DBMS > Apache Pinot vs. EsgynDB vs. Greenplum vs. Hypertable vs. SWC-DB

System Properties Comparison Apache Pinot vs. EsgynDB vs. Greenplum vs. Hypertable vs. SWC-DB

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

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Wide column store

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

pinot.apache.org

www.esgyn.cn

greenplum.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.pinot.apache.org

docs.greenplum.org

Developer

Apache Software Foundation and contributors

Esgyn

Pivotal Software Inc.

Hypertable Inc.

Alex Kashirin

Initial release

2015

2005

2009

2020

Current release

1.0.0, September 2023

7.0.0, September 2023

0.9.8.11, March 2016

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Open Source

GNU version 3. Commercial license available

Open Source

GPL V3

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

Java

C++, Java

C++

Server operating systems

All OS with a Java JDK11 or higher

Linux

Linux
OS X
Windows

an inofficial Windows port is available

Linux

Data scheme

yes

schema-free

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

since Version 4.2

Secondary indexes

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

JDBC
ODBC

C++ API
Thrift

Proprietary protocol
Thrift

Supported programming languages

Go
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

C
Java
Perl
Python
R

C++
Java
Perl
PHP
Python
Ruby

C++

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Source-replica replication

selectable replication factor on file system level

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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