DBMS > Dragonfly vs. Microsoft Azure Cosmos DB vs. SAP HANA vs. TimescaleDB

System Properties Comparison Dragonfly vs. Microsoft Azure Cosmos DB vs. SAP HANA vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

In-memory, column based data store. Available as appliance or cloud service

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

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

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

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

azure.microsoft.com/services/cosmos-db

www.sap.com/products/hana.html

www.timescale.com

Technical documentation

www.dragonflydb.io/docs

learn.microsoft.com/azure/cosmos-db

help.sap.com/hana

docs.timescale.com

Developer

DragonflyDB team and community contributors

Microsoft

SAP

Timescale

Initial release

2023

2014

2010

2017

Current release

1.0, March 2023

2.0 SPS07 (April 4, 2023), April 2023

2.15.0, May 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

Linux

hosted

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

scheme-free

schema-free

yes

Typing

predefined data types such as float or date

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

yes

JSON types

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

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

JDBC
ODBC

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

Supported programming languages

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

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Lua

JavaScript

SQLScript, R

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

publish/subscribe channels provide some trigger functionality

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

yes

Source-replica replication with hot standby and reads on replicas

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

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Password-based authentication

Access rights can be defined down to the item level

yes

fine grained access rights according to SQL-standard

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