DBMS > Drizzle vs. EJDB vs. Manticore Search vs. Microsoft SQL Server vs. RisingWave

System Properties Comparison Drizzle vs. EJDB vs. Manticore Search vs. Microsoft SQL Server vs. RisingWave

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.

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

Multi-storage database for search, including full-text search.

Microsofts flagship relational DBMS

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS
Spatial DBMS

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

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	0.61
Rank	#237	Overall
	#111	Relational DBMS

Website

github.com/Softmotions/ejdb

manticoresearch.com

www.microsoft.com/en-us/sql-server

www.risingwave.com/database

Technical documentation

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

manual.manticoresearch.com

learn.microsoft.com/en-US/sql/sql-server

docs.risingwave.com/docs/current/intro

Developer

Drizzle project, originally started by Brian Aker

Softmotions

Manticore Software

Microsoft

RisingWave Labs

Initial release

2008

2012

2017

1989

2022

Current release

7.2.4, September 2012

6.0, February 2023

SQL Server 2022, November 2022

1.2, September 2023

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

GPLv2

Open Source

GPL version 2

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Rust

Server operating systems

FreeBSD
Linux
OS X

server-less

FreeBSD
Linux
macOS
Windows

Linux
Windows

Docker
Linux
macOS

Data scheme

yes

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes

Standard SQL-types and JSON

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

yes

APIs and other access methods

JDBC

in-process shared library

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
PostgreSQL wire protocol

Supported programming languages

C
C++
Java
PHP

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

UDFs in Python or Java

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Synchronous replication based on Galera library

yes, but depending on the SQL-Server Edition

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

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

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

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Users and Roles

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