DBMS > 4D vs. BigObject vs. Ehcache vs. Microsoft Azure Cosmos DB vs. TimescaleDB

System Properties Comparison 4D vs. BigObject vs. Ehcache vs. Microsoft Azure Cosmos DB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Analytic DBMS for real-time computations and queries

A widely adopted Java cache with tiered storage options

Globally distributed, horizontally scalable, multi-model database service

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

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

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

www.4d.com

bigobject.io

www.ehcache.org

azure.microsoft.com/services/cosmos-db

www.timescale.com

Technical documentation

developer.4d.com

docs.bigobject.io

www.ehcache.org/documentation

learn.microsoft.com/azure/cosmos-db

docs.timescale.com

Developer

4D, Inc

BigObject, Inc.

Terracotta Inc, owned by Software AG

Microsoft

Timescale

Initial release

1984

2015

2009

2014

2017

Current release

v20, April 2023

3.10.0, March 2022

2.15.0, May 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache 2.0

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

Server operating systems

OS X
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

fluentd
ODBC
RESTful HTTP API

JCache

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

4D proprietary IDE
PHP

Java

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Lua

JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Cache Event Listeners

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

by using Terracotta Server

yes

Implicit feature of the cloud service

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Tunable Consistency (Strong, Eventual, Weak)

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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