# charts Published articles for charts. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Tips for running Temporal on Kubernetes DevFeed: [Tips for running Temporal on Kubernetes]() Original publisher: [Read original article]() Author: Cubby Sivasithamparam Published: 2025-12-15T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Kubernetes](), [Helm charts](), [Deployment](), [autoscaling](), [Resilience](), [Latency]() Tags: [autoscaling](), [charts](), [deployment](), [helm](), [how-to](), [kubernetes](), [performance](), [practical](), [production](), [scaling](), [temporal](), [tips]() ### AI overview This article provides practical guidance for deploying and operating Temporal on Kubernetes. It explains how Temporal adds durable state persistence, retries, and recovery, and recommends using Helm charts with external production dependencies rather than bundled development configurations. ### Source excerpt Learn practical tips for deploying and scaling Temporal on Kubernetes. Covers Helm charts, KEDA autoscaling, and production performance tuning. ## Cognitive Load and Web Accessibility: Quick Tips for Clearer Content DevFeed: [Cognitive Load and Web Accessibility: Quick Tips for Clearer Content]() Original publisher: [Read original article]() Author: Marissa Published: 2025-07-24T19:45:00Z Content type: tutorial Language: en Sources: [Blog-Main]() Topics: [Accessibility](), [Web Accessibility (a11y)](), [Web Content Accessibility Guidelines](), [Website](), [Users](), [User interface design]() Tags: [accessibility](), [accessibility-guidelines](), [accessibility-ux](), [autism](), [charts](), [cognitive-load](), [form](), [layout](), [navigation](), [people-with-disabilities](), [quick-tips](), [tips](), [wcag](), [web-accessibility](), [web-content-accessibility-guidelines]() ### AI overview This article explains how cognitive load affects web accessibility, particularly for people with cognitive and learning disabilities. It recommends simplifying complex content, using consistent layouts and navigation, and following the Understandable principle of WCAG. ### Source excerpt The Web Content Accessibility Guidelines (WCAG) is designed to improve experiences for all users with disabilities. That includes people who have cognitive disabilities and learning disabilities -- but those users are often left out of the discussion. ## Enabling Apache ECharts in React for Data Visualization DevFeed: [Enabling Apache ECharts in React for Data Visualization]() Original publisher: [Read original article]() Author: hello@taniarascia.com Published: 2025-03-31T00:00:00Z Content type: tutorial Language: en Sources: [Tania Rascia]() Topics: [Data visualization](), [React](), [Front end](), [JavaScript](), [dashboards]() Tags: [charts](), [dashboards](), [data-visualization](), [front-end](), [javascript](), [react](), [visualization]() ### AI overview A tutorial on creating a small React wrapper for Apache ECharts to build interactive data visualizations, including line, bar, and pie charts. It covers chart initialization, configuration updates, event handling, and resizing. ### Source excerpt Making dashboards with charts and graphs is a pretty common part of the front-end developer experience, as well as deciding which JavaScript... ## Blog: Falcosidekick 2.31.0 DevFeed: [Blog: Falcosidekick 2.31.0]() Original publisher: [Read original article]() Published: 2025-02-04T00:00:00Z Content type: release Language: en Sources: [Falco - Falco](), [Falco - The Falco blog]() Topics: [Falco](), [OpenTelemetry](), [Helm charts](), [Prometheus]() Tags: [changelog](), [charts](), [falco](), [falcosidekick](), [helm-charts](), [observability](), [opentelemetry](), [prometheus](), [release](), [updated](), [upgrade](), [version]() ### AI overview Falcosidekick 2.31.0 adds OTLP metrics forwarding to OpenTelemetry collectors, improves logging and Loki output options, allows NATS/STAN subject customization, and fixes issues involving labels, ClusterPolicyReport creation, HTTP headers, and Prometheus custom fields. Updated Helm charts are also available. ### Source excerpt The year 2025 is well started now. We saw a few days ago the first release of Falco for the year. It's to let fly out a new version of Falcosidekick, the 2.31.0. New output This release comes with a new output only, the last pillar of the observability with [OpenTelemetry].(https://opentelemetry.io/) that missing in Falcosidekick. OTLP Metrics You can now forward the Falco Events to the OpenTelemetery collector or any received understanding the protocol. New features Here's a non exhaustive list of the great features and enhancements which come with this new release: Better logger It was a ToDo for a while (even years), but it's now completed. The log system used by Falcosidekick has been replaced, without any breaking change for the users, but opening the door to more enhancements in the future. More default labels for Loki The log lines forwarded to Loki contain now by default the source namespace and pod name, if present in the alert. It will allow to filter more easily the events you want to display in your dashboards. Thanks to @afreyermuth98. Payload format for Loki Some users asked for the possibility to forward the Falco alerts in their JSON format to Loki. You can now use the setting loki.format for. NATS/STAN subject The template for the subject where to push the messages for NATS/STAN was hardcoded, it can now be overridden with nats/stan.subjecttemplate. See the example config file. Fixes Fix the missing templated fields as labels in Loki payload (PR#1091) Fix the creation error of a ClusterPolicyReport (PR#1100) Fix the missing custom headers for HTTP requests for Loki (PR#1107 thanks to @lsroe) Fix the wrong key format of custom fields for Prometheus (PR#1110 thanks to @rubensf) Conclusion You can find the full changelog here. The respective Helm charts are already updated and allow you to test by yourself all these great new features. Just issue the helm repo update; helm upgrade --reuse-values -n falco command to do so. Once again, thanks to all the ## Interactive Data Visualizations with React DevFeed: [Interactive Data Visualizations with React]() Original publisher: [Read original article]() Author: admin Published: 2024-06-02T08:26:04Z Content type: tutorial Language: en Sources: [Posts on Chris Banes]() Topics: [React](), [Data visualization](), [JavaScript](), [User Interfaces]() Tags: [charts](), [charts-and-graphs](), [d3-js](), [data-visualization](), [javascript](), [react](), [uncategorized]() ### AI overview This tutorial explains how React can be used to create interactive data visualizations. It discusses component-based architecture, dynamic updates through the Virtual DOM, performance, flexibility, and the React visualization ecosystem, including Chart.js and D3.js. ### Source excerpt React, the popular JavaScript library for building user interfaces, is a fantastic tool for creating interactive data visualizations. With its component-based architecture and a wealth of libraries at your disposal, React makes it easier than ever to bring your data to life and engage your audience. But why should you choose React for your data [...] ## Meet "YCharts": an Opensource Jetpack Compose chart library. DevFeed: [Meet "YCharts": an Opensource Jetpack Compose chart library.]() Original publisher: [Read original article]() Author: Codeangi Published: 2023-07-13T06:29:03Z Content type: tutorial Language: en Sources: [ymedialabs-innovation - Medium]() Topics: [Compose](), [Jetpack Compose](), [Android](), [Graphs](), [Library](), [ui](), [Code]() Tags: [android](), [charts](), [charts-and-graphs](), [color](), [compose](), [electronics](), [graphs](), [ios](), [jetpack-compose](), [kotlin](), [kotlin-multiplatform](), [library](), [maven](), [signal](), [time](), [ui]() ### AI overview This article introduces Y-Charts, an open-source Jetpack Compose chart library for Android. It explains how the library integrates charts into existing UI, can be added through Maven, and supports customizable Cartesian, bar, line, and wave charts. ### Source excerpt Meet "Y-Charts": an Opensource Jetpack Compose chart library. GitHub - yml-org/YCharts: YCharts is a graph library for Android. Y-Charts is a Jetpack Compose-based charts/graphs library that enables developers to easily integrate various types of charts/graphs into their existing UI to visually represent statistical data. Jetpack Compose is Android's recommended modern toolkit for building native UI. Y-Charts leverages on powerful features of Jetpack Compose to deliver accurate and accessible results for users, and many reusable components allow it to be concise and lightweight also enabling developers to customize and add new charts with ease. Y-Charts Sample in action.Adding Y-charts to your project. You can add the library via Maven: https://medium.com/media/f3feb939f36855b0cec632a08a6c6fa8/hrefCharts in Y-Charts:1. Cartesian Charts: Cartesian charts, also known as coordinate charts or xy-charts, utilize a rectangular coordinate system with two perpendicular axes: the x-axis (horizontal) and the y-axis (vertical). These charts are particularly effective for displaying quantitative data and relationships between variables. AxisData: The AxisData model supplied to each chart item controls the behavior and appearance of X and Y axis labels, user can opt for default behavior via the builder and can customize according to their needs. Bar Charts: Bar charts use rectangular bars to represent data, where the length or height of each bar corresponds to its value. Y-Charts provides many customization including the orientation of the bar chart to achieve a horizontal-scroll behavior, along with grouping and stacking of bars. This enables users to visualize different types of bar charts with very few code changes. BarchartData and GroupBarChartData enable users to customize this behavior, additionally, users can change the appearance of the bars by using the BarStyle property. BarData represents the data for each bar shown in the chart. Following snippet shows the BarchartD ## How to Plot an ASCII Bar Chart with SQL DevFeed: [How to Plot an ASCII Bar Chart with SQL]() Original publisher: [Read original article]() Author: lukaseder Published: 2022-09-01T08:25:29Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [PostgreSQL](), [SQL](), [Databases](), [data]() Tags: [ascii-charts](), [charts](), [coding](), [configuration](), [database](), [how-to](), [plotting](), [postgresql](), [sql]() ### AI overview A tutorial showing how to use a PostgreSQL SQL query to render ASCII bar charts directly in a SQL editor. It explains the query's data, configuration, and chart-generation sections, using revenue data from the Sakila database and discussing date handling and gap filling. ### Source excerpt No need for expensive Tableau subscriptions. Ditch Microsoft Excel. Just use native PostgreSQL to quickly visualise your data! Here's an idea I had for a while. As you may know, jOOQ can produce fancy charts from your jOOQ results. But that requires you use jOOQ, and you may not be using jOOQ, because you're not ... Continue reading How to Plot an ASCII Bar Chart with SQL -> ## Temporal Transparency Update #7 DevFeed: [Temporal Transparency Update #7]() Original publisher: [Read original article]() Author: Ryland Goldstein Published: 2020-09-04T04:00:00Z Content type: news Language: en Sources: [Temporal Blog]() Topics: [Code](), [Testing](), [Deployment](), [Apache Cassandra](), [Helm charts]() Tags: [cassandra](), [charts](), [deployment](), [product-news](), [temporal](), [testing](), [update]() ### AI overview Temporal's seventh transparency update reports stabilization progress after code completion across its components. It covers deployment infrastructure work, scale-testing scenarios, fault-injection testing, and the investigation of issues found by tests. ### Source excerpt Hey Temporal community, hope everyone is having a fantastic Friday. In our previous update we announced code completion for JavaSDK which also meant reaching the V1 code complete milestone. ## Ruby, ChartKick and chart.js versus Google Charts and the Y Axis Auto Sizing DevFeed: [Ruby, ChartKick and chart.js versus Google Charts and the Y Axis Auto Sizing]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-12-13T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Library](), [Ruby](), [JavaScript](), [Development]() Tags: [chartjs](), [chartkick](), [charts](), [code](), [google](), [google-charts](), [graph](), [graphs](), [js](), [rails](), [ruby](), [yaxis]() ### AI overview This tutorial explains how to use the Ruby ChartKick library with chart.js or Google Charts. It also covers dynamically setting the y-axis minimum and adding chart colors. ### Source excerpt Like a lot of people in the Open Source world, I rarely know actual people in the Open Source world - but I do know github usernames. And one of those names that I truly respect is ankane. I've rarely found a bit of code he wrote that I don't like or use. And while I'm not a big open source creator, I'd like to think that I give back by writing documentation like this blog post. Today I'm talking about the ChartKick library by ankane. ChartKick is an easy way to build JavaScript charts with a single line of Ruby. I've used ChartKick on and off for years but how it works has always been a bit of a mystery. Hopefully this blog post de-mystifies it a bit. ChartKick Can Use Different Graphing Engines I have always used ChartKick in the past just with the default Google Charts engine. Much to my surprise, ChartKick can use chart.js underneath. This is surprising to me and utterly awesome. Here's where I learned this. Using ChartKick with chart.js In application.js, you need both of these: //= require Chart.bundle //= require chartkick Note 1: This assumes that you aren't using yarn / webpacker. Note 2: If you change application.js, you need to restart your development server. Here's a picture of a graph using chart.js: Using ChartKick with Google Charts If you are using ChartKick with Google Charts then you need to remove the line below from application.js: //= require Chart.bundle and then you need to add, into your layout or your html template: <%= javascript_include_tag "https://www.gstatic.com/charts/loader.js" %> Here's a picture of a graph using Google Charts: Y Axis Auto Sizing / Setting Axis Minimums Dynamically The Y axis on my graphs, for years, has always been zero. I had a friend complain about this to me and a documentation grovel (ok I read it more closely than I ever had before) revealed this: min defaults to 0 for charts with non-negative values. Use nil to let the charting library decide. Adding a Bit of Color to Your Graph I haven't used this in quite a ## 7 tips for getting the most out of Crashlytics DevFeed: [7 tips for getting the most out of Crashlytics]() Original publisher: [Read original article]() Author: Jason St. Pierre Published: 2017-09-19T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Crashlytics](), [debugging](), [dashboards](), [Firebase]() Tags: [best-practices](), [charts](), [crashlytics](), [debugging](), [firebase](), [regression](), [tips](), [tutorials]() ### AI overview This tutorial presents seven tips for getting more value from Crashlytics in the Fabric dashboard, including using crash insights, tracking regressions by closing issues, locking issues to reduce clutter, and using wildcard builds. ### Source excerpt News, tutorials, and updates from the Firebase team. ## How to Turn Django Admin Into a Lightweight Dashboard DevFeed: [How to Turn Django Admin Into a Lightweight Dashboard]() Original publisher: [Read original article]() Author: Haki Benita Published: 2017-03-30T21:00:00Z Content type: tutorial Language: en Sources: [Haki Benita]() Topics: [Django](), [dashboards](), [Object-relational mapping]() Tags: [articles](), [charts](), [dashboards](), [django](), [django-admin](), [how-to](), [lightweight]() ### AI overview A tutorial on customizing Django Admin into a lightweight dashboard with a summary table and charts. It uses Django Admin customization, a proxy model, templates, filtering, and aggregation without adding dependencies. ### Source excerpt Django Admin is a powerful tool for managing data in your app. However, it was not designed with summary tables and charts in mind. Luckily, the developers of Django Admin made it easy for us to customize. We are going to turn Django Admin into a dashboard by adding a chart and a summary table. ## Plotting Reddit post frequencies with d3 DevFeed: [Plotting Reddit post frequencies with d3]() Original publisher: [Read original article]() Published: 2016-02-20T00:00:00Z Content type: tutorial Language: en Sources: [Adam Bard]() Topics: [d3.js](), [plotting](), [data](), [Code]() Tags: [charts](), [code](), [d3](), [d3-js](), [data](), [graphs](), [plotting]() ### AI overview A tutorial describing how the author added a 7x24 color-coded grid plot to Later's Reddit post timing analysis page using D3.js. The chart represents posting frequency across hours and days of the week, providing a more precise view than separate bar charts. ### Source excerpt This week I added a better graph to Later (for Reddit)'s post timing analysis page. Previously, the most common time and the most common day were plotted on separate bar charts. However, I noticed that I got called out on this in this article on Medium, which offered a pretty sensible alternative that I wasted no time in ripping off wholesale. I still have the bar graphs in, but I also added a 7x24 color-coded grid plot, allowing for a more accurate view of exactly when the most popular posts were posted. ## Postgres Open 2014, Chicago DevFeed: [Postgres Open 2014, Chicago]() Original publisher: [Read original article]() Published: 2014-09-16T22:00:00Z Content type: news Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [SQL]() Tags: [charts](), [conferences](), [event](), [open-source](), [postgresql](), [sql](), [tool]() ### AI overview A PostgresOpen 2014 conference page describes talks on pgcharts, SQL-based chart creation, SQL window functions, and advanced PostgreSQL features such as ranges. The event combined PostgresOpen with PGConf Silicon Valley. ### Source excerpt A ligthning talk about pgcharts, a tool to turn your SQL queries into charts, and share the result. The PostgreSQL for Developers talk begins with a simple use case for developers to solve entirely in SQL. Discover SQL window functions if you don't know them already, and then see about advanced PostgreSQL stuff such as ranges. ## Turn your PostgreSQL queries into Charts DevFeed: [Turn your PostgreSQL queries into Charts]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2014-08-25T12:09:00Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [SQL](), [App](), [data]() Tags: [application](), [charts](), [compare](), [compiler](), [console](), [debian](), [github](), [install](), [mongodb](), [postgresql](), [sql]() ### AI overview The article introduces pgcharts, an application that turns PostgreSQL SQL queries into charts. Users can edit a query, define categories and data series, add legends, and produce bar, column, pie, or donut charts. At the time of writing, the application had to be compiled with SBCL, with a Debian package planned. ### Source excerpt Earlier this year we did compare compare Aggregating NBA data, PostgreSQL vs MongoDB then talked about PostgreSQL, Aggregates and histograms where we even produced a nice Histogram chart directly within the awesome psql console. Today, let's get that same idea to the next level, with pgcharts: *The new [pgcharts](https://github.com/dimitri/pgcharts) application* The application's specifications are quite simple: edit an SQL query, set your categories and your data series, add in some legends, and get a nice chart. Currently supported are bar, column, pie and donut charts, and we should be able to add anything that http://www.highcharts.com/ has support for. ## Book review: Introduction to Computer Science Using Python (by Charles Dierbach) DevFeed: [Book review: Introduction to Computer Science Using Python (by Charles Dierbach)]() Original publisher: [Read original article]() Author: Guido van Rossum (noreply@blogger.com) Published: 2013-10-28T20:56:00Z Content type: opinion Language: en Sources: [Guido van Rossum]() Topics: [Python](), [Computer science](), [Programming](), [Graphics](), [Simulation]() Tags: [book](), [book-review](), [charts](), [computer-science](), [examples](), [graphics](), [programming](), [python](), [review](), [screenshots](), [simulation]() ### AI overview A review of Charles Dierbach's college-level textbook Introduction to Computer Science Using Python: A Computational Problem-Solving Focus. The reviewer describes its beginner-friendly approach, emphasis on computational problem solving, broad computer science coverage, exercises, examples, and practical Python material including IDLE and turtle graphics. ### Source excerpt After much back and forth I received a nice new Python book in the mail. The book's full title is "Introduction to Computer Science Using Python: A Computational Problem-Solving Focus", and its author is a very experienced educator, Charles Dierbach. This is not your average Python book -- it is a college text intended for first-semester CS courses that happens to use Python. As such, in assumes absolutely no previous programming experience, and it looks like any previous computer experience is optional. Not only that, but the book starts with a step-by-step introduction to the art of computational problem solving. This is an idea that goes well beyond hacking together a website! The book is incredibly thorough: there are exercises throughout the text (not just at the end of each chapter), and it includes a plethora of examples, screenshots, tables, charts, diagrams, and photos. (Yes, my picture is in there -- so are Alan Turing, JFK, and K&R. :-) The author is not afraid of taking a stance; for example, he omits the 'break' and 'continue' statement because they do not fit within the paradigm of structured programming. This actually fits with the general goal of the book, which is to give an overview of many areas of computer science without getting too deep into the minutiae of any topic. I love the final chapter, which is an overview of the history of computing, starting with Charles Babbage and Ada Lovelace. At the same time, the book gives plenty of useful practical information, such as instructions for using IDLE and an extensive explanation of turtle graphics, culminating in a horse race simulation. (The author's Baltimore roots seem to show through here. :-) All in all, I think this book is a great text for anyone teaching CS1 or interested in familiarizing themselves with computer science through serious self-study.