DBMS > Amazon DynamoDB vs. CockroachDB vs. Datomic vs. Faircom DB

System Properties Comparison Amazon DynamoDB vs. CockroachDB vs. Datomic vs. Faircom DB

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store
Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Website

aws.amazon.com/dynamodb

www.cockroachlabs.com

www.datomic.com

www.faircom.com/products/faircom-db

Technical documentation

docs.aws.amazon.com/dynamodb

www.cockroachlabs.com/docs

docs.datomic.com

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

Developer

Amazon

Cockroach Labs

Cognitect

FairCom Corporation

Initial release

2012

2015

2012

1979

Current release

23.1.1, May 2023

1.0.6735, June 2023

V12, November 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache 2.0, commercial license available

commercial

limited edition free

commercial

Restricted, free version available

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, Clojure

ANSI C, C++

Server operating systems

hosted

Linux
macOS
Windows

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Data scheme

schema-free

dynamic schema

yes

schema free, schema optional, schema required, partial schema,

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

yes, wire compatible with PostgreSQL

yes, ANSI SQL with proprietary extensions

APIs and other access methods

RESTful HTTP API

JDBC

RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

Clojure
Java

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

Triggers

yes

by integration with AWS Lambda

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

none

But extensive use of caching in the application peers

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using RAFT

none

But extensive use of caching in the application peers

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

Yes, tunable from durable to delayed durability to in-memory

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 for users and roles can be defined via the AWS Identity and Access Management (IAM)

Role-based access control

Fine grained access rights according to SQL-standard with additional protections for files

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