DBMS > 4D vs. Hypertable vs. Microsoft Azure Cosmos DB vs. ObjectBox vs. XTDB

System Properties Comparison 4D vs. Hypertable vs. Microsoft Azure Cosmos DB vs. ObjectBox vs. XTDB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Application development environment with integrated database management system

An open source BigTable implementation based on distributed file systems such as Hadoop

Globally distributed, horizontally scalable, multi-model database service

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Object oriented DBMS
Vector DBMS

Document store

Secondary database models

Spatial DBMS

Time Series DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS
	#7	Vector DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.4d.com

azure.microsoft.com/services/cosmos-db

github.com/objectbox
objectbox.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

developer.4d.com

learn.microsoft.com/azure/cosmos-db

docs.objectbox.io

www.xtdb.com/docs

Developer

4D, Inc

Hypertable Inc.

Microsoft

ObjectBox Limited

Juxt Ltd.

Initial release

1984

2009

2014

2017

2019

Current release

v20, April 2023

0.9.8.11, March 2016

4.0 (May 2024)

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

Bindings are released under Apache 2.0

Apache License 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Clojure

Server operating systems

OS X
Windows

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes, plus "flex" map-like types

yes, extensible-data-notation format

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

only exact value or prefix value scans

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

C++ API
Thrift

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Proprietary native API

HTTP REST
JDBC

Supported programming languages

4D proprietary IDE
PHP

C++
Java
Perl
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

Clojure
Java

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor on file system level

yes

Implicit feature of the cloud service

Data sync between devices allowing occasional connected databases to work completely offline

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

Users and groups

Access rights can be defined down to the item level

yes

More information provided by the system vendor

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