DBMS > HugeGraph vs. JaguarDB vs. Machbase Neo vs. OpenSearch vs. Sphinx

System Properties Comparison HugeGraph vs. JaguarDB vs. Machbase Neo vs. OpenSearch vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Performant, highly scalable DBMS for AI and IoT applications

TimeSeries DBMS for AIoT and BigData

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

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

Primary database model

Graph DBMS

Key-value store
Vector DBMS

Time Series DBMS

Search engine

Secondary database models

Vector DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

github.com/hugegraph
hugegraph.apache.org

www.jaguardb.com

machbase.com

github.com/opensearch-project
opensearch.org

sphinxsearch.com

Technical documentation

hugegraph.apache.org/docs

www.jaguardb.com/support.html

machbase.com/dbms

opensearch.org/docs/latest

sphinxsearch.com/docs

Developer

Baidu

DataJaguar, Inc.

Machbase

Amazon Web Services

Sphinx Technologies Inc.

Initial release

2018

2015

2013

2021

2001

Current release

0.9

3.3 July 2023

V8.0, August 2023

2.5.0, January 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL V3.0

commercial

free test version available

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

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.

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Implementation language

Java

C++

the server part. Clients available in other languages

Java

C++

Server operating systems

Linux
macOS
Unix

Linux

Linux
macOS
Windows

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

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

also supports composite index and range index

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

Groovy
Java
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

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

Multi-source replication

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Foreign keys

Referential integrity

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

volatile and lookup table

User concepts

Access control

Users, roles and permissions

rights management via user accounts

simple password-based access control

More information provided by the system vendor

Specific characteristics

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
» more

Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
» more

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