DBMS > Apache Pinot vs. Geode vs. HEAVY.AI vs. PouchDB vs. SQLite

System Properties Comparison Apache Pinot vs. Geode vs. HEAVY.AI vs. PouchDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

pinot.apache.org

geode.apache.org

github.com/heavyai/heavydb
www.heavy.ai

pouchdb.com

www.sqlite.org

Technical documentation

docs.pinot.apache.org

geode.apache.org/docs

docs.heavy.ai

pouchdb.com/guides

www.sqlite.org/docs.html

Developer

Apache Software Foundation and contributors

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

HEAVY.AI, Inc.

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2015

2002

2016

2012

2000

Current release

1.0.0, September 2023

1.1, February 2017

5.10, January 2022

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache Version 2; enterprise edition available

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++ and CUDA

JavaScript

Server operating systems

All OS with a Java JDK11 or higher

All OS with a Java VM

the JDK (8 or later) is also required

Linux

server-less, requires a JavaScript environment (browser, Node.js)

server-less

Data scheme

yes

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

via views

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (OQL)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Go
Java
Python

.Net
All JVM based languages
C++
Groovy
Java
Scala

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

View functions in JavaScript

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Round robin

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

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

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Access rights per client and object definable

fine grained access rights according to SQL-standard

More information provided by the system vendor

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.