DBMS > Axibase vs. Dgraph vs. Drizzle vs. FatDB vs. Google Cloud Bigtable

System Properties Comparison Axibase vs. Dgraph vs. Drizzle vs. FatDB vs. Google Cloud Bigtable

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

Distributed and scalable native Graph DBMS

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Primary database model

Time Series DBMS

Graph DBMS

Relational DBMS

Document store
Key-value store

Key-value store
Wide column store

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Website

axibase.com/docs/atsd/finance

dgraph.io

cloud.google.com/bigtable

Technical documentation

dgraph.io/docs

cloud.google.com/bigtable/docs

Developer

Axibase Corporation

Dgraph Labs, Inc.

Drizzle project, originally started by Brian Aker

FatCloud

Google

Initial release

2013

2016

2008

2012

2015

Current release

15585

7.2.4, September 2012

License

Commercial or Open Source

commercial

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

Open Source

Apache 2.0

Open Source

GNU GPL

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++

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
OS X

Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

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

with proprietary extensions

Via inetgration in SQL Server

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JDBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

Supported programming languages

Go
Java
PHP
Python
R
Ruby

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

C
C++
Java
PHP

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

Server-side scripts

Stored procedures

yes

via applications

Triggers

yes

hooks for callbacks inside the server can be used.

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Synchronous replication via Raft

Multi-source replication
Source-replica replication

selectable replication factor

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
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

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

Planned for future releases

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Can implement custom security layer via applications

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

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More resources

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