DBMS > Apache Impala vs. Oracle vs. Riak KV vs. Titan

System Properties Comparison Apache Impala vs. Oracle vs. Riak KV vs. Titan

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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.

Description

Analytic DBMS for Hadoop

Widely used RDBMS

Distributed, fault tolerant key-value store

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Relational DBMS

Key-value store

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

Graph DBMS

Secondary database models

Document store

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

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

Website

impala.apache.org

www.oracle.com/database

github.com/thinkaurelius/titan

Technical documentation

impala.apache.org/impala-docs.html

docs.oracle.com/en/database

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

github.com/thinkaurelius/titan/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Oracle

OpenSource, formerly Basho Technologies

Aurelius, owned by DataStax

Initial release

2013

1980

2009

2012

Current release

4.1.0, June 2022

23c, September 2023

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Erlang

Java

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X

Linux
OS X
Unix
Windows

Data scheme

yes

Schemaless in JSON and XML columns

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

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API
Native Erlang Interface

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

Clojure
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

PL/SQL

also stored procedures in Java possible

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

no "single point of failure"

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

can be realized in PL/SQL

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Relationships in graph

Transaction concepts

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

ACID

isolation level can be parameterized

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

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

yes, using Riak Security

User authentification and security via Rexster Graph Server

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