DBMS > Drizzle vs. GridGain vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Spark SQL

System Properties Comparison Drizzle vs. GridGain vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Spark SQL

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

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.gridgain.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

spark.apache.org/sql

Technical documentation

www.gridgain.com/docs/index.html

learn.microsoft.com/azure/cosmos-db

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Drizzle project, originally started by Brian Aker

GridGain Systems, Inc.

Microsoft

Apache Software Foundation

Initial release

2008

2007

2014

2012

2014

Current release

7.2.4, September 2012

GridGain 8.5.1

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, C++, .Net

Scala

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

with proprietary extensions

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

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

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

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

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

Security Hooks for custom implementations

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

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