DBMS > Google BigQuery vs. KairosDB vs. Quasardb vs. TimesTen vs. Tkrzw

System Properties Comparison Google BigQuery vs. KairosDB vs. Quasardb vs. TimesTen vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Distributed Time Series DBMS based on Cassandra or H2

Distributed, high-performance timeseries database

In-Memory RDBMS compatible to Oracle

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

cloud.google.com/bigquery

github.com/kairosdb/kairosdb

quasar.ai

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

dbmx.net/tkrzw

Technical documentation

cloud.google.com/bigquery/docs

kairosdb.github.io

doc.quasar.ai/master

docs.oracle.com/database/timesten-18.1

Developer

Google

quasardb

Oracle, TimesTen Performance Software, HP

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

Mikio Hirabayashi

Initial release

2010

2013

2009

1998

2020

Current release

1.2.2, November 2018

3.14.1, January 2024

11 Release 2 (11.2.2.8.0)

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

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

commercial

Open Source

Apache 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

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

BSD
Linux
OS X
Windows

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

Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

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

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

Graphite protocol
HTTP REST
Telnet API

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

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

C
C++
Java
PL/SQL

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

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

by using LevelDB

yes

by means of logfiles and checkpoints

yes

In-memory capabilities

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

yes

Transient mode

yes

using specific database classes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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