Native multi-model is competitivePerformance comparison between ArangoDB, MongoDB and Neo4j

Claudius Weinberger (ArangoDB) June, 2015

Native Multi-Model DB

“A native multi-model database is from my perspective a document store (JSON documents), a key/value store and a graph database, all in one database engine and with a unifying query language and API that covers all three data models and even allows to mix them in a single query.” — Claudius Weinberger

Data-SetOur test data are a snapshot from a social network in Slovakia and provided by the Stanford University SNAP collection. It contains 1 632 803 vertices describing people and 30 622 564 edges describing their friendship relation. We use the vertex data for the document tests and the combined vertex and edge data for the graph tests. We wanted to study the standard client/server setup rather than embedding the database into the application, because this model is used more widely and therefore more relevant in practice. We used JavaScript/node.js as client language and environment

Use-Cases• single read: single document reads of profiles (100 000 documents)

• single write: single document writes of profiles (100 000 documents)

• aggregation: aggregation over a single collection (1 632 803 documents) Here, we compute statistics about the age distribution for everyone in the network, simply counting which age occurs how often.

• neighbors: finding direct neighbors plus the neighbors of the neighbors (for 500 vertices)

• shortest path: finding 19 shortest paths (in a highly connected social graph) This answers the question how close to each other two people are in the social network.

The throughput measurements on the test machine for ArangoDB define the baseline (100%) for the comparisons. Lower percentages point to higher throughput - so less is better.

Test Setup• Hardware:

• virtual machine of type n1-standard-16 in Google Compute Engine with 16 virtual cores (on these, a virtual core is implemented as a single hardware hyper-thread on a 2.3 GHz Intel Xeon E5 v3) and altogether 60 GB of RAM.

• the client was an n1-standard-8 (8 vCPU, 30 GB RAM) in the same network.

• Software:

• ArangoDB V2.6.0 alpha3 (pre-release) for x86_64 driver: arangojs in version 3.8.0

• MongoDB V3.0.3 for x86_64, using the WiredTiger storage engine driver: mongodb in v2.0.33, which builds on top of mongodb-core in v1.1.32

• Neo4j Enterprise 2.3 SNAPSHOT running on JDK 1.7.0_79 driver: node-neo4j v2.0.0 RC1

more details in the blog post

feedback welcome

Don’t trust benchmarks. Make your own.

github.com/weinberger/nosql-test