DBMS > Datomic vs. EXASOL vs. HarperDB vs. NSDb vs. Riak KV

System Properties Comparison Datomic vs. EXASOL vs. HarperDB vs. NSDb vs. Riak KV

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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.

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

Technical documentation

docs.datomic.com

www.exasol.com/resources

docs.harperdb.io/docs

nsdb.io/Architecture

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Cognitect

Exasol

HarperDB

OpenSource, formerly Basho Technologies

Initial release

2012

2000

2017

2009

Current release

1.0.6735, June 2023

3.1, August 2021

3.2.0, December 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

free community edition available

Open Source

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

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

Node.js

Java, Scala

Erlang

Server operating systems

All OS with a Java VM

Linux
OS X

Linux
macOS

Linux
OS X

Data scheme

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes: int, bigint, decimal, string

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

all fields are automatically indexed

restricted

SQL

Support of SQL

yes

SQL-like data manipulation statements

SQL-like query language

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

gRPC
HTTP REST
WebSocket

HTTP API
Native Erlang Interface

Supported programming languages

Clojure
Java

Java
Lua
Python
R

.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

Java
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

Custom Functions

since release 3.1

Erlang

Triggers

By using transaction functions

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

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

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

the nodes on which a table resides can be defined

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Atomic execution of specific operations

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

yes, using LMDB

Using Apache Lucene

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 for users, groups and roles according to SQL-standard

Access rights for users and roles

yes, using Riak Security

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