DBMS > EsgynDB vs. etcd vs. IBM Db2 Event Store vs. NSDb

System Properties Comparison EsgynDB vs. etcd vs. IBM Db2 Event Store vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A distributed reliable key-value store

Distributed Event Store optimized for Internet of Things use cases

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

Primary database model

Relational DBMS

Key-value store

Event Store
Time Series DBMS

Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

www.esgyn.cn

etcd.io
github.com/etcd-io/etcd

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

nsdb.io

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

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

nsdb.io/Architecture

Developer

Esgyn

IBM

Initial release

2015

2017

Current release

3.4, August 2019

2.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

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++, Java

C and C++

Java, Scala

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS

Data scheme

yes

schema-free

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

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC
JSON over HTTP

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

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

Java
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Active-active shard replication

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

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

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

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

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