DBMS > GeoSpock vs. Google BigQuery vs. Hazelcast vs. NCache

System Properties Comparison GeoSpock vs. Google BigQuery vs. Hazelcast vs. NCache

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

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

Primary database model

Relational DBMS

Key-value store

Secondary database models

Time Series DBMS

Document store

JSON support with IMDG 3.12

Document store
Search engine

Using distributed Lucene

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

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

Website

geospock.com

cloud.google.com/bigquery

hazelcast.com

www.alachisoft.com/ncache

Technical documentation

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

www.alachisoft.com/resources/docs

Developer

GeoSpock

Google

Hazelcast

Alachisoft

Initial release

2010

2008

2005

Current release

2.0, September 2019

5.3.6, November 2023

5.3.3, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Enterprise Edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Javascript

Java

C#, .NET, .NET Core, Java

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

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

the object must implement a serialization strategy

Secondary indexes

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

SQL-like query language

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

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

support for stored procedures with SQL-Server CLR

Triggers

yes

Events

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

one or two-phase-commit; repeatable reads; read commited

optimistic locking and pessimistic locking

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

User concepts

Access control

Access rights for users can be defined per table

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role-based access control

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

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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