DBMS > Alibaba Cloud Table Store vs. Amazon Neptune vs. Apache Impala vs. MySQL vs. Titan

System Properties Comparison Alibaba Cloud Table Store vs. Amazon Neptune vs. Apache Impala vs. MySQL vs. Titan

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

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Fast, reliable graph database built for the cloud

Analytic DBMS for Hadoop

Widely used open source RDBMS

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Wide column store

Graph DBMS
RDF store

Relational DBMS

Key/Value like access via memcached API

Graph DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

www.alibabacloud.com/product/table-store

aws.amazon.com/neptune

impala.apache.org

www.mysql.com

github.com/thinkaurelius/titan

Technical documentation

www.alibabacloud.com/help/en/tablestore

aws.amazon.com/neptune/developer-resources

impala.apache.org/impala-docs.html

dev.mysql.com/doc

github.com/thinkaurelius/titan/wiki

Developer

Alibaba

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Oracle

since 2010, originally MySQL AB, then Sun

Aurelius, owned by DataStax

Initial release

2016

2017

2013

1995

2012

Current release

4.1.0, June 2022

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

Java

Server operating systems

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

Data scheme

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

HTTP API

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

Java
Python

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Clojure
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

selectable replication factor

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

not for MyISAM storage engine

yes

Relationships in graph

Transaction concepts

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

Atomic single-row operations

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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

User concepts

Access control

Access rights based on subaccounts and tokens

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Users with fine-grained authorization concept

no user groups or roles

User authentification and security via Rexster Graph Server

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