DBMS > EventStoreDB vs. Kyligence Enterprise vs. NSDb

System Properties Comparison EventStoreDB vs. Kyligence Enterprise vs. NSDb

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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.

A distributed analytics engine for big data, built on top of Apache Kylin

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Event Store

Relational DBMS

Time Series DBMS

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

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

www.eventstore.com

kyligence.io/kyligence-enterprise

nsdb.io

Technical documentation

developers.eventstore.com

nsdb.io/Architecture

Developer

Event Store Limited

Kyligence, Inc.

Initial release

2012

2016

2017

Current release

21.2, February 2021

License

Commercial or Open Source

Open Source

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Scala

Server operating systems

Linux
Windows

Linux

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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 fields are automatically indexed

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

Supported programming languages

Java
Scala

Server-side scripts

Stored procedures

Partitioning methods

Methods for storing different data on different nodes

Sharding

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

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More resources

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