---
title: "AWS"
description: "205 posts about AWS, summarised, each linking to the original."
---

# AWS
> 205 posts about AWS, summarised, each linking to the original.

## Articles

### [Scaling Airbnb’s identity graph with a unified knowledge graph infrastructure](https://yomu.fyi/post/scaling-airbnb-s-identity-graph-with-a-unified-knowledge-graph-infrast.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Lucen Zhao
- Published: May 19, 2026

Airbnb migrated its massive identity graph from a third-party graph database vendor to a unified, in-house knowledge graph platform to solve long-tail latency and scaling bottlenecks. Built on JanusGraph with AWS DynamoDB for persistence and OpenSearch for indexing, the new architecture decouples storage operations from graph traversal logic. The migration improved P99 query latency, eliminated routine instance reboots, and supported a tenfold increase in write throughput.


### [Ready for your busiest day: How we scale](https://yomu.fyi/post/ready-for-your-busiest-day-how-we-scale.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Ryan Sherlock
- Published: May 19, 2026
- Type: Explainer

Fin describes how its customer service platform is designed to absorb high-volume events while preserving customer isolation, availability, and operational control. At daily peaks, it handles over 150,000 customer requests per second and more than 70,000 asynchronous requests per second; its source-of-truth data spans 128 Vitess shards, while Elasticsearch supports over 650TB, 1.7 trillion documents, and peaks above 40,000 requests per second. The architecture favors AWS primitives, Vitess managed by PlanetScale, online and incremental migrations, fair queues, feature flags, heartbeat metrics, and automatic rollback, with roughly 250 production ships per workday and merge-to-production time under 10 minutes. The stated conclusion is that scale depends less on headline capacity than on clear scaling levers, tenant fairness, customer-outcome monitoring, reversible change, and routine infrastructure improvement without planned customer-impacting downtime.


### [Promptimus: Improving already good LLM prompts with zero manual engineering](https://yomu.fyi/post/promptimus-improving-already-good-llm-prompts-with-zero-manual-enginee.md)
- Company: amazon.science
- Author: Zhengyuan Shen, Yunfei Bai, Sullam Jeoung, Shuai Wang
- Published: May 14, 2026
- Type: Problem & solution

Enterprise applications rely on mature large language model prompts that incorporate intricate business logic and regulatory constraints, making further optimization and cross-model migration challenging. Promptimus automates prompt refinement through a four-step iterative loop that evaluates candidate prompts against decomposed metric checkpoints, diagnoses failure bottlenecks, and generates targeted improvement strategies. The system operates either via full prompt rewrites in standard mode or through surgical, programmatic find-and-replace edits in edit mode to preserve structured taxonomies and schemas. Tested across multimodal classification and code generation tasks, the framework achieves performance gains with small development datasets of 20 to 50 samples. Amazon is making the model-agnostic optimization system available through Amazon Bedrock to support enterprise generative-AI model migration without manual engineering.


### [Building Blocks for Foundation Model Training and Inference on AWS](https://yomu.fyi/post/building-blocks-for-foundation-model-training-and-inference-on-aws.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Keita Watanabe, Pavel Belevich, Aman Shanbhag
- Published: May 11, 2026
- Type: Explainer

The post explains how foundation-model scaling now spans pre-training, post-training, and test-time compute, creating shared infrastructure requirements rather than a single pre-training curve. It examines how AWS infrastructure and open-source tools support these workloads across accelerated compute, networking, storage, orchestration, ML frameworks, and observability. Technical detail includes NVIDIA H100, H200, B200, and B300 GPU characteristics, NVLink/NVSwitch scale-up communication, and EFA-based scale-out networking for distributed collectives. The architecture also incorporates EC2 P instances, tiered storage, Slurm, Kubernetes, SageMaker HyperPod, CUDA, NCCL, PyTorch, Prometheus, and Grafana. The conclusion presents integration points between these layers as the basis for diagnosing bottlenecks and making scaling decisions across the foundation-model lifecycle.


### [Powering self-driving vehicle analytics at Avride with ClickHouse Cloud](https://yomu.fyi/post/powering-self-driving-vehicle-analytics-at-avride-with-clickhouse-clou.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: May 11, 2026
- Type: Problem & solution

Avride’s expanding autonomous passenger vehicle and delivery robot fleet generated thousands of data points per minute, overwhelming its legacy Apache Iceberg and Parquet metadata indexing architecture. Iceberg’s optimistic concurrency model struggled with parallel writes, pushing ingestion latencies to hours or days, inflating query times to 10–20 seconds, and requiring raw data duplication across storage locations. To resolve these bottlenecks, Avride built a custom indexing and metrics warehouse powered by ClickHouse Cloud on AWS backed by Amazon S3 object storage. The new architecture dropped ingestion delays to seconds, reduced worst-case lookup latencies to under one second, eliminated the need for bespoke caching layers, and removed payload duplication. Beyond operational analytics, the unified ClickHouse platform now supports internal ride-replay debugging tools, simulation workflows, and high-cardinality vehicle C++ performance trace profiling.


### [AI Is Changing What Customers Need From a Database. MongoDB 8.3 Is Built for It](https://yomu.fyi/post/ai-is-changing-what-customers-need-from-a-database-mongodb-8-3-is-buil.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Ben Cefalo
- Published: May 7, 2026
- Type: Announcement

MongoDB announced the general availability of MongoDB 8.3, designed to support high-throughput AI workloads without requiring application code changes. Compared to version 8.0, the release provides a 35 percent boost in write throughput, a 45 percent increase in read speeds, and 15 percent faster ACID transactions. Additionally, cross-region connectivity for AWS PrivateLink is now generally available, enabling private traffic routing between Atlas clusters across distinct AWS regions without public internet exposure. MongoDB Atlas supports deployments across 130 regions spanning AWS, Google Cloud, and Microsoft Azure, including multi-cloud cluster topologies. These infrastructure updates target enterprise demands for sub-100ms retrieval, sub-second context updates, zero downtime, and strict data residency compliance.


### [From SSH to REST: A Security-Driven Modernization of Slack’s EMR Data Pipelines](https://yomu.fyi/post/from-ssh-to-rest-a-security-driven-modernization-of-slack-s-emr-data-p.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Mahendran Vasagam
- Published: May 5, 2026
- Type: Problem & solution

Slack's data platform accumulated over 700 SSH-based Airflow operators executing jobs directly on AWS Elastic MapReduce clusters, creating broad security surfaces and operational instability. Stateful SSH connections frequently failed when Kubernetes pods restarted, leaving orphaned zombie processes and preventing infrastructure modernization such as migrating EMR clusters to child accounts. To eliminate SSH access, Slack adopted a REST-based architecture using Quarry, a gateway providing unified REST APIs and token authentication across compute engines. For non-Hadoop CLI workloads, Slack leveraged YARN Distributed Shell to execute arbitrary shell scripts stored in Amazon S3 within managed, isolated YARN containers. The phased migration achieved zero downtime across eight data regions, unlocking reliable monitoring, simplified onboarding, and future initiatives like Spark on Kubernetes.


### [Gala supercharges analytics performance with ClickHouse on AWS](https://yomu.fyi/post/gala-supercharges-analytics-performance-with-clickhouse-on-aws.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: May 4, 2026
- Type: Problem & solution

Blockchain gaming platform Gala struggled to ingest and query expanding volumes of telemetry and blockchain data on its Databricks-based data infrastructure, resulting in multi-minute query times and heavy engineering maintenance overhead. To solve these bottlenecks, the company migrated its data platform to ClickHouse on AWS, ingesting data through Kafka, Airbyte, Amazon S3, and Fivetran. Gala leveraged ClickHouse's indexing capabilities alongside saved queries and API endpoints to power company-wide Metabase business intelligence dashboards for non-technical teams. Following the migration completed in December 2024, data capacity expanded from 3 TB to 9 TB while query times dropped from minutes to sub-second on previously unoptimized tables. The switch also lowered initial infrastructure costs by 30 percent, with plans underway to implement ClickHouse ClickPipes for improved data processing pipelines.


### [Optimizing ML Workload Network Efficiency (Part I): Feature Trimmer](https://yomu.fyi/post/optimizing-ml-workload-network-efficiency-part-i-feature-trimmer.md)
- Company: [Pinterest](https://yomu.fyi/company/pinterest.md)
- Author: Pinterest Engineering
- Published: May 1, 2026
- Type: Problem & solution

Pinterest deployed a root-leaf architecture for online machine learning serving to decouple CPU-based feature preprocessing from GPU-based inference. Because the root cluster retrieved a union of features across models and fanned out the full set to every leaf, network bandwidth became a severe bottleneck. After initial tests with fbthrift LZ4 compression yielded a 20% bandwidth reduction at the cost of CPU and latency overhead, the team implemented Feature Trimmer. This mechanism uses exported model signatures as the source of truth to prune unused features on the root before transmission. By dispatching only required features per model, the system reduced network bandwidth, decreased latency, and enabled cluster rightsizing that saved over $4M annually.


### [Mercedes-Benz builds a cross-cloud data mesh with Delta Sharing and intelligent replication, cutting costs by 66%](https://yomu.fyi/post/mercedes-benz-builds-a-cross-cloud-data-mesh-with-delta-sharing-and-in.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Alexander Summa, Aleksandar Dragojevic
- Published: Apr 20, 2026
- Type: Problem & solution

Mercedes-Benz built a cross-cloud data mesh to share after-sales data between AWS and Azure while supporting its data-defined vehicle strategy. Existing direct queries incurred egress costs, and weekly full loads delayed updates for datasets that included vehicle over-the-air events and workshop visits; source tables were in Iceberg while many consumers expected Delta. Using Unity Catalog and Delta Sharing, the company federated AWS Glue tables, established cross-metastore shares, and used periodic serverless Sync Jobs with Delta Deep Clone to incrementally replicate selected tables into Azure object storage for local querying. This hybrid model preserves direct sharing for fresher workloads while reducing transfer for less time-sensitive ones. For the initial ten data products, egress costs fell 66%, update frequency improved from weekly to every second day, and the source reports an approximately 93% annual reduction in its 50-use-case comparison.


### [PostgresBench: A Reproducible Benchmark for Postgres Services](https://yomu.fyi/post/postgresbench-a-reproducible-benchmark-for-postgres-services.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Lionel Palacin
- Published: Apr 2, 2026
- Type: Benchmark

PostgresBench is a public, reproducible benchmarking suite created to compare transactional performance across managed PostgreSQL providers. The benchmark uses the standard pgbench tool with a TPC-B-like write-intensive workload executing across 256 clients and 16 threads for ten-minute durations. Evaluators tested five services—including Postgres managed by ClickHouse, AWS Aurora, AWS RDS, Neon, and Crunchy Bridge—under 100 GB and 500 GB scale factors using default configurations in the us-east-2 region. Across tested configurations, Postgres managed by ClickHouse delivered higher transactions per second and lower P99 latencies due to physically co-located NVMe primary storage. These results demonstrate that remote network storage layers, such as EBS volumes, introduce significant latency overhead during sustained write and fsync operations.


### [5 Career Myths From Women in Engineering](https://yomu.fyi/post/5-career-myths-from-women-in-engineering.md)
- Company: [Two Sigma](https://yomu.fyi/company/two-sigma.md)
- Author: Emily Majewski
- Published: Mar 17, 2026
- Type: Explainer

Three engineering leaders at Two Sigma—Angela Wang, Cecilia Ye, and Mae Santos—challenge conventional career progression models by examining five persistent industry myths. Diverse transitions across civil engineering, enterprise architecture, and customer-facing solutions architecture supplied foundational skills for modern roles in feature engineering, reliability engineering, and real-time trading optimizers. The leaders emphasize that deep domain expertise stems from direct proximity to an active system rather than cumulative years of tenure. Beyond core programming capabilities, proactive contributions such as upgrading operational standards and cultivating peer relationships drive career opportunities and leadership recognition. Adapting to emerging technologies and maintaining consistent execution allows engineers to build credibility across evolving technical environments.


### [Observability and OpenTelemetry: Introducing MongoDB Atlas Log Integration](https://yomu.fyi/post/observability-and-opentelemetry-introducing-mongodb-atlas-log-integrat.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Sabina Bates
- Published: Mar 12, 2026
- Type: Announcement

During database latency spikes or unexpected outages, operations teams frequently waste valuable time downloading compressed archives and manually inspecting log files to isolate root causes. MongoDB Atlas resolves this operational friction by introducing a direct log integration feature for dedicated M10+ clusters. The system routes mongod, mongos, and audit logs directly to third-party tools such as Datadog, Splunk, Amazon S3, Google Cloud Storage, and Azure Blob Storage. Furthermore, built-in support for the OpenTelemetry standard enables teams to pipe database logs into any OTel-compatible logging backend. This setup eliminates operational silos, streamlines security auditing, and correlates database query diagnostics with application traces and infrastructure metrics in real time.


### [Introducing Storage Buckets on the Hugging Face Hub](https://yomu.fyi/post/introducing-storage-buckets-on-the-hugging-face-hub.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Lucain Pouget, Eliott Coyac, Adrien Carreira, Victor Mustar, Julien Chaumond, Quentin Lhoest, Pierric Cistac, Sylvestre Bcht, Hugo Larcher, Rajat Arya, Di Xiao, Assaf Vayner
- Published: Mar 10, 2026
- Type: Announcement

Hugging Face introduces Storage Buckets as a mutable, S3-like object storage layer on the Hub for intermediate machine-learning artifacts such as checkpoints, optimizer states, processed shards, logs, and traces. Unlike versioned Models and Datasets repositories, Buckets support frequent writes, overwrites, directory synchronization, and cleanup while retaining standard Hub permissions and public or private visibility. Built on Xet, they chunk and deduplicate content across files, reducing repeated bytes, bandwidth, transfer time, and, for Enterprise customers, billed storage. The CLI and Python APIs support creation, syncing, dry runs, plans, inspection, individual file operations, and programmatic workflows; HfFileSystem and fsspec also allow compatible libraries to read and write through hf:// paths. Pre-warming can place hot data near AWS or GCP compute, while roadmap work aims to connect Buckets directly with versioned repos.


### [Log Drains: Now available on Pro](https://yomu.fyi/post/log-drains-now-available-on-pro.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: steven\_eubank
- Published: Mar 5, 2026
- Type: Announcement

Supabase has launched Log Drains for its Pro tier, allowing users to send Supabase logs to their own logging backend and investigate them alongside the rest of their stack. The feature addresses the separation between Supabase logs and external observability tools by exporting events from Postgres, API Gateway, Auth, Storage, Edge Functions, and Realtime. Logs are sent over HTTP in batches of up to 250 entries or every second, whichever comes first, with Gzip compression when supported by the destination. Supported destinations include Sentry, Grafana Loki, Datadog, AWS S3, Axiom, and generic HTTP endpoints; users configure drains in the dashboard and can create multiple drains, with pricing of $60 per drain per project, $0.20 per million events, and $0.09 per GB of egress.


### [Minions: Stripe’s one-shot, end-to-end coding agents—Part 2](https://yomu.fyi/post/minions-stripe-s-one-shot-end-to-end-coding-agents-part-2.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Alistair Gray
- Published: Feb 19, 2026
- Type: Explainer

Stripe’s minions are an unattended agentic coding flow whose pull requests are human-reviewed but contain no human-written code; more than 1,300 merge weekly. The system runs agents in AWS EC2-based devboxes that are prewarmed to become usable within about 10 seconds, providing parallel, predictable, and isolated environments. Its custom harness uses code-defined blueprints that combine deterministic nodes, such as linting and pushing changes, with agent loops for tasks such as implementation and fixing CI failures. Minions receive automated feedback through local linting, one full CI iteration, and a possible second repair attempt, while devbox isolation and security controls limit destructive actions; Stripe presents the approach as an extension of long-running investments in human developer productivity.


### [Supabase incident on February 12, 2026](https://yomu.fyi/post/supabase-incident-on-february-12-2026.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: paul\_copplestone
- Published: Feb 13, 2026
- Type: Incident / postmortem

Supabase reports a 3-hour-and-42-minute outage on February 12–13, 2026, affecting every service in its us-east-2 region, including Postgres, Auth, Data APIs, Edge Functions, Storage, and Realtime. A deployment of an internal monitoring service enabled AWS VPC Block Public Access in block-bidirectional mode, blocking internet gateway traffic across production VPCs in the region. The incident response was delayed because alerts pointed toward shared services, the relevant CloudTrail event was inconspicuous, pre-production lacked us-east-2, and infrastructure specialists were not involved initially. Supabase restored connectivity by rolling back and removing the monitoring stack, then audited regions, added AWS Organizations policies and IaC safeguards, and planned account isolation, external connectivity probes, production-parity testing, and improved escalation.


### [Balancing cost and reliability for Spark on Kubernetes](https://yomu.fyi/post/balancing-cost-and-reliability-for-spark-on-kubernetes.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Justin Lee
- Published: Feb 12, 2026
- Type: Problem & solution

Notion describes how it reduced Spark compute costs on Kubernetes while maintaining reliability across production workloads. After moving from fixed EC2 clusters on Amazon EMR to EMR on EKS, it used Karpenter, EKS Auto Mode, and the MostAllocated scheduler for dynamic provisioning and bin-packing. Spot Instances then exposed a failure mode: one interruption could remove many bin-packed executors, causing shuffle loss and expensive recomputation. Spot Balancer addresses this by labeling executor creation through a Kubernetes webhook and maintaining each job’s configured spot-to-on-demand ratio, with per-job counts stored in Redis. Named stability weights make the cost-reliability trade-off explicit, and reported savings reached 60–90 percent while jobs that previously failed from interruptions finished reliably.


### [Docker lazy loading at Grab: Accelerating container startup times](https://yomu.fyi/post/docker-lazy-loading-at-grab-accelerating-container-startup-times.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Huong Vuong
- Published: Jan 21, 2026
- Type: Problem & solution

Large Docker images for Airflow and Spark Connect caused minute-scale downloads, delaying cold starts and weakening autoscaling responsiveness. The post describes testing eStargz and Seekable OCI (SOCI) with containerd remote snapshotters, which expose registry-backed layers through a virtual lowerdir and fetch file content on demand. In production, SOCI delivered 30–40% faster startup times for Airflow and Spark Connect, while benchmark results showed SOCI matching standard images’ 5.0-second Airflow application startup; eStargz took 25.0 seconds. Configuration tuning increased download concurrency, unpack concurrency, and chunk size, reducing fresh-node image download time from 60 seconds to 24, a 60% improvement. Grab chose SOCI because Bottlerocket supports it natively, while noting that lazy loading redistributes rather than removes download work and requires workload-specific tuning.


### [PaaS + IaaS: Heroku and AWS for Cloud-Native Enterprise Agility](https://yomu.fyi/post/paas-iaas-heroku-and-aws-for-cloud-native-enterprise-agility.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Gillian Gual Hinkle
- Published: Dec 4, 2025
- Type: Problem & solution

Modern enterprises can combine AWS IaaS infrastructure scale with Heroku’s managed PaaS to reduce operational work and developer cognitive load while retaining control for specialized workloads. Heroku supports Git-based deployment, managed services including Apache Kafka and Heroku Postgres, polyglot applications, and isolated environments such as Private Spaces and Shield Private Spaces. In Salesforce’s example, five applications serving more than 76,000 employees used Heroku to cut expense approval time from two days to two hours and recover $2 million in unused licenses. The architecture also combines Heroku AppLink, Heroku Connect, Heroku Fir’s OpenTelemetry support, and managed Dynos, while AWS remains the stated choice for highly specialized AI training requiring raw GPU access.


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