DBMS > Datomic vs. Geode vs. SQLite vs. Vitess

System Properties Comparison Datomic vs. Geode vs. SQLite vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Widely used embeddable, in-process RDBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	1.51
Rank	#147	Overall
	#25	Key-value stores

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

Technical documentation

docs.datomic.com

geode.apache.org/docs

www.sqlite.org/docs.html

vitess.io/docs

Developer

Cognitect

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

Dwayne Richard Hipp

The Linux Foundation, PlanetScale

Initial release

2012

2002

2000

2013

Current release

1.0.7180, July 2024

1.1, February 2017

3.46.1 (13 August 2024), August 2024

15.0.2, December 2022

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Public Domain

Open Source

Apache Version 2.0, commercial licenses available

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, Clojure

Java

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

server-less

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (OQL)

yes

SQL-92 is not fully supported

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Clojure
Java

.Net
All JVM based languages
C++
Groovy
Java
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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

yes

proprietary syntax

Triggers

By using transaction functions

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication

none

Multi-source replication
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

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes, on a single node

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights per client and object definable

Users with fine-grained authorization concept

no user groups or roles

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