#datastructure — Public Fediverse posts

🎩✨ Behold the majestic skiplist: the data structure nobody asked for but everyone pretends to need! 🤡 This article bravely navigates a sea of #buzzwords and sales pitches, leaving readers wondering if the true purpose of skiplists is to skip the point entirely. 🚀
https://antithesis.com/blog/2026/skiptrees/ #skiplist #datastructure #techhumor #programming #satire #HackerNews #ngated

🎩✨ Behold the majestic skiplist: the data structure nobody asked for but everyone pretends to need! 🤡 This article bravely navigates a sea of #buzzwords and sales pitches, leaving readers wondering if the true purpose of skiplists is to skip the point entirely. 🚀
https://antithesis.com/blog/2026/skiptrees/ #skiplist #datastructure #techhumor #programming #satire #HackerNews #ngated

[New Blog Post] Alpha Equivalent Hash Consing with Thinnings https://www.philipzucker.com/thin_hash_cons_codebruijn/ #logic #datastructure @pigworker

[New Blog Post] Alpha Equivalent Hash Consing with Thinnings https://www.philipzucker.com/thin_hash_cons_codebruijn/ #logic #datastructure @pigworker

My memory isn't getting worse, it's just using exponential decay - by Matthew Kim

https://chunkofcoal.com/posts/exponential-decay/

#WebAssembly #WASM #DataStructure #Algorithm #Algorithms #Rust #RustLang

My memory isn't getting worse, it's just using exponential decay - by Matthew Kim

https://chunkofcoal.com/posts/exponential-decay/

#WebAssembly #WASM #DataStructure #Algorithm #Algorithms #Rust #RustLang

Index, Count, Offset, Size

https://tigerbeetle.com/blog/2026-02-16-index-count-offset-size/

#HackerNews #Index #Count #Offset #Size #DataStructure #Programming

Index, Count, Offset, Size

https://tigerbeetle.com/blog/2026-02-16-index-count-offset-size/

#HackerNews #Index #Count #Offset #Size #DataStructure #Programming

La matematica delle strutture dati racconta una verità semplice: non esiste un modo perfetto per archiviare informazioni.

Ogni sistema è un compromesso tra velocità, memoria e ordine. A volte serve struttura, altre volte un po’ di disordine funziona meglio. Un equilibrio dinamico, in cui l’efficienza nasce proprio dalla varietà delle soluzioni.

https://www.quantamagazine.org/why-theres-no-single-best-way-to-store-information-20260116/

#computerscience #datastructure #data #algorithm #algoritmi #informatica

📜 Discover the Fascinating History of Arrays!

💡 Arrays are everywhere today - from simple algorithms of searching and sorting to technologies like GPUs and AI models.

❓But, one may wonder - what was it like when they were first used?

🔎 Want to know the story behind it?

👉 Check out the article titled: "Story of First Array"
🔗 https://priyabrata-paul-blog.hashnode.dev/story-of-the-first-array

#Science #Computing #DataStructure #Array #History

Matching Algorithm with Recursively Implemented StorAge (MARISA) is a space-efficient, fairly fast, and static trie data structure. MARISA serves as a dictionary structure, and by definition, it supports exact match lookup, which is the basic operation of dictionary. In addition, MARISA supports reverse lookup, common prefix search, and predictive search.

Thanks to @terrtia for the discovery.

#datastructure #computerscience

🔗 https://www.s-yata.jp/marisa-trie/docs/readme.en.html

Matching Algorithm with Recursively Implemented StorAge (MARISA) is a space-efficient, fairly fast, and static trie data structure. MARISA serves as a dictionary structure, and by definition, it supports exact match lookup, which is the basic operation of dictionary. In addition, MARISA supports reverse lookup, common prefix search, and predictive search.

Thanks to @terrtia for the discovery.

#datastructure #computerscience

🔗 https://www.s-yata.jp/marisa-trie/docs/readme.en.html

Grmbl.😩

Implementing an object graph with with loops.

After setup, it needs additional initialization.

The fields getting set will not change again. Ideally would be final (Java, or readonly in TypeScript). And I don't want an "if (bla!=null)" where ever access I them.

In my ideal solution this would be guarded by the type system where the nodes, once initialized, change type. I know roughly how it could be done, but its weird and cumbersome. 😕

#Java #final #datastructure #strongTyping

Grmbl.😩

Implementing an object graph with with loops.

After setup, it needs additional initialization.

The fields getting set will not change again. Ideally would be final (Java, or readonly in TypeScript). And I don't want an "if (bla!=null)" where ever access I them.

In my ideal solution this would be guarded by the type system where the nodes, once initialized, change type. I know roughly how it could be done, but its weird and cumbersome. 😕

#Java #final #datastructure #strongTyping

Hey Mastodon! 👋 I'm diving back into my DSA journey and want to move beyond just solving theoretical problems. I'm looking to build some real-world projects to apply what I'm learning.

Any project ideas where you can genuinely use DSA concepts? Things like a simple recommendation engine, a social network graph, or a pathfinding visualizer. I'm curious about how you would implement these.

#devcommunity #DSA #Projects #Programming #Tech #algorithm #datastructure

Visualize your Python data structures with just one click.
Hash Set: https://memory-graph.com/#codeurl=https://raw.githubusercontent.com/bterwijn/memory_graph/refs/heads/main/src/hash_set.py&breakpoints=32&continues=1&timestep=0.5&play

#python #programming #memory_graph #DataStructure

Visualize your Python data structure with just one click.
Linked List: https://memory-graph.com/#codeurl=https://raw.githubusercontent.com/bterwijn/memory_graph/refs/heads/main/src/linked_list.py&timestep=0.2&play

#Python #memory_graph #DataStructure

Understanding and debugging Data Structures is easier when you can see the structure of your data using memory_graph: https://github.com/bterwijn/memory_graph

Here we show values being added to a Linked List in Cursor AI. When adding the last value '5' we "Step Into" the code to show more of the details: https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/linked_list.gif

🎥 See the Quick Intro video for the VS Code integration: https://youtu.be/23_bHcr7hqo

#Python #LinkedList #DataStructure #memory_graph #debug

Understanding and debugging Data Structures is easier when you can see the structure of your data using memory_graph: https://github.com/bterwijn/memory_graph

In this example we show values being inserted in a Binary Tree. When inserting the last value '29' we "Step Into" the code to show the recursive implementation: https://shorturl.at/bx848

🎥 See the Quick Intro video for the VS Code integration: https://youtu.be/23_bHcr7hqo

#Python #BinaryTree #Tree #DataStructure #memory_graph #debug

Understanding and debugging Data Structures is easier when you can see the structure of your data using memory_graph: https://github.com/bterwijn/memory_graph

In this example we show values being inserted in a Binary Tree. When inserting the last value '29' we "Step Into" the code to show the recursive implementation: https://shorturl.at/bx848

🎥 See the Quick Intro video for the VS Code integration: https://youtu.be/23_bHcr7hqo

#Python #BinaryTree #Tree #DataStructure #memory_graph #debug

Reflecting some more on the Sketchpad & ECS parts of this talk: SideFX Houdini organizes all geometry data in similar vertical silos of points, vertices, edges, faces, prims, each with component IDs, each with its own group of native and user-defined attribs, and with similar powerful "omniscient" visibility/access from anywhere. That structure makes VEX SOPs akin to "systems" in an ECS setup and the handling/scripting itself very fun & powerful. The GUI also provides spreadsheet views of the geometry (again similar to e.g. what FLECS provides for debugging). Considering the age of Houdini, I think this approach is notable...

Blender's BMesh Radial Mesh implementation[1] is more traditional OOP structured, but the core idea of "discs" (aka bi-directional circular lists) of pointers to vertices & edges now seems somewhat relevant to some Sketchpad ideas too. Also a reminder that I really need to find/make time to update & release my own mesh implementation (from 2018) combining ideas from both Houdini & BMesh... It's already been a year (again) since I last talked about & touched it... 😱

[1] https://developer.blender.org/docs/features/objects/mesh/bmesh/

#Blender #Houdini #Mesh #Geometry #DataStructure #ECS #Sketchpad

Reflecting some more on the Sketchpad & ECS parts of this talk: SideFX Houdini organizes all geometry data in similar vertical silos of points, vertices, edges, faces, prims, each with component IDs, each with its own group of native and user-defined attribs, and with similar powerful "omniscient" visibility/access from anywhere. That structure makes VEX SOPs akin to "systems" in an ECS setup and the handling/scripting itself very fun & powerful. The GUI also provides spreadsheet views of the geometry (again similar to e.g. what FLECS provides for debugging). Considering the age of Houdini, I think this approach is notable...

Blender's BMesh Radial Mesh implementation[1] is more traditional OOP structured, but the core idea of "discs" (aka bi-directional circular lists) of pointers to vertices & edges now seems somewhat relevant to some Sketchpad ideas too. Also a reminder that I really need to find/make time to update & release my own mesh implementation (from 2018) combining ideas from both Houdini & BMesh... It's already been a year (again) since I last talked about & touched it... 😱

[1] https://developer.blender.org/docs/features/objects/mesh/bmesh/

#Blender #Houdini #Mesh #Geometry #DataStructure #ECS #Sketchpad

At first glance, bar charts might seem like a simple visualization type. But with a little creativity, they can be enhanced in countless ways to reveal deeper insights and make your data shine.

The attached visual highlights a variety of bar chart styles to inspire your work.

Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #data #tidyverse #rstats #package #datasciencetraining

At first glance, bar charts might seem like a simple visualization type. But with a little creativity, they can be enhanced in countless ways to reveal deeper insights and make your data shine.

The attached visual highlights a variety of bar chart styles to inspire your work.

Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #data #tidyverse #rstats #package #datasciencetraining

I recently made a very popular LinkedIn post about Simpson's Paradox, which resulted in an engaging conversation. Paul Julian made a great comment on the relationship between Mixed Effects Models and Simpson's Paradox that I wanted to share with you.

In the plot below (generated from reproducible code – thanks, Paul!), you can see how different models compare:

Original post: https://www.linkedin.com/posts/joachim-schork_coding-bigdata-rprogramming-activity-7242865910405824512-oNmF/

Further details: http://eepurl.com/gH6myT

#datavisualization #database #datastructure #data

I recently made a very popular LinkedIn post about Simpson's Paradox, which resulted in an engaging conversation. Paul Julian made a great comment on the relationship between Mixed Effects Models and Simpson's Paradox that I wanted to share with you.

In the plot below (generated from reproducible code – thanks, Paul!), you can see how different models compare:

Original post: https://www.linkedin.com/posts/joachim-schork_coding-bigdata-rprogramming-activity-7242865910405824512-oNmF/

Further details: http://eepurl.com/gH6myT

#datavisualization #database #datastructure #data

Using dplyr and ggplot2 in R can significantly streamline your data analysis process, making it easier to work with complex data sets.

I have created a video tutorial in collaboration with Albert Rapp, where I demonstrate how to do this in practice: https://www.youtube.com/watch?v=EKISB0gnue4

#coding #datavisualization #rprogramming #dataviz #statisticalanalysis #package #datastructure #ggplot2 #bigdata #tidyverse

Using dplyr and ggplot2 in R can significantly streamline your data analysis process, making it easier to work with complex data sets.

I have created a video tutorial in collaboration with Albert Rapp, where I demonstrate how to do this in practice: https://www.youtube.com/watch?v=EKISB0gnue4

#coding #datavisualization #rprogramming #dataviz #statisticalanalysis #package #datastructure #ggplot2 #bigdata #tidyverse

🟪 Harnessing the Power of Data Structure to Build Resilient Power Apps

Too often, Power Apps makers jump into app building—without giving enough thought to data structure. In Ep. 55 of #PowerTalks, Griffin Lickfeldt from Citizen Developer explains why getting this step right from the start is critical for building scalable, secure, and AI-ready apps.

💡 The Superpower of Dataverse
🔍 Strategic Table Structuring
⚖️ Future-Proof Your Apps
🔒 Security as a Cornerstone

▶︎ https://www.hubsite365.com/en-ww/citizen-developer/?id=33ec151c-5941-f011-8779-002248826e75&topic=8daf8386-bb75-ea11-a811-000d3a210788&theater=true

#DataStructure #PowerApps #CopilotStudio #CitizenDeveloper

🟪 Harnessing the Power of Data Structure to Build Resilient Power Apps

Too often, Power Apps makers jump into app building—without giving enough thought to data structure. In Ep. 55 of #PowerTalks, Griffin Lickfeldt from Citizen Developer explains why getting this step right from the start is critical for building scalable, secure, and AI-ready apps.

💡 The Superpower of Dataverse
🔍 Strategic Table Structuring
⚖️ Future-Proof Your Apps
🔒 Security as a Cornerstone

▶︎ https://www.hubsite365.com/en-ww/citizen-developer/?id=33ec151c-5941-f011-8779-002248826e75&topic=8daf8386-bb75-ea11-a811-000d3a210788&theater=true

#DataStructure #PowerApps #CopilotStudio #CitizenDeveloper

Mean imputation is a common method for handling missing values in numerical data. It replaces missing values with the mean of the observed values, ensuring the data set remains complete and easy to use.

The image below illustrates the impact of mean imputation. The black line represents the original data distribution before imputation, while the red line shows the data distribution after imputation.

Tutorial: https://statisticsglobe.com/mean-imputation-for-missing-data/

Newsletter: http://eepurl.com/gH6myT

#datastructure

Mean imputation is a common method for handling missing values in numerical data. It replaces missing values with the mean of the observed values, ensuring the data set remains complete and easy to use.

The image below illustrates the impact of mean imputation. The black line represents the original data distribution before imputation, while the red line shows the data distribution after imputation.

Tutorial: https://statisticsglobe.com/mean-imputation-for-missing-data/

Newsletter: http://eepurl.com/gH6myT

#datastructure

🧵 Thread on sorting algorithms.

Starting with #StupidSort 🤪, an inefficient algorithm that sorts by randomly shuffling until the list is ordered.

(1/3) ⬇️

#computerscience #algorithm #sortingalgorithm #datastructure #coding #programming #software #softwaredevelopment #bigo #learntocode #codenewbie

Shoutout to my friend Daniel, who is not on mastodon. He created an uber efficient slice implementation for joining/modifying/copying lists at scale. The blogpost and github README describe the algorithm. It's an interesting technical read if you have 10-20 minutes of spare time.

https://daniel.avery.io/writing/fork-join-data-structures

#code #algorithm #datastructure #random

Shoutout to my friend Daniel, who is not on mastodon. He created an uber efficient slice implementation for joining/modifying/copying lists at scale. The blogpost and github README describe the algorithm. It's an interesting technical read if you have 10-20 minutes of spare time.

https://daniel.avery.io/writing/fork-join-data-structures

#code #algorithm #datastructure #random

Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/

More details are available at this link: http://eepurl.com/gH6myT

#research #datastructure #businessanalyst #data

Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/

More details are available at this link: http://eepurl.com/gH6myT

#research #datastructure #businessanalyst #data

Designing an Efficient Tree Index on Disaggregated Memory

https://cacm.acm.org/research-highlights/designing-an-efficient-tree-index-on-disaggregated-memory/

#computing #datastructure #algorithm #software

Designing an Efficient Tree Index on Disaggregated Memory

https://cacm.acm.org/research-highlights/designing-an-efficient-tree-index-on-disaggregated-memory/

#computing #datastructure #algorithm #software

gganimate is a powerful extension for ggplot2 that transforms static visualizations into dynamic animations. By adding a time dimension, it allows you to illustrate trends, changes, and patterns in your data more effectively.

The attached animated visualization, which I created with gganimate, showcases a ranked bar chart of the top 3 countries for each year based on inflation since 1980.

More information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #datavisualization #tidyverse #ggplot2

Visualizing gene structures in R? gggenes, an extension of ggplot2, simplifies the process of creating clear and informative gene diagrams, making genomic data easier to interpret and share.

Visualization: https://cran.r-project.org/web/packages/gggenes/vignettes/introduction-to-gggenes.html

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #datavisualization #dataanalytics #data #tidyverse #datascientists #ggplot2

Red Green Syntax Trees - an Overview | by Will Speak (aka Plingdollar):

https://willspeak.me/2021/11/24/red-green-syntax-trees-an-overview.html

#Parser #Compiler #DataStructure

@FizzyOrange

Wow, this crate looks like the most feature-rich tree crate I've ever seen!

It seems very underrated (only ~1000 downloads and one star on GitHub (by me)).

Thank you for the suggestion!😊

#Rust #RustLang #DataStructure #Tree #Algorithms

#ReleaseMonday — One of the recent (already very useful!) new package additions to #ThingUmbrella is:

https://thi.ng/leaky-bucket

Leaky buckets are commonly used in communication networks for rate limiting, traffic shaping and bandwidth control, but are equally useful in other domains requiring similar constraints.

A Leaky Bucket is a managed counter with an enforced maximum value (i.e. bucket capacity). The counter is incremented for each a new event to check if it can/should be processed. If the bucket capacity has already been reached, the bucket will report an overflow, which we can then handle accordingly (e.g. by dropping or queuing events). The bucket also has a configurable time interval at which the counter is decreasing (aka the "leaking" behavior) until it reaches zero again (i.e. until the bucket is empty). Altogether, this setup can be utilized to ensure both an average rate, whilst also supporting temporary bursting in a controlled fashion...

Related, I've also updated/simplified the rate limiter interceptor in https://thi.ng/server to utilize this new package...

#ThingUmbrella #DataStructure #RateLimiting #OpenSource #TypeScript #JavaScript

#Development #Guides
Bloom filter · What they are and why they are so powerful https://ilo.im/162mpl

_____
#Programming #Coding #BloomFilter #HashTable #DataStructure #JavaScript #Database #WebDev #Frontend #Backend

I used to think that writing sophisticated R code meant using all the advanced features and chaining long functions together...

Fancy code can be fun, but clean code makes collaboration and debugging so much easier.

Stay informed on data science by joining my free newsletter. Check out this link for more details: http://eepurl.com/gH6myT

#datastructure #datasciencecourse #datasciencetraining

In missing data imputation, it is crucial to compare the distributions of imputed values against the observed data to better understand the structure of the imputed values.

The visualization below can be generated using the following R code:

library(mice)
my_imp <- mice(boys)
densityplot(my_imp)

Take a look here for more details: https://statisticsglobe.com/online-workshop-missing-data-imputation-r

#datastructure #statisticalanalysis #dataanalytics #visualanalytics #pythoncoding #package #datavisualization #datascience

#ITByte: Algorithms and data structures are central to #ComputerScience.

Here is a quick refresher on #DataStructure and #Algorithms. #CS101

https://knowledgezone.co.in/trends/explorer?topic=Data-Structure-Algorithms

Avoiding text overlap in plots is essential for clarity, and R offers a great solution with the ggplot2 and ggrepel packages. By automatically repositioning labels, ggrepel keeps your plot clean and easy to interpret.

Video: https://www.youtube.com/watch?v=5lu4h_CPhi0
Website: https://statisticsglobe.com/avoid-overlap-text-labels-ggplot2-plot-r

Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#pythonprogramminglanguage #statisticalanalysis #datascience #datastructure #package #rstudio

Is there a data structure that can sensibly handle multiple hierarchical classification systems?

e.g. an Orange, in terms of phylogeny is
Plantae->Eudicot->...->Citrus->sinensis

and in terms of usefulness, is
Thing->Food->fruit->orange
(and it could have multiple parents in this taxonomy, e.g. cleaning product)

Bonus points for cool visualisations of this kind information.

#data #dataScience #dataStructure #information #hierarchy #taxonomy #classification #visualisation #dataViz

In statistics, Frequentist and Bayesian approaches are two major methods of inference. While they aim to solve similar problems, they differ in their interpretation of probability and handling of uncertainty.

Frequentists interpret probability as the long-run frequency of events. Parameters (like the mean) are fixed but unknown, and inference relies on analyzing repeated samples.

Learn more: http://eepurl.com/gH6myT

#datascience #datavisualization #datastructure #bigdata #rstats #analysisskill