DBMS > Hypertable vs. Postgres-XL vs. Spark SQL vs. YTsaurus

System Properties Comparison Hypertable vs. Postgres-XL vs. Spark SQL vs. YTsaurus

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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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Wide column store

Relational DBMS

Document store
Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#340	Overall
	#45	Document stores
	#50	Key-value stores

Website

www.postgres-xl.org

spark.apache.org/sql

ytsaurus.tech

Technical documentation

www.postgres-xl.org/documentation

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

ytsaurus.tech/docs/en

Developer

Hypertable Inc.

Apache Software Foundation

Yandex

Initial release

2009

2014

since 2012, originally named StormDB

2014

2023

Current release

0.9.8.11, March 2016

10 R1, October 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

Open Source

Mozilla public license

Open Source

Apache 2.0

Open Source

Apache License, Version 2.0

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++

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

Linux
macOS

Linux
OS X
Windows

Ubuntu

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.

yes

XML type, but no XML query functionality

Secondary indexes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like DML and DDL statements

YQL, an SQL-based language, is supported

APIs and other access methods

C++ API
Thrift

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Python
R
Scala

C++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

none

yes

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

MVCC

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access Control Lists

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