fortran-lang.org

A high-performance Flex Datagrid is a visualization component built to keep a responsive UI while processing thousands of deeply-nested data changes per second. Kinesis Software describes its datagrid as developed for the performance-critical banking sector, with a companion Hierarchical List that maps dynamic hierarchical models into a linear, flattened list. You need one when a standard Flex grid starts dropping frames, lagging on updates, or struggling with nested data — not merely because your dataset is large.

What a Flex Datagrid does

A Flex Datagrid renders rows and columns of data and handles user interaction such as scrolling, selection, and editing. The built-in Flex grid components cover ordinary business screens well. The problem is the update path: when data changes arrive faster than the grid can re-render, the UI stops feeling live.

Kinesis Software's datagrid targets exactly that path — it is described as capable of handling thousands of deeply-nested data changes each second while maintaining responsiveness, and it was built for banking, where stale or frozen screens are not acceptable.

Where standard grids break down

The failure mode is usually not the initial render. It is the steady stream of updates:

• High update frequency. A blotter that receives hundreds or thousands of price or position changes per second forces the grid to reconcile each change.
• Deeply-nested data. When a change touches a nested object, the grid must work out which visible row it affects.
• Responsiveness under load. Once rendering falls behind the data feed, the grid either queues changes or blocks the UI thread.

A grid that renders a static 10,000-row table fine can still fail completely on a live feed of a few hundred changes per second.

Why hierarchical data needs flattening

Hierarchical models — client portfolios, price band groupings — are trees, but a grid displays a flat list of rows. Kinesis Software notes that working with these dynamic models made it critical to develop a structure capable of mapping them into a linear, flattened list, which became the Hierarchical List, scaling gracefully to large numbers of objects.

The practical consequence: when a nested node changes, the flattened structure lets the grid locate and update the corresponding row without walking the whole tree. That is the mechanism that makes deep nesting affordable at high update rates.

When a standard grid is enough

Choose a custom grid when your update rate and data nesting are the bottleneck, and the cost of a frozen screen is high. If your data changes a few times a minute and sits one level deep, a built-in component is the cheaper and simpler answer.

Trade-offs to weigh

• Custom component vs. built-in. A specialized grid means extra code to integrate and maintain, in exchange for responsiveness under load.
• Flattening complexity. Mapping a hierarchical model into a linear list adds a layer you must keep in sync with the source model.
• Version coverage. Kinesis Software's datagrid targets Flex 3.x and 4.x, so confirm it matches your SDK before committing.
• Fit to your update profile. The value shows up only if your data actually arrives in high-frequency, deeply-nested bursts.

The decision rule is straightforward: measure your update rate and nesting depth first. If they are the reason your grid stutters, a high-performance datagrid earns its place; if not, it is complexity you do not need.

A++ is an educational programming language built on the lambda calculus, designed for one purpose: helping people who want to learn programming understand the essentials of the art of programming as quickly and thoroughly as possible. It suits learners who want a deep grasp of core concepts and transferable patterns before (or instead of) wrestling with the many convenience features of mainstream languages. It is not aimed at people who need a language for practical, production-style software work.

The core idea: essentials instead of features

Most languages come with what the A++ project describes as "bells and whistles" — many soft features that are genuinely useful for coping with practical programming problems. A++ deliberately strips these away. Being based on the lambda calculus, it is presented as a hard-core language made of elements that cannot be further split up or disintegrated.

The reasoning behind this: learning the art of programming is not accomplished by becoming familiar with all the handy soft features a language offers. It comes from learning how to cope with programming problems by continuously applying three operations from beginning to end.

ARS Based Programming

A++ organizes programming around three concepts, abbreviated ARS:

• Abstraction — capturing a pattern or behavior as a reusable unit.
• Reference — using what has been abstracted.
• Synthesis — combining abstractions into larger constructs.

Programming with the focus on applying Abstraction, Reference and Synthesis is called ARS Based Programming. In A++, the project states, ARS Based Programming becomes "ultimately visible to a programmer" — the language is structured so these three operations are not hidden behind incidental syntax.

The claim for learners is twofold: quick, deep comprehension of programming, and pattern-recognition skills that apply in most other programming languages.

Implementations

A++ has been implemented in Scheme, Java, C++, C, Perl and Python, using ARS based programming techniques. The Perl and C implementations are presented in detail in the book on A++ (see below); the other implementations are covered in Programmierung pur (Undiluted Programming), published in 2003.

That spread matters for the educational argument: the same ARS approach is shown to carry across very different language families, which supports the idea that what you learn in A++ transfers rather than being tied to one tool.

Books and learning entry points

The language is introduced in A++ – The Smallest Programming Language in the World, which covers both the language and its implementation. It was first published in Darmstadt, Germany in October 2004 (ISBN 3-87820-116-8), with a 2nd edition published in April 2018 by tredition GmbH, Hamburg.

The site also lists newer titles (July 2018), including:

Who should consider it

A++ fits you if:

• You want to understand what programming is at a fundamental level before committing to a mainstream language.
• You are interested in the lambda calculus as a practical foundation rather than only as theory.
• You want patterns you can carry into other languages.

It probably does not fit you if:

• You need to build practical applications now and want a language with extensive libraries and convenience features.
• You are looking for a first language chosen for employability rather than conceptual depth.

For a systematic path in, the book A++ – The Smallest Programming Language in the World is the documented starting point, with A++ and the Lambda-Calculus as the natural companion for the theoretical basis.