DBMS > Amazon DynamoDB vs. Hazelcast vs. Sphinx vs. Splice Machine vs. Transbase

System Properties Comparison Amazon DynamoDB vs. Hazelcast vs. Sphinx vs. Splice Machine vs. Transbase

Editorial information provided by DB-Engines

Name

Description

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

A widely adopted in-memory data grid

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store
Key-value store

Key-value store

Search engine

Relational DBMS

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	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

aws.amazon.com/dynamodb

hazelcast.com

sphinxsearch.com

splicemachine.com

www.transaction.de/en/products/transbase.html

Technical documentation

docs.aws.amazon.com/dynamodb

hazelcast.org/imdg/docs

sphinxsearch.com/docs

splicemachine.com/how-it-works

www.transaction.de/en/products/transbase/features.html

Developer

Amazon

Hazelcast

Sphinx Technologies Inc.

Splice Machine

Transaction Software GmbH

Initial release

2012

2008

2001

2014

1987

Current release

5.3.6, November 2023

3.5.1, February 2023

3.1, March 2021

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license available

commercial

free development license

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++

Java

C and C++

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

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.

yes

the object must implement a serialization strategy

Secondary indexes

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

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.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#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Java

yes

Triggers

yes

by integration with AWS Lambda

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

none

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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)

Role-based access control

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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