DBMS > AgensGraph vs. Google Cloud Bigtable vs. OushuDB vs. TempoIQ

System Properties Comparison AgensGraph vs. Google Cloud Bigtable vs. OushuDB vs. TempoIQ

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Graph DBMS
Relational DBMS

Key-value store
Wide column store

Relational DBMS

Time Series DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Website

bitnine.net/agensgraph

cloud.google.com/bigtable

www.oushu.com/product/oushuDB

tempoiq.com (offline)

Technical documentation

bitnine.net/documentation

cloud.google.com/bigtable/docs

www.oushu.com/documentation

Developer

Bitnine Global Inc.

Google

Oushu

TempoIQ

Initial release

2016

2015

2012

Current release

2.1, December 2018

4.0.1, August 2020

License

Commercial or Open Source

Open Source

Apache 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

Server operating systems

Linux
OS X
Windows

hosted

Linux

Data scheme

depending on used data model

schema-free

yes

schema-free

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

Full-featured ANSI SQL support

APIs and other access methods

Cypher Query Language
JDBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC

HTTP API

Supported programming languages

C
Java
JavaScript
Python

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

C
C++

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Kerberos, SSL and role based access

simple authentication-based access control

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