DBMS > 4D vs. BigObject vs. Microsoft Azure Cosmos DB vs. Titan vs. XTDB

System Properties Comparison 4D vs. BigObject vs. Microsoft Azure Cosmos DB vs. Titan vs. XTDB

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Application development environment with integrated database management system

Analytic DBMS for real-time computations and queries

Globally distributed, horizontally scalable, multi-model database service

Titan is a Graph DBMS optimized for distributed clusters.

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Document store

Secondary database models

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

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.4d.com

bigobject.io

azure.microsoft.com/services/cosmos-db

github.com/thinkaurelius/titan

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

Technical documentation

developer.4d.com

docs.bigobject.io

learn.microsoft.com/azure/cosmos-db

github.com/thinkaurelius/titan/wiki

www.xtdb.com/docs

Developer

4D, Inc

BigObject, Inc.

Microsoft

Aurelius, owned by DataStax

Juxt Ltd.

Initial release

1984

2015

2014

2012

2019

Current release

v20, April 2023

1.19, September 2021

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Apache license, version 2.0

Open Source

MIT License

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

Clojure

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)

hosted

Linux
OS X
Unix
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

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

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

limited SQL, making use of Apache Calcite

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

fluentd
ODBC
RESTful HTTP API

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP REST
JDBC

Supported programming languages

4D proprietary IDE
PHP

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

Clojure
Java
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

Lua

JavaScript

yes

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

yes

via pluggable storage backends

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

Implicit feature of the cloud service

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Relationships in graph

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

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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.

yes

User concepts

Access control

Users and groups

Access rights can be defined down to the item level

User authentification and security via Rexster Graph Server

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