DBMS > Apache Impala vs. RDFox vs. SWC-DB vs. Transwarp Hippo

System Properties Comparison Apache Impala vs. RDFox vs. SWC-DB vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

High performance knowledge graph and semantic reasoning engine

A high performance, scalable Wide Column DBMS

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

Primary database model

Secondary database models

Document store

Relational DBMS

Time Series DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

impala.apache.org

www.oxfordsemantic.tech

github.com/kashirin-alex/swc-db
www.swcdb.org

www.transwarp.cn/en/subproduct/hippo

Technical documentation

impala.apache.org/impala-docs.html

docs.oxfordsemantic.tech

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Oxford Semantic Technologies

Alex Kashirin

Initial release

2013

2017

2020

2023

Current release

4.1.0, June 2022

6.0, Septermber 2022

0.5, April 2021

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL V3

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

C++

Server operating systems

Linux

Linux
macOS
Windows

Linux

Linux
macOS

Data scheme

yes

RDF schemas

schema-free

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

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC

RESTful HTTP API
SPARQL 1.1

Proprietary protocol
Thrift

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
Java

C++

C++
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Roles, resources, and access types

Role based access control and fine grained access rights

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More resources

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