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Posts Tagged ‘INGBank’

Lecturer

Balkrishna Rawool leads IT chapters at ING Bank, focusing on scalable software solutions and Java concurrency. He actively shares insights on Project Loom through conferences and writings, drawing from practical implementations in financial systems.

Abstract

This review dissects Project Loom’s enhancements to Java’s concurrency: virtual threads for efficient multitasking, structured concurrency for task orchestration, and scoped values for secure data sharing. Placed in web development contexts, it explains their interfaces and combined usage via a Spring Boot loan processing app. The evaluation covers integration techniques, traditional threading issues, and effects on legibility, expandability, and upkeep in parallel code.

Project Loom Foundations and Virtual Threads

Project Loom overhauls Java concurrency with lightweight alternatives to OS-bound threads, which limit scale due to overheads. Virtual threads, managed by the JVM, enable vast concurrency on few carriers, ideal for IO-heavy web services.

In the loan app—computing offers via credit, account, and loan calls—virtual threads parallelize without resource strain. Configuring Tomcat to use them boosts TPS from hundreds to thousands, as non-blocking calls unmount threads.

The interface mirrors traditional: Thread.ofVirtual().start(task). Internals use continuations for suspension, allowing carrier reuse. Consequences: lower memory, natural exception flow.

Care needed for pinning: synchronized blocks block carriers; ReentrantLocks avoid this, sustaining performance.

Structured Concurrency for Unified Task Control

Structured concurrency organizes subtasks as cohesive units, addressing executors’ scattering. StructuredTaskScope scopes forks, ensuring completion before progression.

In the app, scoping credit/account/loan forks with ShutdownOnFailure cancels on errors, avoiding leaks. Example:

try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
    var credit = scope.fork(() -> getCredit(request));
    var account = scope.fork(() -> getAccount(request));
    var loan = scope.fork(() -> calculateLoan(request));
    scope.join();
    // Aggregate
} catch (Exception e) {
    // Manage
}

This ensures orderly shutdowns, contrasting unstructured daemons. Effects: simpler debugging, no dangling tasks.

Scoped Values for Immutable Inheritance

Scoped values supplant ThreadLocals for virtual threads, binding data immutably in scopes. ThreadLocals mutate, risking inconsistencies; scoped values inherit safely.

For request IDs in logs: ScopedValue.where(ID, uuid).run(() -> tasks); IDs propagate to forks via scopes.

Example:

ScopedValue.where(REQ_ID, UUID.randomUUID()).run(() -> {
    // Forks access ID
});

This solves ThreadLocal inefficiencies in Loom. Effects: secure sharing in hierarchies.

Combined Usage and Prospects

Synergies yield maintainable concurrency: virtual threads scale, scopes structure, values share. The app processes concurrently yet organized, IDs tracing.

Effects: higher IO throughput, easier upkeep. Prospects: framework integrations reshaping concurrency.

In overview, Loom’s features enable efficient, readable parallel systems.

Links:

• Lecture video: https://www.youtube.com/watch?v=iO79VR0zAhQ
• Balkrishna Rawool on LinkedIn: https://nl.linkedin.com/in/balkrishnarawool
• Balkrishna Rawool on Twitter/X: https://twitter.com/BalaRawool
• ING Bank website: https://www.ing.com/

At Scala Days New York 2016, Tim Soethout, a functional programmer at ING Bank, offered a comprehensive guide to Scala’s implicits, a feature often perceived as magical by developers transitioning from basic to advanced Scala programming. Tim’s presentation bridged this gap, providing clear explanations and practical examples to demonstrate how implicits enhance code expressiveness and flexibility.

Understanding Implicits

Tim Soethout began by defining implicits as a mechanism for providing values or conversions without explicit references, enabling concise and flexible code. Drawing parallels with object-oriented programming, Tim explained that implicits extend “is-a” and “has-a” relationships with “is-viewable-as,” allowing developers to add rich interfaces to existing types. For instance, in Akka, the ! (tell) operator uses an implicit sender parameter, simplifying message passing. Similarly, Scala’s Futures rely on implicit execution contexts to manage asynchronous operations, abstracting thread scheduling from developers.

Compiler Resolution of Implicits

A key focus of Tim’s talk was demystifying how the Scala compiler resolves implicits. He outlined the compiler’s search process, which prioritizes local scope, companion objects, and package objects related to the involved types. Tim cautioned against implicit conversions with mismatched semantics, as they can lead to unexpected behavior. Using a live coding demo, he illustrated how implicits enable expressive DSLs, such as JSON serialization libraries, by automatically resolving type-specific writers, thus reducing boilerplate code.

Type Classes and Extensibility

Tim explored type classes as a powerful application of implicits, allowing non-intrusive library extensions. By defining behaviors like JSON serialization in companion objects, developers can extend functionality without modifying core libraries. He demonstrated this with a JSON writer example, where implicits ensured type-safe serialization for complex data structures. Tim emphasized that this approach fosters loose coupling, making libraries more modular and easier to maintain.

Practical Debugging Tips

Addressing common challenges, Tim offered strategies for debugging implicits, such as inspecting bytecode or leveraging IDEs to trace implicit resolutions. He warned against chaining multiple implicit conversions, as the compiler restricts itself to a single conversion to avoid complexity. By sharing practical examples, Tim equipped developers with the tools to harness implicits effectively, ensuring they enhance rather than obscure code clarity.

Links:

• ING Bank company website