DBMS > GigaSpaces vs. Heroic vs. HugeGraph vs. Newts vs. OpenSearch

System Properties Comparison GigaSpaces vs. Heroic vs. HugeGraph vs. Newts vs. OpenSearch

Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A fast-speed and highly-scalable Graph DBMS

Time Series DBMS based on Cassandra

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

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Secondary database models

Graph DBMS
Search engine

Vector DBMS

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	16.03
Rank	#37	Overall
	#4	Search engines

Website

www.gigaspaces.com

github.com/spotify/heroic

github.com/hugegraph
hugegraph.apache.org

opennms.github.io/newts

github.com/opensearch-project
opensearch.org

Technical documentation

docs.gigaspaces.com/latest/landing.html

spotify.github.io/heroic

hugegraph.apache.org/docs

github.com/OpenNMS/newts/wiki

opensearch.org/docs/latest

Developer

Gigaspaces Technologies

Spotify

Baidu

OpenNMS Group

Amazon Web Services

Initial release

2000

2014

2018

2014

2021

Current release

15.5, September 2020

0.9

2.5.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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.

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Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
macOS
Solaris
Windows

Linux
macOS
Unix

Linux
OS X
Windows

All OS with a Java VM

Data scheme

schema-free

yes

schema-free

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

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.

XML can be used for describing objects metadata

Secondary indexes

yes

via Elasticsearch

yes

also supports composite index and range index

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP REST
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C++
Java
Python
Scala

Groovy
Java
Python

Java

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

yes

Triggers

yes, event driven architecture

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

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

selectable replication factor

based on Cassandra

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

Users, roles and permissions

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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