DBMS > Drizzle vs. EJDB vs. Hypertable vs. ReductStore

System Properties Comparison Drizzle vs. EJDB vs. Hypertable vs. ReductStore

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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.

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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)

An open source BigTable implementation based on distributed file systems such as Hadoop

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

github.com/Softmotions/ejdb

github.com/reductstore
www.reduct.store

Technical documentation

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

www.reduct.store/docs

Developer

Drizzle project, originally started by Brian Aker

Softmotions

Hypertable Inc.

ReductStore LLC

Initial release

2008

2012

2009

2023

Current release

7.2.4, September 2012

0.9.8.11, March 2016

1.9, March 2024

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

GPLv2

Open Source

GNU version 3. Commercial license available

Open Source

Business Source License 1.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C++, Rust

Server operating systems

FreeBSD
Linux
OS X

server-less

Linux
OS X
Windows

an inofficial Windows port is available

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

Secondary indexes

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

JDBC

in-process shared library

C++ API
Thrift

HTTP API

Supported programming languages

C
C++
Java
PHP

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

C++
Java
Perl
PHP
Python
Ruby

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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