DBMS > Databend vs. HyperSQL vs. Realm vs. Transwarp Hippo

System Properties Comparison Databend vs. HyperSQL vs. Realm vs. Transwarp Hippo

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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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Relational DBMS

Document store

Vector DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

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

hsqldb.org

realm.io

www.transwarp.cn/en/subproduct/hippo

Technical documentation

docs.databend.com

hsqldb.org/web/hsqlDocsFrame.html

realm.io/docs

Developer

Databend Labs

Realm, acquired by MongoDB in May 2019

Initial release

2021

2001

2014

2023

Current release

1.0.59, April 2023

2.7.2, June 2023

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

based on BSD license

Open Source

commercial

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

Java

C++

Server operating systems

hosted
Linux
macOS

All OS with a Java VM

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

Android
Backend: server-less
iOS
Windows

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and 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

SQL

Support of SQL

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

HTTP API

JDBC via HTTP
JDBC
ODBC

RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

All languages supporting JDBC/ODBC
Java

.Net
Java

with Android only
Objective-C
React Native
Swift

C++
Java
Python

Server-side scripts

Stored procedures

Java, SQL

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

In-memory capabilities

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

yes

In-Memory realm

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

yes

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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