DBMS > Hypertable vs. Spark SQL vs. SQLite vs. Transbase

System Properties Comparison Hypertable vs. Spark SQL vs. SQLite vs. Transbase

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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.

Description

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

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

Widely used embeddable, in-process RDBMS

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Wide column store

Relational DBMS

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

Score	20.06
Rank	#36	Overall
	#22	Relational DBMS

Score	131.49
Rank	#10	Overall
	#7	Relational DBMS

Score	0.39
Rank	#287	Overall
	#131	Relational DBMS

Website

spark.apache.org/sql

www.sqlite.org

www.transaction.de/en/products/transbase.html

Technical documentation

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

www.sqlite.org/docs.html

www.transaction.de/en/products/transbase/features.html

Developer

Hypertable Inc.

Apache Software Foundation

Dwayne Richard Hipp

Transaction Software GmbH

Initial release

2009

2014

2000

1987

Current release

0.9.8.11, March 2016

3.3.0 ( 2.13), June 2022

3.40.1 (28 December 2022), December 2022

Transbase 8.4, September 2020

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

Open Source

Apache 2.0

Open Source

Public Domain

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

C and C++

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Windows

server-less

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

APIs and other access methods

C++ API
Thrift

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

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

yes

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

fine grained access rights according to SQL-standard

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