DBMS > Drizzle vs. HarperDB vs. Machbase Neo vs. RethinkDB vs. XTDB

System Properties Comparison Drizzle vs. HarperDB vs. Machbase Neo vs. RethinkDB vs. XTDB

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.

Description

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

TimeSeries DBMS for AIoT and BigData

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.harperdb.io

machbase.com

rethinkdb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.harperdb.io/docs

machbase.com/dbms

rethinkdb.com/docs

www.xtdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

HarperDB

Machbase

The Linux Foundation

since July 2017

Juxt Ltd.

Initial release

2008

2017

2013

2009

2019

Current release

7.2.4, September 2012

3.1, August 2021

V8.0, August 2023

2.4.1, August 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

free community edition available

commercial

free test version available

Open Source

Apache Version 2

Open Source

MIT License

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++

Node.js

C++

Clojure

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X

Linux
macOS
Windows

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

string, binary, float, bool, date, geometry

yes, extensible-data-notation format

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

with proprietary extensions

SQL-like data manipulation statements

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

HTTP REST
JDBC

Supported programming languages

C
C++
Java
PHP

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Clojure
Java

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Triggers

hooks for callbacks inside the server can be used.

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

the nodes on which a table resides can be defined

selectable replication factor

Source-replica replication

yes, each node contains all data

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

Transaction concepts

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

ACID

Atomic execution of specific operations

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

volatile and lookup table

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles

simple password-based access control

yes

users and table-level permissions

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