DBMS > Geode vs. PouchDB vs. Snowflake vs. Solr vs. SQLite

System Properties Comparison Geode vs. PouchDB vs. Snowflake vs. Solr vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

JavaScript DBMS with an API inspired by CouchDB

Cloud-based data warehousing service for structured and semi-structured data

A widely used distributed, scalable search engine based on Apache Lucene

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	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

Technical documentation

geode.apache.org/docs

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

www.sqlite.org/docs.html

Developer

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

Apache Software Foundation

Snowflake Computing Inc.

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2002

2012

2014

2006

2000

Current release

1.1, February 2017

7.1.1, June 2019

9.6.0, April 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

commercial

Open Source

Apache Version 2

Open Source

Public Domain

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

Java

JavaScript

Java

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

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

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

server-less

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

dynamic column types

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

via views

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language (OQL)

yes

Solr Parallel SQL Interface

yes

SQL-92 is not fully supported

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

JavaScript

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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

user defined functions

Java plugins

Triggers

yes

Cache Event Listeners

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Sharding

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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

optimistic locking

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

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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