DBMS > Amazon Redshift vs. chDB vs. HugeGraph vs. Sphinx

System Properties Comparison Amazon Redshift vs. chDB vs. HugeGraph vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An embedded SQL OLAP Engine powered by ClickHouse

A fast-speed and highly-scalable Graph DBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Graph DBMS

Search engine

Secondary database models

Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.07
Rank	#376	Overall
	#158	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

aws.amazon.com/redshift

github.com/chdb-io/chdb

github.com/hugegraph
hugegraph.apache.org

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/redshift

doc.chdb.io

hugegraph.apache.org/docs

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

Baidu

Sphinx Technologies Inc.

Initial release

2012

2023

2018

2001

Current release

0.9

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

hosted

server-less

Linux
macOS
Unix

FreeBSD
Linux
NetBSD
OS X
Solaris
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

restricted

yes

also supports composite index and range index

yes

full-text index on all search fields

SQL

Support of SQL

yes

does not fully support an SQL-standard

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

Bun
C
C++
Go
JavaScript (Node.js)
Python
Rust

Groovy
Java
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

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

The original contents of fields are not stored in the Sphinx index.

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

Users, roles and permissions

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