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Is multi-model the future of NoSQL?

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Recently a new breed of "multi-model" databases has emerged. They are a document store, a graph database and a key/value store combined in one program. Therefore they are able to cover a lot of use cases which otherwise would need multiple different database systems. This approach promises a boost to the idea of "polyglot persistence", which has become very popular in recent years although it creates some friction in the form of data conversion and synchronisation between different systems. This is, because with a multi-model database one can enjoy the benefits of polyglot persistence without the disadvantages. In this talk I will explain the motivation behind the multi-model approach, discuss its advantages and limitations, and will then risk to make some predictions about the NoSQL database market in five years time, which I shall only reveal during the talk.

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Is multi-model the future of NoSQL?

  1. 1. Is multi-model the future of NoSQL? Max Neunhöffer Big Data Science Meetup, 15 March 2015 www.arangodb.com
  2. 2. Max Neunhöffer I am a mathematician “Earlier life”: Research in Computer Algebra (Computational Group Theory) Always juggled with big data Now: working in database development, NoSQL, ArangoDB I like: research, hacking, teaching, tickling the highest performance out of computer systems. 1
  3. 3. Document and Key/Value Stores Document store A document store stores a set of documents, which usually means JSON data, these sets are called collections. The database has access to the contents of the documents. each document in the collection has a unique key secondary indexes possible, leading to more powerful queries different documents in the same collection: structure can vary no schema is required for a collection database normalisation can be relaxed Key/value store Opaque values, only key lookup without secondary indexes: =⇒ high performance and perfect scalability 2
  4. 4. Graph databases Graph database A graph database stores a labelled graph. Vertices and edges can be documents. Graphs are good to model relations. graphs often describe data very naturally (e.g. the facebook friendship graph) graphs can be stored using tables, however, graph queries notoriously lead to expensive joins there are interesting and useful graph algorithms like “shortest path” or “neighbourhood” need a good query language to reap the benefits horizontal scalability is troublesome graph databases vary widely in scope and usage, no standard 3
  5. 5. Polyglot Persistence Idea Use the right data model for each part of a system. For an application, persist an object or structured data as a JSON document, a hash table in a key/value store, relations between objects in a graph database, a homogeneous array in a relational DBMS. If the table has many empty cells or inhomogeneous rows, use a column-oriented database. Take scalability needs into account! 4
  6. 6. A typical Use Case — an Online Shop We need to hold customer data: usually homogeneous, but still variations =⇒ use a relational DB: MySQL product data: even for a specialised business quite inhomogeneous =⇒ use a document store: shopping carts: need very fast lookup by session key =⇒ use a key/value store: order and sales data: relate customers and products =⇒ use a document store: recommendation engine data: links between different entities =⇒ use a graph database: 5
  7. 7. Polyglot Persistence is nice, but . . . Consequence: One needs multiple database systems in the persis- tence layer of a single project! Polyglot persistence introduces some friction through data synchronisation, data conversion, increased installation and administration effort, more training needs. Wouldn’t it be nice, . . . . . . to enjoy the benefits without the disadvantages? 6
  8. 8. The Multi-Model Approach Multi-model database A multi-model database combines a document store with a graph database and is at the same time a key/value store. Vertices are documents in a vertex collection, edges are documents in an edge collection. a single, common query language for all three data models is able to compete with specialised products on their turf allows for polyglot persistence using a single database queries can mix the different data models can replace a RDMBS in many cases 7
  9. 9. Use case: Aircraft fleet management One of our customers uses ArangoDB to store each part, component, unit or aircraft as a document model containment as a graph thus can easily find all parts of some component keep track of maintenance intervals perform queries orthogonal to the graph structure thereby getting good efficiency for all needed queries 8
  10. 10. Use case: Family tree management For genealogy, the natural object is a family tree. data naturally comes as a (directed) graph many queries are traversals or shortest path but not all, for example: “all people with name James” in a family tree, sorted by birthday “all family members who studied at Berkeley”, sorted by number of children quite often, queries mixing the different models are useful 9
  11. 11. Recently: Key/Value stores adding other models (by Basho), originally a key/value store, adds support for documents with their 2.0 version (late 2014) (sponsored by Pivotal), originally an in-memory key/value store, has over time added more data types and more complex operations FoundationDB (by FoundationDB) is a key/value store, but is now marketed as a multi-model database by adding additional layers on top OrientDB (by Orient Technologies) started as an object database and nowadays calls itself a multi-model database 10
  12. 12. Recently: DataStax acquired Aurelius In February 2015, DataStax (commercialised version of Cassan- dra (column-oriented)), announced the acquisition of Aurelius, the company behind TitanDB (a distributed graph database on top of Cassandra). In their own words: “Bringing Graph Database Technology To Cassandra.” “Will deliver massively scalable, always-on graph database technology.” “Will simplify the adoption of leading NoSQL technologies to support multi-model use case environments.” 11
  13. 13. Recently: MongoDB 3.0 adds pluggable DB engine is one of the most popular document stores. In February 2015, they announced their 3.0 version, to be released in March, featuring a pluggable storage engine layer transparent on-disk compression etc. This indicates their interest to support more data models than “just documents”. It will be very interesting indeed to see if and how they extend their query-language . . . 12
  14. 14. is a multi-model database (document store & graph database), is open source and free (Apache 2 license), offers convenient queries (via HTTP/REST and AQL), including joins between different collections, configurable consistency guarantees using transactions memory efficient by shape detection, uses JavaScript throughout (Google’s V8 built into server), API extensible by JS code in the Foxx Microservice Framework, offers many drivers for a wide range of languages, is easy to use with web front end and good documentation, and enjoys good community as well as professional support. 13
  15. 15. Configurable consistency ArangoDB offers atomic and isolated CRUD operations for single documents, transactions spanning multiple documents and multiple collections, snapshot semantics for complex queries, very secure durable storage using append only and storing multiple revisions, all this for documents as well as for graphs. In the near future, ArangoDB will implement complete MVCC semantics to allow for lock-free concurrent transactions and offer the same ACID semantics even with sharding. 14
  16. 16. Extensible through JavaScript and Foxx The HTTP API of ArangoDB can be extended by user-defined JavaScript code, that is executed in the DB server for high performance. This is formalised by the Foxx microservice framework, which allows to implement complex, user-defined APIs with direct access to the DB engine. Very flexible and secure authentication schemes can be implemented conveniently by the user in JavaScript. Because JavaScript runs everywhere (in the DB server as well as in the browser), one can use the same libraries in the back-end and in the front-end. =⇒ implement your own micro services 15
  17. 17. The Future of NoSQL: My Observations I observe 2 decades ago the most versatile solutions eventually dominated the relational DB market (Oracle, MySQL, PostgreSQL), the rise of the polyglot persistence idea a trend towards multi-model databases specialised products broadening their scope even relational systems add support for JSON documents devOps gaining influence (Docker phenomenon) 16
  18. 18. The Future of NoSQL: My Predictions In 5 years time . . . the default approach is to use a multi-model database, the big vendors will all add other data models, the NoSQL solutions will conquer a sizable portion of what is now dominated by the relational model, specialized products will only survive, if they find a niche. 17
  19. 19. Links https://www.arangodb.com https://github.com/ArangoDB/guesser 18