DBMS > Axibase vs. eXtremeDB vs. Google BigQuery vs. STSdb vs. TimesTen

System Properties Comparison Axibase vs. eXtremeDB vs. Google BigQuery vs. STSdb vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

Natively in-memory DBMS with options for persistency, high-availability and clustering

Large scale data warehouse service with append-only tables

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

In-Memory RDBMS compatible to Oracle

Primary database model

Time Series DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Relational DBMS

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	0.80
Rank	#214	Overall
	#99	Relational DBMS
	#18	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.mcobject.com

cloud.google.com/bigquery

github.com/STSSoft/STSdb4

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

Technical documentation

www.mcobject.com/docs/extremedb.htm

cloud.google.com/bigquery/docs

docs.oracle.com/database/timesten-18.1

Developer

Axibase Corporation

McObject

Google

STS Soft SC

Oracle, TimesTen Performance Software, HP

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

Initial release

2013

2001

2010

2011

1998

Current release

15585

8.2, 2021

4.0.8, September 2015

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

GPLv2, commercial license available

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

C and C++

Server operating systems

Linux

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

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

primitive types and user defined types (classes)

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.

support of XML interfaces available

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with the option: eXtremeSQL

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

.NET Client API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net
C
C#
C++
Java
Lua
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

PL/SQL

Triggers

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning / sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

none

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

Durability

Support for making data persistent

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

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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