DBMS > Axibase vs. Google BigQuery vs. Lovefield vs. MonetDB vs. TimesTen

System Properties Comparison Axibase vs. Google BigQuery vs. Lovefield vs. MonetDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Large scale data warehouse service with append-only tables

Embeddable relational database for web apps written in pure JavaScript

A relational database management system that stores data in columns

In-Memory RDBMS compatible to Oracle

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

axibase.com/docs/atsd/finance

cloud.google.com/bigquery

google.github.io/lovefield

www.monetdb.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

cloud.google.com/bigquery/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

www.monetdb.org/Documentation

docs.oracle.com/database/timesten-18.1

Developer

Axibase Corporation

Google

MonetDB BV

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2013

2010

2014

2004

1998

Current release

15585

2.1.12, February 2017

Dec2023 (11.49), December 2023

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

commercial

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

Java

JavaScript

Server operating systems

Linux

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

SQL-like query language

yes

SQL-like query language

via JavaScript builder pattern

yes

SQL 2003 with some extensions

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes, in SQL, C, R

PL/SQL

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

using MemoryDB

yes

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

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