DBMS > Google BigQuery vs. Memcached vs. openGauss vs. SAP HANA

System Properties Comparison Google BigQuery vs. Memcached vs. openGauss vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	1.06
Rank	#184	Overall
	#84	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

cloud.google.com/bigquery

www.memcached.org

gitee.com/opengauss
opengauss.org

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/bigquery/docs

github.com/memcached/memcached/wiki

docs.opengauss.org/en
gitee.com/opengauss/docs

help.sap.com/hana

Developer

Google

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Huawei and openGauss community

SAP

Initial release

2010

2003

2019

2010

Current release

1.6.27, May 2024

3.0, March 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

BSD license

Open Source

commercial

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, C++, Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Unix
Windows

Linux

Appliance or cloud-service

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL 2011

yes

APIs and other access methods

RESTful HTTP/JSON API

Proprietary protocol

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash)

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Repcached, a Memcached patch, provides this functionallity

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

using SASL (Simple Authentication and Security Layer) protocol

Access rights for users, groups and roles according to SQL-standard

yes

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