DBMS > atoti vs. Datomic vs. Faircom EDGE vs. HarperDB vs. TimesTen

System Properties Comparison atoti vs. Datomic vs. Faircom EDGE vs. HarperDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

In-Memory RDBMS compatible to Oracle

Primary database model

Object oriented DBMS

Relational DBMS

Key-value store
Relational DBMS

Document store

Relational DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

atoti.io

www.datomic.com

www.faircom.com/products/faircom-edge

www.harperdb.io

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.atoti.io

docs.datomic.com

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

docs.harperdb.io/docs

docs.oracle.com/database/timesten-18.1

Developer

ActiveViam

Cognitect

FairCom Corporation

HarperDB

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2012

1979

2017

1998

Current release

1.0.6735, June 2023

V3, October 2020

3.1, August 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free versions available

commercial

limited edition free

commercial

Restricted, free version available

commercial

free community edition available

commercial

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

Java, Clojure

ANSI C, C++

Node.js

Server operating systems

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

dynamic schema

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

JSON data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

ANSI SQL queries

SQL-like data manipulation statements

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Clojure
Java

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Python

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

Custom Functions

since release 3.1

PL/SQL

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

But extensive use of caching in the application peers

File partitioning

Customizable business rules for partitioning

A table resides as a whole on one (or more) nodes in a cluster

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

the nodes on which a table resides can be defined

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

Immediate Consistency
Tunable Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

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

yes

yes, using LMDB

yes

by means of logfiles and checkpoints

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

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Access rights for users and roles

fine grained access rights according to SQL-standard

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