DBMS > Drizzle vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Rockset vs. Splice Machine

System Properties Comparison Drizzle vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Rockset vs. Splice Machine

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.

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

Relational DBMS
Search engine

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

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

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.kinetica.com

azure.microsoft.com/services/cosmos-db

rockset.com

splicemachine.com

Technical documentation

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

docs.rockset.com

splicemachine.com/how-it-works

Developer

Drizzle project, originally started by Brian Aker

Kinetica

Microsoft

Rockset

Splice Machine

Initial release

2008

2012

2014

2019

2014

Current release

7.2.4, September 2012

7.1, August 2021

3.1, March 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C, C++

C++

Java

Server operating systems

FreeBSD
Linux
OS X

Linux

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

SQL-like query language

Read-only SQL queries, including JOINs

yes

APIs and other access methods

JDBC

JDBC
ODBC
RESTful HTTP API

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

HTTP REST

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C++
Java
PHP

C++
Java
JavaScript (Node.js)
Python

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

Go
Java
JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

JavaScript

yes

Java

Triggers

hooks for callbacks inside the server can be used.

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Automatic sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

Implicit feature of the cloud service

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

GPU vRAM or System RAM

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles on table level

Access rights can be defined down to the item level

Access rights for users and organizations can be defined via Rockset console

Access rights for users, groups and roles according to SQL-standard

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