Introduction to no sql database
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NoSQL databases provide alternatives to traditional relational databases. They are designed to be highly scalable, flexible, and available. Common types of NoSQL databases include key-value stores, document databases, column-oriented databases, and graph databases. Companies like Google, Amazon, and LiveJournal have successfully used NoSQL databases like BigTable, DynamoDB, and Memcached to handle large volumes of data and improve performance.
Introduction to no sql database
- 1. NoSQL It’s about making intelligent choices
- 3. The Relation Model • • • • • • • • • Simplicity and Elegance Well Understood Very Powerful Abstraction Solve Many Storage Problem (Persistent Data) Concurrency Integration A Mostly Standard Model … But It also has its Limitation…
- 5. Issues With Implementing A Relational Database • Agility and Programmability (Impedance Mismatch) • Flexibility • Performance and Scalability • Availability
- 7. NoSQL No SQL Not Only SQL Non-relational Database
- 9. Typical Usage • Image Stores • Key-Based File Systems • Object Cache • Systems Designed to Scale
- 12. Typical Usage • Web Crawler Results • Big Data Problems That Can Relax Consistency Rules
- 14. Column Family
- 15. Typical Usage • • • • • High-Variability Data Document Search Integration Hubs Web Content Management Publishing
- 16. Column Family • Amazon SimpleDB • Cassandra • Hbase • HyperTable
- 17. Graph Database
- 18. Typical Usage • Social Networks • Fraud Detection • Relationship-Heavy Data
- 20. Common Features of NoSQL Databases • Designing Aggregations • • Materializing Summary Data • • Sharding Improving Consistency • • Clusters Improving Scalability and Reducing Network Latency • • Map/Reduce Implementing High Availability • • An aggregate in a NoSQL database is similar to a row in a table in a relational database Data Versioning Schemas and Non-Uniformity
- 23. LiveJournal’s Memcache • Driver • • Need to increase performance of database queries. Finding • By using hashing and caching, data in RAM can be shared. This cuts down the number of read requests sent to the database, increasing performance.
- 25. MapReduce Example – Word Count
- 26. Google’s MapReduce • Driver • Need to index billions of web pages for search using low-cost hardware. • Finding • By using parallel processing, indexing billions of web pages can be done quickly with a large number of commodity processors.
- 27. Google BigTable • Driver • Need to flexibly store tabular data in a distributed system. • Finding • By using a sparse matrix approach, users can think of all data as being stored in a single table with billions of rows and millions of columns without the need for up-front data modeling.
- 28. Amazon’s Dynamo
- 29. Amazon’s Dynamo • Driver • Need to accept a web order 24 hours a day, 7 days a week. • Finding • A key-value store with a simple interface can be replicated even when there are large volumes of data to be processed.
- 31. Key Points • • • • Relational databases have been a successful technology for twenty years, providing persistence, concurrency control, and an integration mechanism. Application developers have been frustrated with the impedance mismatch between the relational model and the in-memory data structures. There is a movement away from using databases as integration points towards encapsulating databases within applications and integrating through services. The most important result of the rise of NoSQL is Polyglot Persistence.
- 32. Key Points • • • The vital factor for a change in data storage was the need to support large volumes of data by running on clusters. Relational databases are not designed to run efficiently on clusters. NoSQL is an accidental neologism. There is no prescriptive definition—all you can make is an observation of common characteristics. The common characteristics of NoSQL databases are • • • • • Not using the relational model Running well on clusters Open Source Built for the 21st century web estates Schemaless