DBMS > Dragonfly vs. Microsoft Azure Cosmos DB vs. VictoriaMetrics vs. XTDB

System Properties Comparison Dragonfly vs. Microsoft Azure Cosmos DB vs. VictoriaMetrics vs. XTDB

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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

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Document store

Secondary database models

Spatial 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	1.23
Rank	#172	Overall
	#15	Time Series DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

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

azure.microsoft.com/services/cosmos-db

victoriametrics.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.dragonflydb.io/docs

learn.microsoft.com/azure/cosmos-db

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

www.xtdb.com/docs

Developer

DragonflyDB team and community contributors

Microsoft

VictoriaMetrics

Juxt Ltd.

Initial release

2023

2014

2018

2019

Current release

1.0, March 2023

v1.91, May 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache Version 2.0

Open Source

MIT License

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++

Clojure

Server operating systems

Linux

hosted

FreeBSD
Linux
macOS
OpenBSD

All OS with a Java 8 (and higher) VM
Linux

Data scheme

scheme-free

schema-free

Typing

predefined data types such as float or date

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

yes

JSON types

yes, extensible-data-notation format

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

yes

SQL

Support of SQL

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

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

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

HTTP REST
JDBC

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

Clojure
Java

Server-side scripts

Stored procedures

Lua

JavaScript

Triggers

publish/subscribe channels provide some trigger functionality

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

Synchronous replication

yes, each node contains all data

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

Eventual Consistency

Foreign keys

Referential integrity

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

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