DBMS > Apache Drill vs. Coveo vs. Drizzle vs. H2 vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Drill vs. Coveo vs. Drizzle vs. H2 vs. Microsoft Azure Cosmos DB

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

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Relational DBMS

Search engine

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	2.14
Rank	#116	Overall
	#11	Search engines

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

drill.apache.org

www.coveo.com

www.h2database.com

azure.microsoft.com/services/cosmos-db

Technical documentation

drill.apache.org/docs

docs.coveo.com

www.h2database.com/html/main.html

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Coveo

Drizzle project, originally started by Brian Aker

Thomas Mueller

Microsoft

Initial release

2012

2008

2005

2014

Current release

1.20.3, January 2023

7.2.4, September 2012

2.2.220, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GNU GPL

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

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

Server operating systems

Linux
OS X
Windows

hosted

FreeBSD
Linux
OS X

All OS with a Java VM

hosted

Data scheme

schema-free

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

with proprietary extensions

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC

JDBC
ODBC

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

Supported programming languages

C++

C#
Java
JavaScript
Python

C
C++
Java
PHP

Java

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

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures and User-Defined Functions

JavaScript

Triggers

yes

hooks for callbacks inside the server can be used.

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

granular access controls, API key management, content filters

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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