DBMS > Hypertable vs. SAP HANA vs. Spark SQL vs. Yaacomo

System Properties Comparison Hypertable vs. SAP HANA vs. Spark SQL vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

An open source BigTable implementation based on distributed file systems such as Hadoop

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Wide column store

Relational DBMS

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	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.sap.com/products/hana.html

spark.apache.org/sql

yaacomo.com

Technical documentation

help.sap.com/hana

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Hypertable Inc.

SAP

Apache Software Foundation

Q2WEB GmbH

Initial release

2009

2010

2014

2009

Current release

0.9.8.11, March 2016

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Apache 2.0

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++

Scala

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

Appliance or cloud-service

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

C++ API
Thrift

JDBC
ODBC

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

yes

fine grained access rights according to SQL-standard

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