Posts Tagged ‘DevoxxFR2013’
[DevoxxFR2013] Les Cast Codeurs Podcast: Reflecting on Four Years of Java Community Insights
Lecturer
Emmanuel Bernard leads development on Hibernate and Quarkus at Red Hat, with expertise in ORM and data management. A Java Champion, he contributes to standards like JPA and Bean Validation. Vincent Massol acts as CTO at XWiki SAS, committing to the XWiki open-source project. He co-authored books on Maven and JUnit, and participates in Les Cast Codeurs podcast. Antonio Goncalves, Principal Software Engineer at Microsoft, founded the Paris Java User Group and authored books on Java EE. He engages in JCP expert groups for Java EE specifications. Guillaume Laforge advocates for Google Cloud Platform, previously managing Groovy. A Java Champion, he co-authored “Groovy in Action” and co-hosts Les Cast Codeurs. Arnaud Héritier manages software factories, committing to Apache Maven. He authored books on Maven and productivity, sharing at community events.
Abstract
This article evaluates the live recording of Les Cast Codeurs Podcast’s fourth anniversary at Devoxx France, hosted by Emmanuel Bernard, Vincent Massol, Antonio Goncalves, Guillaume Laforge, and Arnaud Héritier. It dissects discussions on Java ecosystem trends, conference experiences, and community dynamics. Framed as an informal yet insightful session, the analysis reviews topics like Java 8 features, build tools evolution, and event organization challenges. It assesses the podcast’s role in disseminating knowledge, implications for developer engagement, and reflections on technological shifts. Through anecdotes and audience interactions, it highlights the blend of humor, critique, and foresight that defines the podcast’s appeal in fostering a vibrant French Java community.
Origins and Evolution of Les Cast Codeurs
Les Cast Codeurs emerged from informal discussions among Java enthusiasts, evolving into a staple French-language podcast on Java and related technologies. Emmanuel recounts its inception four years prior, inspired by English counterparts like Java Posse. Initial episodes faced technical hurdles—recording via Skype with varying quality—but persistence yielded over 80 episodes by this milestone.
The format balances news, interviews, and debates, covering Java SE/EE advancements, tools like Maven and Gradle, and broader topics such as cloud computing. Vincent notes the shift from ad-hoc sessions to structured ones, incorporating listener feedback via tools like Google Forms for surveys. This anniversary episode, recorded live at Devoxx France, exemplifies community integration, with audience polls on attendance and preferences.
Growth metrics reveal listenership spikes around releases, averaging thousands per episode. Arnaud highlights international reach, with listeners in French-speaking regions and beyond, underscoring the podcast’s role in bridging linguistic gaps in tech discourse.
Navigating Java Ecosystem Trends and Challenges
Discussions delve into Java 8’s lambda expressions and streams, praised for enhancing code conciseness. Guillaume shares experiences with Groovy’s functional paradigms, drawing parallels to Java’s modernization. Critiques address Oracle’s stewardship post-Sun acquisition, with concerns over delayed releases and community involvement.
Build tools spark debate: Maven’s ubiquity contrasts with Gradle’s rising popularity for Android and flexibility. Antonio advocates for tool-agnostic approaches, while Emmanuel warns of migration costs. The panel concurs on the need for better dependency management, citing transitive conflicts as persistent issues.
Cloud and DevOps trends feature prominently, with reflections on PaaS like Cloud Foundry. Vincent emphasizes automation’s impact on deployment cycles, reducing manual interventions. Security vulnerabilities, like recent Java exploits, prompt calls for vigilant updates and sandboxing.
Community Engagement and Event Reflections
Devoxx France’s organization draws praise for inclusivity and speaker diversity. Arnaud recounts logistical feats—managing 1,000 attendees with volunteer support—highlighting French JUGs’ collaborative spirit. Comparisons to international Devoxx events note unique cultural flavors, like extended lunches fostering networking.
Audience polls reveal demographics: predominantly male, with calls for greater female participation. The panel encourages involvement in JUGs and conferences, citing benefits for skill-sharing and career growth. Humorous anecdotes, like Antonio’s “chouchou” moniker from keynote interactions, lighten the mood, reinforcing the podcast’s approachable style.
Reflections on past guests—industry leaders like James Gosling—underscore the platform’s prestige. Future plans include themed episodes on emerging tech like AI in Java.
Technological Shifts and Future Directions
The session probes Java’s relevance amid alternatives like Scala or Kotlin. Emmanuel defends Java’s ecosystem maturity, while Guillaume highlights Groovy’s interoperability. Discussions on open-source sustainability address funding models, with kudos to foundations like Apache.
Implications for education emphasize podcasts as accessible learning tools, supplementing formal training. The format’s conversational tone demystifies complex topics, aiding newcomers.
In conclusion, Les Cast Codeurs embodies community-driven knowledge dissemination, adapting to Java’s evolution while nurturing inclusivity. Its anniversary celebrates not just longevity but sustained impact on developer discourse.
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[DevoxxFR2013] Building a Complete Information System in 10 Months with Cloud: The Joe Mobile Story
Lecturers
Didier Herbault, the Chief Technical Officer of Joe Mobile, a disruptive Mobile Virtual Network Operator (MVNO) launched by SFR, brings a wealth of experience from over a decade in the telecommunications industry. With a background in web-scale architectures and a passion for applying internet-era principles to traditional telecom systems, Didier orchestrated the creation of Joe Mobile’s entire information system from scratch in an astonishing ten-month timeframe. His philosophy—“safe is risky and risky is safe”—reflects a willingness to embrace calculated risks and innovate rapidly in a highly regulated and competitive market. Under his leadership, Joe Mobile achieved a fully cloud-native stack, eliminating on-premises servers and leveraging Software-as-a-Service (SaaS) solutions for every business function, from CRM to billing.
Cyril Leclerc, at the time of the project, was Technical Director at Xebia, where he served as the lead architect and infrastructure engineer for Joe Mobile’s cloud implementation. A veteran of Java middleware and open-source monitoring, Cyril is a core committer on JMXTrans and a former contributor to Tomcat 4. His expertise in cloud automation, monitoring, and DevOps practices was instrumental in building a scalable, observable, and cost-effective platform. Since this presentation, Cyril has joined CloudBees, where he continues to advance continuous delivery and cloud-native development practices.
Abstract
In the spring of 2012, Joe Mobile set an audacious goal: to launch a fully functional MVNO in France within ten months, competing with established giants like Orange, SFR, and Bouygues Telecom. This presentation details the comprehensive, end-to-end journey of building a complete information system—spanning customer acquisition, billing, CRM, network provisioning, and analytics—entirely on cloud infrastructure, without a single physical server in the company’s offices. Didier Herbault and Cyril Leclerc provide an exhaustive walkthrough of the architectural decisions, technology selections, cultural transformations, and financial strategies that enabled this feat. From selecting Amazon Web Services (AWS) for compute and storage to integrating SaaS solutions like Lithium for community management and SurveyMonkey for customer feedback, every component was chosen for speed, scalability, and operational simplicity. The session delves into the challenges of telecom integration, real-time billing, and regulatory compliance, offering a complete case study in cloud-native disruption. Entrepreneurs, architects, and DevOps practitioners gain a detailed blueprint for launching a technology-driven business at startup speed within a legacy industry.
The MVNO Challenge: Disrupting Telecom with Cloud-Native Agility
The telecommunications industry has traditionally been defined by massive capital expenditures, long procurement cycles, and rigid, on-premises systems. Launching an MVNO— a mobile operator that leases network capacity from a host operator (in this case, SFR)—requires integrating with legacy OSS/BSS (Operations Support Systems/Business Support Systems), provisioning SIM cards, managing real-time rating and billing, and providing customer self-service portals. For a new entrant like Joe Mobile, these requirements posed a formidable barrier: building a traditional system would take years and tens of millions of euros.
Didier Herbault’s vision was to apply web-scale principles to telecom: treat infrastructure as code, leverage SaaS for non-differentiating functions, and build only what provides competitive advantage. The goal was to launch with a minimal viable product in ten months, using cloud to eliminate hardware lead times and SaaS to avoid building commodity systems. Cyril Leclerc was tasked with designing an architecture that could scale from zero to hundreds of thousands of subscribers while maintaining sub-second response times for critical operations like balance checks and plan changes.
Architecture Overview: A Fully Cloud-Native Stack
The Joe Mobile platform was built entirely on AWS, with EC2 for compute, RDS for relational data, ElastiCache for caching, S3 for storage, and CloudFront for content delivery. The core application was a Java/Spring Boot monolith that evolved into microservices as the codebase grew. RabbitMQ handled asynchronous messaging between services, while Hazelcast provided distributed caching and session replication. The frontend was a single-page application built with Backbone.js and RequireJS, served through CloudFront for low-latency delivery.
The billing system was a custom real-time rating engine that processed CDR (Call Detail Records) from SFR’s network, applied plan rules, and updated subscriber balances in PostgreSQL. Amazon SNS/SQS orchestrated event-driven workflows, such as sending SMS notifications for low balance or plan expiration. Elasticsearch powered search and analytics, with Kibana dashboards for operational visibility. CloudWatch collected infrastructure metrics, while JMXTrans exported application metrics to Graphite for graphing and alerting.
SaaS Integration: Leveraging Best-of-Breed Solutions
Joe Mobile adopted a “buy before build” philosophy, integrating SaaS solutions for every non-core function:
- Lithium for community forums and customer support
- Zendesk for ticketing and knowledge base
- SurveyMonkey (now Momentive) for customer satisfaction surveys
- Mailchimp for email marketing
- Google Workspace for collaboration
- Xero for accounting
- Stripe for payment processing
This approach eliminated the need to develop and maintain complex systems for HR, finance, marketing, and support, allowing the engineering team to focus on the core telecom platform. Integration was achieved through REST APIs and webhooks, with Zapier used for rapid prototyping of automation workflows.
The 10-Month Timeline: From Idea to Launch
The project was executed in four phases:
-
Months 1–2: Foundations
Cyril Leclerc provisioned the initial AWS environment, set up CI/CD with Jenkins, and implemented infrastructure as code with CloudFormation. The team adopted Git for version control and JIRA for issue tracking. Core services—customer portal, billing engine, and CRM—were scaffolded. -
Months 3–5: Core Development
The billing engine was built to handle real-time rating for voice, SMS, and data. The customer portal was developed with Backbone.js, allowing users to manage plans, view usage, and top up balances. Integration with SFR’s provisioning systems was achieved via SOAP APIs wrapped in Apache Camel routes. -
Months 6–8: Scaling and Hardening
Load testing with Gatling validated performance under 100,000 concurrent users. Auto-scaling groups were configured to handle traffic spikes. Chaos Monkey (inspired by Netflix) was used to test resilience. Security was hardened with AWS WAF, Shield, and IAM roles. -
Months 9–10: Launch and Stabilization
Beta testing with 1,000 users identified edge cases. The marketing site went live, and the first SIM cards were shipped. Post-launch, the team operated in war room mode, resolving issues in real time.
Cultural Transformation: From Telecom to Web Speed
The most profound change was cultural. The team adopted agile practices with two-week sprints, daily standups, and retrospectives. Pair programming and code reviews ensured quality. Feature flags allowed safe rollout of new capabilities. The absence of physical servers eliminated traditional ops tasks, freeing engineers to focus on code and automation.
Didier Herbault’s credit card became a symbol of the new culture: in the first two months, he personally funded AWS, Papertrail, and other services, bypassing bureaucratic procurement processes. This “just do it” mindset permeated the organization, from engineering to marketing.
Financial Model: OpEx over CapEx
The cloud-first approach transformed the cost structure. Instead of €10 million in upfront hardware, Joe Mobile spent €100,000 in the first year on AWS and SaaS. Reserved instances reduced EC2 costs by 40%, while spot instances handled batch processing at 90% discount. The total cost of ownership was 70% lower than a traditional setup, with the added benefit of infinite scalability.
Outcomes and Lessons Learned
Joe Mobile launched on time, with 99.99% uptime and sub-second response times for critical operations. The platform scaled to 500,000 subscribers within 18 months. Key lessons:
- Cloud eliminates barriers to entry in capital-intensive industries
- SaaS accelerates time-to-market for non-differentiating functions
- Culture eats strategy for breakfast—agile, empowered teams are essential
- Start small, iterate fast—MVP first, perfection later
Conclusion: Cloud as a Disruptive Force
Joe Mobile’s ten-month journey from concept to launch demonstrates that cloud, when combined with SaaS and agile practices, can disrupt even the most entrenched industries. Didier and Cyril’s comprehensive approach—technical, cultural, and financial—offers a complete playbook for entrepreneurs seeking to build world-class systems at startup speed.
Links
Hashtags: #CloudNative #MVNO #SaaS #AWS #DevOps #TelecomDisruption #DidierHerbault #CyrilLeclerc
[DevoxxFR2013] The Lightning Memory-Mapped Database: A Revolutionary Approach to High-Performance Key-Value Storage
Lecturer
Howard Chu stands as one of the most influential figures in open-source systems programming, with a career that spans decades of foundational contributions to the software ecosystem. As the founder and Chief Technology Officer of Symas Corporation, he has dedicated himself to building robust, high-performance solutions for enterprise and embedded environments. His involvement with the OpenLDAP Project began in 1999, and by 2007 he had assumed the role of Chief Architect, guiding the project through significant scalability improvements that power directory services for millions of users worldwide. Chu’s earlier work in the 1980s on GNU tools and his invention of parallel make — a feature that enables concurrent compilation of source files and is now ubiquitous in build systems like GNU Make — demonstrates his deep understanding of system-level optimization and concurrency. The creation of the Lightning Memory-Mapped Database (LMDB) emerged directly from the practical challenges faced in OpenLDAP, where existing storage backends like Berkeley DB introduced unacceptable overhead in data copying, deadlock-prone locking, and maintenance-intensive compaction. Chu’s design philosophy emphasizes simplicity, zero-copy operations, and alignment with modern hardware capabilities, resulting in a database library that not only outperforms its predecessors by orders of magnitude but also fits entirely within a typical CPU’s L1 cache at just 32KB of object code. His ongoing work continues to influence a broad range of projects, from authentication systems to mobile applications, cementing his legacy as a pioneer in efficient, reliable data management.
Abstract
The Lightning Memory-Mapped Database (LMDB) represents a paradigm shift in embedded key-value storage, engineered by Howard Chu to address the critical performance bottlenecks encountered in the OpenLDAP Project when using Berkeley DB. This presentation provides an exhaustive examination of LMDB’s design principles, architectural innovations, and operational characteristics, demonstrating why it deserves the moniker “lightning.” Chu begins with a detailed analysis of Berkeley DB’s shortcomings — including data copying between kernel and user space, lock-based concurrency leading to deadlocks, and periodic compaction requirements — and contrasts these with LMDB’s solutions: direct memory mapping via POSIX mmap() for zero-copy access, append-only writes for instantaneous crash recovery, and multi-version concurrency control (MVCC) for lock-free, linearly scaling reads. He presents comprehensive benchmarks showing read throughput scaling perfectly with CPU cores, write performance exceeding SQLite by a factor of twenty, and a library footprint so compact that it executes entirely within L1 cache. The session includes an in-depth API walkthrough, transactional semantics, support for sorted duplicates, and real-world integrations with OpenLDAP, Cyrus SASL, Heimdal Kerberos, SQLite, and OpenDKIM. Attendees gain a complete understanding of how LMDB achieves unprecedented efficiency, simplicity, and reliability, making it the ideal choice for performance-critical applications ranging from embedded devices to high-throughput enterprise systems.
The Imperative for Change: Berkeley DB’s Limitations in High-Performance Directory Services
The development of LMDB was not an academic exercise but a direct response to the real-world constraints imposed by Berkeley DB in the OpenLDAP environment. OpenLDAP, as a mission-critical directory service, demands sub-millisecond response times for millions of authentication and authorization queries daily. Berkeley DB, while robust, introduced several fundamental inefficiencies that became unacceptable under such loads.
The most significant issue was data copying overhead. Berkeley DB maintained its own page cache in user space, requiring data to be copied from kernel buffers to this cache and then again to the application buffer — a process that violated the zero-copy principle essential for minimizing latency in I/O-bound operations. This double-copy penalty became particularly egregious with modern solid-state drives and multi-core processors, where memory bandwidth is often the primary bottleneck.
Another critical flaw was lock-based concurrency. Berkeley DB’s fine-grained locking mechanism, while theoretically sound, frequently resulted in deadlocks under high contention, especially in multi-threaded LDAP servers handling concurrent modifications. The overhead of lock management and deadlock detection negated much of the benefit of parallel processing.
Finally, compaction and maintenance represented an operational burden. Berkeley DB required periodic compaction to reclaim space from deleted records, a process that could lock the database for minutes or hours in large installations, rendering the system unavailable during peak usage periods.
These limitations collectively threatened OpenLDAP’s ability to scale with growing enterprise demands, prompting Chu to design a completely new storage backend from first principles.
Architectural Foundations: Memory Mapping, Append-Only Writes, and Flattened B+Trees
LMDB’s architecture is built on three core innovations that work in concert to eliminate the aforementioned bottlenecks.
The first and most fundamental is direct memory mapping using POSIX mmap(). Rather than maintaining a separate cache, LMDB maps the entire database file directly into the process’s virtual address space. This allows data to be accessed via pointers with zero copying — the operating system’s virtual memory manager handles paging transparently. This approach leverages decades of OS optimization for memory management while eliminating the complexity and overhead of a user-space cache.
The second innovation is append-only write semantics. When a transaction modifies data, LMDB does not update pages in place. Instead, it appends new versions of modified pages to the end of the file and updates the root pointer atomically using msync(). This design to instantaneous crash recovery — in the event of a system failure, the previous root pointer remains valid, and no log replay or checkpoint recovery is required. The append-only model also naturally supports Multi-Version Concurrency Control (MVCC), where readers access a consistent snapshot of the database without acquiring locks, while writers operate on private copies of pages.
The third architectural choice is a flattened B+tree structure. Traditional B+trees maintain multiple levels of internal nodes, each requiring additional I/O to traverse. LMDB stores all nodes at the leaf level, with internal nodes containing only keys and child pointers. This reduces tree height and minimizes the number of page fetches required for lookup operations. Keys within pages are maintained in sorted order, enabling efficient range scans and supporting sorted duplicates for multi-valued attributes common in directory schemas.
API Design: Simplicity and Power in Harmony
Despite its sophisticated internals, LMDB’s API is remarkably concise and intuitive, reflecting Chu’s philosophy that complexity should be encapsulated, not exposed. The core operations fit within a handful of functions:
MDB_env *env;
mdb_env_create(&env);
mdb_env_set_mapsize(env, 10485760); // 10MB initial size
mdb_env_open(env, "./mydb", MDB_NOSUBDIR, 0664);
MDB_txn *txn;
mdb_txn_begin(env, NULL, 0, &txn);
MDB_dbi dbi;
mdb_dbi_open(txn, "mytable", MDB_CREATE, &dbi);
MDB_val key = {5, "hello"};
MDB_val data = {5, "world"};
mdb_put(txn, dbi, &key, &data, 0);
mdb_txn_commit(txn);
For databases supporting duplicate values:
mdb_dbi_open(txn, "multival", MDB_CREATE | MDB_DUPSORT, &dbi);
mdb_put(txn, dbi, &key, &data1, 0);
mdb_put(txn, dbi, &key, &data2, MDB_APPENDDUP); // Preserves sort order
The API supports full ACID transactions with nested transactions, cursor-based iteration, and range queries. Error handling is straightforward, with return codes indicating success or specific failure conditions.
Performance Characteristics: Linear Scaling and Unparalleled Efficiency
LMDB’s performance profile is nothing short of revolutionary, particularly in read-heavy workloads. Benchmarks conducted by Chu demonstrate:
- Read scaling: Perfect linear scaling with CPU cores, achieving over 1.5 million operations per second on an 8-core system. This is possible because readers never contend for locks and operate on consistent snapshots.
- Write performance: Approximately 100,000 operations per second, compared to Berkeley DB’s 5,000 and SQLite’s similar range — a 20x improvement.
- Memory efficiency: The shared memory mapping means multiple processes accessing the same database share physical RAM, dramatically reducing per-process memory footprint.
- Cache residency: At 32KB of object code, the entire library fits in L1 cache, eliminating instruction cache misses during operation.
These metrics translate directly to real-world gains. OpenLDAP with LMDB handles 10 times more queries per second than with Berkeley DB, while SQLite gains a 2x speedup when using LMDB as a backend.
Operational Excellence: Zero Maintenance and Instant Recovery
LMDB eliminates the operational overhead that plagues traditional databases. There is no compaction, vacuuming, or index rebuilding required. The database file grows only with actual data, and deleted records are reclaimed automatically during transaction commits. The map size, specified at environment creation, can be increased dynamically without restarting the application.
Crash recovery is instantaneous — the last committed root pointer is always valid, and no transaction log replay is needed. This makes LMDB ideal for embedded systems and mobile devices where reliability and quick startup are paramount.
Concurrency Model: One Writer, Unlimited Readers
LMDB enforces a strict concurrency model: one writer at a time, unlimited concurrent readers. This design choice, while seemingly restrictive, actually improves performance. Chu’s testing revealed that even with Berkeley DB’s multi-writer support, placing a global lock around the database increased write throughput. The overhead of managing multiple concurrent writers — deadlock detection, lock escalation, and cache invalidation — often outweighs the benefits.
For applications requiring multiple writers, separate LMDB environments can be used, or higher-level coordination (e.g., via a message queue) can serialize write access. An experimental patch exists to allow concurrent writes to multiple databases within a single environment, but the single-writer model remains the recommended approach for maximum performance.
Ecosystem Integrations: Powering Critical Infrastructure
LMDB’s versatility is evident in its adoption across diverse projects:
- OpenLDAP: The primary motivation, enabling directory servers to handle millions of entries with sub-millisecond latency
- Cyrus SASL: Efficient storage of authentication credentials
- Heimdal Kerberos: High-throughput ticket management in distributed authentication
- SQLite: As a backend, providing embedded SQL with LMDB’s speed and reliability
- OpenDKIM: Accelerating domain key lookups for email authentication
These integrations demonstrate LMDB’s ability to serve as a drop-in replacement for slower, more complex storage engines.
Future Directions: Replication and Distributed Systems
While LMDB focuses on local storage, Chu envisions its use as a high-performance backend for distributed NoSQL systems like Riak and HyperDex, which provide native replication. This separation of concerns allows LMDB to excel at what it does best — ultra-fast, reliable local access — while leveraging other systems for network coordination.
The library’s compact size and zero-dependency design make it particularly attractive for edge computing, IoT devices, and mobile applications, where resource constraints are severe.
Conclusion: Redefining the Possible in Database Design
The Lightning Memory-Mapped Database represents a triumph of focused engineering over feature bloat. By ruthlessly optimizing for the common case — read-heavy workloads with occasional writes — and leveraging modern OS capabilities like mmap(), Howard Chu created a storage engine that is simultaneously faster, simpler, and more reliable than its predecessors. LMDB proves that sometimes the most revolutionary advances come not from adding features, but from removing complexity. For any application where performance, reliability, and simplicity matter, LMDB is not just an option — it is the new standard.
Links
[DevoxxFR2013] Developers: Prima Donnas of the 21st Century? — A Provocative Reflection on Craft, Value, and Responsibility
Lecturer
Hadi Hariri stands at the intersection of technical depth and human insight as a developer, speaker, podcaster, and Technical Evangelist at JetBrains. For over a decade, he has traversed the global conference circuit, challenging audiences to confront uncomfortable truths about their profession. A published author and frequent contributor to developer publications, Hadi brings a rare blend of architectural expertise and communication clarity. Based in Spain with his wife and three sons, he leads the .NET Malaga User Group and holds prestigious titles including ASP.NET MVP and Insider. Yet beneath the credentials lies a relentless advocate for software as a human endeavor — not a technological one.
Abstract
This is not a technical talk. There will be no code, no frameworks, no live demos. Instead, Hadi Hariri delivers a searing, unfiltered indictment of the modern developer psyche. We proclaim ourselves misunderstood geniuses, central to business success yet perpetually underappreciated. We demand the latest tools, resent managerial oversight, and cloak personal ambition in the language of craftsmanship. But what if the real problem is not “them” — it’s us?
Through sharp wit, brutal honesty, and relentless logic, Hadi dismantembles the myths we tell ourselves: that communication is someone else’s job, that innovation resides in syntax, that our discomfort with business priorities justifies disengagement. This session is a mirror — polished, unforgiving, and essential. Leave your ego at the door, or stay seated and miss the point.
The Myth of the Misunderstood Genius
We gather in echo chambers — conferences, forums, internal chat channels — to commiserate about how management fails to grasp our brilliance. We lament that stakeholders cannot appreciate the elegance of our dependency injection, the foresight of our microservices, the purity of our functional paradigm. We position ourselves as the unsung heroes of the digital age, laboring in obscurity while others reap the rewards.
Yet when pressed, we retreat behind JIRA tickets, estimation buffers, and technical debt backlogs. We argue passionately about tabs versus spaces, spend days evaluating build tools, and rewrite perfectly functional systems because the new framework promises salvation. We have mistaken activity for impact, novelty for value, and personal preference for professional necessity.
Communication: The Silent Killer of Influence
The single greatest failure of the developer community is not technical — it is communicative. We speak in acronyms and abstractions: DI, IoC, CQRS, DDD. We present architecture diagrams as if they were self-evident. We say “it can’t be done” when we mean “I haven’t considered the trade-offs.” We fail to ask “why” because we assume the answer is beneath us.
Consider a simple feature request: “The user should be able to reset their password.” A typical response might be: “We’ll need a new microservice, a message queue, and a Redis cache for rate limiting.” The business hears cost, delay, and complexity. What they needed was: “We can implement this securely in two days using the existing authentication flow, with an optional enhancement for audit logging if compliance requires it.”
The difference is not technical sophistication — it is empathy, clarity, and alignment. Until we learn to speak the language of outcomes rather than implementations, we will remain marginalized.
The Silver Bullet Delusion
Every year brings a new savior: a framework that will eliminate boilerplate, a methodology that will banish chaos, a cloud service that will scale infinitely. We chase these mirages with religious fervor, abandoning yesterday’s solution before it has proven its worth. We rewrite backend systems in Node.js, then Go, then Rust — not because the business demanded it, but because we read a blog post.
This is not innovation. It is distraction. It is the technical equivalent of rearranging deck chairs on the Titanic. The problems that truly matter — unclear requirements, legacy constraints, human error, organizational inertia — are immune to syntax. No process can compensate for poor judgment, and no tool can replace clear thinking.
Value Over Vanity: Redefining Success
We measure ourselves by metrics that feel good but deliver nothing: lines of code written, test coverage percentages, build times in milliseconds. We celebrate the deployment of a new caching layer while users wait longer for search results. We optimize the developer experience at the expense of the user experience.
True value resides in outcomes: a feature that increases revenue, a bug fix that prevents customer churn, a performance improvement that saves server costs. These are not glamorous. They do not trend on Hacker News. But they are the reason our profession exists.
Ask yourself with every commit: Does this make someone’s life easier? Does it solve a real problem? If400 If the answer is no, you are not innovating — you are indulging.
The Privilege We Refuse to Acknowledge
Most professions are defined by repetition. The accountant reconciles ledgers. The lawyer drafts contracts. The mechanic replaces brakes. Day after day, the same patterns, the same outcomes, the same constraints.
We, by contrast, are paid to solve novel problems. We are challenged to learn continuously, to adapt to shifting requirements, to create systems that impact millions. We work in air-conditioned offices, collaborate with brilliant minds, and enjoy flexibility that others can only dream of. We are not underpaid or underappreciated — we are extraordinarily privileged.
And yet we complain. We demand ping-pong tables and unlimited vacation while nurses work double shifts, teachers buy school supplies out of pocket, and delivery drivers navigate traffic in the rain. Our discomfort is not oppression — it is entitlement.
Innovation as Human Impact
Innovation is not a technology. It is not a framework, a language, or a cloud provider. Innovation is the act of making someone’s life better. It is the medical system that detects cancer earlier. It is the banking app that prevents fraud. It is the e-commerce platform that helps a small business reach new customers.
Even in enterprise software — often derided as mundane — we have the power to reduce frustration, automate drudgery, and free human attention for higher purposes. Every line of code is an opportunity to serve.
A Call to Maturity
The prima donnas of the 21st century are not the executives demanding impossible deadlines. They are not the product managers changing requirements. They are us — the developers who believe our discomfort entitles us to disengagement, who confuse technical preference with professional obligation, who prioritize our learning over the user’s needs.
It is time to grow up. To communicate clearly. To focus on outcomes. To recognize our privilege and wield it responsibly. The world does not owe us appreciation — it owes us the opportunity to make a difference. Let us stop wasting it.
Links
[DevoxxFR2013] Returns all active nodes with response times
Targeted actions follow:
mco service restart service=httpd -F osfamily=RedHat
This restarts Apache only on RedHat-based systems—in parallel across thousands of nodes. Filters support complex queries:
mco find -F country=FR -F environment=prod
MCollective plugins extend functionality: package installation, file deployment, or custom scripts. Security relies on SSL certificates and message signing, preventing unauthorized commands.
## Integrating Puppet and MCollective: A Synergistic Workflow
Pelisse combines both tools for full lifecycle management. Puppet bootstraps nodes—installing the MCollective agent during initial provisioning. Once enrolled, MCollective triggers Puppet runs on demand:
mco puppet runonce -I /web\d+.prod.fr/
This forces configuration convergence across matching web servers. For dependency-aware deployments, MCollective sequences actions:
1. Install database backend
2. Validate connectivity (via facts)
3. Deploy application server
4. Start services
Pelisse shares a real-world example: upgrading JBoss clusters. MCollective drains traffic from nodes, Puppet applies the new WAR, then MCollective re-enables load balancing—all orchestrated from a single command.
## Tooling Ecosystem: Foreman, Hiera, and Version Control
Foreman provides a web dashboard for Puppet—visualizing reports, managing node groups, and scheduling runs. It integrates with LDAP for access control and supports ENC (External Node Classifier) scripts to assign classes dynamically.
Hiera separates configuration data from logic, using YAML or JSON backends:
[DevoxxFR2013] Distributed DDD, CQRS, and Event Sourcing – Part 1/3: Time as a Business Core
Lecturer
Jérémie Chassaing is an architect at Siriona, focusing on scalable systems for hotel channel management. Author of thinkbeforecoding.com, a blog on Domain-Driven Design, CQRS, and Event Sourcing, he founded Hypnotizer (1999) for interactive video and BBCG (2004) for P2P photo sharing. His work emphasizes time-centric modeling in complex domains.
Abstract
Jérémie Chassaing posits time as central to business logic, advocating Event Sourcing to capture temporal dynamics in Domain-Driven Design. He integrates Distributed DDD, CQRS, and Event Sourcing to tackle scalability, concurrency, and complexity. Through examples like order management, Chassaing analyzes event streams over relational models, demonstrating eventual consistency and projection patterns. The first part establishes foundational shifts from CRUD to event-driven architectures, setting the stage for distributed implementations.
Time’s Primacy in Business Domains
Chassaing asserts time underpins business: reacting to events, analyzing history, forecasting futures. Traditional CRUD ignores temporality, leading to lost context. Event Sourcing records immutable facts—e.g., OrderPlaced, ItemAdded—enabling full reconstruction.
This contrasts relational databases’ mutable state, where updates erase history. Events form audit logs, facilitating debugging and compliance.
Domain-Driven Design Foundations: Aggregates and Bounded Contexts
DDD models domains via aggregates—consistent units like Order with line items. Bounded contexts delimit scopes, preventing model pollution.
Distributed DDD extends this to microservices, each owning a context. CQRS separates commands (writes) from queries (reads), enabling independent scaling.
CQRS Mechanics: Commands, Events, and Projections
Commands mutate state, emitting events. Handlers project events to read models:
case class OrderPlaced(orderId: UUID, customer: String)
case class ItemAdded(orderId: UUID, item: String, qty: Int)
// Command handler
def handle(command: AddItem): Unit = {
// Validate
emit(ItemAdded(command.orderId, command.item, command.qty))
}
// Projection
def project(event: ItemAdded): Unit = {
updateReadModel(event)
}
Projections denormalize for query efficiency, accepting eventual consistency.
Event Sourcing Advantages: Auditability and Scalability
Events form immutable logs, replayable for state recovery or new projections. This decouples reads/writes, allowing specialized stores—SQL for reporting, NoSQL for search.
Chassaing addresses concurrency via optimistic locking on aggregate versions. Distributed events use pub/sub (Kafka) for loose coupling.
Challenges and Patterns: Idempotency and Saga Management
Duplicates require idempotent handlers—e.g., check event IDs. Sagas coordinate cross-aggregate workflows, reacting to events and issuing commands.
Chassaing warns of “lasagna architectures”—layered complexity—and advocates event-driven simplicity over tiered monoliths.
Implications for Resilient Systems: Embracing Eventual Consistency
Event Sourcing yields antifragile designs: failures replay from logs. Distributed CQRS scales horizontally, handling “winter is coming” loads.
Chassaing urges rethinking time in models, shifting from mutable entities to immutable facts.
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[DevoxxFR2013] Speech Technologies for Web Development: From APIs to Embedded Solutions
Lecturer
Sébastien Bratières has developed voice-enabled products across Europe since 2001, spanning telephony at Tellme, embedded systems at Voice-Insight, and chat-based dialogue at As An Angel. He currently leads Quint, the voice division of Dawin GmbH. Holding degrees from École Centrale Paris and an MPhil in Speech Processing from the University of Cambridge, he remains active in machine learning research at Cambridge.
Abstract
Sébastien Bratières surveys the landscape of speech recognition technologies available to web developers, contrasting cloud-based APIs with embedded solutions. He covers foundational concepts—acoustic models, language models, grammar-based versus dictation recognition—while evaluating practical trade-offs in latency, accuracy, and deployment. The presentation compares CMU Sphinx, Google Web Speech API, Nuance Developer Network, and Windows Phone 8 Speech API, addressing error handling, dialogue management, and offline capabilities. Developers gain a roadmap for integrating voice into web applications, from rapid prototyping to production-grade systems.
Core Concepts in Speech Recognition: Models, Architectures, and Trade-offs
Bratières introduces the speech recognition pipeline: audio capture, feature extraction, acoustic modeling, language modeling, and decoding. Acoustic models map sound to phonemes; language models predict word sequences.
Grammar-based recognition constrains input to predefined phrases, yielding high accuracy and low latency. Dictation mode supports free-form speech but demands larger models and increases error rates.
Cloud architectures offload processing to remote servers, reducing client footprint but introducing network latency. Embedded solutions run locally, enabling offline use at the cost of computational resources.
Google Web Speech API: Browser-Native Recognition in Chrome
Available in Chrome 25+ beta, the Web Speech API exposes speech recognition via JavaScript. Bratières demonstrates:
const recognition = new webkitSpeechRecognition();
recognition.lang = 'fr-FR';
recognition.onresult = event => console.log(event.results[0][0].transcript);
recognition.start();
Strengths include ease of integration, continuous updates, and multilingual support. Limitations: Chrome-only, requires internet, and lacks fine-grained control over models.
CMU Sphinx: Open-Source Flexibility for Custom Deployments
CMU Sphinx offers fully customizable, embeddable recognition. PocketSphinx runs on resource-constrained devices; Sphinx4 targets server-side Java applications.
Bratières highlights model training: adapt acoustic models to specific domains or accents. Grammar files (JSGF) define valid utterances, enabling precise command-and-control interfaces.
Deployment options span browser via WebAssembly, mobile via native libraries, and server-side processing. Accuracy rivals commercial solutions with sufficient training data.
Nuance Developer Network and Windows Phone 8 Speech API: Enterprise-Grade Alternatives
Nuance provides cloud and embedded SDKs with industry-leading accuracy, particularly in noisy environments. The developer network offers free tiers for prototyping, scaling to paid plans.
Windows Phone 8 integrates speech via the SpeechRecognizerUI class, supporting grammar-based and dictation modes. Bratières notes seamless integration with Cortana but platform lock-in.
Practical Considerations: Latency, Error Handling, and Dialogue Management
Latency varies: cloud APIs achieve sub-second results under good network conditions; embedded systems add processing delays. Bratières advocates progressive enhancement—fallback to text input on failure.
Error handling strategies include confidence scores, n-best lists, and confirmation prompts. Dialogue systems use finite-state machines or statistical models to maintain context.
Embedded and Offline Challenges: Current State and Future Outlook
Bratières addresses offline recognition demand, citing truck drivers embedding systems for navigation. Commercial embedded solutions exist but remain costly.
Open-source alternatives lag in accuracy, particularly for dictation. He predicts convergence: WebAssembly may bring Sphinx-class recognition to browsers, while edge computing reduces cloud dependency.
Conclusion: Choosing the Right Speech Stack
Bratières concludes that no universal solution exists. Prototype with Google Web Speech API for speed; transition to CMU Sphinx or Nuance for customization or offline needs. Voice enables natural interfaces, but success hinges on managing expectations around accuracy and latency.
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[DevoxxFR2013] Dispelling Performance Myths in Ultra-High-Throughput Systems
Lecturer
Martin Thompson stands as a preeminent authority in high-performance and low-latency engineering, having accumulated over two decades of expertise across transactional and big-data realms spanning automotive, gaming, financial, mobile, and content management sectors. As co-founder and former CTO of LMAX, he now consults globally, championing mechanical sympathy—the harmonious alignment of software with underlying hardware—to craft elegant, high-velocity solutions. His Disruptor framework exemplifies this philosophy.
Abstract
Martin Thompson systematically dismantles entrenched performance misconceptions through rigorous empirical analysis derived from extreme low-latency environments. Spanning Java and C implementations, third-party libraries, concurrency primitives, and operating system interactions, he promulgates a “measure everything” ethos to illuminate genuine bottlenecks. The discourse dissects garbage collection behaviors, logging overheads, parsing inefficiencies, and hardware utilization, furnishing actionable methodologies to engineer systems delivering millions of operations per second at microsecond latencies.
The Primacy of Empirical Validation: Profiling as the Arbiter of Truth
Thompson underscores that anecdotal wisdom often misleads in performance engineering. Comprehensive profiling under production-representative workloads unveils counterintuitive realities, necessitating continuous measurement with tools like perf, VTune, and async-profiler.
He categorizes fallacies into language-specific, library-induced, concurrency-related, and infrastructure-oriented myths, each substantiated by real-world benchmarks.
Garbage Collection Realities: Tuning for Predictability Over Throughput
A pervasive myth asserts that garbage collection pauses are an inescapable tax, best mitigated by throughput-oriented collectors. Thompson counters that Concurrent Mark-Sweep (CMS) consistently achieves sub-10ms pauses in financial trading systems, whereas G1 frequently doubles minor collection durations due to fragmented region evacuation and reference spidering in cache structures.
Strategic heap sizing to accommodate young generation promotion, coupled with object pooling on critical paths, minimizes pause variability. Direct ByteBuffers, often touted for zero-copy I/O, incur kernel transition penalties; heap-allocated buffers prove superior for modest payloads.
Code-Level Performance Traps: Parsing, Logging, and Allocation Patterns
Parsing dominates CPU cycles in message-driven architectures. XML and JSON deserialization routinely consumes 30-50% of processing time; binary protocols with zero-copy parsers slash this overhead dramatically.
Synchronous logging cripples latency; asynchronous, lock-free appenders built atop ring buffers sustain millions of events per second. Thompson’s Disruptor-based logger exemplifies this, outperforming traditional frameworks by orders of magnitude.
Frequent object allocation triggers premature promotions and GC pressure. Flyweight patterns, preallocation, and stack confinement eliminate heap churn on hot paths.
Concurrency Engineering: Beyond Thread Proliferation
The notion that scaling threads linearly accelerates execution collapses under context-switching and contention costs. Thompson advocates thread affinity to physical cores, aligning counts with hardware topology.
Contented locks serialize execution; lock-free algorithms leveraging compare-and-swap (CAS) preserve parallelism. False sharing—cache line ping-pong between adjacent variables—devastates throughput; 64-byte padding ensures isolation.
Infrastructure Optimization: OS, Network, and Storage Synergy
Operating system tuning involves interrupt coalescing, huge pages to reduce TLB misses, and scheduler affinity. Network kernel bypass (e.g., Solarflare OpenOnload) shaves microseconds from round-trip times.
Storage demands asynchronous I/O and batching; fsync calls must be minimized or offloaded to dedicated threads. SSD sequential writes eclipse HDDs, but random access patterns require careful buffering.
Cultural and Methodological Shifts for Sustained Performance
Thompson exhorts engineering teams to institutionalize profiling, automate benchmarks, and challenge assumptions relentlessly. The Disruptor’s single-writer principle, mechanical sympathy, and batching yield over six million operations per second on commodity hardware.
Performance is not an afterthought but an architectural cornerstone, demanding cross-disciplinary hardware-software coherence.
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[DevoxxFR2013] The Classpath Persists, Yet Its Days Appear Numbered
Lecturer
Alexis Hassler has devoted more than fifteen years to Java development. Operating independently, he engages in programming while also guiding enterprises through training and advisory roles to refine their Java-based workflows and deployment strategies. As co-leader of the Lyon Java User Group, he plays a pivotal part in orchestrating community gatherings, including the acclaimed annual Mix-IT conference held in Lyon.
Abstract
Alexis Hassler meticulously examines the enduring complexities surrounding Java’s classpath and classloading mechanisms, drawing a sharp contrast between conventional hierarchical approaches and the rise of sophisticated modular frameworks. By weaving historical insights with hands-on illustrations and deep integration of JBoss Modules, he unravels the intricacies of dependency clashes, application isolation techniques, and viable transition pathways. The exploration extends to profound consequences for application server environments, delivering practical remedies to alleviate classpath-induced frustrations while casting an anticipatory gaze toward the transformative potential of Jigsaw.
Tracing the Roots: Classloaders and the Enduring Classpath Conundrum
Hassler opens by invoking Mark Reinhold’s bold 2009 JavaOne proclamation that the classpath’s demise was imminent, a statement that fueled expectations of modular systems seamlessly resolving all dependency conflicts. Despite the passage of four years, the classpath remains a fixture within the JDK and application server landscapes, underscoring its stubborn resilience.
Within the JDK, classloaders operate through a delegation hierarchy: the Bootstrap classloader handles foundational rt.jar components, the Extension classloader manages optional javax packages, and the Application classloader oversees user-defined code. This parent-first delegation model effectively safeguards core class integrity yet frequently precipitates version mismatches when disparate libraries demand conflicting implementations.
Hassler vividly demonstrates notorious pitfalls, such as the perplexing ClassNotFoundException that arises despite a JAR’s presence in the classpath or the insidious NoDefClassError triggered by incompatible transitive dependencies. These issues originate from the classpath’s flat aggregation paradigm, which indiscriminately merges all artifacts without regard for scoping or versioning nuances.
Hierarchical Containment Strategies in Application Servers: The Tomcat Paradigm
Application servers like Tomcat invert the delegation flow to enforce robust isolation among deployed artifacts. The WebappClassLoader prioritizes local resources before escalating unresolved requests to parent loaders, thereby permitting each web application to maintain its own dependency ecosystem.
This inverted hierarchy facilitates per-application versioning, substantially mitigating library collisions. Hassler delineates Tomcat’s layered loader architecture, encompassing common, server, shared, and per-webapp classloaders, each serving distinct scoping responsibilities.
Nevertheless, memory leaks persist as a formidable challenge, particularly during hot redeployments when static fields retain references to obsolete classes, inflating PermGen space. Mitigation demands meticulous resource cleanup through context listeners and disciplined finalization practices.
Modular Paradigms on the Horizon: OSGi, Jigsaw, and the Pragmatism of JBoss Modules
OSGi introduces the concept of bundles equipped with explicit import and export declarations, complete with version range specifications. This dynamic loading and unloading capability proves ideal for plugin architectures, though it necessitates substantial refactoring of existing codebases.
Project Jigsaw, slated for Java 9, aspires to embed modularity natively through module declarations that articulate precise dependencies. Despite repeated delays, its eventual integration promises standardized resolution, yet its absence compels interim solutions.
JBoss Modules, already battle-tested within JBoss AS7, employs a dependency graph resolution mechanism. Modules are defined with dedicated resource paths and dependency linkages, enabling parallel coexistence of multiple library versions. Hassler elucidates a module descriptor:
<module xmlns="urn:jboss:module:1.1" name="com.example.app">
<resources>
<resource-root path="app.jar"/>
</resources>
<dependencies>
<module name="javax.api"/>
<module name="org.hibernate" slot="4.3"/>
</dependencies>
</module>
This structure empowers fine-grained version isolation, exemplified by simultaneous deployment of Hibernate 3 and 4 instances.
Hands-On Deployment Scenarios: JBoss Modules in Standalone and Tomcat Environments
Within JBoss AS7, modules reside in a dedicated directory structure, and applications declare dependencies via jboss-deployment-structure.xml manifests. Standalone execution leverages module-aware classloaders, either through MANIFEST entries or programmatic instantiation.
Hassler showcases a proof-of-concept integration with Tomcat, wherein a custom ClassLoader delegates to JBoss Modules, thereby endowing legacy web containers with modern dependency management. The prototype, available on GitHub, acknowledges limitations in hot-redeployment memory cleanup but validates conceptual soundness.
This adaptability extends modular benefits to environments traditionally tethered to classpath constraints.
Forward-Looking Consequences for Java Ecosystems: Transition Pathways and Jigsaw’s Promise
Classpath tribulations exact a heavy toll on developer productivity, manifesting in protracted debugging sessions and fragile builds. Modular frameworks counter these by enhancing maintainability, accelerating startup through lazy initialization, and fortifying deployment reliability.
Migration hurdles encompass tooling maturity and knowledge gaps, yet the advantages—conflict elimination, streamlined packaging—outweigh transitional friction. Hassler advocates incremental adoption, leveraging JBoss Modules as a bridge to Jigsaw’s eventual standardization.
In conclusion, while the classpath lingers, modular evolution heralds its obsolescence, equipping practitioners with robust tools to transcend historical limitations.
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[DevoxxFR2013] Groovy and Statically Typed DSLs
Lecturer
Guillaume Laforge manages the Groovy project and leads JSR-241 for its standardization. As Vice President of Technology at G2One, he delivers services around Groovy/Grails. Co-author of “Groovy in Action,” he evangelizes at global conferences.
Cédric Champeau contributes to Groovy core at SpringSource (VMware division). Previously at Lingway, he applied Groovy industrially in DSLs, scripting, workflows.
Abstract
Guillaume Laforge and Cédric Champeau explore Groovy’s evolution in crafting statically typed domain-specific languages (DSLs). Building on runtime metaprogramming, Groovy 2.1 introduces compile-time features for type safety without sacrificing flexibility. They demonstrate extensions, AST transformations, and error reporting, culminating in advanced builders surpassing Java’s checks, illustrating implications for robust, expressive DSL design.
Groovy’s DSL Heritage: Dynamic Foundations and Metaprogramming
Laforge recaps Groovy’s DSL prowess: flexible syntax, runtime interception (invokeMethod, getProperty), closures for blocks.
Examples: method missing for fluent APIs, expando meta-classes for adaptations.
This dynamism accelerates development but risks runtime errors. Groovy 2 adds optional static typing (@TypeChecked), clashing initially with dynamic DSLs.
Bridging Static and Dynamic: Compile-Time Extensions
Champeau introduces Groovy 2.1’s static compile-time metaprogramming. @CompileStatic enables type checking; extensions handle DSL specifics.
Trait-like extensions via extension modules: add methods to classes statically.
// Extension class
class HtmlExtension {
static NodeBuilder div(Element self, Closure c) { /* build */ }
}
Register in META-INF, usable in typed code with error propagation.
This preserves DSL fluency under static compilation.
AST Transformations for Deeper Integration
Custom AST transformations inject code during compilation. @Builder variants, delegation.
For DSLs: transform method calls into builders, validate arguments statically.
Example: markup builder with type-checked HTML generation, reporting mismatches at compile-time.
Champeau details global transformations for cross-cutting concerns.
Advanced Type Checking: Custom Error Reporting and Beyond Java
Laforge showcases @TypeChecked with custom type checkers. Override doVisit for context-specific rules.
@TypeChecked
void script() {
html {
div(id: 'main') { /* content */ }
}
}
Checker ensures div accepts valid attributes, closures; errors reference user code lines.
Groovy exceeds Java: infer types in dynamic contexts, enforce domain rules unavailable in Java.
Builder Patterns and Real-World Applications
Demonstrate HTML DSL: nested closures build node trees, statically verified.
Grails integration: apply to GSPs for compile-time validation.
Champeau notes Grails’ metaprogramming complexity as ideal testbed—getProperty, MOP, AST all in play.
Implications for DSL Engineering: Safety, Productivity, Evolution
Static typing catches errors early, aids IDE support (autocompletion, refactoring). Dynamic essence retained via extensions.
Trade-offs: setup complexity; mitigated by community modules.
Future: deeper Grails incorporation, enhanced tooling.
Laforge and Champeau position Groovy as premier for type-safe yet expressive DSLs, blending agility with reliability.