Nosql data models
6 likes2,588 views
The document discusses different NoSQL data models including key-value, document, column family, and graph models. It provides examples of popular NoSQL databases that implement each model such as Redis, MongoDB, Cassandra, and Neo4j. The document argues that these NoSQL databases address limitations of relational databases in supporting modern web applications with requirements for scalability, flexibility, and high performance.
![Why NoSQL
• “The whole point of seeking alternatives [to
RDBMS systems] is that you need to solve a
problem that relational databases are a bad
fit for.” Eric Evans - Rackspace
11](https://image.slidesharecdn.com/nosql-data-models-150216094551-conversion-gate01/85/Nosql-data-models-11-320.jpg)
![Why NoSQL [cont'd]
• ACID does not scale
• Web applications have different needs
– Scalability
– Elasticity
– Flexible schema/ semi-structured data
– Geographically distributed
• Web applications do not always need
– Transaction
– Strong consistency
– Complex queries
12](https://image.slidesharecdn.com/nosql-data-models-150216094551-conversion-gate01/85/Nosql-data-models-12-320.jpg)
Ad
More Related Content
What's hot (20)
Viewers also liked (20)
Ad
Similar to Nosql data models (20)
Ad
More from Viet-Trung TRAN (20)
Recently uploaded (20)
Nosql data models
- 1. NoSQL data models Viet-Trung Tran is.hust.edu.vn/~trungtv/ 1
- 2. Eras of Databases • Why NoSQL? 2
- 5. ICDE 2005 conference 5 The last 25 years of commercial DBMS development can be summed up in a single phrase: "one size fits all". This phrase refers to the fact that the tradi.onal DBMS architecture (originally designed and op.mized for business data processing) has been used to support many data-‐centric applica.ons with widely varying characterisCcs and requirements. In this paper, we argue that this concept is no longer applicable to the database market, and that the commercial world will fracture into a collecCon of independent database engines, some of which may be unified by a common front-‐end parser. We use examples from the stream-‐ processing market and the data-‐warehouse market to bolster our claims. We also briefly discuss other markets for which the tradiConal architecture is a poor fit and argue for a criCcal rethinking of the current factoring of systems services into products.
- 10. 10
- 11. Why NoSQL • “The whole point of seeking alternatives [to RDBMS systems] is that you need to solve a problem that relational databases are a bad fit for.” Eric Evans - Rackspace 11
- 12. Why NoSQL [cont'd] • ACID does not scale • Web applications have different needs – Scalability – Elasticity – Flexible schema/ semi-structured data – Geographically distributed • Web applications do not always need – Transaction – Strong consistency – Complex queries 12
- 13. NoSQL use cases • Massive data volume (Big volume) – Google, Amazon, Yahoo, Facebook – 10-100K servers • Extreme query workload • Schema evolution 13
- 14. Relational data model revisited • Data is usually stored in row by row manner (row store) • Standardized query language (SQL) • Data model defined before you add data • Joins merge data from multiple tables – Results are tables • Pros: Mature ACID transactions with fine- grain security controls, widely used • Cons: Requires up front data modeling, does not scale well 14 Oracle, MySQL, PostgreSQL, MicrosoP SQL Server, IBM DB/2
- 15. Key/value data model • Simple key/value interface – GET, PUT, DELETE • Value can contain any kind of data • Pros • Cons • Berkley DB, Memcache, DynamoDB, Redis, Riak 15
- 16. Key/value vs. table • A table with two columns and a simple interface – Add a key-value – For this key, give me the value – Delete a key • Super fast and easy to scale (no joins) 16
- 19. Memcached • Open source in-memory key-value caching system • Make effective use of RAM on many distributed web servers • Designed to speed up dynamic web applications by alleviating database load – Simple interface for highly distributed RAM caches – 30ms read times typical • Designed for quick deployment, ease of development • APIs in many languages 19
- 20. • Open source in-memory key-value store with optional durability • Focus on high speed reads and writes of common data structures to RAM • Allows simple lists, sets and hashes to be stored within the value and manipulated • Many features that developers like expiration, transactions, pub/sub, partitioning 20
- 21. • Scalable key-value store • Fastest growing product in Amazon's history • Focus on throughput on storage and predictable read and write times • Strong integration with S3 and Elastic MapReduce 21
- 22. • Open source distributed key-value store with support and commercial versions by Basho • A "Dynamo-inspired" database • Focus on availability, fault-tolerance, operational simplicity and scalability • Support for replication and auto-sharding and rebalancing on failures • Support for MapReduce, fulltext search and secondary indexes of value tags • Written in ERLANG 22
- 23. Column family store • Dynamic schema, column-oriented data model • Sparse, distributed persistent multi- dimensional sorted map (row, column (family), timestamp) -> cell contents 23
- 24. Column families • Group columns into "Column families" • Group column families into "Super-Columns" • Be able to query all columns with a family or super family • Similar data grouped together to improve speed 24
- 25. Column family data model vs. relational • Sparse matrix, preserve table structure – One row could have millions of columns but can be very sparse • Hybrid row/column stores • Number of columns is extendible – New columns to be inserted without doing an "alter table" 25
- 26. Bigtable • ACM TOCS 2008 • Fault-tolerant, persistent • Scalable – Thousands of servers – Terabytes of in-memory data – Petabyte of disk-based data – Millions of reads/writes per second, efficient scans • Self-managing – Servers can be added/ removed dynamically – Servers adjust to load imbalance 26
- 27. • Open-source Bigtable, written in JAVA • Part of Apache Hadoop project 27
- 28. Hadoop? 28
- 29. • Apache open source column family database • Supported by DataStax • Peer-to-peer distribution model • Strong reputation for linear scale out (millions of writes/ second) • Written in Java and works well with HDFS and MapReduce 29
- 30. Graph data model • Core abstractions: Nodes, Relationships, Properties on both 30
- 31. Graph database (store) • A database stored data in an explicitly graph structure • Each node knows its adjacent nodes • Queries are really graph traversals 31
- 32. Compared to Relational Databases OpCmized for aggregaCon OpCmized for connecCons
- 33. Compared to Key Value Stores OpCmized for simple look-‐ups OpCmized for traversing connected data
- 34. Compared to Document Stores OpCmized for “trees” of data OpCmized for seeing the forest and the trees, and the branches, and the trunks
- 35. 35
- 36. 36
- 37. • Graph database designed to be easy to use by Java developers • Disk-based (not just RAM) • Full ACID • High Availability (with Enterprise Edition) • 32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties • Embedded java library • REST API 37
- 38. Document store • Documents, not value, not tables • JSON or XML formats • Document is identified by ID • Allow indexing on properties 38
- 39. Relational data mapping • T1–HTML into Objects • T2–Objects into SQL Tables • T3–Tables into Objects • T4–Objects into HTML 39
- 40. Web Service in the middle • T1 – HTML into Java Objects • T2 – Java Objects into SQL Tables • T3 – Tables into Objects • T4 – Objects into HTML • T5 – Objects to XML • T6 – XML to Objects 40 T1 T3 T2 T4 Object Middle Tier Relational Database Web Browser T5 Web Service T6
- 41. Discussion • Object-relational mapping has become one of the most complex components of building applications today – Java Hibernate Framework – JPA • To avoid complexity is to keep your architecture very simple 41
- 42. Document mapping • Documents in the database • Documents in the application • No object middle tier • No "shredding" • No reassembly • Simple! 42 ApplicaCon Layer Database Document Document
- 43. • Open Source JSON data store created by 10gen • Master-slave scale out model • Strong developer community • Sharding built-in, automatic • Implemented in C++ with many APIs (C++, JavaScript, Java, Perl, Python etc.) 43
- 44. • Apache project • Open source JSON data store • Written in ERLANG • RESTful JSON API • B-Tree based indexing, shadowing b-tree versioning • ACID fully supported • View model • Data compaction • Security 44