DBMS > Amazon DynamoDB vs. AnzoGraph DB vs. Hazelcast vs. Sphinx vs. STSdb

System Properties Comparison Amazon DynamoDB vs. AnzoGraph DB vs. Hazelcast vs. Sphinx vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

A widely adopted in-memory data grid

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

aws.amazon.com/dynamodb

cambridgesemantics.com/anzograph

hazelcast.com

sphinxsearch.com

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/dynamodb

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

hazelcast.org/imdg/docs

sphinxsearch.com/docs

Developer

Amazon

Cambridge Semantics

Hazelcast

Sphinx Technologies Inc.

STS Soft SC

Initial release

2012

2018

2008

2001

2011

Current release

2.3, January 2021

5.3.6, November 2023

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free trial version available

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license 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

Linux

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

full-text index on all search fields

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JCache
JPA
Memcached protocol
RESTful HTTP API

Proprietary protocol

.NET Client API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C++
Java
Python

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Event Listeners, Executor Services

Triggers

yes

by integration with AWS Lambda

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

yes

Replicated Map

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

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

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users and roles

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.