DBMS > Atos Standard Common Repository vs. Drizzle vs. EJDB vs. EXASOL vs. Google Cloud Bigtable

System Properties Comparison Atos Standard Common Repository vs. Drizzle vs. EJDB vs. EXASOL vs. Google Cloud Bigtable

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

Document store
Key-value store

Relational DBMS

Document store

Relational DBMS

Key-value store
Wide column store

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

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

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

github.com/Softmotions/ejdb

www.exasol.com

cloud.google.com/bigtable

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

www.exasol.com/resources

cloud.google.com/bigtable/docs

Developer

Atos Convergence Creators

Drizzle project, originally started by Brian Aker

Softmotions

Exasol

Google

Initial release

2016

2008

2012

2000

2015

Current release

1703

7.2.4, September 2012

License

Commercial or Open Source

commercial

Open Source

GNU GPL

Open Source

GPLv2

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

FreeBSD
Linux
OS X

server-less

hosted

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

string, integer, double, bool, date, object_id

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

LDAP

JDBC

in-process shared library

.Net
JDBC
ODBC
WebSocket

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

Supported programming languages

All languages with LDAP bindings

C
C++
Java
PHP

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Java
Lua
Python
R

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

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

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

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

yes

User concepts

Access control

LDAP bind authentication

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles according to SQL-standard

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

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