DBMS > Drizzle vs. FileMaker vs. Ignite vs. Microsoft SQL Server vs. SurrealDB

System Properties Comparison Drizzle vs. FileMaker vs. Ignite vs. Microsoft SQL Server vs. SurrealDB

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.

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Microsofts flagship relational DBMS

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Document store
Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

www.claris.com/filemaker

ignite.apache.org

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

surrealdb.com

Technical documentation

www.claris.com/resources/documentation

apacheignite.readme.io/docs

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

surrealdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

Claris

a subsidiary of Apple

Apache Software Foundation

Microsoft

SurrealDB Ltd

Initial release

2008

1983

2015

1989

2022

Current release

7.2.4, September 2012

19.4.1, November 2021

Apache Ignite 2.6

SQL Server 2022, November 2022

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C++, Java, .Net

C++

Rust

Server operating systems

FreeBSD
Linux
OS X

iOS

client part only
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

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.

yes

A http query request can return the data in XML format

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

via plugins

ANSI-99 for query and DML statements, subset of DDL

yes

SQL-like query language

APIs and other access methods

JDBC

Filemaker WebDirect
JDBC
ODBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C
C++
Java
PHP

PHP

C#
C++
Java
PHP
Python
Ruby
Scala

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

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

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

Triggers

hooks for callbacks inside the server can be used.

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

Source-replica replication, warm standby

since Version 14

yes (replicated cache)

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

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

simple rights management via user accounts and connection to external directory services

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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