DBMS > Postgres-XL vs. ScyllaDB vs. Titan vs. Trafodion

System Properties Comparison Postgres-XL vs. ScyllaDB vs. Titan vs. Trafodion

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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.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Cassandra and DynamoDB compatible wide column store

Titan is a Graph DBMS optimized for distributed clusters.

Transactional SQL-on-Hadoop DBMS

Primary database model

Secondary database models

Document store
Spatial DBMS

Key-value store

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

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Website

www.postgres-xl.org

www.scylladb.com

github.com/thinkaurelius/titan

trafodion.apache.org

Technical documentation

www.postgres-xl.org/documentation

docs.scylladb.com

github.com/thinkaurelius/titan/wiki

trafodion.apache.org/documentation.html

Developer

ScyllaDB

Aurelius, owned by DataStax

Apache Software Foundation, originally developed by HP

Initial release

2014

since 2012, originally named StormDB

2015

2012

2014

Current release

10 R1, October 2018

ScyllaDB Open Source 5.4.1, January 2024

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Mozilla public license

Open Source

Open Source (AGPL), commercial license available

Open Source

Apache license, version 2.0

Open Source

Apache 2.0

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.

Scylla Cloud: Create real-time applications that run at global scale with Scylla Cloud, the industry’s most powerful NoSQL DBaaS

Implementation language

C++

Java

C++, Java

Server operating systems

Linux
macOS

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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

XML type, but no XML query functionality

Secondary indexes

yes

cluster global secondary indices

yes

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like DML and DDL statements (CQL)

yes

APIs and other access methods

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

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Clojure
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

yes, Lua

yes

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes

via pluggable storage backends

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

yes

Transaction concepts

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

ACID

MVCC

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

In-memory capabilities

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

yes

in-memory tables

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more

Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
» more

Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
» more

Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more

Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more

Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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