This document provides an overview of Cassandra, including:
- Why Cassandra is used for big data applications handling large volumes of data.
- How Cassandra's distributed architecture provides high availability and horizontal scalability.
- Details of Cassandra's write path, including how writes are replicated across nodes and how consistency is ensured.
- Examples of modeling data in Cassandra, including choices for primary keys, clustering columns, and other techniques.
- Common use cases where Cassandra is applicable, such as sensor data, fraud detection, and personalization engines.
NYC Java Meetup - Profiling and PerformanceJason Shao
A brief overview of some of the tools that ship with the Java platform that can be used to troubleshoot performance issues, and common production/performance problems
Paolo Alvarado Customer Support Engineer, Fastly at Altitude 2016
Customer Support Engineer Paolo Alvarado discusses various useful features of advanced Varnish Configuration Language (VCL).
PostgreSQL Portland Performance Practice Project - Database Test 2 HowtoMark Wong
Fourth presentation in a speaker series sponsored by the Portland State University Computer Science Department. The series covers PostgreSQL performance with an OLTP (on-line transaction processing) workload called Database Test 2 (DBT-2). This presentation is a set of examples to go along with the live presentation given on March 12, 2009.
Slides from Secon'2015 - Software Developers Conference. Penza, Russia.
The database is an essential element of any project. The database must be stable and provide good performance. If you plan to use PostgreSQL in your project, you will run into question the choice of operating system. Linux is one of the most popular operating system today. The combination of flexibility and stability makes Linux a good candidate as a platform for PostgreSQL. However, the default settings are suitable for a wide range of workloads. In this report, I will talk about what settings should pay attention and how they affect the performance of PostgreSQL. Which of these settings are more important, and what - no. How do the PostgreSQL more predictable and stable under normal circumstances or in cases of increasing load.
Apache Cassandra is a scalable, fault-tolerant database that has found its way into more than 25% of the Fortune 100 and continues to enjoy significant adoption in the marketplace. In this talk we'll introduce you to Cassandra, explore some of its internals, and discuss CQL (the SQL-like query language for Cassandra). We'll finish by talking about how some companies are using it for services you probably interact with in your daily life. You'll leave with all the tools you need to start exploring Cassandra on your own.
NYC Java Meetup - Profiling and PerformanceJason Shao
A brief overview of some of the tools that ship with the Java platform that can be used to troubleshoot performance issues, and common production/performance problems
Paolo Alvarado Customer Support Engineer, Fastly at Altitude 2016
Customer Support Engineer Paolo Alvarado discusses various useful features of advanced Varnish Configuration Language (VCL).
PostgreSQL Portland Performance Practice Project - Database Test 2 HowtoMark Wong
Fourth presentation in a speaker series sponsored by the Portland State University Computer Science Department. The series covers PostgreSQL performance with an OLTP (on-line transaction processing) workload called Database Test 2 (DBT-2). This presentation is a set of examples to go along with the live presentation given on March 12, 2009.
Slides from Secon'2015 - Software Developers Conference. Penza, Russia.
The database is an essential element of any project. The database must be stable and provide good performance. If you plan to use PostgreSQL in your project, you will run into question the choice of operating system. Linux is one of the most popular operating system today. The combination of flexibility and stability makes Linux a good candidate as a platform for PostgreSQL. However, the default settings are suitable for a wide range of workloads. In this report, I will talk about what settings should pay attention and how they affect the performance of PostgreSQL. Which of these settings are more important, and what - no. How do the PostgreSQL more predictable and stable under normal circumstances or in cases of increasing load.
Apache Cassandra is a scalable, fault-tolerant database that has found its way into more than 25% of the Fortune 100 and continues to enjoy significant adoption in the marketplace. In this talk we'll introduce you to Cassandra, explore some of its internals, and discuss CQL (the SQL-like query language for Cassandra). We'll finish by talking about how some companies are using it for services you probably interact with in your daily life. You'll leave with all the tools you need to start exploring Cassandra on your own.
You've been hearing all the doom and gloom about the first quarter earnings season but is it really that bad? We have all the latest data. These are the 4 top earnings charts this week.
Third normal form? That’s so 20th century. Learn the newest techniques to make your Cassandra database sing from the rafters in performance and scalability. AND it uses concepts that you already know and apply every day. You can do this. This is the must-see half hour of your professional life! These developers found a new way to work with databases. First you will be shocked, then you will be inspired!
DataStax: Old Dogs, New Tricks. Teaching your Relational DBA to fetchDataStax Academy
Do you love some Cassandra, but that relational brain is still on? You aren't alone. Let's take that OLAP data model and get it OLTP. This will be an updated talk with some of the new features brought to you by Cassandra 3.0. Real techniques to translate application patterns into effective models. Common pitfalls that can slow you down and send you running back to RDBMS land. Don't do it! Finally, if you didn't get it right the first time, I'll show you how to fix that data model without any downtime. Turn a hot cup of fail into a tall glass of awesome!
Beyond php - it's not (just) about the codeWim Godden
Most PHP developers focus on writing code. But creating Web applications is about much more than just wrting PHP. Take a step outside the PHP cocoon and into the big PHP ecosphere to find out how small code changes can make a world of difference on servers and network. This talk is an eye-opener for developers who spend over 80% of their time coding, debugging and testing.
Beyond php - it's not (just) about the codeWim Godden
Most PHP developers focus on writing code. But creating Web applications is about much more than just wrting PHP. Take a step outside the PHP cocoon and into the big PHP ecosphere to find out how small code changes can make a world of difference on servers and network. This talk is an eye-opener for developers who spend over 80% of their time coding, debugging and testing.
Declarative benchmarking of cassandra and it's data modelsMonal Daxini
With the Netflix’s large cassandra footprint there are lots of interesting data models both new and evolving and we have different versions of cassandra.
Hence, developing or evolving scalable data models takes iterations in application code, schema and configurations to achieve desired functional and scalability requirements.
I will share use cases and details about how we make it easy for engineers to validate Cassandra data models across versions, and configuration tweaks to assure application scalability.
Beyond the Query – Bringing Complex Access Patterns to NoSQL with DataStax - ...StampedeCon
Learn how to model beyond traditional direct access in Apache Cassandra. Utilizing the DataStax platform to harness the power of Spark and Solr to perform search, analytics, and complex operations in place on your Cassandra data!
This article contains information about performance optimization of Unity3D games for android. Different solutions provided both for CPU and GPU. Also here you can find methodology which will help you to detect performance problems, analyze them and perform appropriate optimization.
Introduction to data modeling with apache cassandraPatrick McFadin
Are you using relational databases and wonder how to get started with data modeling and Apache Cassandra? Here is a starting tour of how to get started. Translating from the knowledge you already have to the knowledge you need to effective with Cassandra development. We cover patterns and anti-patterns. Get going today!
Cassandra Day Chicago 2015: Apache Cassandra Data Modeling 101DataStax Academy
Speaker(s): Patrick McFadin, Chief Evangelist for Apache Cassandra at DataStax
Relational systems have always been built on the premise of modeling relationships. As you will see, static schema, one-to-one, many-to-many still have a place in Cassandra. From the familiar, we’ll go into the specific differences in Cassandra and tricks to make your application fast and resilient.
Speaker(s): Patrick McFadin, Chief Evangelist for Apache Cassandra at DataStax
Relational systems have always been built on the premise of modeling relationships. As you will see, static schema, one-to-one, many-to-many still have a place in Cassandra. From the familiar, we’ll go into the specific differences in Cassandra and tricks to make your application fast and resilient.
Relational systems have always been built on the premise of modeling relationships. As you will see, static schema, one-to-one, many-to-many still have a place in Cassandra. From the familiar, we’ll go into the specific differences in Cassandra and tricks to make your application fast and resilient.
GraphQL, GRPC, REST, WebFlux, OData il existe une multitude de protocoles et formats pour implementer des API au dessus d'une base de donnees comme Cassandra. Chez DataStax nous avons eu l'occasion de toutes les implementer pour tester. Je vous propose un tour d'horizon des différentes solutions avec les pros, les cons et surtout beaucoup de code !
Cassandra Day London 2015: Building Your First Application in Apache CassandraDataStax Academy
Speaker(s): Luke Tillman, Apache Cassandra Language Evangelist at DataStax
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
Event sourcing - what could possibly go wrong ? Devoxx PL 2021Andrzej Ludwikowski
Yet another presentation about Event Sourcing? Yes and no. Event Sourcing is a really great concept. Some could say it’s a Holy Grail of the software architecture. I might agree with that, while remembering that everything comes with a price. This session is a summary of my experience with ES gathered while working on 3 different commercial products. Instead of theoretical aspects, I will focus on possible challenges with ES implementation. What could explode (very often with delayed ignition)? How and where to store events effectively? What are possible schema evolution solutions? How to achieve the highest level of scalability and live with eventual consistency? And many other interesting topics that you might face when experimenting with ES.
Yet another presentation about Event Sourcing? Yes and no. Event Sourcing is a really great concept. Some could say it’s a Holy Grail of the software architecture. I might agree with that, while remembering that everything comes with a price. This session is a summary of my experience with ES gathered while working on 3 different commercial products. Instead of theoretical aspects, I will focus on possible challenges with ES implementation. What could explode (very often with delayed ignition)? How and where to store events effectively? What are possible schema evolution solutions? How to achieve the highest level of scalability and live with eventual consistency? And many other interesting topics that you might face when experimenting with ES.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
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https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Why Mobile App Regression Testing is Critical for Sustained Success_ A Detail...kalichargn70th171
A dynamic process unfolds in the intricate realm of software development, dedicated to crafting and sustaining products that effortlessly address user needs. Amidst vital stages like market analysis and requirement assessments, the heart of software development lies in the meticulous creation and upkeep of source code. Code alterations are inherent, challenging code quality, particularly under stringent deadlines.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
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✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
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#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
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#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
3. Why cassandra?
- BigData!!!
- Volume (petabytes of data, trillions of entities)
- Velocity (real-time, streams, millions of transactions per second)
- Variety (un-, semi-, structured)
- Near-linear horizontal scaling (in proper use cases)
- Fully distributed, with no single point of failure
- Data replication
- By default
33. Immediate vs. Eventual Consistency
- if (writeCL + readCL) > replication_factor then immediate consistency
- writeCL=ALL, readCL=1
- writeCL=1, readCL=ALL
- writeCL,readCL=QUORUM
- If "stale" is measured in milliseconds,
how much are those milliseconds worth?
Node 1
Node 2
Node 3
Node 4
Client
RF=3
34. Modeling - new mindset
- QDD, Query Driven Development
- Nesting is ok
- Duplication is ok
- Writes are cheap
38. QDD - Physical model
- Technology dependent
- Analysis and validation (finding problems)
- Physical optimization (fixing problems)
- Data types
39. Physical storage
- Primary key
- Partition key
CREATE TABLE videos (
id int,
title text,
runtime int,
year int,
PRIMARY KEY (id)
);
id | title | runtime | year
----+---------------------+---------+------
1 | dzien swira | 93 | 2002
2 | chlopaki nie placza | 96 | 2000
3 | psy | 104 | 1992
4 | psy 2 | 96 | 1994
1
title runtime year
dzien swira 93 2002
2
title runtime year
chlopaki... 96 2000
3
title runtime year
psy 104 1992
4
title runtime year
psy 2 96 1994
SELECT FROM videos
WHERE title = ‘dzien swira’
40. Physical storage
CREATE TABLE videos_with_clustering (
title text,
runtime int,
year int,
PRIMARY KEY ((title), year)
);
- Primary key (could be compound)
- Partition key
- Clustering column (order, uniqueness)
title | year | runtime
-------------+------+---------
godzilla | 1954 | 98
godzilla | 1998 | 140
godzilla | 2014 | 123
psy | 1992 | 104
godzilla
1954 runtime
98
1998 runtime
140
2014 runtime
123
1992 runtime
104
psy
SELECT FROM videos_with_clustering
WHERE title = ‘godzilla’
41. Physical storage
CREATE TABLE videos_with_composite_pk(
title text,
runtime int,
year int,
PRIMARY KEY ((title, year))
);
- Primary key (could be compound)
- Partition key (could be composite)
- Clustering column (order, uniqueness)
title | year | runtime
-------------+------+---------
godzilla | 1954 | 98
godzilla | 1998 | 140
godzilla | 2014 | 123
psy | 1992 | 104
godzilla:1954
runtime
93
godzilla:1998
runtime
140
godzilla:2014
runtime
123
psy:1992
runtime
104
SELECT FROM videos_with_composite_pk
WHERE title = ‘godzilla’
AND year = 1954
42. Modeling - clustering column(s)
Q: Retrieve videos an actor has appeared in (newest first).
43. Modeling - clustering column(s)
CREATE TABLE videos_by_actor (
actor text,
added_date timestamp,
video_id timeuuid,
character_name text,
description text,
encoding frozen<video_encoding>,
tags set<text>,
title text,
user_id uuid,
PRIMARY KEY ( )
) WITH CLUSTERING ORDER BY ( );
Q: Retrieve videos an actor has appeared in (newest first).
44. Modeling - clustering column(s)
CREATE TABLE videos_by_actor (
actor text,
added_date timestamp,
video_id timeuuid,
character_name text,
description text,
encoding frozen<video_encoding>,
tags set<text>,
title text,
user_id uuid,
PRIMARY KEY ((actor), added_date)
) WITH CLUSTERING ORDER BY (added_date desc);
Q: Retrieve videos an actor has appeared in (newest first).
45. Modeling - clustering column(s)
CREATE TABLE videos_by_actor (
actor text,
added_date timestamp,
video_id timeuuid,
character_name text,
description text,
encoding frozen<video_encoding>,
tags set<text>,
title text,
user_id uuid,
PRIMARY KEY ((actor), added_date, video_id)
) WITH CLUSTERING ORDER BY (added_date desc);
Q: Retrieve videos an actor has appeared in (newest first).
46. Modeling - clustering column(s)
CREATE TABLE videos_by_actor (
actor text,
added_date timestamp,
video_id timeuuid,
character_name text,
description text,
encoding frozen<video_encoding>,
tags set<text>,
title text,
user_id uuid,
PRIMARY KEY ((actor), added_date, video_id, character_name)
) WITH CLUSTERING ORDER BY (added_date desc);
Q: Retrieve videos an actor has appeared in (newest first).
47. Modeling - compound partition key
CREATE TABLE temperature_by_day (
weather_station_id text,
date text,
event_time timestamp,
temperature text
PRIMARY KEY ( )
) WITH CLUSTERING ORDER BY ( );
Q: Retrieve last 1000 measurement from given day.
48. Modeling - compound partition key
CREATE TABLE temperature_by_day (
weather_station_id text,
date text,
event_time timestamp,
temperature text
PRIMARY KEY ((weather_station_id), date, event_time)
) WITH CLUSTERING ORDER BY (event_time desc);
Q: Retrieve last 1000 measurement from given day.
1 day = 86 400 rows
1 week = 604 800 rows
1 month = 2 592 000 rows
1 year = 31 536 000 rows
49. Modeling - compound partition key
CREATE TABLE temperature_by_day (
weather_station_id text,
date text,
event_time timestamp,
temperature text
PRIMARY KEY ((weather_station_id, date), event_time)
) WITH CLUSTERING ORDER BY (event_time desc);
Q: Retrieve last 1000 measurement from given day.
50. Modeling - TTL
CREATE TABLE temperature_by_day (
weather_station_id text,
date text,
event_time timestamp,
temperature text
PRIMARY KEY ((weather_station_id, date), event_time)
) WITH CLUSTERING ORDER BY (event_time desc);
Retention policy - keep data only from last week.
INSERT INTO temperature_by_day … USING TTL 604800;
51. Modeling - bit map index
CREATE TABLE car (
year timestamp,
model text,
color timestamp,
vehicle_id int,
//other columns
PRIMARY KEY ((year, model, color), vehicle_id)
);
Q: Find car by year and/or model and/or color.
INSERT INTO car (year, model, color, vehicle_id, ...) VALUES (2000, 'Multipla', 'blue', 13, ...);
INSERT INTO car (year, model, color, vehicle_id, ...) VALUES (2000, 'Multipla', '', 13, ...);
INSERT INTO car (year, model, color, vehicle_id, ...) VALUES (2000, '', 'blue', 13, ...);
INSERT INTO car (year, model, color, vehicle_id, ...) VALUES (2000, '', '', 13, ...);
INSERT INTO car (year, model, color, vehicle_id, ...) VALUES ('', 'Multipla', 'blue', 13, ...);
INSERT INTO car (year, model, color, vehicle_id, ...) VALUES ('', 'Multipla', '', 13, ...);
INSERT INTO car (year, model, color, vehicle_id, ...) VALUES ('', '', 'blue', 13, ...);
SELECT * FROM car WHERE year=2000 and model=’’ and color=’blue’;
52. Modeling - wide rows
CREATE TABLE user (
email text,
name text,
age int,
PRIMARY KEY (email)
);
Q: Find user by email.
53. Modeling - wide rows
CREATE TABLE user (
domain text,
user text,
name text,
age int,
PRIMARY KEY ((domain), user)
);
Q: Find user by email.
54. Modeling - versioning with lightweight transactions
CREATE TABLE document (
id text,
content text,
version int,
locked_by text,
PRIMARY KEY ((id))
);
INSERT INTO document (id, content , version ) VALUES ( 'my doc', 'some content', 1)
IF NOT EXISTS;
UPDATE document SET locked_by = 'andrzej' WHERE id = 'my doc' IF locked_by = null;
UPDATE document SET content = 'better content', version = 2, locked_by = null
WHERE id = 'my doc' IF locked_by = 'andrzej';
55. Modeling - JSON with UDT and tuples
{
"title": "Example Schema",
"type": "object",
"properties": {
"firstName": “andrzej”,
"lastName": “ludwikowski”,
"age": {
"description": "Age in years",
"type": "integer",
"minimum": 0
}
},
“x_dimension”: “1”,
“y_dimension”: “2”,
}
CREATE TYPE age (
description text,
type int,
minimum int
);
CREATE TYPE prop (
firstName text,
lastName text,
age frozen <age>
);
CREATE TABLE json (
title text,
type text,
properties list<frozen <prop>>,
dimensions tuple<int, int>
PRIMARY KEY (title)
);
56. Common use cases
- Sensor data (Zonar)
- Fraud detection (Barracuda)
- Playlist and collections (Spotify)
- Personalization and recommendation engines (Ebay)
- Messaging (Instagram)