DBMS > Hazelcast vs. HugeGraph vs. Microsoft Azure Cosmos DB vs. Sphinx vs. Trafodion

System Properties Comparison Hazelcast vs. HugeGraph vs. Microsoft Azure Cosmos DB vs. Sphinx vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

A widely adopted in-memory data grid

A fast-speed and highly-scalable Graph DBMS

Globally distributed, horizontally scalable, multi-model database service

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Key-value store

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

hazelcast.com

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/cosmos-db

sphinxsearch.com

trafodion.apache.org

Technical documentation

hazelcast.org/imdg/docs

hugegraph.apache.org/docs

learn.microsoft.com/azure/cosmos-db

sphinxsearch.com/docs

trafodion.apache.org/documentation.html

Developer

Hazelcast

Baidu

Microsoft

Sphinx Technologies Inc.

Apache Software Foundation, originally developed by HP

Initial release

2008

2018

2014

2001

2014

Current release

5.3.6, November 2023

0.9

3.5.1, February 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2, commercial licence available

Open Source

Apache 2.0

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++

C++, Java

Server operating systems

All OS with a Java VM

Linux
macOS
Unix

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

also supports composite index and range index

yes

All properties auto-indexed by default

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Proprietary protocol

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Groovy
Java
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

asynchronous Gremlin script jobs

JavaScript

Java Stored Procedures

Triggers

yes

Events

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

Implicit feature of the cloud service

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

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

yes

Implicit feature of the cloud service

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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.

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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