DBMS > Alibaba Cloud Table Store vs. Datomic vs. NCache vs. RocksDB vs. Stardog

System Properties Comparison Alibaba Cloud Table Store vs. Datomic vs. NCache vs. RocksDB vs. Stardog

Editorial information provided by DB-Engines

Name

Description

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Open-Source and Enterprise in-memory Key-Value Store

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.alibabacloud.com/product/table-store

www.datomic.com

www.alachisoft.com/ncache

rocksdb.org

www.stardog.com

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.datomic.com

www.alachisoft.com/resources/docs

github.com/facebook/rocksdb/wiki

docs.stardog.com

Developer

Alibaba

Cognitect

Alachisoft

Facebook, Inc.

Stardog-Union

Initial release

2016

2012

2005

2013

2010

Current release

1.0.6735, June 2023

5.3.3, April 2024

8.11.4, April 2024

7.3.0, May 2020

License

Commercial or Open Source

commercial

limited edition free

Open Source

Enterprise Edition available

Open Source

BSD

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

C#, .NET, .NET Core, Java

C++

Java

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

Linux

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

HTTP API

RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

C++ API
Java API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Java
Python

Clojure
Java

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

support for stored procedures with SQL-Server CLR

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

By using transaction functions

yes

Notifications

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

But extensive use of caching in the application peers

yes

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

But extensive use of caching in the application peers

yes, with selectable consistency level

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

Atomic single-row operations

ACID

optimistic locking and pessimistic locking

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights based on subaccounts and tokens

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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