Jonathan Lalou's Blog

Archive for the ‘en-US’ Category

ls /sys/firmware/efi # exists → UEFI

> **Security Note**: UEFI Secure Boot prevents unsigned kernels from loading—a critical defense in enterprise environments.

### 1.2 Bootloader: GRUB2

dmesg | grep -i “EFI v”

Joe Cieplinski, Creative Director at Bombing Brain Interactive, imparts wisdom on navigating the indie development landscape, drawing from his journey with apps like Teleprompt+ and Setlists. A former Apple Store presenter and app designer since 2008, Joe shares monetization tactics, customer engagement, and marketing essentials for sustaining a living through mobile software. His session, infused with anecdotes from his iOS and OS X ventures, underscores premium pricing, in-app purchases, and responsive support as pillars of success.

Indie developers, Joe contends, thrive by treating apps as products with ongoing value. He recounts Bombing Brain’s evolution, from manual support to PDF/iBooks guides, reducing queries while building loyalty. Effective strategies, he illustrates, blend quality delivery with community interaction, turning users into advocates.

Monetization Models and Pricing Wisdom

Joe advocates premium upfront pricing for perceived value, citing Teleprompt+’s $19.99 tagline drawing discerning users. Subscriptions and in-app upgrades, he demos, extend revenue—unlocking features like cloud sync fosters recurring income.

Avoid free apps’ ad pitfalls, Joe warns; targeted promotions on App Store or forums yield better conversions than broad ads.

Customer Support and Resource Creation

Exemplary support, Joe emphasizes, differentiates indies. He shares Tim Mosley’s empathetic responses, turning complaints into testimonials. Comprehensive manuals—100-page PDFs for Teleprompt+—preempt queries, saving time while showcasing depth.

Social media, though secondary for his audience, amplifies reach; Twitter/App.net engagements build rapport.

Building Reputation and Marketing Tactics

Reputation accrues through consistent excellence, Joe asserts. Cross-platform ports, like Teleprompt+ for iPad/iPhone/OS X, expand ecosystems. Collaborations with friends, as in his trio, infuse passion into products.

Marketing favors organic growth: user reviews, niche forums, and targeted outreach outperform paid campaigns for bootstrapped teams.

Lessons from Bombing Brain’s Journey

Joe reflects on x2y’s launch, balancing innovation with sustainability. He urges indies to prioritize joy—his “lifestyle” approach sustains creativity amid challenges.

This blueprint, Joe concludes, equips solo creators for enduring viability in app markets.

Links:

• Joe Cieplinski on LinkedIn
• Bombing Brain Interactive company website

FROM nginx:alpine
COPY –from=builder /app/dist /usr/share/nginx/html
EXPOSE 80

This pattern reduces final image size from hundreds of megabytes to tens of megabytes. **Layer caching** optimization requires careful instruction ordering:

COPY package.json package-lock.json ./
RUN npm ci
COPY . .

Copying dependency manifests first maximizes cache reuse during development.

## Networking Models and Service Discovery
Docker’s default bridge network isolates containers on a single host. Production environments demand multi-host communication. **Overlay networks** create virtual networks across swarm nodes:

docker network create –driver overlay –attachable prod-net
docker service create –network prod-net –name api myapp:latest

Docker’s built-in DNS enables service discovery by name. For external traffic, **ingress routing meshes** like Traefik or NGINX provide load balancing, TLS termination, and canary deployments.

## Persistent Storage for Stateful Applications
Stateless microservices dominate container use cases, but databases and queues require durable storage. **Docker volumes** offer the most flexible solution:

docker volume create postgres-data
docker run -d \
–name postgres \
-v postgres-data:/var/lib/postgresql/data \
-e POSTGRES_PASSWORD=secret \
postgres:13

For distributed environments, **CSI (Container Storage Interface)** plugins integrate with Ceph, GlusterFS, or cloud-native storage like AWS EBS.

## Orchestration and Automated Operations
Docker Swarm provides native clustering with zero external dependencies:

docker swarm init
docker stack deploy -c docker-compose.yml myapp
“`

For advanced workloads, Kubernetes offers:
– Deployments for rolling updates and self-healing.
– Horizontal Pod Autoscaling based on CPU/memory or custom metrics.
– ConfigMaps and Secrets for configuration management.

Migration paths typically begin with stateless services in Swarm, then progress to Kubernetes for stateful and machine-learning workloads.

Security Hardening and Compliance

Production containers must follow security best practices:
– Run as non-root users: USER appuser in Dockerfile.
– Scan images with Trivy or Clair in CI/CD pipelines.
– Apply seccomp and AppArmor profiles to restrict system calls.
– Use RBAC and Network Policies in Kubernetes to enforce least privilege.

Production Case Studies and Operational Wisdom

Spotify manages thousands of microservices using Helm charts and custom operators. Airbnb leverages Kubernetes for dynamic scaling during peak booking periods. The New York Times uses Docker for CI/CD acceleration, reducing deployment time from hours to minutes.

Common lessons include:
– Monitor with Prometheus and Grafana.
– Centralize logs with ELK or Loki.
– Implement distributed tracing with Jaeger or Zipkin.
– Use chaos engineering to validate resilience.

Strategic Impact on DevOps Culture

Docker fundamentally accelerates the CI/CD pipeline and enables immutable infrastructure. Success requires cultural alignment: developers embrace infrastructure-as-code, operations teams adopt GitOps workflows, and security integrates into every stage. Orchestration platforms bridge the gap between development velocity and operational stability.

Links:

• Jérôme Petazzoni on LinkedIn
• Docker Documentation

Venkat Subramaniam, an award-winning author and founder of Agile Developer, Inc., guides developers through the paradigm shift of functional programming on the JVM. Renowned for Functional Programming in Java and his global mentorship, Venkat uses Java 8, Groovy, and Scala to illustrate functional tenets. His session contrasts imperative statements with composable expressions, demonstrating how to leverage lambda expressions and higher-order functions for elegant, maintainable code.

Functional programming, Venkat posits, transcends syntax—it’s a mindset fostering immutability and data flow. Through practical examples, he showcases Groovy’s idiomatic functional constructs and Scala’s expression-driven purity, equipping attendees to rethink application design.

Functional Principles and Expressions

Venkat contrasts statements—imperative, mutation-driven blocks—with expressions, which compute and return values. He demos a Java 8 stream pipeline, transforming data without side effects, versus a loop’s mutability.

Expressions, Venkat emphasizes, enable seamless composition, fostering cleaner, more predictable codebases.

Groovy’s Functional Idioms

Groovy, though not purely functional, excels in functional style, Venkat illustrates. He showcases collect and findAll for list transformations, akin to Java 8 streams, with concise closures.

These idioms, he notes, simplify data processing, making functional patterns intuitive for Java developers.

Scala’s Expression-Driven Design

Scala’s expression-centric nature shines in Venkat’s examples: every construct returns a value, enabling chaining. He demos pattern matching and for-comprehensions, streamlining complex workflows.

This purity, Venkat argues, minimizes state bugs, aligning with functional ideals.

Higher-Order Functions and Composition

Venkat explores higher-order functions, passing lambdas as arguments. A Groovy example composes functions to filter and map data, while Scala’s currying simplifies partial application.

Such techniques, he asserts, enhance modularity, enabling parallelization for performance-critical tasks.

Practical Adoption and Parallelization

Venkat advocates starting with small functional refactors, like replacing loops with streams. He demos parallel stream processing in Java 8, leveraging multi-core CPUs.

This pragmatic approach, he concludes, bridges imperative habits with functional elegance, boosting scalability.

Links:

• Venkat Subramaniam on LinkedIn
• Agile Developer company website

FROM node:16 AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

Lecturer

Jérôme Petazzoni works as a Docker expert and previously served as an engineer at Docker Inc. He brings a strong background in system administration and distributed systems. Jérôme has deployed containerized workloads at scale in production environments. He frequently speaks on container orchestration, security, and operational best practices.

Abstract

This article distills hard-won production experience with Docker, covering networking, persistent storage, orchestration, and security. It examines common operational pitfalls—such as container sprawl, image bloat, and network complexity—and presents proven solutions including multi-stage builds, overlay networks, and Kubernetes integration. Real-world case studies from leading organizations illustrate strategies for achieving reliability, scalability, and security in containerized production systems.

Image Optimization and Build Strategies

Docker images must remain lean to ensure fast deployments and efficient resource usage. Multi-stage builds separate build-time dependencies from runtime artifacts:

“`

Antonio Goncalves and Arun Gupta, luminaries in Java EE advocacy, deliver a comprehensive exploration of Java EE 7’s advancements, blending simplification with expanded capabilities. Antonio, a senior architect and author of Beginning Java EE 6 Platform with GlassFish 3, collaborates with Arun, Red Hat’s Director of Developer Advocacy and former Java EE pioneer at Sun Microsystems, to unveil WebSocket, JSON processing, and enhanced CDI and JTA features. Their session, rich with demos, highlights how these innovations bolster HTML5 support and streamline enterprise development.

Java EE 7, they assert, refines container services while embracing modern web paradigms. From WebSocket’s real-time communication to CDI’s unified bean management, they showcase practical integrations, ensuring developers can craft scalable, responsive applications.

WebSocket for Real-Time Communication

Antonio introduces WebSocket, a cornerstone for HTML5’s bidirectional connectivity. He demonstrates @ServerEndpoint-annotated classes, crafting a chat application where messages flow instantly, bypassing HTTP’s overhead.

Arun details encoders/decoders, transforming POJOs to wire-ready text or binary frames, streamlining data exchange for real-time apps like live dashboards.

JSON Processing and JAX-RS Enhancements

Arun explores JSON-P (JSR 353), parsing and generating JSON with a fluid API. He demos building JSON objects from POJOs, integrating with JAX-RS’s HTTP client for seamless RESTful interactions.

This synergy, Antonio notes, equips developers to handle data-driven web applications, aligning with HTML5’s data-centric demands.

CDI and Managed Bean Alignment

Antonio unveils CDI’s evolution, unifying managed beans with injectable interceptors. He showcases constructor injection and method-level validation, simplifying dependency management across EJBs and servlets.

Arun highlights JTA’s declarative transactions, enabling @Transactional annotations to streamline database operations, reducing boilerplate.

Simplified JMS and Batch Processing

Arun introduces JMS 2.0’s simplified APIs, demonstrating streamlined message publishing. The new Batch API (JSR 352), Antonio adds, orchestrates chunk-based processing for large datasets, with demos showcasing job definitions.

These enhancements, they conclude, enhance usability, pruning legacy APIs while empowering enterprise scalability.

Resource Definitions and Community Engagement

Antonio details expanded resource definitions, configuring data sources via annotations. Arun encourages JCP involvement, noting Java EE 8’s community-driven roadmap.

Their demos—leveraging GlassFish—illustrate practical adoption, inviting developers to shape future specifications.

Links:

• Antonio Goncalves on LinkedIn
• Arun Gupta on LinkedIn
• Red Hat company website

Case

My application consists in a WAR I deploy on Tomcat or Jetty during the development phase. I execute Eclipse Jetty either in standalone, or via Maven Jetty plugin – most of the time.

On updating and deploying the application on my laptop (which is not my primary development machine), I get the following error with Maven:
[java]java.lang.Exception: Timeout scanning annotations[/java]
Unlike, with a standalone instance of Jetty, the WAR is successfully deployed.

Complete stacktrace

[java]2014-09-08 22:28:50.669:INFO:oeja.AnnotationConfiguration:main: Scanned 1 container path jars, 87 WEB-INF/lib jars, 1 WEB-INF/classes dirs in 65922ms for context o.e.j.m.p.JettyWebAppContext@13bb109{/,[file:/D:/JLALOU/development/forfait-XXX-XXX/XXX-web/src/main/webapp/, jar:file:/C:/Users/jlalou/.m2/repository/org/primefaces/extensions/primefaces-extensions/2.0.0/primefaces-extensions-2.0.0.jar!/META-INF/resources/, jar:file:/C:/Users/jlalou/.m2/repository/org/primefaces/themes/bootstrap/1.0.10/bootstrap-1.0.10.jar!/META-INF/resources/, jar:file:/C:/Users/jlalou/.m2/repository/org/primefaces/primefaces/5.0/primefaces-5.0.jar!/META-INF/resources/, jar:file:/C:/Users/jlalou/.m2/repository/com/sun/faces/jsf-impl/2.2.6/jsf-impl-2.2.6.jar!/META-INF/resources/],STARTING}{file:/D:/JLALOU/development/forfait-XXX-XXX/XXX-web/src/main/webapp/}
2014-09-08 22:28:50.670:WARN:oejw.WebAppContext:main: Failed startup of context o.e.j.m.p.JettyWebAppContext@13bb109{/,[file:/D:/JLALOU/development/forfait-XXX-XXX/XXX-web/src/main/webapp/, jar:file:/C:/Users/jlalou/.m2/repository/org/primefaces/extensions/primefaces-extensions/2.0.0/primefaces-extensions-2.0.0.jar!/META-INF/resources/, jar:file:/C:/Users/jlalou/.m2/repository/org/primefaces/themes/bootstrap/1.0.10/bootstrap-1.0.10.jar!/META-INF/resources/, jar:file:/C:/Users/jlalou/.m2/repository/org/primefaces/primefaces/5.0/primefaces-5.0.jar!/META-INF/resources/, jar:file:/C:/Users/jlalou/.m2/repository/com/sun/faces/jsf-impl/2.2.6/jsf-impl-2.2.6.jar!/META-INF/resources/],STARTING}{file:/D:/JLALOU/development/forfait-XXX-XXX/XXX-web/src/main/webapp/}
java.lang.Exception: Timeout scanning annotations
at org.eclipse.jetty.annotations.AnnotationConfiguration.scanForAnnotations(AnnotationConfiguration.java:571)
at org.eclipse.jetty.annotations.AnnotationConfiguration.configure(AnnotationConfiguration.java:441)
at org.eclipse.jetty.webapp.WebAppContext.configure(WebAppContext.java:466)
at org.eclipse.jetty.webapp.WebAppContext.startContext(WebAppContext.java:1342)
at org.eclipse.jetty.server.handler.ContextHandler.doStart(ContextHandler.java:745)
at org.eclipse.jetty.webapp.WebAppContext.doStart(WebAppContext.java:492)
at org.eclipse.jetty.maven.plugin.JettyWebAppContext.doStart(JettyWebAppContext.java:282)
at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:68)
at org.eclipse.jetty.util.component.ContainerLifeCycle.start(ContainerLifeCycle.java:117)
at org.eclipse.jetty.util.component.ContainerLifeCycle.doStart(ContainerLifeCycle.java:99)
at org.eclipse.jetty.server.handler.AbstractHandler.doStart(AbstractHandler.java:60)
at org.eclipse.jetty.server.handler.ContextHandlerCollection.doStart(ContextHandlerCollection.java:154)
at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:68)
at org.eclipse.jetty.util.component.ContainerLifeCycle.start(ContainerLifeCycle.java:117)
at org.eclipse.jetty.util.component.ContainerLifeCycle.doStart(ContainerLifeCycle.java:99)
at org.eclipse.jetty.server.handler.AbstractHandler.doStart(AbstractHandler.java:60)
at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:68)
at org.eclipse.jetty.util.component.ContainerLifeCycle.start(ContainerLifeCycle.java:117)
at org.eclipse.jetty.server.Server.start(Server.java:358)
at org.eclipse.jetty.util.component.ContainerLifeCycle.doStart(ContainerLifeCycle.java:99)
at org.eclipse.jetty.server.handler.AbstractHandler.doStart(AbstractHandler.java:60)
at org.eclipse.jetty.server.Server.doStart(Server.java:325)
at org.eclipse.jetty.maven.plugin.JettyServer.doStart(JettyServer.java:68)
at org.eclipse.jetty.util.component.AbstractLifeCycle.start(AbstractLifeCycle.java:68)
at org.eclipse.jetty.maven.plugin.AbstractJettyMojo.startJetty(AbstractJettyMojo.java:564)
at org.eclipse.jetty.maven.plugin.AbstractJettyMojo.execute(AbstractJettyMojo.java:360)
at org.eclipse.jetty.maven.plugin.JettyRunMojo.execute(JettyRunMojo.java:168)
at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo(DefaultBuildPluginManager.java:133)
at org.apache.maven.lifecycle.internal.MojoExecutor.execute(MojoExecutor.java:208)
at org.apache.maven.lifecycle.internal.MojoExecutor.execute(MojoExecutor.java:153)
at org.apache.maven.lifecycle.internal.MojoExecutor.execute(MojoExecutor.java:145)
at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject(LifecycleModuleBuilder.java:108)
at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject(LifecycleModuleBuilder.java:76)
at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build(SingleThreadedBuilder.java:51)
at org.apache.maven.lifecycle.internal.LifecycleStarter.execute(LifecycleStarter.java:116)
at org.apache.maven.DefaultMaven.doExecute(DefaultMaven.java:361)
at org.apache.maven.DefaultMaven.execute(DefaultMaven.java:155)
at org.apache.maven.cli.MavenCli.execute(MavenCli.java:584)
at org.apache.maven.cli.MavenCli.doMain(MavenCli.java:213)
at org.apache.maven.cli.MavenCli.main(MavenCli.java:157)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced(Launcher.java:289)
at org.codehaus.plexus.classworlds.launcher.Launcher.launch(Launcher.java:229)
at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode(Launcher.java:415)
at org.codehaus.plexus.classworlds.launcher.Launcher.main(Launcher.java:356)[/java]

Explanation

Since the release of 9.1 branch, Jetty server limits the scan time for annotations at 60 seconds, by default.
The exception is raised here: source
[java]boolean timeout = !latch.await(getMaxScanWait(context), TimeUnit.SECONDS);

if (LOG.isDebugEnabled())
{
for (ParserTask p:_parserTasks)
LOG.debug("Scanned {} in {}ms", p.getResource(), TimeUnit.MILLISECONDS.convert(p.getStatistic().getElapsed(), TimeUnit.NANOSECONDS));

LOG.debug("Scanned {} container path jars, {} WEB-INF/lib jars, {} WEB-INF/classes dirs in {}ms for context {}",
_containerPathStats.getTotal(), _webInfLibStats.getTotal(), _webInfClassesStats.getTotal(),
(TimeUnit.MILLISECONDS.convert(System.nanoTime()-start, TimeUnit.NANOSECONDS)),
context);
}

if (timeout)
me.add(new Exception("Timeout scanning annotations"));[/java]

Fix

As a quick fix, on launching Maven, add the option -Dorg.eclipse.jetty.annotations.maxWait=120 (set a higher value if needed):
[java]mvn jetty:run -Dorg.eclipse.jetty.annotations.maxWait=120[/java]

You can also set this property directly in jetty-*.xml configuration files, for webapp or even for a complete server.

Lecturer

Ben Christensen works as a software engineer at Netflix. He leads the development of reactive libraries for the JVM. Ben serves as a core contributor to RxJava. He possesses extensive experience in constructing resilient, low-latency systems for streaming platforms. His expertise centers on applying functional reactive programming principles to microservices architectures.

Abstract

This article provides an in-depth exploration of RxJava, Netflix’s implementation of Reactive Extensions for the JVM. It analyzes the Observable pattern as a foundation for composing asynchronous and event-driven programs. The discussion covers essential operators for data transformation and composition, schedulers for concurrency management, and advanced error handling strategies. Through concrete Netflix use cases, the article demonstrates how RxJava enables non-blocking, resilient applications and contrasts this approach with traditional callback-based paradigms.

The Observable Pattern and Push vs. Pull Models

RxJava revolves around the Observable, which functions as a push-based, composable iterator. Unlike the traditional pull-based Iterable, Observables emit items asynchronously to subscribers. This fundamental duality enables uniform treatment of synchronous and asynchronous data sources:

Observable<String> greeting = Observable.just("Hello", "RxJava");
greeting.subscribe(System.out::println);

The Observer interface defines three callbacks: onNext for data emission, onError for exceptions, and onCompleted for stream termination. RxJava enforces strict contracts for backpressure—ensuring producers respect consumer consumption rates—and cancellation through unsubscribe operations.

Operator Composition and Declarative Programming

RxJava provides over 100 operators that transform, filter, and combine Observables in a declarative manner. These operators form a functional composition pipeline:

Observable.range(1, 10)
          .filter(n -> n % 2 == 0)
          .map(n -> n * n)
          .subscribe(square -> System.out.println("Square: " + square));

The flatMap operator proves particularly powerful for concurrent operations, such as parallel API calls:

Observable<User> users = getUserIds();
users.flatMap(userId -> userService.getDetails(userId), 5)
     .subscribe(user -> process(user));

This approach eliminates callback nesting (callback hell) while maintaining readability and composability. Marble diagrams visually represent operator behavior, illustrating timing, concurrency, and error propagation.

Concurrency Control with Schedulers

RxJava decouples computation from threading through Schedulers, which abstract thread pools:

Observable.just(1, 2, 3)
          .subscribeOn(Schedulers.io())
          .observeOn(Schedulers.computation())
          .map(this::cpuIntensiveTask)
          .subscribe(result -> display(result));

Common schedulers include:
– Schedulers.io() for I/O-bound operations (network, disk).
– Schedulers.computation() for CPU-bound tasks.
– Schedulers.newThread() for fire-and-forget operations.

This abstraction enables non-blocking I/O without manual thread management or blocking queues.

Error Handling and Resilience Patterns

RxJava treats errors as first-class citizens in the data stream:

Observable risky = Observable.create(subscriber -> {
    subscriber.onNext(computeRiskyValue());
    subscriber.onError(new RuntimeException("Failed"));
});
risky.onErrorResumeNext(throwable -> Observable.just("Default"))
     .subscribe(value -> System.out.println(value));

Operators like retry, retryWhen, and onErrorReturn implement resilience patterns such as exponential backoff and circuit breakers—critical for microservices in failure-prone networks.

Netflix Production Use Cases

Netflix employs RxJava across its entire stack. The UI layer composes multiple backend API calls for personalized homepages:

Observable<Recommendation> recs = userIdObservable
    .flatMap(this::fetchUserProfile)
    .flatMap(profile -> Observable.zip(
        fetchTopMovies(profile),
        fetchSimilarUsers(profile),
        this::combineRecommendations));

The API gateway uses RxJava for timeout handling, fallbacks, and request collapsing. Backend services leverage it for event processing and data aggregation.

Broader Impact on Software Architecture

RxJava embodies the Reactive Manifesto principles: responsive, resilient, elastic, and message-driven. It eliminates common concurrency bugs like race conditions and deadlocks. For JVM developers, RxJava offers a functional, declarative alternative to imperative threading models, enabling cleaner, more maintainable asynchronous code.

Links:

• Ben Christensen on LinkedIn
• RxJava GitHub
• Netflix Tech Blog