DBMS > EventStoreDB vs. Microsoft Azure Cosmos DB vs. TinkerGraph vs. VictoriaMetrics

System Properties Comparison EventStoreDB vs. Microsoft Azure Cosmos DB vs. TinkerGraph vs. VictoriaMetrics

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Editorial information provided by DB-Engines

Name

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Globally distributed, horizontally scalable, multi-model database service

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

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

Primary database model

Event Store

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.88
Rank	#186	Overall
	#1	Event Stores

Score	40.98
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.18
Rank	#293	Overall
	#23	Graph DBMS

Score	0.56
Rank	#228	Overall
	#17	Time Series DBMS

Website

www.eventstore.com

azure.microsoft.com/services/cosmos-db

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

victoriametrics.com

Technical documentation

developers.eventstore.com

docs.microsoft.com/azure/cosmos-db

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

Developer

Event Store Limited

Microsoft

VictoriaMetrics

Initial release

2012

2014

2009

2018

Current release

21.2, February 2021

v1.77, May 2022

License

Commercial or Open Source

Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

Java

Server operating systems

Linux
Windows

hosted

FreeBSD
Linux
macOS
OpenBSD

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

APIs and other access methods

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

TinkerPop 3

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

Supported programming languages

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

Groovy
Java

Server-side scripts

Stored procedures

JavaScript

Triggers

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

yes

Implicit feature of the cloud service

none

Synchronous replication

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

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

none

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

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

optional

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

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

High performance, horizontally scalable time series database and monitoring solution...
» more

Competitive advantages

Specifically designed for highly loaded workloads Addresses most of the scalability...
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Typical application scenarios

When the current monitoring solution can't keep up with increasing workload Kubernetes...
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Key customers

Grammarly, Wix.com, adidas, Sensedia, Brandwatch, CERN, COLOPL, Razorpay See our...
» more

Market metrics

6k stars and 100+ contributors on GitHub 45M+ downloads Thousands of active users...
» more

Licensing and pricing models

VictoriaMetrics comes in three versions: an open source version, an Enterprise version...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.