DBMS > etcd vs. Google BigQuery vs. MonetDB vs. NuoDB vs. Titan

System Properties Comparison etcd vs. Google BigQuery vs. MonetDB vs. NuoDB vs. Titan

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

A distributed reliable key-value store

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Key-value store

Relational DBMS

Graph DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Website

etcd.io
github.com/etcd-io/etcd

cloud.google.com/bigquery

www.monetdb.org

www.3ds.com/nuodb-distributed-sql-database

github.com/thinkaurelius/titan

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

doc.nuodb.com

github.com/thinkaurelius/titan/wiki

Developer

Google

MonetDB BV

Dassault Systèmes

originally NuoDB, Inc.

Aurelius, owned by DataStax

Initial release

2010

2004

2013

2012

Current release

3.4, August 2019

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla Public License 2.0

commercial

limited edition free

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

C++

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

hosted

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

APIs and other access methods

gRPC
JSON over HTTP

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in SQL, C, R

Java, SQL

yes

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding via remote tables

data is dynamically stored/cached on the nodes where it is read/written

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

none

Source-replica replication available in experimental status

yes

Managed transparently by NuoDB

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

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

Temporary table

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

User authentification and security via Rexster Graph Server

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