DBMS > IBM Db2 Event Store vs. JaguarDB vs. LeanXcale vs. NSDb

System Properties Comparison IBM Db2 Event Store vs. JaguarDB vs. LeanXcale vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Performant, highly scalable DBMS for AI and IoT applications

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Event Store
Time Series DBMS

Key-value store
Vector DBMS

Key-value store
Relational DBMS

Time Series DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

www.ibm.com/products/db2-event-store

www.jaguardb.com

www.leanxcale.com

nsdb.io

Technical documentation

www.ibm.com/docs/en/db2-event-store

www.jaguardb.com/support.html

nsdb.io/Architecture

Developer

IBM

DataJaguar, Inc.

LeanXcale

Initial release

2017

2015

2017

Current release

2.0

3.3 July 2023

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GPL V3.0

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

C++

the server part. Clients available in other languages

Java, Scala

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

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

yes

through the embedded Spark runtime

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

through Apache Derby

SQL-like query language

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

gRPC
HTTP REST
WebSocket

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
Java
Scala

Java
Scala

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

Using Apache Lucene

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

rights management via user accounts

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