DBMS > ObjectBox vs. Sadas Engine vs. Sphinx vs. Splice Machine vs. ToroDB

System Properties Comparison ObjectBox vs. Sadas Engine vs. Sphinx vs. Splice Machine vs. ToroDB

Editorial information provided by DB-Engines

Name

Description

Extremely fast embedded database for small devices, IoT and Mobile

SADAS Engine is a columnar DBMS specifically designed for high performance in Data warehouse environments

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 MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.75
Rank	#195	Overall
	#9	Object oriented DBMS

Score	0.00
Rank	#334	Overall
	#139	Relational DBMS

Score	6.56
Rank	#57	Overall
	#6	Search engines

Score	0.74
Rank	#196	Overall
	#99	Relational DBMS

Score	0.01
Rank	#330	Overall
	#47	Document stores

Website

Technical documentation

docs.objectbox.io

www.sadasengine.com/index.php/downloads/documentation-columnar-db

sphinxsearch.com/docs

doc.splicemachine.com

www.torodb.com/stampede/docs

Developer

ObjectBox Limited

SADAS s.r.l.

Sphinx Technologies Inc.

Splice Machine

8Kdata

Initial release

2017

2006

2001

2014

2016

Current release

8.0

3.3.1 , July 2020

2.0

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free trial version available

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license available

Open Source

AGPL-V3

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.

Implementation language

C and C++

C++

Java

Server operating systems

Android
iOS
Linux
macOS
Windows

AIX
Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

yes

APIs and other access methods

Proprietary native API

JDBC
ODBC
Proprietary protocol

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
C
C#
C++
Groovy
Java
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

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

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

online/offline synchronization between client and server

none

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

managed by 'Learn by Usage'

yes

User concepts

Access control

yes

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

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

THE ONLY SCALE-OUT SQL RDBMS WITH BUILT-IN MACHINE LEARNING For data-rich, real-time...
» more

Competitive advantages

Operational Workloads: Full ACID support with high performance at scale Multi-row...
» more

Typical application scenarios

New AI/ML and IoT Applications Data Warehouse Offloads Application Modernization...
» more

Licensing and pricing models

Kubernetes Community Edition: https://hub.helm.sh/charts/splicemachine/splice-helm...
» more

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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.

Related products and services

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More resources

DB-Engines blog posts

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