# performance-optimization Published articles for performance-optimization. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time DevFeed: [Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time]() Original publisher: [Read original article]() Author: Egor Klimov Published: 2026-09-16T12:34:47Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [OpenTelemetry](), [Microservices](), [observability](), [telemetry](), [Traces](), [ide]() Tags: [all-things-web](), [backstage](), [environment-variables](), [goland](), [ide](), [idea](), [intellij-idea](), [intellij-platform](), [jetbrains](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [plugin-development](), [plugin-highlights](), [plugins](), [pycharm](), [research](), [rider](), [telemetry](), [traces](), [webstorm]() ### AI overview This article explains how the JetBrains OpenTelemetry Plugin generates a service map from runtime telemetry. It describes using logs, metrics, and especially standardized trace spans to visualize how microservices communicate, along with the plugin's lightweight local OpenTelemetry backend. ### Source excerpt We've all been there: you join a new project, and the first thing you ask for is the architecture diagram. You're handed a diagram that looks great, but after a week of debugging, you realize it's six months out of date. Service A hasn't talked to Service B since the spring, and there's a new [...] ## Optimizing CPU-side Rendering Code DevFeed: [Optimizing CPU-side Rendering Code]() Original publisher: [Read original article]() Author: Clay John Published: 2026-09-15T17:00:00Z Content type: tutorial Language: en Sources: [Godot Engine Official]() Topics: [Godot](), [Optimization](), [cpu](), [GPU](), [shaders]() Tags: [batching](), [bug](), [cpu](), [gpu](), [optimization](), [performance](), [performance-optimization](), [progress-report](), [shaders]() ### AI overview This article explains how Godot optimizes CPU-side rendering code. It describes balancing CPU and GPU workloads, identifying performance bottlenecks, investigating solutions, measuring results, and repeating the process. ### Source excerpt Optimizing CPU code is a lot of fun. Here's how we do it ## Inside SubcomposeLayout: Jetpack Compose's Most Misunderstood API DevFeed: [Inside SubcomposeLayout: Jetpack Compose's Most Misunderstood API]() Original publisher: [Read original article]() Author: Shreyas Patil Published: 2026-07-10T12:05:00Z Content type: article Language: en Sources: [Shreyas Patil's Blog]() Topics: [Jetpack Compose](), [Compose](), [ui]() Tags: [android](), [android-app-development](), [compose](), [declarative](), [deep-dive](), [internals](), [jetpack-compose](), [kotlin](), [learn](), [performance](), [performance-optimization](), [ui](), [uiux]() ### AI overview A deep dive into Jetpack Compose's SubcomposeLayout, explaining how it performs composition during measurement or placement, stalls the normal layout pipeline, and can create performance costs. The article also outlines when standard custom layouts or SubcomposeLayout are appropriate. ### Source excerpt A deep dive into the internals of SubcomposeLayout in Jetpack Compose. Learn how subcomposition actually works, why it breaks the phase rules, and the real node-thrashing cost it carries. ## How Gradle Is Javamaxxing DevFeed: [How Gradle Is Javamaxxing]() Original publisher: [Read original article]() Author: Laura Kassovic Published: 2026-05-28T04:00:00Z Content type: article Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Java](), [toolchains](), [toolchain]() Tags: [bytecode](), [compiler](), [garbage-collectors](), [gradle](), [java](), [java-8](), [jdk](), [jvm](), [openjdk](), [optimization](), [performance-optimization](), [release]() ### AI overview Gradle aggressively adopts new JDK releases because newer JVMs can improve build speed, memory use, startup behavior, and maintainability. JVM toolchains allow Gradle to run on JDK 26 while compiling and testing projects for older Java versions, including producing Java 8-compatible bytecode. ### Source excerpt Some people optimize one part of their lives to the absolute limit, then past it. They call it "-maxxing." Houseplantmaxxing. Sleepmaxxing. Tokenmaxxing. The Gradle Build Tool is Javamaxxing. TL;DR Breathe easy knowing you can run modern Gradle on JDK 26 while still shipping Java 8-compatible artifacts: Gradle aggressively adopts new JDKs because newer JVMs make builds faster, leaner, and easier to maintain. Upgrading the JDK that runs Gradle is often the easiest performance win. JVM toolchains fully separate the JVM running Gradle, the JVM compiling your code, and the JVM running your tests. NOT TL;DR We aggressively adopt new JDK releases as soon as we responsibly can. Not because it's trendy. Because newer JDKs make Gradle faster, leaner, and easier to maintain. Every OpenJDK release ships improvements to the JVM; garbage collectors, compiler infrastructure, startup behavior, memory layout, and runtime APIs. As a high-performance JVM build tool, Gradle benefits directly from all of it. The question people immediately ask is: Does this mean my project also has to run on the newest JDK? No. That coupling effectively disappeared once Gradle introduced JVM toolchains. With toolchains, the JVM that runs Gradle is completely separate from the JVM that compiles, tests, and executes your code. You can run Gradle on JDK 26 today and still produce Java 8 bytecode. What Is the Current State of Gradle and Java? We add daemon support for every new JDK as soon as the ecosystem permits: Java 24 in Gradle 8.14.0, Java 25 in 9.1.0 (released two days after JDK 25 GA), Java 26 in 9.4.0. The full mapping is in the Compatibility Matrix. We then bump the minimum JVM required to execute Gradle as users move. Gradle 9.0.0, released on July 31, 2025, raised the minimum JVM required to run the Gradle daemon to Java 17. That was the first daemon JVM floor increase since Gradle 5.0 made Java 8 the minimum back in 2018. The next major transition is already underway, Gradle 10.0.0 will likely ## How Bitsets Supercharged Our Backend: Faster String Overlap Checks at Scale DevFeed: [How Bitsets Supercharged Our Backend: Faster String Overlap Checks at Scale]() Original publisher: [Read original article]() Author: Jiaqi Zhu Published: 2026-03-25T19:02:18Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Optimization](), [Back end](), [Database](), [data](), [Caching]() Tags: [access-control](), [backend](), [backend-development](), [caching](), [complexity](), [database](), [distributed-systems](), [memory](), [optimization](), [performance](), [performance-optimization](), [real-time](), [software-engineering]() ### AI overview This article describes a backend performance problem involving overlap checks between large sets of strings. It explains that nested loops and hash-based comparisons became bottlenecks as data volumes and request loads increased, while local caching improved lookup speed but created significant memory pressure. The article introduces bitset optimization as the approach used to transform these overlap checks. ### Source excerpt Introduction Ever wondered why your backend slows to a crawl when comparing massive sets of strings? Here's how we turned a bottleneck into a lightning-fast operation using bitsets. Efficiently checking for overlaps between large sets of strings is a common challenge in high-throughput backend services. Traditional approaches -- such as nested loops or hash-based comparisons -- can quickly become performance bottlenecks, especially as data volumes scale and both memory and response time are critical. This is particularly true in environments like Walmart's, where backend systems routinely process millions of records per second to power search, recommendation, and fraud detection features. As our applications grew, we observed that even well-optimized hash set operations struggled to keep up with the demands of real-time processing. In this blog, I'll share how we leveraged bitset optimization to transform our string set overlap checks. The Problem: When "Fast Enough" Isn't Enough Our service is designed to handle high volumes of requests, each containing multiple sets of strings mapped to unique keys -- such as user IDs, product SKUs, or transaction identifiers. For every incoming key, we must efficiently retrieve the corresponding set of strings from our backend database and determine if there is any overlap with the provided set in the request. This overlap check is a critical step in workflows like deduplication, access control, and real-time validation. However, as the number of keys and the size of each string set grow, the computational and memory demands of these operations can escalate rapidly. The Memory Bottleneck To mitigate performance bottlenecks, we initially explored caching the database string sets locally within each pod. While this approach did improve lookup speed, it introduced a new challenge: memory consumption. As our dataset grew, the memory footprint of these in-memory caches ballooned, reaching as high as an unsustainable level in the worst-ca ## Upgrade to Puma 7 to Fix Keep-Alive Request Fairness in Ruby Apps DevFeed: [Upgrade to Puma 7 to Fix Keep-Alive Request Fairness in Ruby Apps]() Original publisher: [Read original article]() Author: Richard Schneeman Published: 2025-11-05T21:42:16Z Content type: article Language: en Sources: [Heroku]() Topics: [upgrade](), [Ruby](), [Heroku](), [Latency](), [servers](), [Low Latency]() Tags: [engineering](), [heroku](), [latency](), [performance-optimization](), [ruby](), [servers](), [upgrade](), [web]() ### AI overview The article explains a keep-alive scheduling bug in Puma 6 and earlier that could let one connection monopolize a thread and delay queued requests. Puma 7 introduces proportional sleeping to balance connections across workers, and Puma 7.1.0 restores the fast-inline optimization with fairness safeguards. It also describes the upgrade and verification commands. ### Source excerpt Puma 7 is here, and that means your Ruby app is now keep-alive ready. This bug, which existed in Puma for years, caused one out of every 10 requests to take 10x longer by unfairly "cutting in line." In this post, I'll cover how web servers work, what caused this bad behavior in Puma, and [...] The post Upgrade to Puma 7 and Unlock the Power of Fair Scheduled Keep-alive appeared first on Heroku. ## Learn How to Lower Heroku Dyno Latency through Persistent Connections (Keep-alive) DevFeed: [Learn How to Lower Heroku Dyno Latency through Persistent Connections (Keep-alive)]() Original publisher: [Read original article]() Author: Richard Schneeman Published: 2025-11-04T16:27:37Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Heroku](), [Latency](), [Persistence](), [web applications](), [HTTP](), [networking]() Tags: [engineering](), [heroku](), [how-to](), [http](), [latency](), [networking](), [performance](), [performance-optimization](), [persistence](), [web-applications]() ### AI overview This tutorial explains how persistent keep-alive connections can reduce Heroku dyno latency by amortizing the overhead of new TCP connections and avoiding TCP slow start for later requests. It introduces the relationship between HTTP requests, TCP connections, and web server behavior. ### Source excerpt The Performance Penalty of Repeated Connections Before the latest improvements to the Heroku Router, every connection between the router and your application dyno risked incurring the latency penalty of a TCP slow start. To understand why this is a performance bottleneck for modern web applications, we must look at the fundamentals of the Transmission Control [...] The post Learn How to Lower Heroku Dyno Latency through Persistent Connections (Keep-alive) appeared first on Heroku. ## BV Hosted Display - The New Baseline for CWV metrics DevFeed: [BV Hosted Display - The New Baseline for CWV metrics]() Original publisher: [Read original article]() Author: Veda Published: 2025-09-23T07:51:42Z Content type: tutorial Language: en Sources: [Bazaarvoice]() Topics: [benchmarking](), [Core Web Vitals](), [performance-optimization](), [Web](), [Mobile](), [cpu](), [Android](), [iOS](), [5G](), [iphone]() Tags: [5g](), [android](), [bazaarvoice](), [benchmarking](), [benchmarkingstrategy](), [core-web-vitals](), [corewebvitals](), [cpu](), [cwv](), [engineering](), [ios](), [iphone](), [lcp](), [lighthouse](), [mobile](), [mobilefirst](), [mobileperformance](), [pagespeed](), [performance](), [performance-optimization](), [performanceoptimization](), [seo](), [userexperience](), [ux](), [web-performance](), [webperformance]() ### AI overview This article argues that the throttled Moto G4 baseline traditionally used for Core Web Vitals no longer accurately represents many e-commerce users. It proposes a data-driven benchmarking strategy based on actual customer devices, operating systems, hardware, and network conditions. ### Source excerpt The Old Way vs. The Reality of Your Customers For years, the gold standard for benchmarking web performance, particularly for Google's Core Web Vitals (CWV), has been a mobile device baseline--specifically, a throttled connection and CPU designed to simulate a Moto G4. This approach was established with good intentions: to ensure websites are accessible to [...] ## Using heroku run for Interactive One-Off Dynos on Heroku Fir DevFeed: [Using heroku run for Interactive One-Off Dynos on Heroku Fir]() Original publisher: [Read original article]() Author: Su Glasgo Published: 2025-09-08T21:33:43Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Command-line interface](), [Database Migration](), [Authentication]() Tags: [cli](), [cloud-infrastructure](), [database-migration](), [dynos](), [engineering](), [fir](), [heroku](), [opentelemetry](), [performance-optimization](), [security](), [troubleshooting]() ### AI overview This article explains how Heroku Fir supports interactive CLI sessions through dedicated one-off dynos. It describes using these isolated dynos for administrative tasks, database migrations, real-time diagnostics, and troubleshooting, with public SSH key authentication required. ### Source excerpt When Production Goes Sideways Imagine this: It's 2 AM, your phone buzzes with an alert, and your dashboards are screaming. Production is down. Sound familiar? An automated health check has failed, and your internal dashboards are showing a spike in errors. You've just pushed a new release that included a critical database schema change, and [...] The post Triage and Fix with Confidence: heroku run and OTel on Heroku Fir appeared first on Heroku. ## Exploring PausableComposition internals in Jetpack Compose DevFeed: [Exploring PausableComposition internals in Jetpack Compose]() Original publisher: [Read original article]() Author: Shreyas Patil Published: 2025-07-14T05:34:31Z Content type: tutorial Language: en Sources: [Shreyas Patil's Blog]() Topics: [Compose](), [Jetpack Compose]() Tags: [android](), [android-app-development](), [code](), [compose](), [declarative](), [internals](), [jank](), [jetpack-compose](), [kotlin](), [kotlin-multiplatform](), [main-thread](), [performance](), [performance-optimization](), [ui](), [uiux]() ### AI overview This article explores the internals of PausableComposition, an internal API introduced in Compose 1.9.X. It explains how composition can be divided into smaller pieces and prepared asynchronously to reduce main-thread work and help prevent dropped frames during scrolling. ### Source excerpt Explore the internals of PausableComposition in Jetpack Compose. Learn how Compose manages state and effects when a composition is paused or resumed. ## OpenTelemetry, Kubernetes, and Fir: Putting it All Together DevFeed: [OpenTelemetry, Kubernetes, and Fir: Putting it All Together]() Original publisher: [Read original article]() Author: Ken W. Alger Published: 2025-07-09T15:00:13Z Content type: article Language: en Sources: [Heroku]() Topics: [OpenTelemetry](), [Kubernetes](), [Heroku](), [observability](), [telemetry](), [Cloud](), [Microservice]() Tags: [alternatives](), [buildpacks](), [cloud-infrastructure](), [datadog](), [engineering](), [fir](), [heroku](), [jaeger](), [kubernetes](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [sdks](), [telemetry]() ### AI overview This article explains how Heroku's Fir cloud platform uses Kubernetes for automated scaling, self-healing, and resource management while exposing a Heroku interface to users. It also describes how OpenTelemetry standardizes telemetry collection and enables distributed tracing, performance diagnosis, and routing data to different monitoring backends without vendor lock-in. ### Source excerpt Modern cloud-native architectures are composed of multiple microservices running across dynamic environments. Effectively diagnosing performance issues, bottlenecks, or failures requires comprehensive observability. For this, many organizations look to OpenTelemetry, which provides a standardized approach to capturing and analyzing telemetry data. Fir is Heroku's next generation cloud platform, designed to offer more modern cloud-native capabilities with [...] The post OpenTelemetry, Kubernetes, and Fir: Putting it All Together appeared first on Heroku. ## Heroku Postgres 17 with the New Upgrade Process: Faster Performance, Easier Upgrade DevFeed: [Heroku Postgres 17 with the New Upgrade Process: Faster Performance, Easier Upgrade]() Original publisher: [Read original article]() Author: Jonathan Brown Published: 2025-05-29T15:32:49Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [PostgreSQL](), [upgrade](), [Database](), [JSON](), [Monitoring](), [Latency]() Tags: [availability](), [database](), [database-performance](), [heroku](), [heroku-postgres](), [monitoring](), [news](), [performance](), [performance-optimization](), [platform-updates](), [postgresql](), [sql-json](), [upgrade]() ### AI overview Heroku announces the general availability of Heroku Postgres 17 and makes a new version-upgrade method the default. The release includes query and write performance improvements, enhanced JSON support, lower vacuum memory usage, and expanded observability features. ### Source excerpt We're excited to announce the general availability of Heroku Postgres version 17, packed with new features and enhancements to your database performance. And that's not all - we're also introducing a game-changing feature that streamlines your upgrade experience. This new method of version upgrade is now the default, so you can try it to upgrade [...] The post Heroku Postgres 17 with the New Upgrade Process: Faster Performance, Easier Upgrade appeared first on Heroku. ## SignalR Scalability: Scaling Real-Time SignalR Applications on Heroku DevFeed: [SignalR Scalability: Scaling Real-Time SignalR Applications on Heroku]() Original publisher: [Read original article]() Author: Andrew Fawcett Published: 2025-05-21T16:26:16Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [SignalR](), [Scalability](), [Heroku](), [Redis](), [.NET](), [WebSocket]() Tags: [developer-tools](), [engineering](), [guide](), [heroku](), [net](), [performance-optimization](), [redis](), [scalability](), [signalr](), [sticky-sessions](), [websocket]() ### AI overview A Heroku tutorial explains how to scale a real-time .NET SignalR application across multiple servers or dynos. It covers using a Redis backplane and sticky sessions to maintain stable WebSocket connections. ### Source excerpt SignalR makes it easy to add real-time functionality to .NET web applications--things like live chat, instant notifications, or interactive dashboards. But what happens when your app starts to grow? A single server can only take you so far. At some point, you'll need to take advantage of SignalR scaling features to scale out your app. [...] The post SignalR Scalability: Scaling Real-Time SignalR Applications on Heroku appeared first on Heroku. ## Optimizing Enterprise Operations with Heroku's Advanced Logging Features DevFeed: [Optimizing Enterprise Operations with Heroku's Advanced Logging Features]() Original publisher: [Read original article]() Author: Julián Duque Published: 2025-05-13T15:00:13Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Logging](), [log management](), [distributed-systems]() Tags: [backend](), [cloud](), [cloud-infrastructure](), [distributed](), [distributed-systems](), [ecosystem](), [heroku](), [logging](), [monitoring](), [operations](), [performance-optimization](), [private-spaces](), [security-compliance](), [solarwinds](), [visibility]() ### AI overview The article explains how logging supports enterprise operations and presents Heroku's Private Space Logging as a way to centralize logs from applications and related services. It also mentions scalability, security, data residency, log drains, and compatibility with monitoring tools. ### Source excerpt Logging is the unsung hero of enterprise operations--quietly saving the day, one log line at a time. Imagine trying to maintain successful applications without knowing what's happening inside them. This would be like flying a plane blindfolded at night, in a storm, with no instruments. Spoiler alert: Neither scenario would end well! Today's distributed systems [...] The post Optimizing Enterprise Operations with Heroku's Advanced Logging Features appeared first on Heroku. ## CPU Performance Optimization notes DevFeed: [CPU Performance Optimization notes]() Original publisher: [Read original article]() Author: Graham King Published: 2025-03-29T16:15:00Z Content type: tutorial Language: en Sources: [Graham King]() Topics: [cpu](), [Optimization](), [performance-optimization](), [Cache](), [Code](), [data](), [floating-point]() Tags: [array](), [arrays](), [cache](), [code](), [cpu](), [cpu-optimization](), [data](), [floating-point](), [hardware](), [loops](), [memory](), [optimization](), [performance](), [performance-optimization](), [software]() ### AI overview Raw notes on CPU performance optimization covering data locality, cache behavior, memory alignment, data layout, prefetching, integer and floating-point types, powers of two, vectorization, and predictable branching. ### Source excerpt Raw notes on CPU optimization ## Heroku Eventing: A Router for All Your Events DevFeed: [Heroku Eventing: A Router for All Your Events]() Original publisher: [Read original article]() Author: Kim Harrison Published: 2025-03-04T15:01:00Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [event driven](), [Publish-subscribe pattern](), [Authentication](), [API](), [Monitoring]() Tags: [api](), [app-architecture](), [architecture](), [authentication](), [event-driven](), [events](), [heroku](), [kafka](), [monitoring](), [news](), [observability](), [performance-optimization](), [product-features]() ### AI overview Heroku introduces Heroku Eventing, a pilot tool for managing, subscribing to, and publishing events across services. It provides a unified API, centralized event management, integrations with Heroku Kafka and Postgres, secure credential storage, and monitoring across connected sources. ### Source excerpt Managing event-driven architecture can be challenging. For many organizations, this includes a diverse set of eventing services and buses, often across multiple organizations. Developers must manage authentication and pub/sub services across teams and applications. We're thrilled to introduce Heroku Eventing, a powerful tool designed to help teams manage events more efficiently and securely. This new [...] The post Heroku Eventing: A Router for All Your Events appeared first on Heroku. ## Configuring Azure Kubernetes Service Control Planes and Node Pools DevFeed: [Configuring Azure Kubernetes Service Control Planes and Node Pools]() Original publisher: [Read original article]() Author: Sumit Prasad Published: 2024-12-12T12:53:34Z Content type: tutorial Language: en Sources: [Myntra]() Topics: [Azure Kubernetes Service](), [Kubernetes](), [Azure](), [Containers](), [Orchestration](), [Scalability](), [Architecture & Design](), [DevOps]() Tags: [aks](), [aks-best-practices](), [aks-upgrade](), [architecture](), [azure](), [azure-kubernetes-service](), [best-practices](), [components](), [coredns-scaling](), [devops](), [getting-started](), [guide](), [ingress](), [kubernetes](), [performance-optimization](), [scalability]() ### AI overview This guide explains how to configure Azure Kubernetes Service (AKS) for performance and scalability. It describes the managed control plane, worker-node-based node pools, workload separation, and taints used to isolate system, ingress, platform, and application workloads. ### Source excerpt Introduction In this guide, we'll explore the intricacies of configuring Azure Kubernetes Service (AKS) for optimal performance and scalability. As a fully managed container orchestration service by Microsoft Azure, AKS simplifies deploying, managing, and scaling containerized applications using Kubernetes. Drawing from our extensive experience running AKS at scale, this blog aims to provide practical insights and solutions to common challenges faced by DevOps engineers, system administrators, and Kubernetes enthusiasts. We'll walk you through the AKS setup, offering step-by-step guidance, best practices, and tips from real-world scenarios. From fine-tuning the control plane to optimizing node pools, our goal is to equip you with the knowledge to leverage AKS in production environments fully. Join us as we delve into key considerations, navigate potential pitfalls, and share advanced configurations. Whether you're just getting started with AKS or looking to enhance your existing setup, this blog promises a comprehensive dive into Azure Kubernetes Service, informed by our hands-on experiences and lessons learned. AKS Architecture The AKS control plane, a managed service provided by Azure, oversees the management and orchestration of the Kubernetes cluster. It handles tasks such as scheduling, scaling, and monitoring the cluster. Node pools, or worker nodes, are the virtual machine scale sets that run the containers in the AKS cluster, executing workloads and running applications. Our AKS architecture, illustrated in the diagram below, includes multiple node pools, each dedicated to a specific purpose: System Node Pool: Hosts system-critical pods or add-on services managed by AKS. User Node Pools: Three user node pools are designated for: Platform components Ingress Application pods To ensure each node pool serves its intended purpose, we apply taints during their creation: The system node pool has a taint with the key-value pair CriticalAddonsOnly=true:NoSchedule. Th ## Best Practices for Optimizing Your Enterprise Cloud Applications with New Relic DevFeed: [Best Practices for Optimizing Your Enterprise Cloud Applications with New Relic]() Original publisher: [Read original article]() Author: Julián Duque Published: 2024-10-09T00:13:37Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [observability](), [Cloud]() Tags: [add-ons](), [cloud](), [dynos](), [ecosystem](), [heroku](), [memory](), [metrics](), [observability](), [performance](), [performance-optimization](), [product-features](), [root-cause-analysis]() ### AI overview This article explains how Heroku's built-in observability features help monitor enterprise cloud applications. It covers application metrics, alerts, runtime metrics, response time, throughput, memory usage, and dyno load, and discusses using the New Relic add-on for more granular data and complex use cases. ### Source excerpt If your cloud application performs poorly or is unreliable, users will walk away, and your enterprise will suffer. To know what's going on inside of your million-concurrent-user application (Don't worry, you'll get there!), you need observability. Observability gives you the insights you need to understand how your application behaves. As your application and architecture scale [...] The post Best Practices for Optimizing Your Enterprise Cloud Applications with New Relic appeared first on Heroku. ## Evolving the Backend Storage for Platform Metrics DevFeed: [Evolving the Backend Storage for Platform Metrics]() Original publisher: [Read original article]() Author: David Murray Published: 2024-05-09T22:00:00Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Back end](), [Time Series](), [Apache Cassandra](), [Kafka](), [stream-processing](), [autoscaling]() Tags: [apache-kafka](), [app-architecture](), [autoscaling](), [aws](), [backend](), [cassandra](), [cloud-infrastructure](), [data-analytics](), [database](), [developer-tools](), [engineering](), [heroku](), [kafka](), [metrics](), [performance-optimization](), [platform](), [platform-updates](), [stream-processing](), [time-series]() ### AI overview Heroku describes an infrastructure upgrade that migrated the storage backend for platform metrics. The article explains how MetaaS collects application observations, aggregates them into time-series statistics, and uses Kafka, stream-processing jobs, and Cassandra to support dashboard metrics, alerting, autoscaling, retention, and querying. ### Source excerpt One of our most important goals at Heroku is to be boring. Don't get us wrong, we certainly hope that you're excited about the Heroku developer experience -- as heavy users of Heroku ourselves, we certainly are! But, even more so, we hope that you don't have to spend all that much time thinking about [...] The post Evolving the Backend Storage for Platform Metrics appeared first on Heroku. ## Improved Heroku App Performance with HTTP/2 DevFeed: [Improved Heroku App Performance with HTTP/2]() Original publisher: [Read original article]() Author: Ethan Limchayseng Published: 2024-05-08T00:20:00Z Content type: release Language: en Sources: [Heroku]() Topics: [HTTP](), [Heroku](), [Latency](), [Compression](), [web applications](), [networking](), [TLS (Transport Layer Security)]() Tags: [app](), [app-architecture](), [cloud-infrastructure](), [compression](), [dynos](), [heroku](), [http](), [latency](), [networking](), [news](), [performance-optimization](), [platform](), [private-spaces](), [release](), [tls]() ### AI overview Heroku announces public beta support for HTTP/2 on Common Runtime and Private Spaces. The release uses multiplexing and header compression to reduce latency and improve web application performance, while HTTP/2 terminates at the Heroku router and traffic is forwarded to apps over HTTP/1.1. ### Source excerpt Introduction: We're excited to announce public beta support for HTTP/2 on both Heroku Common Runtime and Private Spaces. HTTP/2 support is one of the most requested and desired improvements for the Heroku platform. HTTP/2 is significantly faster than HTTP 1.1 by introducing features like multiplexing and header compression to reduce latency and therefore improve the [...] The post Improved Heroku App Performance with HTTP/2 appeared first on Heroku. ## Expanded Memory and Compute with Heroku's New Larger Dynos DevFeed: [Expanded Memory and Compute with Heroku's New Larger Dynos]() Original publisher: [Read original article]() Author: Ethan Limchayseng Published: 2024-03-28T02:25:00Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [cpu](), [Cache](), [big-data](), [Data analysis]() Tags: [big-data](), [cache](), [cli](), [cloud-infrastructure](), [compute](), [cpu](), [data](), [distributed](), [dynos](), [hadoop](), [heroku](), [how-to](), [machine-learning](), [memory](), [news](), [performance-optimization](), [pricing](), [private-spaces](), [real-time](), [spark](), [video]() ### AI overview Heroku introduces nine larger dyno types across its Performance, Private, and Shield tiers, adding higher memory and CPU limits for compute-intensive workloads. The new sizes support use cases including real-time analytics, caching, machine learning, video encoding, and simulations. ### Source excerpt Introduction Heroku is excited to introduce nine new dyno types to our fleets and product offerings. In 2014, we introduced Performance-tier dynos, giving our customers fully dedicated resources to run their most compute-intensive workloads. Now in 2024, today's standards are rapidly increasing as complex applications and growing data volumes consume more memory and carry heavier [...] The post Expanded Memory and Compute with Heroku's New Larger Dynos appeared first on Heroku. ## Heroku adds Basic dynos for Enterprise customers on the Common Runtime DevFeed: [Heroku adds Basic dynos for Enterprise customers on the Common Runtime]() Original publisher: [Read original article]() Author: Vivek Viswanathan Published: 2024-01-23T23:52:00Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [App](), [Development](), [Testing]() Tags: [development](), [dynos](), [enterprise](), [heroku](), [heroku-enterprise](), [news](), [performance-optimization](), [platform](), [pricing](), [testing]() ### AI overview Heroku has added Basic dynos for Enterprise customers on the Common Runtime. Basic dynos support app development, testing, reference apps, and small-scale projects, and consume 0.28 dyno units compared with the existing minimum of 1 dyno unit for Standard-1X dynos. ### Source excerpt Heroku is improving the cost-effectiveness of Heroku Enterprise with the addition of Basic dynos. Now, Enterprise customers can unlock the power of Basic dynos on the Common Runtime. Basic dynos enable seamless app development and testing on the Heroku platform in the most efficient and cost-effective way. As part of our efforts to simplify our [...] The post Innovating on Heroku is now more cost-effective appeared first on Heroku. ## Simpleperf case study: Fast initialization of TFLite's Memory Arena DevFeed: [Simpleperf case study: Fast initialization of TFLite's Memory Arena]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-09T16:00:00Z Content type: tutorial Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Lite](), [Optimization](), [Machine learning](), [Android](), [Development](), [Code]() Tags: [adb](), [cache](), [display](), [models](), [performance](), [performance-optimization](), [profiling](), [reduce](), [tensorflow-lite](), [visualization]() ### AI overview This developer article presents a case study on optimizing TensorFlow Lite memory arena initialization. It explains how to profile an on-device machine-learning pipeline with Simpleperf, generate profile data, visualize it with pprof, and identify runtime bottlenecks such as ArenaPlanner::ExecuteAllocations. ### Source excerpt Posted by Alan Kelly, Software Engineer One of our previous articles, Optimizing TensorFlow Lite Runtime Memory, discusses how TFLite's memory arena minimizes memory usage by sharing buffers between tensors. This means we can run models on even smaller edge devices. In today's article, I will describe the performance optimization of the memory arena initialization so that our users get the benefit of low memory usage with little additional overhead. ML is normally deployed on-device as part of a larger pipeline. TFLite is used because it's fast and lightweight, but the rest of the pipeline must also be fast. Profiling on the target device with representative data lets us identify the slowest parts of the pipeline so that we can optimize the most important part of the code. In this article, I will describe the profiling and optimization of TFLite's memory arena with instructions on how to use Simpleperf and visualize the results. Sample commands are given. It is assumed that the Android NDK is installed and that you have a development device that you can connect to using adb. Simpleperf Simpleperf comes with some scripts to make it easier to use. run_simpleperf_on_device.py pushes simpleperf to the device and runs your binary with the given arguments. /usr/lib/android-ndk/simpleperf/run_simpleperf_on_device.py record -call-graph fp /data/local/tmp/my_binary arg0 arg1 ... This will generate the output file perf.data which you must then copy back to your computer. adb pull /data/local/tmp/perf.data You then generate the binary cache which contains all the information needed later to generate a useful profile. /usr/lib/android-ndk/simpleperf/binary_cache_builder.py -lib /your/binarys/folder -i perf.data And generate the proto buffer used for visualization: /usr/lib/android-ndk/simpleperf/pprof_proto_generator.py --ndk_path=/path/to/android-ndk -i perf.data -o profile.proto You can then display the results this using pprof: pprof -http :8888 profile.proto And open localhos ## Heroku Adds Third Availability Zone for Private Spaces DevFeed: [Heroku Adds Third Availability Zone for Private Spaces]() Original publisher: [Read original article]() Author: Ethan Limchayseng Published: 2023-06-15T23:00:04Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [Availability](), [spaces](), [Amazon Web Services]() Tags: [announce](), [availability](), [aws](), [cloud-infrastructure](), [data-centers](), [developer-tools](), [dynos](), [features](), [heroku](), [news](), [outages](), [performance-optimization](), [platform](), [private-spaces](), [product](), [product-features]() ### AI overview Heroku added a third availability zone to Private Spaces. The change is managed by Heroku, requires no customer action or additional cost, and is intended to improve resilience during availability zone outages. ### Source excerpt Heroku is excited to announce the addition of a third availability zone (AZ) for our Private Spaces product offering. Three availability zones make Private Space apps more resilient to outages. We've prioritized this improvement as part of our focus on mission-critical features to make the Heroku Platform even more reliable. The changeover to three availability [...] The post Heroku Adds Third Availability Zone for Private Spaces appeared first on Heroku. [Next page]()