DBMS > Drizzle vs. LeanXcale vs. Realm vs. Sphinx

System Properties Comparison Drizzle vs. LeanXcale vs. Realm vs. Sphinx

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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.

Description

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store

Search engine

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

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.leanxcale.com

realm.io

sphinxsearch.com

Technical documentation

realm.io/docs

sphinxsearch.com/docs

Developer

Drizzle project, originally started by Brian Aker

LeanXcale

Realm, acquired by MongoDB in May 2019

Sphinx Technologies Inc.

Initial release

2008

2015

2014

2001

Current release

7.2.4, September 2012

3.5.1, February 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

GPL version 2, commercial licence available

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++

Server operating systems

FreeBSD
Linux
OS X

Android
Backend: server-less
iOS
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

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

full-text index on all search fields

SQL

Support of SQL

yes

with proprietary extensions

yes

through Apache Derby

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Proprietary protocol

Supported programming languages

C
C++
Java
PHP

C
Java
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

Triggers

hooks for callbacks inside the server can be used.

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

In-Memory realm

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

yes

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