DBMS > Google BigQuery vs. Quasardb vs. Riak KV vs. Titan

System Properties Comparison Google BigQuery vs. Quasardb vs. Riak KV vs. Titan

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Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Large scale data warehouse service with append-only tables

Distributed, high-performance timeseries database

Distributed, fault tolerant key-value store

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Graph DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Website

cloud.google.com/bigquery

quasar.ai

github.com/thinkaurelius/titan

Technical documentation

cloud.google.com/bigquery/docs

doc.quasar.ai/master

www.tiot.jp/riak-docs/riak/kv/latest

github.com/thinkaurelius/titan/wiki

Developer

Google

quasardb

OpenSource, formerly Basho Technologies

Aurelius, owned by DataStax

Initial release

2010

2009

2012

Current release

3.14.1, January 2024

3.2.0, December 2022

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Erlang

Java

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux
OS X

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

with tags

restricted

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

HTTP API
Native Erlang Interface

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

Sharding

no "single point of failure"

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Relationships in graph

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

yes, using Riak Security

User authentification and security via Rexster Graph Server

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