DBMS > Apache Pinot vs. Couchbase vs. LeanXcale

System Properties Comparison Apache Pinot vs. Couchbase vs. LeanXcale

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

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

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Website

pinot.apache.org

www.couchbase.com

www.leanxcale.com

Technical documentation

docs.pinot.apache.org

docs.couchbase.com

Developer

Apache Software Foundation and contributors

Couchbase, Inc.

LeanXcale

Initial release

2015

2011

2015

Current release

1.0.0, September 2023

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

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

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

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++, Go and Erlang

Server operating systems

All OS with a Java JDK11 or higher

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

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

yes

through Apache Derby

APIs and other access methods

JDBC

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

Go
Java
Python

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C
Java
Scala

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

horizontal partitioning

Automatic Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center 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

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

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

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.

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