DBMS > ObjectBox vs. Pinecone vs. SAP HANA vs. Splice Machine vs. Yanza

System Properties Comparison ObjectBox vs. Pinecone vs. SAP HANA vs. Splice Machine vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Extremely fast embedded database for small devices, IoT and Mobile

A managed, cloud-native vector database

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

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

Time Series DBMS for IoT Applications

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	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	3.23
Rank	#92	Overall
	#2	Vector DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

objectbox.io

www.pinecone.io

www.sap.com/products/hana.html

splicemachine.com

yanza.com

Technical documentation

docs.objectbox.io

docs.pinecone.io/docs/overview

help.sap.com/hana

splicemachine.com/how-it-works

Developer

ObjectBox Limited

Pinecone Systems, Inc

SAP

Splice Machine

Yanza

Initial release

2017

2019

2010

2014

2015

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

AGPL 3.0, commercial license available

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

Java

Server operating systems

Android
iOS
Linux
macOS
Windows

hosted

Appliance or cloud-service

Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

Proprietary native API

RESTful HTTP API

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

HTTP API

Supported programming languages

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

Python

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

SQLScript, R

yes

Java

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

online/offline synchronization between client and server

yes

Multi-source replication
Source-replica replication

none

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

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

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

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

More information provided by the system vendor

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