DBMS > Google Cloud Datastore vs. Kingbase vs. Memcached vs. SAP HANA vs. Tkrzw

System Properties Comparison Google Cloud Datastore vs. Kingbase vs. Memcached vs. SAP HANA vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

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

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

In-memory key-value store, originally intended for caching

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

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

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

cloud.google.com/datastore

www.kingbase.com.cn

www.memcached.org

www.sap.com/products/hana.html

dbmx.net/tkrzw

Technical documentation

cloud.google.com/datastore/docs

github.com/memcached/memcached/wiki

help.sap.com/hana

Developer

Google

BeiJing KINGBASE Information technologies inc.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

SAP

Mikio Hirabayashi

Initial release

2008

1999

2003

2010

2020

Current release

V8.0, August 2021

1.6.25, March 2024

2.0 SPS07 (April 4, 2023), April 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

BSD license

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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

C and Java

C++

Server operating systems

hosted

Linux
Windows

FreeBSD
Linux
OS X
Unix
Windows

Appliance or cloud-service

Linux
macOS

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

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

SQL

Support of SQL

SQL-like query language (GQL)

Standard with numerous extensions

yes

APIs and other access methods

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

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

Proprietary protocol

JDBC
ODBC

Supported programming languages

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

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

SQLScript, R

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

none

Repcached, a Memcached patch, provides this functionallity

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

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.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

using specific database classes

User concepts

Access control

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

fine grained access rights according to SQL-standard

yes

using SASL (Simple Authentication and Security Layer) protocol

yes

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