DBMS > Axibase vs. Google Cloud Datastore vs. MaxDB vs. Memcached vs. SAP HANA

System Properties Comparison Axibase vs. Google Cloud Datastore vs. MaxDB vs. Memcached vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

A robust and reliable RDBMS optimized to run all major SAP solutions

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

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

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

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

axibase.com/docs/atsd/finance

cloud.google.com/datastore

maxdb.sap.com

www.memcached.org

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/datastore/docs

maxdb.sap.com/documentation

github.com/memcached/memcached/wiki

help.sap.com/hana

Developer

Axibase Corporation

Google

SAP, acquired from Software AG (Adabas-D) in 1997

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

SAP

Initial release

2013

2008

1984

2003

2010

Current release

15585

7.9.10.12, February 2024

1.6.27, May 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Limited community edition free

Open Source

BSD license

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

Java

C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
OS X
Unix
Windows

Appliance or cloud-service

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
WebDAV

Proprietary protocol

JDBC
ODBC

Supported programming languages

Go
Java
PHP
Python
R
Ruby

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

.Net
C#
Java
Perl
PHP
Python

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

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

SQLScript, R

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

Source-replica replication

none

Repcached, a Memcached patch, provides this functionallity

yes

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

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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