DBMS > ObjectBox vs. SAP HANA vs. Sphinx vs. Weaviate

System Properties Comparison ObjectBox vs. SAP HANA vs. Sphinx vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

Extremely fast embedded database for small devices, IoT and Mobile

In-memory, column based data store. Available as appliance or cloud service

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

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Secondary database models

Time Series DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.22
Rank	#172	Overall
	#5	Object oriented DBMS

Score	45.84
Rank	#22	Overall
	#16	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Score	1.89
Rank	#138	Overall
	#4	Vector DBMS

Website

objectbox.io

www.sap.com/products/hana.html

sphinxsearch.com

github.com/weaviate/weaviate
weaviate.io

Technical documentation

docs.objectbox.io

help.sap.com/hana

sphinxsearch.com/docs

weaviate.io/developers/weaviate

Developer

ObjectBox Limited

SAP

Sphinx Technologies Inc.

Weaviate B.V.

Initial release

2017

2010

2001

2019

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.5.1, February 2023

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

GPL version 2, commercial licence available

Open Source

commercial license available with Weaviate Enterprise

Cloud-based only

Only available as a cloud service

also available as a cloud based 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++

Server operating systems

Android
iOS
Linux
macOS
Windows

Appliance or cloud-service

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

GraphQL is used as query language

APIs and other access methods

Proprietary native API

JDBC
ODBC

Proprietary protocol

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

online/offline synchronization between client and server

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

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

yes

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

All companies that have data.
» more

Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.