DBMS > LeanXcale vs. SAP HANA vs. SpaceTime vs. TimesTen

System Properties Comparison LeanXcale vs. SAP HANA vs. SpaceTime vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

SpaceTime is a spatio-temporal DBMS with a focus on performance.

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	0.28
Rank	#286	Overall
	#41	Key-value stores
	#130	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.leanxcale.com

www.sap.com/products/hana.html

www.mireo.com/spacetime

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

help.sap.com/hana

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

LeanXcale

SAP

Mireo

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

2010

2020

1998

Current release

2.0 SPS07 (April 4, 2023), April 2023

Release 22.1

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

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++

Server operating systems

Appliance or cloud-service

Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

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

through Apache Derby

yes

A subset of ANSI SQL is implemented

yes

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

RESTful HTTP API

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

C
Java
Scala

C#
C++
Python

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

SQLScript, R

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Fixed-grid hypercubes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Real-time block device replication (DRBD)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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