Top 5 Considerations When Evaluating NoSQL
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This document discusses the top 5 considerations for evaluating NoSQL databases: data model, query model, consistency model, APIs, and ecosystem. It provides an overview of common NoSQL data models including key-value, wide-column, and document and describes how their features suit different use cases. The consistency models of eventual and strong consistency are also compared in terms of their tradeoffs for read/write throughput and risk of stale data.
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Top 5 Considerations When Evaluating NoSQL
- 1. Top 5 Considerations When Evaluating NoSQL Graham Neray Product @grahamneray
- 2. 2 • NoSQL Confusion • Common Ground • 5 Considerations – Data model – Query Model – Consistency Model – APIs – Ecosystem • Wrap-up Agenda
- 3. ...in means other than the tabular relations used in relational databases. Wikipedia
- 4. 4 NoSQL is a catch-all Relational Everything Else
- 6. Same Different Not relational Data model Query Indexing Storage Consistency Scalability HighAvailability Durability Atomicity
- 7. 7 How the World Has Responded
- 9. 9 Some common ground • Scalability • High Availability • Schema Flexibility (sort of)
- 10. 10 5 Dimensions • Data Model • Query Model • Consistency Model • APIs • Ecosystem
- 12. 12 Data Model - Use Cases Key-Value Wide-Column Document Graph • General Purpose • Basic, static access patterns • Time series data (wide column) • Social Networks • Neural Maps • Relationship
- 13. 13 Query Model Key-Value Wide-Column Document Graph • Rich queries • Indexes • Basic k/v queries• Rich queries • Relationship analysis
- 14. 14 Query Model – Use Case Key-Value Wide-Column Document Graph
- 15. 15 Consistency Model Consistent Eventually Consistent • Writes are immediately visible • Data always up to date • What developers expect • Writes NOT immediately visible • Can help read or write throughput • Risk of stale data • More complexity
- 16. 16 Consistency Model – Use Case Consistent Eventually Consistent • Anything where it’s important to see most up-to-date data, e.g., inventory levels, real- time analytics, collaboration apps • Anything where it’s not crucial to see most up- to-date data, e.g., logs (high-write), archives (high-read)
- 17. 17 APIs • No standard in NoSQL • Significant variation • Main options – Idiomatic Drivers – Thrift or RESTful APIs Java Python
- 18. 18 Ecosystem • Company • Community • Partners
- 19. 19 MongoDB Overview 400+ employees 1,000+ customers Over $231 million in funding13 offices around the world
- 20. 20 7,000,000+ MongoDB Downloads 200,000+ Online Education Registrants 35,000+ MongoDB User Group Members 35,000+ MongoDB Management Service (MMS) Users 500+ Technology and Services Partners 1,000+ Customers Across All Industries The Largest Ecosystem
- 21. 21 • 10 of the Top Financial Services Institutions • 10 of the Top Electronics Companies • 10 of the Top Media and Entertainment Companies • 10 of the Top Retailers • 10 of the Top Telcos • 8 of the Top Technology Companies • 6 of the Top Healthcare Companies Fortune 500 & Global 500
- 24. 24 Whitepaper Top 5 Considerations When Evaluating NoSQL Databases
- 25. 25 MongoDB World June 23-25 world.mongodb.com Code: 25GN for 25% off
Editor's Notes
- A NoSQL or Not Only SQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.
- The term NoSQL is terrible. While in some respects it captures the excitement around a slew of new databases that challenge the relational model, it conflates all sorts of different database types that are more different than they are similar
- Comparing a document database like MongoDB to a key-value store, for example, is like comparing a smartphone to a beeper. A beeper is exceptionally useful for getting a simple message from Point A to Point B. It’s fast. It’s reliable. But it’s nowhere near as functional as a smartphone, which can quickly and reliably transmit messages, but can also do so much more. Both are useful, but the smartphone fits a far broader range of applications than the more limited beeper.
- McAfee GTI analyzes cyberthreats from all angles, identifying threat relationships, such as malware used in network intrusions, websites hosting malware, botnet associations, and more. Threat information is extremely time sensitive; knowing about a threat from weeks ago is useless. In order to provide up to date, comprehensive threat information, McAfee needs to quickly process terabytes of different data types (such as IP address or domain) into meaningful relationships: e.g. Is this web site good or bad? What other sites have been interacting with it? The success of the cloud-based system also depends on a bidirectional data flow: GTI gathers data from millions of client sensors and provides real-time intelligence back to these end products, at a rate of 100 billion queries per month. Tried using Hbase but complexity of queries made it a nogo
- Document databases and graph databases can be consistent or eventually consistent. MongoDB provides tunable consistency. By default, data is consistent — all writes and reads go to the primary copy of the data. Key-value and wide column stores are typically eventually consistent.
- When does it matter? Buzzfeed real-time analytics needed strong consistency
- Company - Users should consider the health of the company or project when evaluating a database. It is important not only that the product continues to exist, but also to evolve and to provide new features. Having a strong, experienced support organization capable of providing services globally is another relevant consideration. Community - There are significant advantages of having a strong community around a technology, particularly databases. A database with a strong community of users makes it easier to find and hire developers that are familiar with the product. It makes it easier to find information, documentation and code samples. It also helps organizations retain key technical talent. Lastly, a strong community encourages other technology vendors to develop integrations and to participate in the ecosystem. Partners – When deploying a new technology it’s important that you can bring your existing technologies with you. You’ve made investments in technologies already – operating systems, BI tools, ETL tools, etc. – and you should be able to leverage those investments even with a new database. There is significant variation in terms of integrations and support for adjacent technologies, so you want to keep that in mind when evaluating these products.
- I’d be remiss if I didn’t give you a sense for where MongoDB falls on this spectrum
- Customers (not just users)
- Indeed: #2 just after HTML and ahead of iOS, Android, Hadoop Jasper: Demand for MongoDB, the document-oriented NoSQL database, saw the biggest spike with over 200% growth in 2011. 451 Group: Bigger than next 3 or 4 COMBINED; biggest quarter-over-quarter and year-over-year growth (again)
- Indeed: #2 just after HTML and ahead of iOS, Android, Hadoop Jasper: Demand for MongoDB, the document-oriented NoSQL database, saw the biggest spike with over 200% growth in 2011. 451 Group: Bigger than next 3 or 4 COMBINED; biggest quarter-over-quarter and year-over-year growth (again)