DBMS > Databend vs. HEAVY.AI vs. Newts vs. Stardog vs. SwayDB

System Properties Comparison Databend vs. HEAVY.AI vs. Newts vs. Stardog vs. SwayDB

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 high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Time Series DBMS based on Cassandra

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

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

github.com/heavyai/heavydb
www.heavy.ai

opennms.github.io/newts

www.stardog.com

swaydb.simer.au

Technical documentation

docs.databend.com

docs.heavy.ai

github.com/OpenNMS/newts/wiki

docs.stardog.com

Developer

Databend Labs

HEAVY.AI, Inc.

OpenNMS Group

Stardog-Union

Simer Plaha

Initial release

2021

2016

2014

2010

2018

Current release

1.0.59, April 2023

5.10, January 2022

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

GNU Affero GPL V3.0

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

Rust

C++ and CUDA

Java

Scala

Server operating systems

hosted
Linux
macOS

Linux

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

schema-free

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP REST
Java API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

All languages supporting JDBC/ODBC/Thrift
Python

Java

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

selectable replication factor

based on Cassandra

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

yes

ACID

Atomic execution of operations

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

User concepts

Access control

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

Access rights for users and roles

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