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commit aa8a03c78017c04924f6dbfd29c36175f24d0f19
Author: Paul Thomas Au <p...@traverstodd.com>
AuthorDate: Mon Feb 5 14:20:58 2024 -0800
blog post mathematical cql function in cassandra 5.0
patch by Paul Thomas Au; reviewed by Mick Semb Wever for CASSANDRA-19372
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- Replace post tile, date, description and link to you post.
////
+//start card
+[openblock,card shadow relative test]
+----
+[openblock,card-header]
+------
+[discrete]
+=== Apache Cassandra 5.0 Features: Mathematical CQL Functions
+[discrete]
+==== February 5, 2024
+------
+[openblock,card-content]
+------
+Apache Cassandra excels in scalable data handling with powerful CQL math
functions. From basic arithmetic to advanced stats, it enables efficient,
real-time processing and supports diverse domains, enhancing overall
application performance.
+[openblock,card-btn card-btn--blog]
+--------
+[.btn.btn--alt]
+xref:blog/Apache-Cassandra-5.0-Features-Mathematical-CQL-Functions.adoc[Read
More]
+--------
+
+------
+----
+//end card
+
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+= Apache Cassandra 5.0 Features: Mathematical CQL Functions
+:page-layout: single-post
+:page-role: blog-post
+:page-post-date: Feb 5, 2024
+:page-post-author: The Apache Cassandra Community
+:description:
+:keywords:
+
+__Apache Cassandra 5.0 is the project’s major release for 2023, and it
promises some of the biggest changes for Cassandra to date. After more than a
decade of engineering work dedicated to stabilizing and building Cassandra as a
distributed database, we now look forward to introducing a host of exciting
features and enhancements that empower users to take their data-driven
applications to the next level - including machine learning and artificial
intelligence.__
+
+__This blog series aims to give a deeper dive into some of the key features of
Cassandra 5.0.__
+
+Apache Cassandra is renowned for its ability to handle massive amounts of data
across multiple nodes with unparalleled scalability. But one of its
lesser-known yet powerful features lies in its support for
https://cassandra.apache.org/doc/stable/cassandra/cql/operators.html[mathematical
functions^] within the Cassandra Query Language (CQL). These functions bring a
data manipulation and analysis capability, allowing developers to perform
complex mathematical operations directly within the [...]
+
+From basic arithmetic operations to advanced statistical calculations,
Cassandra's CQL functions empower users to perform computations at the database
level, reducing the need for data extraction and processing on the application
side. Apache Cassandra's mathematical CQL functions provide several benefits,
including:
+
+
+=== Efficient Data Processing
+
+Performing mathematical operations directly within Cassandra at the
coordinator level reduces the need to transfer large volumes of data to the
application layer for processing. This improves overall efficiency by
minimizing data movement across the network.
+
+=== Scalability
+
+Cassandra is designed to scale horizontally by adding more nodes to the
cluster. Mathematical CQL functions can be executed across distributed nodes,
allowing for parallel processing of computations using partition keys. This
scalability is crucial for handling large datasets and accommodating growing
workloads.
+
+=== Reduced Data Transfer Overhead
+
+By executing mathematical operations at the database level, only the results
need to be transferred to the application, reducing the amount of data
transmitted over the network. This is particularly advantageous in distributed
environments where minimizing data transfer can significantly improve
performance.
+
+=== Enhanced Real-time Processing
+
+Cassandra is known for its ability to handle real-time data. The availability
of mathematical functions allows for on-the-fly calculations, enabling
real-time processing of data within the database as it is inserted and updated.
This is especially valuable in applications where low-latency responses are
critical.
+
+=== Support for Diverse Domains
+
+The range of mathematical functions in Cassandra caters to diverse domains.
Whether working with financial data, scientific measurements, or spatial data,
these functions provide a versatile toolkit for handling various types of
numerical operations, making Cassandra applicable to a wide array of use cases.
+
+=== Consistent Data Model
+
+Mathematical CQL functions adhere to Cassandra's consistency model. When a
query is executed, the data is read from the number of replicas required to
satisfy the consistency requested by the user. As a result, data used in
mathematical operations match existing Cassandra consistency guarantees,
ensuring data integrity and reliability in a distributed environment.
+
+=== Mathematical and Advanced Functions
+
+Cassandra supports fundamental mathematical functions such as ABS() for
absolute values, ROUND() for rounding numbers, and SQRT() for square roots as
well as advanced mathematical functions like trigonometric operations (e.g.,
SIN(), COS(), TAN()). These functions expand Cassandra’s utility in
applications dealing with spatial data or scenarios requiring complex
mathematical computations - and all within the database itself.
+
+Including mathematical CQL functions in Apache Cassandra provides developers
with a powerful set of tools to handle numerical operations efficiently,
enabling scalable and real-time data processing within the distributed database
environment. These functions contribute to the overall performance,
flexibility, and versatility of applications built on the Cassandra platform.
+
+== Learn More About Apache Cassandra
+
+For more information about Apache Cassandra or to join the community
discussion, you can join us on these channels:
+
+* https://cassandra.apache.org/_/index.html[Apache Cassandra Website]
+* https://the-asf.slack.com/ssb/redirect[ASF Slack^]
+* https://www.youtube.com/@PlanetCassandra[Planet Cassandra Youtube^]
+* https://www.meetup.com/cassandra-global/[Planet Cassandra Global Meetup
Group^]
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