DBMS > 4D vs. CockroachDB vs. HarperDB vs. Riak KV vs. SQream DB

System Properties Comparison 4D vs. CockroachDB vs. HarperDB vs. Riak KV vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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.

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.

Distributed, fault tolerant key-value store

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Document store

Key-value store

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

Relational DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

www.4d.com

www.cockroachlabs.com

www.harperdb.io

sqream.com

Technical documentation

developer.4d.com

www.cockroachlabs.com/docs

docs.harperdb.io/docs

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

docs.sqream.com

Developer

4D, Inc

Cockroach Labs

HarperDB

OpenSource, formerly Basho Technologies

SQream Technologies

Initial release

1984

2015

2017

2009

2017

Current release

v20, April 2023

24.1.0, May 2024

3.1, August 2021

3.2.0, December 2022

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0, commercial license available

commercial

free community edition available

Open Source

Apache version 2, commercial enterprise edition

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

Node.js

Erlang

C++, CUDA, Haskell, Java, Scala

Server operating systems

OS X
Windows

Linux
macOS
Windows

Linux
OS X

Linux

Data scheme

yes

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes, ANSI Standard SQL Types

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

restricted

SQL

Support of SQL

yes

close to SQL 92

yes, wire compatible with PostgreSQL

SQL-like data manipulation statements

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API
Native Erlang Interface

.Net
JDBC
ODBC

Supported programming languages

4D proprietary IDE
PHP

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

.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

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

Erlang

user defined functions in Python

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

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

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

Sharding

no "single point of failure"

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication using RAFT

yes

the nodes on which a table resides can be defined

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

Users and groups

Role-based access control

Access rights for users and roles

yes, using Riak Security

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More resources

DB-Engines blog posts

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