Jonathan Lalou's Blog

At KotlinConf2024, Jason Parachoniak, a Google Workspace engineer, detailed Google’s shift from a Java-based multiplatform system to Kotlin Multiplatform (KMP), starting with Google Docs. For over a decade, Workspace has relied on shared code for consistency across platforms, like Gmail’s synchronization layer. Jason shared how KMP enhances this approach, leveraging Kotlin’s ecosystem for better performance and native interop. The talk highlighted lessons from the migration, focusing on build efficiency, runtime latency, and memory challenges, offering insights for large-scale KMP adoption.

Why Kotlin Multiplatform for Workspace

Google Workspace has long used multiplatform code to ensure consistency, such as identical email drafts across devices in Gmail or uniform document models in Docs. Jason explained that their Java-based system, using transpilers like J2ObjC, was effective but complex. KMP offers a modern alternative, allowing developers to write Kotlin code that compiles to native platforms, improving runtime performance and ecosystem integration. By targeting business logic—everything beyond the UI—Workspace ensures native-feel apps while sharing critical functionality, aligning with user expectations for productivity tools.

Google Docs: The Migration Testbed

The migration began with Google Docs, chosen for its heavily annotated codebase, which tracks build performance, latency, and memory usage. Jason described how Docs is rolling out on KMP, providing metrics to refine the Kotlin compiler and runtime. This controlled environment allowed Google to compare KMP against their legacy system, ensuring parity before expanding to other apps. Collaboration with JetBrains and the Android team has been key, with iterative improvements driven by real-world data, setting a foundation for broader Workspace adoption.

Tackling Build Performance

Build performance posed challenges, as Google’s Bazel-like system resembles clean builds, unlike Gradle’s incremental approach. Jason recounted a 10-minute build time increase after a Kotlin Native update optimized LLVM bitcode generation. While this improved binary size and speed, it slowed builds. Profiling revealed a slow LLVM pass, already fixed in a newer version. Google patched LLVM temporarily, reducing build times from 30 to 8 minutes, and is working with JetBrains to update Kotlin Native’s LLVM, prioritizing stability alongside the K2 compiler rollout.

Optimizing Runtime Latency

Runtime latency, critical for Workspace apps, required Kotlin Native garbage collection (GC) tweaks. Jason noted that JetBrains proactively adjusted GC before receiving Google’s metrics, but further heuristics were needed as latency issues emerged. String handling in the interop layer also caused bottlenecks, addressed with temporary workarounds. Google is designing long-term fixes with JetBrains, ensuring smooth performance across platforms. These efforts highlight KMP’s potential for high-performance apps, provided runtime challenges are systematically resolved through collaboration.

Addressing Memory Usage

Memory usage spikes were a surprise, particularly between iOS 15 and 16. Jason explained that iOS 16’s security-driven constant pool remapping marked Kotlin Native’s vtables as dirty, consuming megabytes of RAM. Google developed a heap dump tool generating HPROF files, compatible with IntelliJ’s Java heap analysis, to diagnose issues. This tool is being upstreamed to Kotlin Native’s runtime, enhancing debugging capabilities. These insights are guiding Google’s memory optimization strategy, ensuring KMP meets Workspace’s stringent performance requirements as the migration expands.

Links:

• KotlinConf website
• Kotlin website
• JetBrains website
• Jason Parachoniak’s Talk
• Kotlin on Twitter

Georgios Andrianakis, a Quarkus engineer at Red Hat, presented a 46-minute talk at Devoxx Greece 2025, dissecting benchmarking fallacies, based on a talk by performance expert Francisco Negro.

The Benchmarketing Problem

Andrianakis introduced “benchmarketing,” where benchmarks are manipulated for marketing. Inspired by Negro’s frustration with a claim that Helidon outperformed Quarkus in a TechEmpower benchmark, he explored how data can be misrepresented. Benchmarks should be relevant, representative, equitable, repeatable, cost-effective, scalable, and transparent. A misleading article claimed Helidon’s superiority, but Negro’s investigation revealed unfair comparisons, sparking this talk to expose such fallacies.

Dissecting a Flawed Claim

Focusing on equity, Negro analyzed the TechEmpower benchmark, which tests web frameworks on tasks like JSON serialization and database queries. The claim hinged on a test where Helidon used a raw database driver (Vert.x for PostgreSQL), while Quarkus used a full object-relational mapper (ORM) like Hibernate, incurring performance penalties. Filtering for full ORM tests, Quarkus topped the charts, with Helidon absent. Comparing both without ORMs, Quarkus still outperformed. This exposed the claim’s inequity, as it wasn’t apples-to-apples, misleading readers.

Critical Thinking in Benchmarks

Andrianakis emphasized skepticism, citing Hitchens’ Razor: claims without evidence can be dismissed. Using Brendan Gregg’s USE method, Negro identified CPU saturation, not database I/O, as the bottleneck, debunking assumptions. He urged active benchmarking—monitoring errors and resources—and measuring one level deeper to understand performance. Awareness of biases, like confirmation bias, and avoiding assumptions of malice over incompetence, ensures fair evaluation of benchmark claims.

Links

• TechEmpower
• Devoxx Greece

Lecturer

Per Minborg is a Java Core Library Developer at Oracle, specializing in language features and performance improvements. He has contributed to projects enhancing Java’s concurrency and immutability models, drawing from his background in database acceleration tools like Speedment.

Abstract

This investigation explores Java’s ‘final’ keyword as a mechanism for immutability, examining its semantic guarantees from compilation to runtime execution. It contextualizes the balance between mutable flexibility and immutable predictability, delving into JVM trust dynamics and recent proposals like strict finals. Through code demonstrations and JVM internals, the narrative assesses approaches for stronger immutability, including lazy constants, and their effects on threading, optimization, and application speed. Forward implications for Java’s roadmap are considered, emphasizing enhanced reliability and efficiency.

Balancing Mutability and Immutability in Java

Java’s design philosophy accommodates both mutability for dynamic state changes and immutability for stability, yet tensions arise in performance and safety. Immutability, praised in resources like Effective Java, ensures objects maintain a single state, enabling safe concurrent access without locks. Immutable instances can be cached or reused, functioning reliably as keys in collections—unlike mutables that may cause hash inconsistencies after alterations.

Mutability, however, provides adaptability for evolving data, crucial in interactive systems. The ‘final’ keyword aims to enforce constancy, but runtime behaviors complicate this. Declaring ‘final int value = 42;’ implies permanence, yet advanced techniques like reflection with Unsafe can modify it, breaching expectations.

This duality impacts optimizations: trusted finals allow constant propagation, minimizing memory accesses. Yet, Java permits post-constructor updates for deserialization, eroding trust. Demonstrations show reflection altering finals, highlighting vulnerabilities where JVMs must hedge against changes, forgoing inlining.

Historically, Java prioritized flexibility, allowing such mutations for practicality, but contemporary demands favor integrity. Initiatives like “final truly final” seek to ban post-construction changes, bolstering trust for aggressive enhancements.

JVM Trust Mechanisms and Final Field Handling

JVM trust refers to assuming ‘final’ fields remain unchanged post-initialization, enabling efficiencies like constant folding. Current semantics, however, permit reflective or deserialization modifications, limiting optimizations.

Examples illustrate:

class Coordinate {
    final int coord;
    Coordinate(int coord) { this.coord = coord; }
}

Post-constructor, ‘coord’ is trusted, supporting optimizations. Reflection via Field.setAccessible(true) overrides this, as Unsafe manipulations demonstrate. Performance tests reveal trusted finals outperform volatiles in accesses, but untrusted ones underperform.

Java’s model historically allowed mutations for deserialization, but stricter enforcement proposals aim to restrict this. Implications include safer multi-threading, as finals ensure visibility without volatiles.

Advancements in Strict Finals and Leak Prevention

Strict finals strengthen guarantees by prohibiting constructor leaks—publishing ‘this’ before all finals are set. This prevents partial states in concurrent environments, where threads might observe defaults like zero before assignments.

Problematic code:

class LeakyClass {
    final int val = 42;
    LeakyClass() { Registry.register(this); } // Leak
}

Leaks risk threads seeing val=0. Strict finals reject this at compile time, enforcing safe initialization.

Methodologically, this requires refactoring to avoid early publications, but yields threading reliability and optimization opportunities. Benchmarks quantify benefits: trusted paths execute quicker, with fewer barriers.

Lazy Constants for Deferred Initialization

Previewed in Java 25, lazy constants merge mutable deferral with final optimizations. Declared ‘lazy final int val;’, they compute once on access via suppliers:

lazy final int heavy = () -> heavyComputation();

JVM views them as constants after initialization, supporting folding. Use cases include infrequent or expensive values, avoiding startup costs.

Unlike volatiles, lazy constants ensure at-most-once execution, with threads blocking on contention. This fits singletons or caches, surpassing synchronized options in efficiency.

Roadmap and Performance Consequences

Strict finals and lazy constants fortify Java’s immutability, complementing concurrent trends. Consequences include accelerated, secure code, with JVMs exploiting trust for vector operations.

Developers balance: finals for absolutes, lazies for postponements. Roadmaps indicate stabilization in Java 26, expanding usage.

In overview, these evolutions make ‘final’ definitively final, boosting Java’s sturdiness and proficiency.

Links:

• Lecture video: https://www.youtube.com/watch?v=J754RsoUd00
• Per Minborg on Twitter/X: https://twitter.com/PMinborg
• Oracle website: https://www.oracle.com/

Amid the mesmerizing merger of microcontrollers and modern markup, Peter Gallagher, a prolific .NET pioneer and podcast personality, prototypes a pivotal plaything: a prehensile prosthesis powered by Pi, .NET 8, Blazor’s bounty, and SignalR’s synergy. As the mind behind “The .NET Core Podcast” and a maestro of minimal viable marvels, Peter parades the panoply—from GPIO gymnastics to VR vistas—proving platform potency in playful pursuits. His hands-on homage, humming with hardware harmony, heralds hobbyists to harness high-level harmony for haptic happenings.

Peter polls the populace: Raspberry aficionados abound, yet arm artisans are scarce—his spur to share schematics, sans soldering sermons. .NET 8’s native nod to ARM architectures animates accessibility, eclipsing esoteric embeds.

Wiring the Wonder: GPIO and Servo Symphonies

Genesis germinates in GPIO groundwork: Pi’s pins, PWM-proficient, pulse servos to swivel shoulders, elbows, wrists. Peter parades the paraphernalia: SG90 servos, jumper joys, breadboard bastions—budget below 50 quid.

Code commandeers: Iot.Device.Bindings beckon, Servo class summoning sweeps—angle aspirations from 0 to 180. Peter’s prototype: console commands calibrating claws, clutching candies in cinematic clips.

Blazor’s Bridge: Browser-Borne Brawn

Blazor bridges the breach: WebAssembly wielding webcams, SignalR streaming signals—real-time relays routing remote reaches. Peter’s portal: Razor renders ranges, sliders summoning servo shifts; hubs herald hubbub, harmonizing host and handler.

He highlights hitches: CORS courtesies, container conundrums—Pi’s paucity of ports prompting proxies. Yet, yields yield yawns: yawns of yawp, where web wielders wrench wrenches wirelessly.

VR’s Vanguard: Questing for Quarters

Quest 3 quests quaternary: Unity unleashes OpenXR, hand-tracking hailing haptics—gestures grasping grippers. Peter’s phantasm: VR viewport voyaging Pi’s panorama, passthrough peering at prehensile prowess.

Integration ignites: SignalR surges selections, servos saluting salutes—synthetic senses simulating seizures. Peter’s plea: print, procure, partake—his GitHub grotto granting guides.

Horizons of Hardware: Harmonizing Hopes

Peter’s panegyric: .NET’s ubiquity unlocks universes—embedded epics, VR ventures—vitalizing visions. His valediction: venture ventures, validate validations—birthday bonuses beckon bold builders.

Links:

• Peter Gallagher on LinkedIn
• The .NET Core Podcast

Keynote Speakers

Alex Vanyo works as a Developer Relations Engineer at Google, concentrating on adaptive applications for the Android platform. His expertise encompasses user interface design and responsive layouts, contributing to tools that facilitate cross-device compatibility.

Emilie Roberts serves as a Developer Relations Engineer at Google, specializing in Android integration with Chrome OS. She advocates for optimized experiences on large-screen devices, drawing from her background in software engineering to guide developers in multi-form factor adaptations.

Abstract

This analysis explores the principles of adaptive development for Android applications, emphasizing strategies to ensure seamless performance across diverse hardware ecosystems including smartphones, tablets, foldables, automotive interfaces, and extended reality setups. It examines emerging platform modifications in Android 16, updates to Jetpack libraries, and innovative tooling in Android Studio, elucidating their conceptual underpinnings, implementation approaches, and potential effects on user retention and developer workflows. By evaluating practical demonstrations and case studies, the discussion reveals how these elements promote versatile, future-proof software engineering in a fragmented device landscape.

Rationale for Adaptive Strategies in Expanding Ecosystems

Alex Vanyo and Emilie Roberts commence by articulating the imperative for adaptive methodologies in Android development, tracing the evolution from monolithic computing to ubiquitous mobile paradigms. They posit that contemporary applications must transcend single-form-factor constraints to embrace an array of interfaces, from wrist-worn gadgets to vehicular displays and immersive headsets. This perspective is rooted in the observation that users anticipate fluid functionality across all touchpoints, transforming software from mere utilities into integral components of daily interactions.

Contextually, this arises from Android’s proliferation beyond traditional handhelds. Roberts highlights the integration of adaptive apps into automotive environments via Android Automotive OS and Android Auto, where permitted categories can now operate in parked modes without necessitating bespoke versions. This leverages existing mobile codebases, extending reach to in-vehicle screens that serve as de facto tablets.

Furthermore, Android 16 introduces desktop windowing enhancements, enabling phones, foldables, and tablets to morph into free-form computing spaces upon connection to external monitors. With over 500 million active large-screen units, this shift democratizes desktop-like productivity, allowing arbitrary resizing and multitasking. Vanyo notes the foundational AOSP support for connected displays, poised for developer previews, which underscores a methodological pivot toward hardware-agnostic design.

The advent of Android XR further diversifies the landscape, positioning headsets as spatial computing hubs where apps inhabit immersive realms. Home space mode permits 2D window placement in three dimensions, akin to boundless desktops, while full space grants exclusive environmental control for volumetric content. Roberts emphasizes that Play Store-distributed mobile apps inherently support XR, with adaptive investments yielding immediate benefits in this nascent arena.

Implications manifest in heightened user engagement; multi-device owners exhibit tripled usage in streaming services compared to single-device counterparts. Methodologically, this encourages a unified codebase strategy, averting fragmentation while maximizing monetization. However, it demands foresight in engineering to accommodate unforeseen hardware, fostering resilience against ecosystem volatility.

Core Principles and Mindset of Adaptive Design

Delving into the ethos, Vanyo defines adaptivity as a comprehensive tactic that anticipates the Android spectrum’s variability, encompassing screen dimensions, input modalities, and novel inventions. This mindset advocates for a singular application adaptable to phones, tablets, foldables, Chromebooks, connected displays, XR, and automotive contexts, eschewing siloed variants.

Roberts illustrates via personal anecdote: transitioning from phone-based music practice to tablet or monitor-enhanced sessions necessitates consistent features like progress tracking and interface familiarity. Disparities risk user attrition, as alternatives offering cross-device coherence gain preference. This user-centric lens complements business incentives, where adaptive implementations correlate with doubled retention rates, as evidenced by games like Asphalt Legends Unite.

Practically, demonstrations of the Socialite app—available on GitHub—exemplify this through a list-detail paradigm via Compose Adaptive. Running identical code across six devices, it dynamically adjusts: XR home space resizes panes fluidly, automotive interfaces optimize for parked interactions, and desktop modes support free-form windows. Such versatility stems from libraries detecting postures like tabletop on foldables, enabling tailored views without codebase bifurcation.

Analytically, this approach mitigates development overhead by centralizing logic, yet requires vigilant testing against configuration shifts to preserve state and avoid visual artifacts. Implications extend to inclusivity, accommodating diverse user scenarios while positioning developers to capitalize on emerging markets like XR, projected to burgeon.

Innovations in Tooling and Libraries for Responsiveness

Roberts and Vanyo spotlight Compose Adaptive 1.1, a Jetpack library facilitating responsive UIs via canonical patterns. It categorizes windows into compact, medium, and expanded classes, guiding layout decisions—e.g., bottom navigation for narrow views versus side rails for wider ones. The library’s supporting pane abstraction manages list-detail flows, automatically transitioning based on space availability.

Code exemplar:

val supportingPaneScaffoldState = rememberSupportingPaneScaffoldState(
    initialValue = SupportingPaneScaffoldValue.Hidden
)
SupportingPaneScaffold(
    state = supportingPaneScaffoldState,
    mainPane = { ListContent() },
    supportingPane = { DetailContent() }
)

This snippet illustrates dynamic pane revelation, adapting to resizes without explicit orientation handling. Navigation 3 complements this, decoupling navigation graphs from UI elements for reusable, posture-aware routing.

Android Studio’s enhancements, like the adaptive UI template wizard, streamline initiation by generating responsive scaffolds. Visual linting detects truncation or overflow in varying configurations, while emulators simulate XR and automotive scenarios for holistic validation.

Methodologically, these tools embed adaptivity into workflows, leveraging Compose’s declarative paradigm for runtime adjustments. Contextually, they address historical assumptions about fixed orientations, preparing for Android 16’s disregard of such restrictions on large displays. Implications include reduced iteration cycles and elevated quality, though necessitate upskilling in reactive design principles.

Platform Shifts and Preparation for Android 16

A pivotal revelation concerns Android 16’s cessation of honoring orientation, resizability, and aspect ratio constraints on displays exceeding 600dp. Targeting SDK 36, activities must accommodate arbitrary shapes, ignoring portrait/landscape mandates to align with user preferences. This standardization echoes OEM overrides, enforcing free-form adaptability.

Common pitfalls include clipped elements, distorted previews, or state loss during rotations—issues users encounter via overrides today. Vanyo advises comprehensive testing, layout revisions, and state preservation. Transitional aids encompass opt-out flags until SDK 37, user toggles, and game exemptions via manifest or Play categories.

For games, Unity 6 integrates configuration APIs, enabling seamless handling of size and density alterations. Samples guide optimizations, while titles like Dungeon Hunter 5 demonstrate foldable integrations yielding retention boosts.

Case studies reinforce: Luminar Neo’s Compose-built editor excels offline via Tensor SDK; Cubasis 3 offers robust audio workstations on Chromebooks; Algoriddim’s djay explores XR scratching. These exemplify methodological fusion of libraries and testing, implying market advantages through device ubiquity.

Strategic Implications and Forward Outlook

Adaptivity emerges as a strategic imperative amid Android’s diversification, where single codebases span ecosystems, enhancing loyalty and revenue. Platform evolutions like desktop windowing and XR demand foresight, with tools mitigating complexities.

Future trajectories involve deeper integrations, potentially with AI-driven layouts, ensuring longevity. Developers are urged to iterate compatibly, avoiding presumptions to future-proof against innovations, ultimately enriching user experiences across the Android continuum.

Links:

• Alex Vanyo on LinkedIn
• Alex Vanyo on GitHub
• Emilie Roberts on LinkedIn
• Lecture Video

From Novice to Custom PCB

Embarking on a journey from minimal electronics knowledge to designing a custom printed circuit board (PCB) is a daunting yet rewarding endeavor. At OxidizeConf2024, Josh Junon from GitButler shared his nine-month odyssey of building a PCB with an STM32 microcontroller, powered by async Rust firmware. Josh’s candid narrative detailed his trials, errors, and eventual success in creating a component for kernel CI/CD testing pipelines, offering valuable lessons for software developers venturing into hardware.

With a strong software background but little electronics experience, Josh tackled the complexities of PCB design, from selecting components to soldering hair-thin parts. Using Rust’s async capabilities, he developed firmware that leveraged interrupts for efficient communication, integrating the PCB into a larger project. His story underscores Rust’s versatility in bridging software and hardware, enabling developers to create reliable, high-performance embedded systems without extensive hardware expertise.

Practical Lessons in Hardware Development

Josh’s presentation was a treasure trove of practical advice. He emphasized the importance of verifying component sizes early, as datasheets often understate their minuteness. For instance, selecting appropriately sized parts saved costs and prevented assembly errors. He also advised against prioritizing aesthetics, such as costly black solder masks, in favor of affordable green ones. These lessons, born from trial and error, highlight the importance of aligning hardware choices with project constraints, particularly for budget-conscious developers.

Rust’s async ecosystem, including libraries like embassy, facilitated Josh’s firmware development. The STM32F4 (or possibly F7) microcontroller, though potentially overpowered, provided a robust platform for his needs. By sharing his GitHub repository, Josh invites community feedback, fostering collaboration to refine his approach. His experience demonstrates how Rust’s safety guarantees and modern tooling can empower software developers to tackle hardware challenges effectively.

Balancing Cost and Learning

Cost management was a recurring theme in Josh’s journey. He cautioned against over-purchasing components and investing in expensive equipment early on, noting that basic tools suffice for initial projects. Custom stencil sizes, while tempting, added unnecessary costs, and Josh recommended reusing standard boards for versatility. Despite these challenges, the learning outcomes were profound, equipping Josh with skills in microcontrollers, interrupts, and embedded programming that enhanced his broader project.

Josh’s success highlights Rust’s role in democratizing hardware development. By leveraging Rust’s ecosystem and community resources, he transformed a side quest into a valuable contribution to kernel testing. His call to “just do it” inspires developers to explore hardware with Rust, proving that persistence and community support can yield remarkable results in unfamiliar domains.

Links:

• GitButler company website

The concluding panel of KotlinConf2025 offered a vibrant and candid discussion, serving as the capstone to the conference. The diverse group of experts from JetBrains, Netflix, and Google engaged in a wide-ranging dialogue, reflecting on the state of Kotlin, its evolution, and the path forward. They provided a unique blend of perspectives, from language design and backend development to mobile application architecture and developer experience. The conversation was an unfiltered look into the challenges and opportunities facing the Kotlin community, touching on everything from compiler performance to the future of multiplatform development.

The Language and its Future

A central theme of the discussion was the ongoing development of the Kotlin language itself. The panel members, including Simon from the K2 compiler team and Michael from language design, shared insights into the rigorous process of evolving Kotlin. They addressed questions about new language features and the careful balance between adding functionality and maintaining simplicity. A notable point of contention and discussion was the topic of coroutines and the broader asynchronous programming landscape. The experts debated the best practices for managing concurrency and how Kotlin’s native features are designed to simplify these complex tasks. There was a consensus that while new features are exciting, the primary focus remains on stability, performance, and enhancing the developer experience.

The State of Multiplatform Development

The conversation naturally shifted to Kotlin Multiplatform (KMP), which has become a cornerstone of the Kotlin ecosystem. The panelists explored the challenges and successes of building applications that run seamlessly across different platforms. Representatives from companies like Netflix and AWS, who are using KMP for large-scale projects, shared their experiences. They discussed the complexities of managing shared codebases, ensuring consistent performance, and maintaining a robust build system. The experts emphasized that while KMP offers immense benefits in terms of code reuse, it also requires a thoughtful approach to architecture and toolchain management. The panel concluded that KMP is a powerful tool, but its success depends on careful planning and a deep understanding of the underlying platforms.

Community and Ecosystem

Beyond the technical discussions, the panel also reflected on the health and vibrancy of the Kotlin community. A developer advocate, SA, and others spoke about the importance of fostering an inclusive environment and the role of the community in shaping the language. They highlighted the value of feedback from developers and the critical role it plays in guiding the direction of the language and its tooling. The discussion also touched upon the broader ecosystem, including the various libraries and frameworks that have emerged to support Kotlin development. The panel’s enthusiasm for the community was palpable, and they expressed optimism about Kotlin’s continued growth and adoption in the years to come.

Links:

• Google company website
• JetBrains company website
• Netflix company website

class Account:
def init(self, balance):
self.balance = balance

def transaction(self, amount):
    if amount > 0:
        self.balance += amount
    else:
        if abs(amount) <= self.balance:
            self.balance += amount
        else:
            raise ValueError("Insufficient funds")

Understanding the Shift in Observability Landscape

If your organization relies on the Elastic Stack (ELK—Elasticsearch, Logstash, Kibana) for log aggregation and basic telemetry, you are likely familiar with the challenges inherent in self-managing disparate data streams. The ELK stack provides powerful, flexible, open-source tools for search and visualization.

However, the major commercial platforms—Splunk, DataDog, and Dynatrace—represent a significant evolutionary step toward unified, full-stack observability and automated root cause analysis. They promise to shift the user’s focus from searching for data to receiving contextualized answers.

For engineers fluent in ELK’s log-centric model and KQL, understanding these competitors requires grasping their fundamental differences in data ingestion, correlation, and intelligence.

1. Splunk: The Enterprise Log King and SIEM Powerhouse

Splunk stands as the most direct philosophical competitor to the ELK Stack, built on the principle of analyzing “machine data” (logs, events, and metrics). Its defining characteristics are its powerful query language and its leadership in the Security Information and Event Management (SIEM) space.

Key Concepts

• Indexer vs. Elasticsearch: Similar to Elasticsearch, the Indexer stores and processes data. However, Splunk primarily employs Schema-on-Read—meaning field definitions are applied at the time of search, not ingestion. This offers unparalleled flexibility for unstructured log data but can introduce query complexity.
• Forwarders vs. Beats/Logstash: Splunk uses Universal Forwarders (UF) (lightweight agents, similar to Beats) and Heavy Forwarders (HF), which can perform pre-processing and aggregation (similar to Logstash) before sending data to the Indexers.

The Power of Search Processing Language (SPL)

While ELK uses the Lucene-based KQL, Splunk relies on the proprietary Search Processing Language (SPL).

SPL is a pipeline-based language, where commands are chained together using the pipe symbol (|). This architecture allows for advanced data transformation, statistical analysis, and correlation after the initial data retrieval.

Specialized Feature: Enterprise Security (SIEM)

Splunk is the market leader in SIEM, using the operational intelligence collected by the platform for dedicated security analysis, threat detection, and compliance auditing. This dedicated security layer extends far beyond the core log analysis features of standard ELK deployments.

2. DataDog: The Cloud-Native Unifier via Tagging

DataDog is a pure Software-as-a-Service (SaaS) solution built explicitly for modern, dynamic, and distributed cloud environments. Its strength lies in unifying the three pillars of observability (logs, metrics, and traces) through a standardized tagging mechanism.

The Unified Agent and APM Focus

• Unified Agent: Unlike the ELK stack, where the three pillars often require distinct configurations (Metricbeat, Filebeat, Elastic APM Agent), the DataDog Agent is a single, lightweight installation that collects logs, infrastructure metrics, and application traces automatically.
• Native APM and Distributed Tracing: DataDog provides best-in-class Application Performance Monitoring (APM). It instruments your code to capture Distributed Traces (the journey of a request across services). This allows engineers to move seamlessly from a high-level metric graph to a detailed, code-level flame graph showing latency attribution.

Correlation through Tagging and Facets

DataDog abstracts much of the complex querying away by leveraging pervasive tags.

• Tags: Every piece of data (log line, metric point, trace segment) is automatically stamped with consistent tags (env:prod, service:frontend, region:us-east-1).
• Facets: These tags become clickable filters (Facets) in the UI, allowing engineers to filter and correlate data instantly across the entire platform. This shifts the operational paradigm from writing complex KQL searches to rapidly filtering data by context.

Specialized Features: RUM and Synthetic Monitoring

DataDog offers deep insight into user experience:

• Real User Monitoring (RUM): Tracks the performance and error rates experienced by actual end-users in their browsers or mobile apps.
• Synthetic Monitoring: Simulates critical user flows (e.g., logging in, checking out) from various global locations to proactively identify availability and performance issues before users are impacted.

3. Dynatrace: AI-Powered Automation and Answer Delivery

Dynatrace is an enterprise-grade SaaS platform distinguished by its commitment to automation and its reliance on the Davis® AI engine to provide “answers, not just data.” It is designed to minimize configuration time and accelerate Mean Time To Resolution (MTTR).

The OneAgent and Smartscape® Topology

• OneAgent vs. Manual Agents: The OneAgent is Dynatrace’s most powerful differentiator. Installed once per host, it automatically discovers and monitors all processes, applications, and services without manual configuration.
• Smartscape®: This feature creates a real-time, interactive dependency map of your entire environment—from cloud infrastructure up through individual application services. This map is crucial, as it provides the context needed for the AI engine to function correctly.

Davis® AI: Root Cause Analysis (RCA) vs. Threshold Alerting

This intelligent layer is the core of Dynatrace, offering a radical departure from traditional threshold alerting used in most ELK deployments.

Specialized Feature: PurePath® Technology

Dynatrace’s proprietary tracing technology captures every transaction end-to-end, providing deep, code-level visibility into every tier of an application stack. This level of granularity is essential for complex microservices environments where a single user request might traverse dozens of components.

Conclusion: Shifting from Data Search to Answer Delivery

For teams transitioning from the highly customizable but labor-intensive ELK stack, the primary shift required is recognizing the value of automation and correlation:

While the initial investment and cost of these commercial platforms are higher than open-source ELK, their value proposition lies in the reduction of operational toil, faster incident resolution, and the ability to scale modern, complex microservice architectures with true confidence.

As generative paradigms proliferate, scripting’s syntax strains under AI’s amorphous allure—prompts as prosaic prose, yet perilous in precision. Yohan Lasorsa, Microsoft’s principal developer advocate and Angular GDE, unveiled GenAIScript at dotJS 2025, a JS-inflected idiom abstracting LLM labyrinths into lucid loops. With 15 years traversing IoT’s interstices to cloud’s canopies, Yohan likened this lexicon to jQuery’s jubilee: DOM’s discord domesticated, now GenAI’s gyrations gentled for mortal makers.

Yohan’s yarn recalled jQuery’s jihad: browser balkanization banished, events etherealized—20 years on, GenAI’s gale mirrors, models multiplying, APIs anarchic. GenAIScript’s grace: JS carapace cloaking complexities—await ai.chat('prompt') birthing banter, ai.forEach(items, 'summarize') distilling dossiers. Demos danced: file foragers (fs.readFile), prompt pipelines (ai.pipe(model).chat(query)), even AST adventurers refactoring Angular artifacts—CLI’s churn supplanted by semantic sorcery.

This superstructure spans: agents’ autonomy (ai.agent({tools})), RAG’s retrieval (ai.retrieve({query, store})), even vision’s vignettes (ai.vision(image)). Yohan’s yield: ergonomics eclipsing exhaustion—built-ins for Bedrock, Ollama; extensibility via plugins. Caveat’s cadence: tool for tinkering, not titanic tomes—yet frameworks’ fledglings may flock hither.

GenAIScript’s gospel: prompting’s poetry, scripted sans strife—democratizing discernment in AI’s ascent.

jQuery’s Echo in AI’s Era

Yohan juxtaposed jQuery’s quirk-quelling with GenAI’s gale—models’ menagerie, APIs’ anarchy. GenAIScript’s girdle: JS’s jacket jacketting journeys—chat’s cadence, forEach’s finesse.

Patterns’ Parade and Potentials

Agents’ agency, RAG’s recall—pipelines pure, vision’s vista. Yohan’s yarns: Angular migrations mended, Bedrock bridged—plugins’ pliancy promising proliferation.

Links:

• Yohan Lasorsa on LinkedIn
• GenAIScript