ArangoDB— Is Multi-Modelthe Future of NoSQL?

Max Neunhöffer

Oslo, 23 October 2014

www.arangodb.com

Max Neunhöffer

I am amathematician

“Earlier life”: Research in Computer Algebra(Computational Group Theory)Always juggled with big dataNow: working in database development, NoSQL, ArangoDBI like:

research,hacking,teaching,tickling the highest performance out of computer systems.

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ArangoDB GmbH

triAGENS GmbH offers consulting services since 2004:software architectureproject managementsoftware developmentbusiness analysis

a lot of experience with specialised database systems.have done NoSQL, before the term was coined at all2011/2012, an idea emerged:to build the database one had wished to have all those years!development of ArangoDB as open source software since 2012ArangoDB GmbH: spin-off to take care of ArangoDB (2014)

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Polyglot Persistence

IdeaUse the right data model for each part of a system.

For an application, persistan 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, usea column-based database.

Take scalability needs into account!

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Document and key/value storesDocument storeA document store stores a set of documents, which usuallymeans JSON data, these sets are called collections. Thedatabase has access to the contents of the documents.each document in the collection has a unique keysecondary indexes possible, leading to more powerful queriesdifferent documents in the same collection: structure can varyno schema is required for a collectiondatabase normalisation can be relaxed“Special case”: key/value storeOpaque values, restrict to key lookup without secondaryindexes:

=⇒ high performance and perfect scalability4

Graph databasesGraph databaseA graph database stores a labelled graph. Vertices andedges can be documents. Graphs are good to modelrelations.graphs often describe data very naturally (e.g. the facebookfriendship graph)graphs can be stored using tables, however, graph queriesnotoriously lead to expensive joinsthere are interesting and useful graph algorithms like “shortestpath” or “neighbourhood”need a good query language to reap the benefitshorizontal scalability is troublesomegraph databases vary widely in scope and usage, no standard

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A typical Use Case— an Online Shop

We need to holdcustomer data: usually homogeneous, but still variations=⇒ use a document store:product data: even for a specialised business quiteinhomogeneous=⇒ 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:

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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 throughdata synchronisation,data conversion,increased installation and administration effort,more training needs.Wouldn’t it be nice, . . .. . . to enjoy the benefits without paying with thedisadvantages?

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The Multi-Model Approach

Multi-model databaseA Multi-model database combines a document store with agraph 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 modelsis able to compete with specialised products on their turfallows for polyglot persistence using a single databasequeries can mix the different data modelscan replace a RDMBS in many cases

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A Map of the NoSQL Landscape

Transaction Processing DBs

Analytic processing DBs

Map/reduce

Column Stores

Extensibility

Documents

Massively distributed

Graphs

Structured

Data

Key/Value

Complex queries

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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),memory efficient by shape detection,uses JavaScript throughout (Google’s V8 built into server),API extensible by JavaScript code in the Foxx framework,offers many drivers for a wide range of languages,is easy to use with web front end and good documentation,enjoys good professional as well as community supportand has sharding since Version 2.0.

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A Map of the NoSQL Landscape

Transaction Processing DBs

Analytic processing DBs

Map/reduce

Column Stores

Extensibility

Documents

Massively distributed

Graphs

Structured

Data

Key/Value

Complex queries

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The ArangoDB Territory

Transaction Processing DBs

Analytic processing DBs

Map/reduce

Column Stores

Extensibility

Documents

Massively distributed

Graphs

Structured

Data

Key/Value

Complex queries

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Strong Consistency

ArangoDB offersatomic and isolated CRUD operations for single documents,transactions spanning multiple documents and multiplecollections,snapshot semantics for complex queries,very secure durable storage using append only and storingmultiple revisions,all this for documents as well as for graphs.

In the (not too distant) future, ArangoDB willoffer the same ACID semantics even with sharding,implement complete MVCC semantics to allow for lock-freeconcurrent transactions.

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Replication and Sharding— horizontal scalability

Right now, ArangoDB provideseasy setup of (asynchronous) replication,which allows read access parallelisation (master/slaves setup),sharding with automatic data distribution to multiple servers.

Very soon, ArangoDB will featurefault tolerance by automatic failover and synchronousreplication in cluster mode,zero administration by a self-reparing and self-balancingcluster architecture.

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Powerful query language: AQL

The built in Arango Query Language AQL allowscomplex, powerful and convenient queries,with transaction semantics,allowing to do joins,with user definable functions (in JavaScript).AQL is independent of the driver used andoffers protection against injections by design.

For Version 2.3, we are reengineering the AQL query engine:use a C++ implementation for high performance,optimise distributed queries in the cluster.

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Extensible through JavaScript and FoxxThe 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 framework,which allows to implement complex, user-defined APIs withdirect access to the DB engine.Very flexible and secure authentication schemes can beimplemented conveniently by the user in JavaScript.Because JavaScript runs everywhere (in the DB server as wellas in the browser), one can use the same libraries in theback-end and in the front-end.

=⇒ implement your own micro services16