DBMS > BoltDB vs. Couchbase vs. OpenMLDB vs. Pinecone

System Properties Comparison BoltDB vs. Couchbase vs. OpenMLDB vs. Pinecone

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Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

An open-source machine learning database that provides a feature platform for training and inference

A managed, cloud-native vector database

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Website

github.com/boltdb/bolt

www.couchbase.com

openmldb.ai

www.pinecone.io

Technical documentation

docs.couchbase.com

openmldb.ai/docs/zh/main

docs.pinecone.io/docs/overview

Developer

Couchbase, Inc.

4 Paradigm Inc.

Pinecone Systems, Inc

Initial release

2013

2011

2020

2019

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

2024-2 February 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

commercial

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, C++, Go and Erlang

C++, Java, Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

hosted

Data scheme

schema-free

Fixed schema

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

JDBC
SQLAlchemy

RESTful HTTP API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Go
Java
Python
Scala

Python

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Triggers

yes

via the TAP protocol

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

fine grained access rights according to SQL-standard

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