Posts Tagged ‘NodeJS’
[NodeCongress2021] Nodejs Runtime Performance Tips – Yonatan Kra
Amidst the clamor of high-stakes deployments, where milliseconds dictate user satisfaction and fiscal prudence, refining Node.js execution emerges as a paramount pursuit. Yonatan Kra, software architect at Vonage and avid runner, recounts a pivotal incident—a customer’s frantic call amid a faltering microservice, where a lone sluggish routine ballooned latencies from instants to eternities. This anecdote catalyzes his compendium of runtime enhancements, gleaned from battle-tested optimizations.
Yonatan initiates with diagnostic imperatives: Chrome DevTools’ performance tab chronicles timelines, flagging CPU-intensive spans. A contrived endpoint—filtering arrays via nested loops—exemplifies: record traces reveal 2-3 second overruns, dissected via flame charts into redundant iterations. Remedies abound: hoist computations outside loops, leveraging const for immutables; Array.prototype.filter supplants bespoke sieves, slashing cycles by orders.
Garbage collection looms large; Yonatan probes heap snapshots, unveiling undisposed allocations. An interval emitter appending to external arrays evades reclamation, manifesting as persistent blue bars—unfreed parcels. Mitigation: nullify references post-use, invoking gc() in debug modes for verification; gray hues signal success, affirming leak abatement.
Profiling Memory and Function Bottlenecks
Memory profiling extends to production shadows: –inspect flags remote sessions, timeline instrumentation captures allocations sans pauses. Yonatan demos: API invocations spawn specials, uncollected until array clears, transforming azure spikes to ephemeral grays. For functions, Postman sequences gauge holistically—from ingress to egress—isolating laggards for surgical tweaks.
Yonatan dispels myths: performance isn’t arcane sorcery but empirical iteration—profile relentlessly, optimize judiciously. His zeal, born of crises, equips Node.js stewards to forge nimble, leak-free realms, where clouds yield dividends and users endure no stutter.
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[NodeCongress2021] Push Notifications: Can’t Live With Em, Can’t Live Without Em – Avital Tzubeli
In an era where digital alerts permeate daily rhythms, the orchestration of push notifications embodies a delicate equilibrium between immediacy and reliability. Avital Tzubeli, a backend engineer at Vonage, unravels this dynamic through her recounting of the message bus at the heart of their communications platform—a conduit dispatching 16 million dispatches daily, contending with temporal pressures and infrastructural strains. Drawing from Hebrew folklore, where a louse embarks on a globetrotting odyssey, Avital likens notifications to intrepid voyagers navigating service boundaries.
Avital’s tale unfolds across Vonage’s ecosystem: inbound triggers from Frizzle ingress via RabbitMQ queues, auto-scaling consumers in HTTP services validate payloads, appending trace IDs for audit trails. Continuation Local Storage (CLS-Hooked) embeds identifiers in request scopes, facilitating log enrichment without prop modifications. As payloads traverse to PushMe—Vonage’s dispatch hub—interceptors affix traces to Axios headers, ensuring end-to-end visibility.
This choreography yields sub-15ms latencies: Frizzle to HTTP in milliseconds, thence to PushMe, culminating in device delivery via APNS or FCM. Avital spotlights middleware elegance—CLS-Hooked instances persist contexts, auto-injecting IDs into logs or headers, oblivious to underlying transports.
Architectural Resilience and Observability
Resilience pivots on RabbitMQ’s durability: dead-letter exchanges quarantine failures, retries exponential backoffs temper bursts. Monitoring via Grafana dashboards tracks queue depths, consumer lags; alerts preempt pileups. Avital shares code vignettes—middleware instantiation, trace retrieval, log augmentation—revealing CLS-Hooked’s prowess in decoupling concerns.
For broader applicability, Avital posits analogous buses for event sourcing or microservice fan-outs: RabbitMQ’s ACK semantics guarantee at-least-once semantics, complemented by idempotent handlers. Blaming externalities like Apple for undelivered alerts underscores the perils of third-party dependencies, yet Vonage’s stack—Node.js scripts fueling the frenzy—exemplifies robust engineering.
Avital’s odyssey, though sans parasitic flair, affirms notifications’ global sprint, propelled by vigilant teams and scalable sinews.
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[NodeCongress2021] Machine Learning in Node.js using Tensorflow.js – Shivay Lamba
The fusion of machine learning capabilities with server-side JavaScript environments opens intriguing avenues for developers seeking to embed intelligent features directly into backend workflows. Shivay Lamba, a versatile software engineer proficient in DevOps, machine learning, and full-stack paradigms, illuminates this intersection through his examination of TensorFlow.js within Node.js ecosystems. As an open-source library originally developed by the Google Brain team, TensorFlow.js democratizes access to sophisticated neural networks, allowing practitioners to train, fine-tune, and infer models without forsaking the familiarity of JavaScript syntax.
Shivay’s narrative commences with the foundational allure of TensorFlow.js: its seamless portability across browser and Node.js contexts, underpinned by WebGL acceleration for tensor operations. This universality sidesteps the silos often encountered in traditional ML stacks, where Python dominance necessitates cumbersome bridges. In Node.js, the library harnesses native bindings to leverage CPU/GPU resources efficiently, enabling tasks like image classification or natural language processing to unfold server-side. Shivay emphasizes practical onboarding—install via npm, import tf, and instantiate models—transforming abstract algorithms into executable logic.
Consider a sentiment analysis endpoint: load a pre-trained BERT variant, preprocess textual inputs via tokenizers, and yield probabilistic outputs—all orchestrated in asynchronous handlers to maintain Node.js’s non-blocking ethos. Shivay draws from real-world deployments, where such integrations power recommendation engines or anomaly detectors in e-commerce pipelines, underscoring the library’s scalability for production loads.
Streamlining Model Deployment and Inference
Deployment nuances emerge as Shivay delves into optimization strategies. Quantization shrinks model footprints, slashing latency for edge inferences, while transfer learning adapts pre-trained architectures to domain-specific corpora with minimal retraining epochs. He illustrates with a convolutional neural network for object detection: convert ONNX formats to TensorFlow.js via converters, bundle with webpack for serverless functions, and expose via Express routes. Monitoring integrates via Prometheus metrics, tracking inference durations and accuracy drifts.
Challenges abound—memory constraints in containerized setups demand careful tensor management, mitigated by tf.dispose() invocations. Shivay advocates hybrid approaches: offload heavy training to cloud TPUs, reserving Node.js for lightweight inference. Community extensions, like @tensorflow/tfjs-node-gpu, amplify throughput on NVIDIA hardware, aligning with Node.js’s event-driven architecture.
Shivay’s exposition extends to ethical considerations: bias audits in datasets ensure equitable outcomes, while federated learning preserves privacy in distributed training. Through these lenses, TensorFlow.js transcends novelty, evolving into a cornerstone for ML-infused Node.js applications, empowering creators to infuse intelligence without infrastructural overhauls.
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[NodeCongress2021] From 1 to 101 Lambda Functions in Production: Evolving a Serverless Architecture – Slobodan Stojanovic
Charting a server’s demise unearths tales of unchecked escalation, yet Slobodan Stojanovic’s chronicle of Vacation Tracker—from solitary Lambda to century-strong ensemble—illuminates adaptive mastery. As co-founder and CTO at Cloud Horizon, Slobodan recounts bootstrapping a PTO sentinel for Slack, evolving through GraphQL mazes to serve millions, all while curbing costs under $2K since 2018.
Slobodan’s saga ignites in 2017: hackathon sparks, landing page lures 100+ waitlisters. 2018’s MVP—single Lambda parses Slack commands, DynamoDB persists—morphs via Serverless Framework, then Claudia.js for API orchestration.
Navigating Architectural Metamorphoses
Hexagonal tenets decouple: ports/adapters insulate cores, easing mocks for units. Early monolith yields to CQRS—separate read/write Lambdas—bolstering scalability. GraphQL unifies: Apollo resolvers dispatch to specialists, DynamoDB queries aggregate.
Migrations pivot: Mongo to Dynamo via interface swaps, data shuttles offline. Integrations? LocalStack emulates AWS; CI spins ephemeral tables, asserts via before/after hooks.
Monitoring, Costs, and Team Triumphs
Datadog dashboards query errs; alerts ping anomalies. Bugs bite—Dynamo scans balloon bills to $300/month, fixed via queries slashing RPS. Onboarding thrives: hexagonal clarity, workshops demystify.
Slobodan’s axioms: evolve with scale, hexagonal/CQRS affinity, integration rigor, vigilant oversight. Free webinars beckon, perpetuating serverless lore.
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[NodeCongress2021] The Micro-Frontend Revolution at Amex – Ruben Casas
Orchestrating frontend sprawl for legions of coders while infusing modern stacks like Node.js and React demands architectural ingenuity. Ruben Casas, software engineer at American Express, chronicles their micro-frontend odyssey—a 2016 vanguard yielding seamless compositions for millions, sans monolithic morass.
Ruben’s tale unfurls with a CTO’s conundrum: ballooning teams clash against legacy behemoths, spawning coordination quagmires and sync lags. Microservices scaled backends; frontends craved analogs—autonomous squads wielding isolated codebases, horizontal velocity.
Forging Modular Compositions
Amex’s OneApp framework—open-source beacon—espouses iframe-free integration: Webpack bundles modules to CDN artifacts, runtime loaders fetch per-route payloads. Ruben diagrams: root orchestrates, injecting via shadow DOM for scoped styles/scripts, mitigating clashes.
Prod hums via module maps—versioned manifests—pulling from CDNs; updates propagate sans restarts, hot-swapping in-memory. Development mirrors: Docker-spun OneApp proxies local clones amid prod stubs, isolating tweaks.
Deployment Dynamics and Cultural Catalysts
CIs per-repo trigger tests—units, integrations—publishing to CDNs; OneApp ingests, composing fluidly. Ruben lauds scalability: thousands collaborate frictionlessly, upgrades cascade independently.
Yet, patterns, not panaceas—tailor to contexts. OneApp’s GitHub invites forks, embodying Amex’s trailblazing ethos.
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[NodeCongress2021] Demystifying Memory Leaks in JavaScript – Ruben Bridgewater
Unraveling the enigma of escalating heap usage transforms from arcane ritual to methodical pursuit under Ruben Bridgewater’s guidance. As principal software architect at Datadog and Node.js Technical Steering Committee member, Ruben demystifies leaks—unfreed allocations snowballing to OOM crashes or inflated bills—via V8’s innards and profiling arsenal.
Ruben invokes Wikipedia: leaks arise from mismanaged RAM, no longer needed yet unreclaimed, yielding upward trajectories on usage graphs versus steady baselines. JavaScript’s GC—mark-sweep for majors, scavenge for minors—orchestrates reclamation, yet closures, globals, or detached DOM snare objects in retention webs.
Profiling the Culprits
Chrome DevTools reigns: timelines chart allocations, heap snapshots freeze states for delta diffs—2.4MB spikes spotlight string hordes in func contexts. Ruben demos: inspect reveals var string chains, tracing to errant accumulators.
Clinic.js automates: clinic doctor flags leaks via flame graphs; heap-profiler pinpoints retainers. Production? APMs like Datadog monitor baselines, alerting deviations—avoid snapshots’ pauses therein.
Browser parity extends tooling: inspect Memory tab mirrors Node’s inspector.
Remediation Roadmaps
Ruben’s playbook: surveil via APMs, snapshot judiciously (controlled environs), diff deltas for deltas, excise roots—globals to WeakMaps, arrays to Sets. Data choices matter—primitives over objects; restarts as Hail Marys.
Ken Thompson’s quip—ditching code boosts productivity—caps Ruben’s ode to parsimony. Memory’s dual toll—fiscal, performative—demands preemption, yielding snappier, thriftier apps.
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[NodeCongress2021] The Security Toolbox For Node – Milecia McGregor
Fortifying Node.js bastions against pervasive threats demands a curated arsenal, blending vigilance with automation. Milecia McGregor, senior software engineer at Conducto, assembles this kit, dissecting OWASP’s top perils and arming attendees with battle-tested countermeasures. From dependency audits to server sentinels, her compendium ensures sprints proceed apace while vulnerabilities wane.
Milecia commences with reconnaissance: npm audit scans repos for exploits, flagging severity via exit codes integrable to CI. Snyk elevates this, fusing vuln databases with fix PRs, while Dependabot automates updates—proactive bulwarks against supply-chain snares like left-pad debacles.
Safeguarding Dependencies and Inputs
Injections top OWASP’s docket; Milecia prescribes parameterized queries via Knex or Sequelize, thwarting SQLi. XSS bows to sanitized outputs—DOMPurify scrubs payloads—while CSRF yields to csurf’s tokens. Auth falters sans salting; bcrypt hashes credentials, JWTs secure sessions with HS256.
Broken access? Role-based guards via Passport middleware enforce hierarchies. Sensitive leaks? dotenv .gitignore guards env vars; helmet configures headers, quelling MIME sniffing and clickjacking.
Validation anchors integrity: Joi schemas parse inputs, rejecting malformations; validator.js tackles emails, phones—eschewing bespoke parsers.
Encrypting Flows and Throttling Threats
Data en route merits crypto-js’s AES, obfuscating intercepts. Servers crave HTTPS—certbot automates Let’s Encrypt—rate-limit via express-rate-limit, capping barrages at 100/min/IP. DDoS? Cloudflare proxies absorb volleys.
Milecia extols reuse: helmet’s quick wins, Kali Linux’s adversarial lens. Her takeaways—leverage extant libs, preempt breaches, probe attacker tactics—empower swift fortifications, harmonizing security with agility.
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[NodeCongress2021] Safely Handling Dynamic Data with TypeScript – Ethan Arrowood
In the realm of full-stack development, where APIs shuttle payloads across boundaries, ensuring type fidelity amid flux poses a perennial puzzle. Ethan Arrowood, a software engineer at Microsoft, navigates this terrain adeptly, advocating schemas as sentinels against runtime surprises. His discourse spotlights TypeScript’s prowess in taming erratic inputs—from form submissions to auth tokens—via symbiotic validation frameworks.
Ethan posits data as the lifeblood of modern apps: JSON’s ubiquity powers endpoints, yet its pliancy invites mismatches. Consider an employee dossier: id, name, employed boolean, company, age, projects array. Static typings guard assignments, but external fetches evade compile-time checks, risking undefined accesses or coerced primitives. Ethan’s remedy? Leverage JSON Schema for declarative constraints, transmuting fluid objects into rigid molds.
Bridging Schemas and Static Guarantees
Enter @sinclair/typebox, a runtime validator that births schemas from TypeScript generics, yielding dual benefits: enforcement and inference. Ethan illustrates with Fastify routes: define bodySchema as TypeBox’s TObject, embedding TString for id/name, TOptional(TBoolean) for employed, mirroring anticipated shapes. This artifact doubles as validator—Fastify’s schema prop ingests it for payload scrutiny—and type oracle, infusing handlers with precise annotations.
In practice, a POST endpoint parses body as TInfer, affording intellisense: body.name yields string, body.age number|undefined. Ethan live-codes this synergy, hovering reveals nested generics—TArray(TString) for projects—ensuring downstream ops like array iterations sidestep guards. Should validation falter, Fastify aborts with 400s, averting tainted flows.
This fusion extends to broader ecosystems: io-ts for branded types, Zod for ergonomic chaining. Ethan cautions reliance on validation logic; a flawed schema propagates peril, echoing JavaScript’s untyped underbelly. Yet, when aligned, it forges ironclad pipelines, where dynamic ingress aligns seamlessly with static egress.
Real-World Integrations and Ecosystem Synergies
Ethan’s Fastify demo crystallizes the workflow: register plugins, await readiness, log addresses— all scaffolded atop schema-derived types. VS Code’s hover unveils the schema’s blueprint, from optional fields to array innards, streamlining refactoring. For authentication, schemas vet JWT claims; forms, user inputs—universal applicability.
Gratitude flows to undraw for visuals, highlight.js for syntax, and tmcw/big for slides, underscoring open-source’s scaffolding role. Ethan’s ethos—connect via GitHub/Twitter—invites dialogue, amplifying Node.js and TypeScript’s communal momentum. By entwining validation with typing, developers reclaim assurance, rendering volatile data a predictable ally in resilient architectures.
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[NodeCongress2021] Instrumenting Node.js Internals – Alejandro Oviedo
Delving into the intricacies of runtime diagnostics reveals a persistent challenge for Node.js developers: unraveling opaque behaviors in live applications without invasive alterations. Alejandro Oviedo, a backend specialist from Buenos Aires, confronts this head-on by unveiling “instrument,” an open-source utility he crafted to illuminate network flows, filesystem interactions, and module loadings. This innovation stems from his encounters with elusive glitches, where conventional logging falls short, compelling a quest for non-disruptive observability.
Alejandro’s journey underscores a universal frustration—debugging sans exceptions or traces leaves one adrift, akin to navigating fog-shrouded waters. Even in controlled dev setups, grasping async invocations or dependency chains demands more than intuition. His tool intervenes subtly, wrapping native modules like HTTP, HTTPS, or FS to log invocations without reshaping source code, thus preserving original outputs while appending diagnostic summaries.
Enhancing Visibility Through Modular Wrappers
At the heart of instrument lies a configuration-driven approach, where users specify modules in an instrument.config.js file—HTTP for endpoint reconnaissance, REQUIRE for dynamic imports. Alejandro demonstrates with npm’s version query: invoking via instrument yields the anticipated 8.2.1 for Mocha, trailed by a concise report on GET requests to registry.npmjs.org, complete with user-agent headers and CI flags. This granularity exposes externalities, from URL patterns to payload details, sans performance penalties in non-prod realms.
Extending to refactoring scenarios, imagine auditing dynamic loads in an HTTP server; static analyzers falter against runtime evaluations, but instrument excels, flagging module_a.js imports across probes. Alejandro stresses its dev-centric ethos: add as a devDependency, execute with npx instrument node app.js, and harvest insights effortlessly. Caveats abound—overhead precludes prod use, and nascent bugs invite community scrutiny via GitHub.
Yet, this simplicity belies profound utility. By demystifying internals, developers sidestep trial-and-error marathons, accelerating triage from hours to moments. Alejandro’s creation not only empowers solo coders but fosters collaborative ecosystems, where shared configs standardize diagnostics across teams. In an era of sprawling Node.js deployments, such tools bridge the observability chasm, ensuring applications hum reliably under scrutiny.
Fostering Community-Driven Refinements
Alejandro invites scrutiny, urging PRs and issues on the repository, while teasing a Q&A for deeper dives. His Buenos Aires roots and international contributions—local meetups to global forums—infuse the project with grassroots vigor, mirroring Node.js’s collaborative spirit. As environments evolve, instrument’s extensibility promises adaptations, perhaps integrating with APMs for holistic tracing.
Through this lens, troubleshooting morphs from art to science, equipping practitioners to dissect and mend with precision. Alejandro’s endeavor reminds us: true resilience blooms from visibility, not obscurity.
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[NodeCongress2021] Security Testing for JS Apps, Node Congress – Ryan Severns
Application security need not impede developer agility; instead, it can integrate seamlessly into workflows. Ryan Severns, co-founder of StackHawk, presents a streamlined approach to vulnerability detection in JavaScript ecosystems, leveraging automation to unearth issues pre-production.
StackHawk automates dynamic analysis against JS apps and APIs—REST, GraphQL—flagging SQL injections or data leaks via CI/CD scans. On pull requests, scans mimic attacks, surfacing flaws with request/response evidence, expediting triages.
Automating Scans with ZAP Foundations
Built atop OWASP ZAP, StackHawk configures effortlessly for Node.js stacks, scanning SPAs or backends sans code mods. Post-scan, dashboards highlight exploits, with remediation docs and Jira integrations deferring low-risks, respecting only novel threats.
Integrating into DevSecOps Pipelines
Ryan emphasizes workflow harmony: GitHub Actions triggers validate endpoints, blocking merges on criticals while queuing fixes. Free tiers invite experimentation, blending security into Node.js velocity without friction.