ld an index per uage query Secondary Indexes Yes Yes Yes Atomicity Single document Single document Yes - advanced Native drivers ; Interface REST Native drivers REST add-on Map/Reduce, serv Server-side batch d Map/Reduce er-side javascri Yes (SQL) ata manipulation pt Written in Erlang C++ C++ Eventually consi Strong consisten Strong consiste stent (master-ma cy. Eventually ncy. Eventuall Distributed Consist ster replication consistent reads y consistent re ency Model with versioning from secondaries ads from second and version reco are available. aries are avail nciliation) able.
- 30. References • NoSQL solutions: Membase, Redis, CouchDB and MongoDB : http://blog.fedecarg.com/2011/01/25/nosql-solutions-membase-redis- couchdb-and-mongodb/ • Visual Guide to NoSQL Systems : http://blog.nahurst.com/visual-guide-to- nosql-systems • MongoDB, CouchDB, MySQL Compare Grid : http://www.mongodb.org/display/DOCS/MongoDB,+CouchDB,+MySQL+Compare+Grid • SQL to Mongo Mapping Chart : http://www.mongodb.org/display/DOCS/SQL+to+Mongo+Mapping+Chart • Memcached FAQ : http://code.google.com/p/memcached/wiki/FAQ#Simple_query_result_caching • Couchbase 2.0 Manual : http://docs.couchbase.org/couchbase-manual-2.0.pdf • Building Timeline : Facebook http://www.facebook.com/notes/facebook- engineering/building-timeline-scaling-up-to-hold-your-life- story/10150468255628920