DBMS > Databend vs. Graph Engine vs. HyperSQL vs. ReductStore

System Properties Comparison Databend vs. Graph Engine vs. HyperSQL vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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.

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Time Series DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.graphengine.io

hsqldb.org

github.com/reductstore
www.reduct.store

Technical documentation

docs.databend.com

www.graphengine.io/docs/manual

hsqldb.org/web/hsqlDocsFrame.html

www.reduct.store/docs

Developer

Databend Labs

Microsoft

ReductStore LLC

Initial release

2021

2010

2001

2023

Current release

1.0.59, April 2023

2.7.2, June 2023

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

based on BSD license

Open Source

Business Source License 1.1

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

Rust

.NET and C

Java

C++, Rust

Server operating systems

hosted
Linux
macOS

.NET

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Docker
Linux
macOS
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP API

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C#
C++
F#
Visual Basic

All languages supporting JDBC/ODBC
Java

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

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