DBMS > Cassandra vs. HyperSQL vs. PlanetScale vs. Spark SQL

System Properties Comparison Cassandra vs. HyperSQL vs. PlanetScale vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Scalable, distributed, serverless MySQL database platform built on top of Vitess

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

Primary database model

Wide column store

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

Document store
Spatial DBMS

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

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

cassandra.apache.org/doc/latest

hsqldb.org/web/hsqlDocsFrame.html

planetscale.com/docs

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

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

PlanetScale

Apache Software Foundation

Initial release

2008

2001

2020

2014

Current release

4.1.3, July 2023

2.7.2, June 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

based on BSD license

commercial

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Scala

Server operating systems

BSD
Linux
OS X
Windows

All OS with a Java VM

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

Docker
Linux
macOS

Linux
OS X
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 equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

JDBC
ODBC

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java, SQL

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 object

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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News

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11 June 2024

Introducing Tejas Kumar, Developer Relations Engineer
7 June 2024

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5 June 2024

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3 June 2024

Focus on Creativity, Not Clusters: DataStax Mission Control in Action!
30 May 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.