DBMS > EsgynDB vs. ReductStore vs. Sequoiadb vs. Splice Machine

System Properties Comparison EsgynDB vs. ReductStore vs. Sequoiadb vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

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

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

NewSQL database with distributed OLTP and SQL

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Time Series DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.esgyn.cn

github.com/reductstore
www.reduct.store

www.sequoiadb.com

splicemachine.com

Technical documentation

www.reduct.store/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

splicemachine.com/how-it-works

Developer

Esgyn

ReductStore LLC

Sequoiadb Ltd.

Splice Machine

Initial release

2015

2023

2013

2014

Current release

1.9, March 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Open Source

Server: AGPL; Client: Apache V2

Open Source

AGPL 3.0, commercial license available

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++, Rust

C++

Java

Server operating systems

Linux

Docker
Linux
macOS
Windows

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP API

proprietary protocol using JSON

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C++
JavaScript (Node.js)
Python
Rust

.Net
C++
Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

simple password-based access control

Access rights for users, groups and roles according to SQL-standard

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