DBMS > GridGain vs. Heroic vs. NCache vs. Percona Server for MongoDB vs. SAP HANA

System Properties Comparison GridGain vs. Heroic vs. NCache vs. Percona Server for MongoDB vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Key-value store
Relational DBMS

Secondary database models

Document store
Search engine

Using distributed Lucene

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

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

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

www.gridgain.com

github.com/spotify/heroic

www.alachisoft.com/ncache

www.percona.com/mongodb/software/percona-server-for-mongodb

www.sap.com/products/hana.html

Technical documentation

www.gridgain.com/docs/index.html

spotify.github.io/heroic

www.alachisoft.com/resources/docs

docs.percona.com/percona-distribution-for-mongodb

help.sap.com/hana

Developer

GridGain Systems, Inc.

Spotify

Alachisoft

Percona

SAP

Initial release

2007

2014

2005

2015

2010

Current release

GridGain 8.5.1

5.3.3, April 2024

3.4.10-2.10, November 2017

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Enterprise Edition available

Open Source

GPL Version 2

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

Java

C#, .NET, .NET Core, Java

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Appliance or cloud-service

Data scheme

yes

schema-free

yes

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.

yes

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

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.

yes

APIs and other access methods

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Server-side scripts

Stored procedures

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

support for stored procedures with SQL-Server CLR

JavaScript

SQLScript, R

Triggers

yes (cache interceptors and events)

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

yes, with selectable consistency level

Source-replica replication

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

via In-Memory Engine

yes

User concepts

Access control

Security Hooks for custom implementations

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

Access rights for users and roles

yes

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

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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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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