DBMS > Oracle Berkeley DB vs. Pinecone vs. StarRocks

System Properties Comparison Oracle Berkeley DB vs. Pinecone vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

A managed, cloud-native vector database

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Vector DBMS

Relational DBMS

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

Score	1.90
Rank	#127	Overall
	#22	Key-value stores
	#3	Native XML DBMS

Score	3.13
Rank	#88	Overall
	#3	Vector DBMS

Score	0.75
Rank	#218	Overall
	#101	Relational DBMS

Website

www.oracle.com/database/technologies/related/berkeleydb.html

www.pinecone.io

www.starrocks.io

Technical documentation

docs.oracle.com/cd/E17076_05/html/index.html

docs.pinecone.io/docs/overview

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Pinecone Systems, Inc

The Linux Foundation

since Feb 2023

Initial release

1994

2019

2020

Current release

18.1.40, May 2020

3.3, June 2024

License

Commercial or Open Source

Open Source

commercial license available

commercial

Open Source

Apache Version 2.0

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

C, Java, C++ (depending on the Berkeley DB edition)

C++, Java

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

RESTful HTTP API

JDBC
MySQL protocol

Supported programming languages

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Python

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Role based access control and fine grained access rights

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