DBMS > GeoSpock vs. HyperSQL vs. Postgres-XL vs. Sequoiadb vs. Spark SQL

System Properties Comparison GeoSpock vs. HyperSQL vs. Postgres-XL vs. Sequoiadb vs. Spark SQL

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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

NewSQL database with distributed OLTP and SQL

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

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

geospock.com

Technical documentation

hsqldb.org/web/hsqlDocsFrame.html

www.postgres-xl.org/documentation

www.sequoiadb.com/en/index.php?m=Files&a=index

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

Developer

GeoSpock

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2001

2014

since 2012, originally named StormDB

2013

2014

Current release

2.0, September 2019

2.7.2, June 2023

10 R1, October 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

Mozilla public license

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, Javascript

Java

C++

Scala

Server operating systems

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
macOS

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

distributed, parallel query execution

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC

HTTP API

JDBC via HTTP
JDBC
ODBC

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

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC
Java

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

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java, SQL

user defined functions

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

horizontal partitioning

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

Document is locked during a transaction

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

Access rights for users can be defined per table

fine grained access rights according to SQL-standard

simple password-based access control

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More resources

Recent citations in the news

How GeoSpock is supercharging geospatial analytics
23 February 2021, ComputerWeekly.com

nChain leads investment round in extreme-scale data firm GeoSpock
2 October 2020, CoinGeek

Smart Cities, Autonomous Vehicles, Artificial General Intelligence Robotics: Q&A with Steve Marsh, GeoSpock
16 May 2018, ExchangeWire

GeoSpock’s extreme-scale data mission in $5.4m funding boost
8 October 2020, Cambridge Independent

UK-based database GeoSpock bags $5.4m, to expand into
6 October 2020, Tech in Asia

provided by Google News

Introduction to JDBC with HSQLDB tutorial
14 November 2022, TheServerSide.com

HyperSQL DataBase flaw leaves library vulnerable to RCE
24 October 2022, The Daily Swig

provided by Google News

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

Performance Insights from Sigma Rule Detections in Spark Streaming
1 June 2024, Towards Data Science

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

provided by Google News

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