DBMS > Google Cloud Bigtable vs. Google Cloud Datastore vs. NCache vs. Tkrzw

System Properties Comparison Google Cloud Bigtable vs. Google Cloud Datastore vs. NCache vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Key-value store
Wide column store

Document store

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	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

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

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

cloud.google.com/bigtable

cloud.google.com/datastore

www.alachisoft.com/ncache

dbmx.net/tkrzw

Technical documentation

cloud.google.com/bigtable/docs

cloud.google.com/datastore/docs

www.alachisoft.com/resources/docs

Developer

Google

Alachisoft

Mikio Hirabayashi

Initial release

2015

2008

2005

2020

Current release

5.3.3, April 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Enterprise Edition available

Open Source

Apache Version 2.0

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

C#, .NET, .NET Core, Java

C++

Server operating systems

hosted

Linux
Windows

Linux
macOS

Data scheme

schema-free

Typing

predefined data types such as float or date

yes, details here

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

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

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

support for stored procedures with SQL-Server CLR

Triggers

Callbacks using the Google Apps Engine

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication using Paxos

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

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

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Atomic single-row operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

using specific database classes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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

More information provided by the system vendor

Specific characteristics

NCache has been the market leader in .NET Distributed Caching since 2005 . NCache...
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Competitive advantages

NCache is 100% .NET/ .NET Core based which fully supports ASP.NET Core Sessions ,...
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Typical application scenarios

NCache enables industries like retail, finance, banking IoT, travel, ecommerce, healthcare...
» more

Key customers

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

Market metrics

Market Leader in .NET Distributed Caching since 2005.
» more

Licensing and pricing models

NCache Open Source is free on an as-is basis without any support. NCache Enterprise...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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