DBMS > AlaSQL vs. Ignite vs. InfinityDB vs. NCache vs. RocksDB

System Properties Comparison AlaSQL vs. Ignite vs. InfinityDB vs. NCache vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A Java embedded Key-Value Store which extends the Java Map interface

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

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

Primary database model

Document store
Relational DBMS

Key-value store
Relational DBMS

Key-value store

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

alasql.org

ignite.apache.org

boilerbay.com

www.alachisoft.com/ncache

rocksdb.org

Technical documentation

github.com/AlaSQL/alasql

apacheignite.readme.io/docs

boilerbay.com/infinitydb/manual

www.alachisoft.com/resources/docs

github.com/facebook/rocksdb/wiki

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation

Boiler Bay Inc.

Alachisoft

Facebook, Inc.

Initial release

2014

2015

2002

2005

2013

Current release

Apache Ignite 2.6

4.0

5.3.3, April 2024

9.2.1, May 2024

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache 2.0

commercial

Open Source

Enterprise Edition available

Open Source

BSD

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.

Implementation language

JavaScript

C++, Java, .Net

Java

C#, .NET, .NET Core, Java

C++

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
Windows

Linux

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

partial

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

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

ANSI-99 for query and DML statements, subset of DDL

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

APIs and other access methods

JavaScript API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

IDistributedCache
JCache
LINQ
Proprietary native API

C++ API
Java API

Supported programming languages

JavaScript

C#
C++
Java
PHP
Python
Ruby
Scala

Java

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

support for stored procedures with SQL-Server CLR

Triggers

yes

yes (cache interceptors and events)

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

none

yes, with selectable consistency level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

READ-COMMITTED or SERIALIZED

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

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Optimistic locking for transactions; no isolation for bulk loads

optimistic locking and pessimistic locking

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

In-memory capabilities

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

yes

User concepts

Access control

Security Hooks for custom implementations

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

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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