DBMS > AgensGraph vs. Dragonfly vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Cosmos DB

System Properties Comparison AgensGraph vs. Dragonfly vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Graph DBMS
Relational DBMS

Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

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

Website

bitnine.net/agensgraph

github.com/dragonflydb/dragonfly
www.dragonflydb.io

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

azure.microsoft.com/services/cosmos-db

Technical documentation

bitnine.net/documentation

www.dragonflydb.io/docs

docs.heavy.ai

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

Developer

Bitnine Global Inc.

DragonflyDB team and community contributors

HEAVY.AI, Inc.

Kinetica

Microsoft

Initial release

2016

2023

2016

2012

2014

Current release

2.1, December 2018

1.0, March 2023

5.10, January 2022

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

BSL 1.1

Open Source

Apache Version 2; enterprise edition available

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, C++

Server operating systems

Linux
OS X
Windows

Linux

hosted

Data scheme

depending on used data model

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

Proprietary protocol

RESP - REdis Serialization Protocol

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

C
Java
JavaScript
Python

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

All languages supporting JDBC/ODBC/Thrift
Python

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

yes

Lua

user defined functions

JavaScript

Triggers

publish/subscribe channels provide some trigger functionality

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

no, but can be realized using table inheritance

Sharding

Round robin

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

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

Atomic execution of command blocks and scripts

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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

Password-based authentication

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Access rights can be defined down to the item level

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