DBMS > Brytlyt vs. Drizzle vs. Heroic vs. Kinetica vs. Microsoft Azure Cosmos DB

System Properties Comparison Brytlyt vs. Drizzle vs. Heroic vs. Kinetica 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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

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

Website

brytlyt.io

github.com/spotify/heroic

www.kinetica.com

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.brytlyt.io

spotify.github.io/heroic

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

Developer

Brytlyt

Drizzle project, originally started by Brian Aker

Spotify

Kinetica

Microsoft

Initial release

2016

2008

2014

2012

2014

Current release

5.0, August 2023

7.2.4, September 2012

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

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++ and CUDA

C++

Java

C, C++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X

Linux

hosted

Data scheme

yes

schema-free

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

via Elasticsearch

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

JavaScript

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

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

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

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

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