DBMS > ObjectBox vs. Pinecone vs. Splice Machine vs. Transbase vs. Yanza

System Properties Comparison ObjectBox vs. Pinecone vs. Splice Machine vs. Transbase 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

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

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Time Series 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	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

objectbox.io

www.pinecone.io

splicemachine.com

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

yanza.com

Technical documentation

docs.objectbox.io

docs.pinecone.io/docs/overview

splicemachine.com/how-it-works

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

Developer

ObjectBox Limited

Pinecone Systems, Inc

Splice Machine

Transaction Software GmbH

Yanza

Initial release

2017

2019

2014

1987

2015

Current release

3.1, March 2021

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

AGPL 3.0, commercial license available

commercial

free development license

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

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

C and C++

Server operating systems

Android
iOS
Linux
macOS
Windows

hosted

Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
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
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

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

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

online/offline synchronization between client and server

Multi-source replication
Source-replica 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

yes

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

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

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Pinecone launches its serverless vector database out of preview
21 May 2024, TechCrunch

AWS Marketplace: Pinecone Vector Database - Annual Commit Comments
4 June 2024, AWS Blog

Pinecone launches serverless edition of its vector database on AWS
22 May 2024, SiliconANGLE News

Pinecone’s new serverless database may see few takers, analysts say
17 January 2024, InfoWorld

Pinecone Makes Accurate, Fast, Scalable Generative AI Accessible to Organizations Large and Small with Launch of ...
21 May 2024, PR Newswire

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

ETL: The Silent Killer of Big Data Projects
23 July 2015, insideBIGDATA

provided by Google News

Share this page