Posts Tagged ‘NodeCongress2021’
[NodeCongress2021] Can We Double HTTP Client Throughput? – Matteo Collina
HTTP clients, the sinews of distributed dialogues, harbor untapped vigor amid presumptions of stasis. Matteo Collina, Node.js TSC stalwart, Fastify co-architect, and Pino progenitor, challenges this inertia, unveiling Undici—a HTTP/1.1 vanguard doubling, nay tripling, Node’s native throughput via HOL-blocking evasion.
Matteo’s odyssey traces TCP/IP genesis: Nagle’s algorithm coalesces packets, delaying ACKs—elegant for telnet, anathema for HTTP’s pipelined pleas. Keep-alive sustains sockets, multiplexing requests; yet core http’s single-flight per connection bottlenecks bursts.
Undici disrupts: connection pools parallelize, pipelining dispatches volleys sans serialization. Matteo benchmarks: native peaks at baselines; Undici’s agents—configurable concurrency—surge 3x, streams minimizing JSON parses.
Mitigating Head-of-Line Shadows
HOL’s specter—prior stalls cascade—yields to Undici’s ordered queues, responses slotted sans reordering. Matteo codes: fetch wrappers proxy natives, agents tune origins—pipelining: true unleashes floods.
Comparisons affirm: Undici’s strictness trumps core’s leniency, APIs diverge—request/stream for granularity. Fastify proxy’s genesis birthed Undici, Robert Nagy’s polish primed production.
Matteo’s clarion—agents mandatory, Undici transformative—ushers HTTP’s renaissance, slashing latencies in microservice meshes.
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[NodeCongress2021] Panel Discussion – Node.js in the Cloud
Cloud paradigms reshape Node.js landscapes, blending serverless ephemera with containerized constancy, as dissected in this convocation. Moderated discourse features Ali Spittel, AWS Amplify advocate and digital nomad; Eran Hammer, Sideway founder weaving narrative webs; Ruben Casas, American Express engineer pioneering micro-frontends; and Slobodan Stojanovic, Cloud Horizon CTO scaling Vacation Tracker’s serverless saga.
Ali champions Amplify’s frictionless ingress: Git-based deploys, CI/CD alchemy transmute code to globals—Lambda for backends, AppSync for GraphQL. Eran probes costs: fixed fleets versus invocation metering, cold starts’ latency tax. Ruben extols IaC: CDK’s constructs blueprint stacks, Terraform’s declarative drifts ensure idempotence.
Slobodan chronicles evolution: singleton Lambda to hexagonal CQRS ensembles, LocalStack mocks integrations. Consensus: serverless abstracts ops, yet demands async mastery—promises over callbacks, hexagonal ports insulate.
Deployment Dynamics and Cost Conundrums
Deploys diverge: Amplify’s wizardry suits solos, Claudia.js blueprints APIs. Containers—Docker/K8s—orchestrate statefuls, Fargate abstracts. Costs confound: Slobodan’s $250/month belies bugs’ $300 spikes; alarms mitigate.
Ali lauds functions’ scalability sans provisioning; Eran tempers with vendor lock perils. Ruben integrates OneApp’s runtime swaps.
Observability and IoT Intersections
Tracing threads via X-Ray/OpenTelemetry; Datadog dashboards divine. IoT? Node’s WebSockets shine—process streams via Amplify, hexagonal fits serverless.
Panel’s tapestry—diverse voices—illuminates Node.js’s cloud ascent, from fledgling functions to enterprise echelons.
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[NodeCongress2021] Don’t Try This at Home: Synchronous I/O in Node.js – Anna Henningsen
Node.js’s asynchronous creed—non-blocking I/O as ethos—clashes intriguingly with synchronous imperatives, where immediacy trumps concurrency. Anna Henningsen, erstwhile Node.js TSC member now at MongoDB’s dev tools cadre, probes this tension, cataloging detours from the async path and gleaning internals’ revelations. Pronouns she/her, Anna balances core contributions with family joys, her moniker addaleax echoing across Twitter and GitHub.
Anna queries the aversion: sync ops monopolize threads, stalling event loops—left pane’s stalled fetches versus right’s parallel prowess. Yet, exigencies persist: CLI bootstraps, config reads—fs.readFileSync reigns for startup simplicity.
Navigating Sync Detours and Their Perils
Anna enumerates evasions: worker_threads offloads to pools, yielding promises—fs.promises.readFile in isolates, main-thread yields via Atomics.wait. Threads excel for CPU hogs, but I/O yields context switches, inflating overheads.
Child processes fork interpreters, stdin/stdout pipes async, but spawnSync blocks—IPC for coordination. Anna demos: execSync shells commands, perils in untrusted inputs.
Domains? Deprecated, error silos sans true parallelism. Async_hooks? Context propagation, not computation.
Enter Anna’s brainchild: synchronous workers—native addons spawning interpreters, runUntil blocks main on promises, full API access sans multi-threading. Node 15.5+ requisites, experimental tag.
MongoDB’s Babel transpilation awaits sync-as-call sites, best-effort awaits. Anna’s taxonomy—drawbacks galore—affirms async’s supremacy, yet equips edge cases with informed arsenals.
Experimental Horizons and Practical Caveats
Anna’s holiday hack—runnable on GitHub—invites tinkering, crashes notwithstanding. Her MongoDB pivot underscores sync’s niche: edge functions crave immediacy, transpilation bridges gaps.
Anna’s disquisition, laced with humor, fortifies Node.js fidelity to flux, while charting sync’s shadowed trails.
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[NodeCongress2021] Logging, Metrics, and Tracing with Node.js – Thomas Hunter II
Observability pillars—logs, gauges, spans—form the triad illuminating Node.js constellations, where opacity breeds outages. Thomas Hunter II, a Node.js luminary and author of “Distributed Systems with Node.js,” dissects these sentinels, adapting book chapters to unveil their synergies in service scrutiny.
Thomas frames logging as cloud-elevated console.logs: structured JSON extrudes states, severity tiers—error to silly—filter verbosity. Winston orchestrates: transports serialize to stdout/files, Pino accelerates with async flushes. Conventions prescribe correlation IDs, timestamps; aggregators like ELK ingest for faceted searches.
Metrics quantify aggregates: counters tally invocations, histograms bin latencies. Prometheus scrapes via prom-client, Grafana visualizes trends—spikes foretell fractures. Thomas codes a registry: gauge tracks heap, histogram times handlers, alerting deviations.
Tracing reconstructs causal chains: spans encapsulate ops, propagators thread contexts. OpenTelemetry standardizes; Jaeger self-hosts hierarchies, timelines dissect 131ms journeys—Memcache to Yelp. Datadog APM auto-instruments, flame graphs zoom Postgres/AWS latencies.
Instrumentation Patterns and Visualization Nuances
Thomas prototypes: async_hooks namespaces contexts, cls-r tracers bridge async gulfs. Zipkin’s dependency DAGs, Datadog’s y-axis strata—live Lob.com postcard fetches—demystify depths.
Thomas’s blueprint—Winston for persistence, Prometheus for pulses, Jaeger for journeys—equips Node.js artisans to navigate nebulous networks with crystalline clarity.
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[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.