DBMS > Drizzle vs. GridGain vs. Memgraph vs. Microsoft SQL Server vs. Vertica

System Properties Comparison Drizzle vs. GridGain vs. Memgraph vs. Microsoft SQL Server vs. Vertica

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.

GridGain is an in-memory computing platform, built on Apache Ignite

An open source graph database built for real-time streaming and compatible with Neo4j

Microsofts flagship relational DBMS

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	3.19
Rank	#94	Overall
	#8	Graph DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

www.gridgain.com

memgraph.com

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

www.vertica.com

Technical documentation

www.gridgain.com/docs/index.html

memgraph.com/docs

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

vertica.com/documentation

Social network pages

Developer

Drizzle project, originally started by Brian Aker

GridGain Systems, Inc.

Memgraph Ltd

Microsoft

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2008

2007

2017

1989

2005

Current release

7.2.4, September 2012

GridGain 8.5.1

SQL Server 2022, November 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

BSL 1.1; commercial license for enterprise edition available

commercial

restricted free version is available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, C++, .Net

C and C++

C++

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Solaris
Windows

Linux

Linux
Windows

Linux

Data scheme

yes

schema-free and schema-optional

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

with proprietary extensions

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

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC

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

Bolt protocol
Cypher query language

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

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C
C++
Java
PHP

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

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

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

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

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

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

hooks for callbacks inside the server can be used.

yes (cache interceptors and events)

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

dynamic graph partitioning

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

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

Multi-source replication using RAFT

yes, but depending on the SQL-Server Edition

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID

with snapshot isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with periodic snapshot and write-ahead logging (WAL) of changes

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

Security Hooks for custom implementations

Users, roles and permissions

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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Licensing and pricing models

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