DBMS > Drizzle vs. Ignite vs. Microsoft Azure Cosmos DB vs. Qdrant

System Properties Comparison Drizzle vs. Ignite vs. Microsoft Azure Cosmos DB vs. Qdrant

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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.

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.

Globally distributed, horizontally scalable, multi-model database service

A high-performance vector database with neural network or semantic-based matching

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Website

ignite.apache.org

azure.microsoft.com/services/cosmos-db

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

apacheignite.readme.io/docs

learn.microsoft.com/azure/cosmos-db

qdrant.tech/documentation

Developer

Drizzle project, originally started by Brian Aker

Apache Software Foundation

Microsoft

Qdrant

Initial release

2008

2015

2014

2021

Current release

7.2.4, September 2012

Apache Ignite 2.6

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

Open Source

Apache Version 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++

C++, Java, .Net

Rust

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Solaris
Windows

hosted

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

Numbers, Strings, Geo, Boolean

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

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

with proprietary extensions

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

SQL-like query language

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

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

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
Go
Java
JavaScript (Node.js)
Python
Rust

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

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

Collection-level replication

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

Eventual Consistency, tunable 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

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

Key-based authentication

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