# table Published articles for table. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Running out of runway: Migrating Temporal Cloud's billing store to ClickHouse DevFeed: [Running out of runway: Migrating Temporal Cloud's billing store to ClickHouse]() Original publisher: [Read original article]() Author: Paul Oh Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [clickhouse](), [Cloud](), [data](), [Databases]() Tags: [clickhouse](), [cloud](), [control-plane](), [cost](), [data](), [database](), [databases](), [parallel](), [results](), [table](), [temporal-voices]() ### AI overview Temporal Cloud migrated its billing store to ClickHouse after its previous warehouse had limited capacity. The migration provided three to five years of projected headroom, reduced steady-state costs by about 70%, and delivered usage data to customers roughly 30 minutes sooner. Temporal validated billing accuracy by running both databases in parallel for six weeks. ### Source excerpt How Temporal Cloud migrated its billing store to ClickHouse, gaining years of headroom, cutting steady-state costs by about 70%, and improving data freshness. ## Mikrotik Winbox 4.3 is out DevFeed: [Mikrotik Winbox 4.3 is out]() Original publisher: [Read original article]() Author: j2sw Published: 2026-07-29T14:38:07Z Content type: release Language: en Sources: [Justin Wilson (j2sw)]() Topics: [MikroTik](), [Windows](), [Zip](), [GUI](), [layout]() Tags: [2026](), [feature](), [file](), [form](), [input](), [layout](), [mikrotik](), [network-engineering-resources](), [performance](), [redesign](), [render](), [right](), [sorting](), [table](), [toolbar](), [ui](), [v3](), [version](), [widget](), [winbox](), [windows](), [zip]() ### AI overview MikroTik WinBox 4.3 is a release update that adds a Windows ARM64 package and introduces or improves table, form, toolbar, settings, font, login, window-selection, layout, and scrolling behavior. ### Source excerpt What's new in 4.3 (2026-Jul-20 10:14): *) app: add Windows for arm64 version in file WinBox_Windows_arm64.zip;*) form: make multiline input field height logic as in WinBox v3;*) table: add quick comment feature (allow multi-row commenting);*) table: allow opening "Table settings" also via right mouse click;*) table: calculate implicit height to fit 16 regular rows including ... Read more The post Mikrotik Winbox 4.3 is out appeared first on Justin Wilson (j2sw). ## Quick update: May 2026 DevFeed: [Quick update: May 2026]() Original publisher: [Read original article]() Author: LoRa Left-handed operational Radio authority; Obviously Published: 2026-05-18T00:00:00Z Content type: news Language: en Sources: [Community blog on PINE64]() Topics: [Home Assistant](), [lora](), [Matter](), [Pinephone](), [zigbee](), [Kernel](), [Pinephone-Pro](), [USB](), [Crystal](), [Dongle]() Tags: [2026](), [community](), [crystal](), [dongle](), [eol](), [home-assistant](), [kernel](), [lcd](), [pinedio](), [pinephone](), [pinephone-pro](), [pinetime](), [pinevoice](), [protocol](), [table](), [update](), [usb](), [zigbee]() ### AI overview Pine64's May 2026 update covers the PineVoice Home Assistant speaker, PineTime and PineTime Pro hardware, PineDio USB LoRa development, and recent PineTab2, PinePhone, PinePhone Pro, and PineTab-V software updates. It also reports instability in the PineDio adapter caused by an overheating crystal oscillator. ### Source excerpt Table of contents Whats been going on recently? PineVoice PineTime/PineTime Pro LoRa? (Left-handed operational Radio authority, obviously) Important recent community updates: Do you have something to share? Contact Community PineStore Whats been going on recently? Hello everyone, we wanted to do a quick update on what's been happening recently. Life has been pretty busy but there's lots of nice things happening in the background that we'd like to share. PineVoice The PineVoice Home Assistant speaker (formally PineVox) is in its final stages. Production begins. Further on this, there will be limited bundle with this device which will include a PineVoice, Zigbee dongle, power supply and two matter modules. Pair it with a SBC of choice and run your Home Assistant instance locally! PineTime/PineTime Pro The current PineTime LCD is EOL and a new one is replacing it, though it is essentially the same display. These will be shipping in the next PineTime batch. New PineTime Pro samples have been received by developers. Shiny! LoRa? (Left-handed operational Radio authority, obviously) The PineDio USB LoRa Adapter is at the focus of a new effort to begin progress on potential new LoRa devices. MeshCore is the current protocol target for the USB LoRa Adapter along with help from the pyMC_core library project. The current problem with the device is that the crystal oscillator is heating up too much and can cause clock frequency drifting instability. In a nutshell that means your messages come out garbled. This may eventually be fixed with a TXCO (Temperature-Compensated Crystal Oscillator) to maintain a stable clock frequency. Important recent community updates: DanctNIX PineTab2 updates (including factory image) Adam Piggz: PinePhone, PinePhone Pro, PineTab2 all updated to kernel 7.0, and improved USB config. Zypper ref and dup to update. machaddr@mastodon.sdf.org: with their new Gentoo PineTab-V image Do you have something to share? Do you have news to share with the communit ## Why JOIN USING Can Lead to Errors in SQL DevFeed: [Why JOIN USING Can Lead to Errors in SQL]() Original publisher: [Read original article]() Author: lukaseder Published: 2026-05-04T14:02:39Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [SQL](), [Databases](), [PostgreSQL]() Tags: [audit-columns](), [errors](), [join](), [postgresql](), [production](), [schema](), [sql](), [statement](), [syntax](), [table]() ### AI overview The article explains why JOIN USING and NATURAL JOIN can make SQL queries fragile as schemas evolve. Adding a same-named column can introduce ambiguity or change join behavior, causing existing queries to fail, so explicit join conditions are recommended for production queries. ### Source excerpt Some SQL operators are as esoteric as they're powerful. One of the oldest operator that you've likely hardly ever used in real world applications is NATURAL JOIN which is the default in relational algebra. We've covered a funky use-case for NATURAL JOIN earlier on this blog. The main reason why it's not very useful is ... Continue reading Why JOIN USING Can Lead to Errors in SQL -> ## Table-Per-Tenant vs Shared Table: The Multi-Tenancy Tradeoff in Postgres DevFeed: [Table-Per-Tenant vs Shared Table: The Multi-Tenancy Tradeoff in Postgres]() Original publisher: [Read original article]() Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Multi-tenancy](), [Database](), [Software as a service]() Tags: [customers](), [database](), [indexes](), [isolation](), [migration](), [multi-tenancy](), [orders](), [postgres](), [saas](), [table]() ### AI overview This tutorial compares table-per-tenant and shared-table designs for multi-tenant SaaS applications in Postgres. It explains how tenant counts, data size, schema changes, scale, query planning, migrations, monitoring, and data-deletion requirements affect the choice. ### Source excerpt You are building a SaaS product. Every customer has their own orders, invoices, and documents... ## Waiting for PostgreSQL 19 - Add support for EXCEPT TABLE in ALTER PUBLICATION. DevFeed: [Waiting for PostgreSQL 19 - Add support for EXCEPT TABLE in ALTER PUBLICATION.]() Original publisher: [Read original article]() Author: depesz Published: 2026-03-26T11:46:24Z Content type: article Language: en Sources: [select \* from depesz;]() Topics: [PostgreSQL](), [syntax]() Tags: [alter](), [commit](), [except](), [logical](), [pg19](), [postgresql](), [publication](), [replication](), [table](), [uncategorized](), [waiting]() ### AI overview This article discusses a PostgreSQL 19 patch adding EXCEPT TABLE support to ALTER PUBLICATION. The change allows modifying a publication's table exclusion list, though the example notes that specifying an exclusion for one publication replaced its previous exclusion list. ### Source excerpt On 20th of March 2026, Amit Kapila committed patch: Add support for EXCEPT TABLE in ALTER PUBLICATION. Following commit fd366065e0, which added EXCEPT TABLE support to CREATE PUBLICATION, this commit extends ALTER PUBLICATION to allow modifying the exclusion list. New Syntax: ALTER PUBLICATION name SET publication_all_object [, ... ] where publication_all_object is ... Continue reading "Waiting for PostgreSQL 19 - Add support for EXCEPT TABLE in ALTER PUBLICATION." ## Implementing Model Monitoring on Databricks DevFeed: [Implementing Model Monitoring on Databricks]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2025-08-06T16:55:23Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [Monitoring](), [model-serving](), [dashboards](), [Inference](), [Logging](), [Data Quality]() Tags: [databricks](), [metrics](), [mlops](), [model-monitoring](), [model-serving](), [monitoring](), [request](), [table]() ### AI overview Lecture 10 in an MLOps with Databricks course demonstrates model monitoring using inference tables and Lakehouse Monitoring. It covers collecting inference logs, creating a structured monitoring table, scheduling refreshes, and building a dashboard to visualize metrics and detect drift. ### Source excerpt Lecture 10 of MLOps with Databricks course ## Meet Keycloak at KubeCon India in August DevFeed: [Meet Keycloak at KubeCon India in August]() Original publisher: [Read original article]() Author: Alexander Schwartz Published: 2025-06-12T00:00:00Z Content type: article Language: en Sources: [Keycloak Blog]() Topics: [Keycloak](), [Extension](), [Authentication](), [openid]() Tags: [2025](), [3](), [authentication](), [contributors](), [extensions](), [github](), [idm](), [india](), [kerberos](), [keycloak](), [kubecon](), [ldap](), [location](), [maintainers](), [openid-connect](), [saml](), [sso](), [table]() ### AI overview Keycloak will participate in KubeCon + CloudNativeCon India 2025 in Hyderabad. The article lists talks on custom access tokens and OpenID Connect, and gives times and location details for meeting Keycloak maintainers and contributors at the project table. ### Source excerpt Last year's KubeCon India was a great success, and Keycloak will be part of this year's edition in Hyderabad on August 7-8. Register today to get tickets for the standard rate. A lot of people use Keycloak and develop extensions in for Keycloak in India, so we are thrilled to connect with the community. Connect with me in this GitHub discussion to have your contributions or projects mentioned in the talk! & '' ## Year in Review: 2024 DevFeed: [Year in Review: 2024]() Original publisher: [Read original article]() Author: hello@taniarascia.com Published: 2025-03-27T00:00:00Z Content type: article Language: en Sources: [Tania Rascia]() Topics: [coding](), [CSS](), [React](), [Web Development](), [Web platform]() Tags: [bluesky](), [burnout](), [career](), [coding](), [css](), [hooks](), [keyboard](), [meta](), [pagination](), [projects](), [react](), [redesign](), [review](), [side-projects](), [sorting](), [table](), [variables](), [year-in-review]() ### AI overview A 2024 year-in-review from a principal engineer who continues to work primarily with React and CSS. The author reflects on balancing a full-time engineering role with coding, writing, side projects, and maintaining a personal website, while highlighting several articles and a blog redesign completed during the year. ### Source excerpt I'm trying to win the award for "Latest Year in Review". 🏆 I don't know if anyone can challenge me at this point, as it's almost thirdway... ## Think About SQL MERGE in Terms of a RIGHT JOIN DevFeed: [Think About SQL MERGE in Terms of a RIGHT JOIN]() Original publisher: [Read original article]() Author: lukaseder Published: 2025-03-13T14:45:53Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [SQL](), [etl](), [rdbms]() Tags: [databricks](), [etl](), [firebird](), [merge](), [outer-join](), [postgresql](), [right-join](), [schema](), [semantics](), [source](), [sql](), [sql-server](), [statement](), [syntax](), [table](), [target-table](), [when-matched](), [when-not-matched](), [when-not-matched-by-source]() ### AI overview The article explains SQL MERGE by relating its target-and-source behavior to a RIGHT JOIN. It covers updating matching rows, inserting nonmatching rows, deletion behavior, and the restriction against duplicate matches. ### Source excerpt RIGHT JOIN is an esoteric feature in the SQL language, and hardly ever seen in the real world, because almost every RIGHT JOIN can just be expressed as an equivalent LEFT JOIN. The following two statements are equivalent: It's not unreasonable to expect these two statements to produce the same execution plan on most RDBMS, ... Continue reading Think About SQL MERGE in Terms of a RIGHT JOIN -> ## Inserting One New Element into Hashes of Varying Sizes DevFeed: [Inserting One New Element into Hashes of Varying Sizes]() Original publisher: [Read original article]() Author: Pat Shaughnessy Published: 2025-02-04T00:00:00Z Content type: tutorial Language: en Sources: [Pat Shaughnessy]() Topics: [Ruby](), [hash](), [Benchmark](), [Code]() Tags: [benchmark](), [hash](), [internals](), [ruby](), [table](), [updating-ruby-under-a-microscope]() ### AI overview This article presents a Ruby experiment measuring the time required to insert one new element into hashes of different sizes. Using Ruby 3.4.1, it observes timing spikes when the hash grows to certain sizes, consistent with rehashing or redistribution. ### Source excerpt I've started working on a new edition of Ruby Under a Microscope that covers Ruby 3.x. I'm working on this in my spare time, so it will take a while. Leave a comment or drop me a line and I'll email you when it's finished. RUM includes a serie ## HTML Tables with Horizontal Scroll and Sticky Headers DevFeed: [HTML Tables with Horizontal Scroll and Sticky Headers]() Original publisher: [Read original article]() Author: hello@taniarascia.com Published: 2024-10-09T00:00:00Z Content type: tutorial Language: en Sources: [Tania Rascia]() Topics: [HTML](), [Web Development](), [modern web development]() Tags: [container](), [css](), [design](), [dockerignore-usage](), [example](), [headers](), [helps](), [html](), [javascript](), [table](), [tables]() ### AI overview A tutorial on building HTML tables that support horizontal scrolling and sticky headers. It explains why sticky positioning can fail inside an overflow container and presents a table-wrapper height as the solution, producing a vertically and horizontally scrollable datagrid-style view. ### Source excerpt Turns out, creating an HTML table that has both horizontal scroll and fixed headers can be a tricky problem. As another developer said about... ## Chain-of-table: Evolving tables in the reasoning chain for table understanding DevFeed: [Chain-of-table: Evolving tables in the reasoning chain for table understanding]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-11T19:08:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Chain-of-thought](), [Natural language processing](), [Large Language Model](), [data](), [LLM evaluation / benchmarking]() Tags: [actor](), [ai](), [awards](), [benchmarks](), [chain-of-thought](), [data](), [information](), [language-models](), [machine-intelligence](), [models](), [natural-language-processing](), [performance](), [reasoning](), [research](), [table](), [verify]() ### AI overview The article presents Chain-of-Table, a framework for table understanding that trains large language models to reason step by step by iteratively updating a table. The approach uses in-context learning to generate operations dynamically and reports improved results on the WikiTQ, TabFact, and FeTaQA benchmarks. ### Source excerpt Posted by Zilong Wang, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural language processing (NLP). Researchers in this field have aimed to leverage language models to help users answer questions, verify statements, and analyze data based on tables. However, language models are trained over large amounts of plain text, so the inherently structured nature of tabular data can be difficult for language models to fully comprehend and utilize. Recently, large language models (LLMs) have achieved outstanding performance across diverse natural language understanding (NLU) tasks by generating reliable reasoning chains, as shown in works like Chain-of-Thought and Least-to-Most. However, the most suitable way for LLMs to reason over tabular data remains an open question. In "Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding", we propose a framework to tackle table understanding tasks, where we train LLMs to outline their reasoning step by step, updating a given table iteratively to reflect each part of a thought process, akin to how people solve the table-based problems. This enables the LLM to transform the table into simpler and more manageable segments so that it can understand and analyze each part of the table in depth. This approach has yielded significant improvements and achieved new state-of-the-art results on the WikiTQ, TabFact, and FeTaQA benchmarks. The figure below shows the high-level overview of the proposed Chain-of-Table and other methods. Given a complex table where a cyclist's nationality and name are in the same cell, (a) generic, multi-step reasoning is unable to provide the correct answer (b) program-aided reasoning generates and executes programs (e.g., SQL queries) to deliver the answ ## Workaround for MySQL's "can't specify target table for update in FROM clause" Error DevFeed: [Workaround for MySQL's "can't specify target table for update in FROM clause" Error]() Original publisher: [Read original article]() Author: lukaseder Published: 2023-12-20T14:20:29Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [MySQL](), [SQL](), [bug](), [rdbms]() Tags: [bug](), [for-update](), [from-clause](), [jooq-in-use](), [mariadb](), [mysql](), [rdbms](), [sql](), [table](), [target-table](), [update]() ### AI overview The article explains MySQL error 1093, which occurs when an UPDATE statement references its target table in a FROM clause. It presents a jOOQ transformation as a workaround and notes that similar workarounds appear in the MySQL documentation. ### Source excerpt In MySQL, you cannot do this: The UPDATE statement will raise an error as follows: SQL Error [1093] [HY000]: You can't specify target table 't' for update in FROM clause People have considered this to be a bug in MySQL for ages, as most other RDBMS can do this without any issues, including MySQL clones: ... Continue reading Workaround for MySQL's "can't specify target table for update in FROM clause" Error -> ## Threats: The Table of Contents DevFeed: [Threats: The Table of Contents]() Original publisher: [Read original article]() Author: Adam Published: 2023-01-16T00:00:00Z Content type: article Language: en Sources: [Shostack & Friends Blog]() Topics: [spoofing]() Tags: [books](), [contents](), [guide](), [spoofing](), [table]() ### AI overview The article presents the table of contents for Threats: What Every Engineer Should Learn From Star Wars. It explains that the book covers threats such as spoofing and authenticity, with each chapter organized around the threat, mechanisms, scenarios or technologies, and defenses. The book is announced as available for preorder and sale on January 25th. ### Source excerpt Like the Force, each threat has a light side, and a dark side. ## How to Sort, Filter, and Paginate a Table with JavaScript DevFeed: [How to Sort, Filter, and Paginate a Table with JavaScript]() Original publisher: [Read original article]() Author: hello@taniarascia.com Published: 2021-10-22T00:00:00Z Content type: tutorial Language: en Sources: [Tania Rascia]() Topics: [React](), [JavaScript](), [Front end](), [Sorting]() Tags: [api](), [filter](), [front-end](), [how-to](), [javascript](), [react](), [rest](), [sorting](), [table]() ### AI overview A tutorial on building a React table with front-end pagination, sorting, and filtering for strings, booleans, numbers, and dates, without relying on styling or framework libraries. ### Source excerpt One thing I've had to do at every job I've had is implement a table on the front end of an application that has sorting, filtering, and... ## How and When to Use Context in React with Hooks DevFeed: [How and When to Use Context in React with Hooks]() Original publisher: [Read original article]() Author: hello@taniarascia.com Published: 2021-06-15T00:00:00Z Content type: tutorial Language: en Sources: [Tania Rascia]() Topics: [React](), [hooks](), [context](), [Redux](), [Code]() Tags: [api](), [code](), [dashboard](), [graph](), [hooks](), [javascript](), [react](), [redux](), [state](), [state-management](), [table]() ### AI overview A tutorial on using React Context with modern functional components and hooks. It explains that Context can pass data across components and is useful for localized, reusable state, while Redux remains more suitable for large-scale global state management. ### Source excerpt A while ago, I wrote an article about Using Context API in React. However, most of my examples on that page used Class components, , and... ## How Do You Know What ActiveRecord Table Has a user\_id Attribute? DevFeed: [How Do You Know What ActiveRecord Table Has a user\_id Attribute?]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-12-28T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Development](), [data-modeling](), [business logic]() Tags: [active-record](), [activerecord](), [business-logic](), [class](), [classes](), [console](), [development](), [id](), [jumpstart](), [metaprogramming](), [migration](), [migrations](), [rails](), [relationships](), [schema](), [table]() ### AI overview This article explains how to identify application classes whose ActiveRecord-backed tables have a user_id attribute. It introduces a DataObject class with a .has_user_id method that can be run in the console, helping the author add belongs_to user relationships without repeatedly checking the schema. ### Source excerpt Even though I'm a firm, firm believer in agile, I've recently been experimenting with a throwback to waterfall style development and it very quickly left me with a fully featured data structure of tables and relationships modeled as example data. What I did is very rapidly write a series of migrations and then populate them to represent a sample "installation". My goal with this approach was to play to my strengths - data modeling - and avoid getting tied down in user interface stuff (my weakness). This was a very, very interesting approach and what I found was that I got much further along the lines of the "guts" of an application simply because I never got discouraged by: Oh Shite - I know this should look good but I'm too much of a hoser to make it look good; I guess I'll put it aside and go watch TV The downside to this is that I never bothered setting up the normal associations that you do when you write a migration; I simply thought about this application in terms of the example data: the users who would be using the system the data objects that they would create how the data objects would interact with each other One of the things that I noticed when I started filling in the basics like "belongs_to :user" was that I kept constantly jumping between the class I was working on and the schema file. Finally it hit me - what I needed was a method that I could execute in the console that would tell me what classes had a user_id attribute. And so I wrote a class called DataObject (for an ActiveRecord class which stores data) and a method .has_user_id. What I was looking for was output that looked like this: > DataObject.has_user_id Initiative Yes - has a user_id field KeyResultOwner Yes - has a user_id field KeyResult Yes - has a user_id field ObjectiveOwner Yes - has a user_id field ObjectiveType Objective Yes - has a user_id field OkrTeamMember Yes - has a user_id field OkrTeam Yes - has a user_id field OrganizationGroup Organization Quarter ResponsibilityRole Stat ## Implementing Safe ActiveRecord Like Queries for Rails DevFeed: [Implementing Safe ActiveRecord Like Queries for Rails]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-11-11T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Rails](), [SQL](), [Databases]() Tags: [activerecord](), [database](), [limit](), [parameter](), [rails](), [sql](), [table]() ### AI overview A Rails tutorial explains how to build safer ActiveRecord LIKE queries using Arel table fields and parameterized query construction. It demonstrates searching a Metric model's note field with filtering, ordering, limits, and pagination. ### Source excerpt In any SQL based database, a like query is generally an SQL injection attack waiting to happen because the underlying sql statement looks like this: SELECT id FROM posts WHERE name LIKE '%foo%' Note: A 30 year old thank you goes out to InfoWorld and Joe Celko who beat into his reader's brains the concept of capitalizing SQL statements for better legibility. Thank you Joe. A seemingly solid StackOverflow post gives this recommendation: title = Model.arel_table[:title] Model.where(title.matches("%#{query}%")) Please note that Model needs to be replaced with the name of your table. Let's say that our table was named Metric and we have a normal simple_form object for Metric coming into our Rails app with a parameter named q and we have a real world Rails app with a limit clause and pagination. Here's how this would look: @q = params[:metric][:q] note = Metric.arel_table[:note] @metrics = current_user.metrics.where(note.matches("%#{@q}%")).order("date_created_at desc").limit(@limit).page(params[:page]) So: @q represents the incoming query note represents the field in our Metrics table that we want to search agains @metrics is the collection of data returned by the search and the where clause is "note.matches("%#{@q}%")" to find any instances of the term @q within the note field ## Ember Table 2.0 Release DevFeed: [Ember Table 2.0 Release]() Original publisher: [Read original article]() Author: Cory Forsyth Published: 2019-08-12T17:52:23Z Content type: release Language: en Sources: [Addepar]() Topics: [Ember](), [Library](), [Sorting](), [Documentation](), [Testing](), [migration]() Tags: [2](), [datatables](), [documentation](), [ember](), [feature](), [features](), [migration](), [performance](), [release](), [sorting](), [table](), [testing]() ### AI overview Addepar announces Ember Table 2.0, a ground-up reimplementation of its data table library with a new API, improved performance for large tables, and greater flexibility. The release adds features including nested subcolumns, occlusion rendering, sorting, resizing, and interactive, versioned documentation. ### Source excerpt I'm excited to announce the release of Ember Table 2.0. This release is the culmination of more than 18 months of effort across several Addepar developers and external contributors. This release represents a ground-up re-implementation of Ember Table with a new, modern, and future-compatible API. At Addepar, the new Ember Table 2.0 ("ET2") brings even better performance on large tables and its enhanced flexibility is a better fit for the advanced use cases that Addepar clients have. Ember Table in use in the Ember Inspector extension Over the past year we've adopted ET2 into much of Addepar's product, and in an upcoming release we will have completed our migration away from the legacy version. That milestone, and the corresponding stability requirements, merit an official major release. Since this release diverges significantly from Ember Table 1.0, we renamed the original version of the library and repo to "ember-table-legacy" to make it easy for legacy codebases to continue using the older version. If you've been waiting to try ET2, now is a great time. It's a powerful and flexible data table library designed to be approachable yet full of features to accommodate complex needs, such as: fixed headers, footers, and columns row selection and sorting tree tables with group collapsing column resizing and reordering nested subcolumns (an all-new feature in ET2 that supports complex table headers, for example to create pivot tables) ability to render thousands of rows performantly via occlusion rendering (leveraging vertical-collection) compatibility with Ember versions 1.13 through 3.10, with a progressively improved API exported test object for testing tables in your app Ember Table supports sorting, column reordering, nested data, and more Additionally, significant effort went into revamping ET2 documentation. The docs use ember-cli-addon-docs so that they are searchable and versioned, with expanded sections and interactive demos. To start using the new ET2, add `emb ## A New Functional Approach to Complex Types in Apache Hive DevFeed: [A New Functional Approach to Complex Types in Apache Hive]() Original publisher: [Read original article]() Author: Jan Filipiak Software programmer; Constantly Surprised By How Stupid Computers Are Still Knows Where His Towel Is Published: 2019-01-30T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [SQL](), [NoSQL](), [Databases](), [Apache Spark]() Tags: [arrays](), [data-science](), [java](), [jvm](), [maps](), [mysql](), [nosql](), [open-source](), [sql](), [structure](), [table]() ### AI overview This article examines how Apache Hive handles complex data types such as maps, arrays, and structs. It explains how Hive can process these types through SQL, custom programs, JVM-based user-defined functions, and table-generating functions such as explode() and inline(). ### Source excerpt When faced with the challenge to store, retrieve and process small or large amounts of data, structured query languages are typically not far away. These languages serve as a nice abstraction be... ## Optimizing the Django Admin Paginator DevFeed: [Optimizing the Django Admin Paginator]() Original publisher: [Read original article]() Author: Haki Benita Published: 2018-11-05T22:00:00Z Content type: tutorial Language: en Sources: [Haki Benita]() Topics: [Django](), [Scalability](), [optimize]() Tags: [articles](), [count](), [django](), [django-admin](), [optimizing](), [override](), [pages](), [performance](), [query](), [scalability](), [table]() ### AI overview This article examines the Django admin paginator's performance on large tables. It explains that counting all rows can dominate page-load time and discusses optimizing Django admin for consistent performance as datasets grow. ### Source excerpt I often talk about making Django scale but what does it actually mean? It means getting consistent performance regardless of the amount of data. In this article we tackle The last nail in Django admin's scalability coffin - the paginator. ## PostgreSQL Data Types: ENUM DevFeed: [PostgreSQL Data Types: ENUM]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2018-05-02T09:00:26Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [data type](), [enum](), [MySQL]() Tags: [alter](), [data-type](), [database](), [enum](), [join](), [mysql](), [postgresql](), [table]() ### AI overview This tutorial compares PostgreSQL ENUM types with reference tables and foreign keys. It explains how PostgreSQL registers ENUM types, how values are stored, and why changing accepted values is a DDL operation with transaction restrictions. The article recommends ENUMs for very static value sets and reference tables when values or ordering may change. ### Source excerpt Continuing our series of PostgreSQL Data Types today we're going to introduce the PostgreSQL ENUM type. This data type has been added to PostgreSQL in order to make it easier to support migrations from MySQL. Proper relational design would use a reference table and a foreign key instead. ## Regular Expressions and Grouping Sets DevFeed: [Regular Expressions and Grouping Sets]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2017-08-14T14:37:53Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [Regular expression](), [SQL](), [data-processing](), [Open Data](), [Structured-data](), [CSV](), [elt](), [etl]() Tags: [array](), [csv](), [data-processing](), [elt](), [etl](), [functions](), [postgresql](), [regexp](), [regular-expressions](), [sql](), [table]() ### AI overview A PostgreSQL tutorial showing how to split hierarchical, comma-separated values from a messy column with regular-expression functions, normalize the results, and analyze category distributions with grouping sets, CUBE, and ROLLUP. It also contrasts ETL with ELT. ### Source excerpt There's a very rich set of PostgreSQL functions to process text, you can find them all at the String Functions and Operators documentation chapter, with functions such as overlay, substring, position or trim. Or aggregates such as string_agg. And then regular expression functions, including the very powerful regexp_split_to_table. [Next page]()