---
title: "Performance"
description: "817 posts about Performance, summarised, each linking to the original."
---

# Performance
> 817 posts about Performance, summarised, each linking to the original.

## Articles

### [How we made ClickStack 5x faster for ClickHouse observability](https://yomu.fyi/post/how-we-made-clickstack-5x-faster-for-clickhouse-observability.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 2, 2026
- Type: Problem & solution

ClickStack’s inherited OpenTelemetry Collector schema was struggling with slow searches, Top N queries, and aggregations that could time out on large observability workloads. The team prioritized common log and trace access patterns, including exact-attribute searches, body-text queries, histograms, individual log lookups, and distinct-key or value introspection. They used fixed production-like workloads and iterative testing with ClickCannon, Kubernetes-managed environments, ephemeral ClickHouse Cloud instances, and controlled schema, query, and version comparisons. The resulting redesign combined primary-key changes, text indexes, query rewrites, alias columns, materialized views, and newer ClickHouse features, reducing representative query latency by more than 5× while balancing ingestion, storage, and operational efficiency.


### [Any Apple update can break our app. Here's how we find out first.](https://yomu.fyi/post/any-apple-update-can-break-our-app-here-s-how-we-find-out-first.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Dan Mindru, Oleh Stasula
- Published: Jul 2, 2026
- Type: Problem & solution

Usage is an Apple ecosystem resource monitor that relies on undocumented APIs vulnerable to silent data structure changes across OS updates. To maintain stability across iPhone, iPad, and Mac without degrading device performance, the development team uses Sentry across separate component projects in a unified workspace. Beyond capturing standard crashes, the team wraps Swift errors with unexpected raw data payloads to quickly diagnose undocumented API structure drift while scrubbing sensitive user values. They also employ Sentry Size Analysis as a regression gate to monitor release payload deltas, preventing unintended asset omissions or bloat from slipping through code reviews. This multi-layered monitoring setup enables the team to detect and resolve platform-induced breakages before most end users encounter them.


### [How do you turn AI coding chaos into a repeatable playbook?](https://yomu.fyi/post/how-do-you-turn-ai-coding-chaos-into-a-repeatable-playbook.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Eira May
- Published: Jul 2, 2026
- Type: Explainer

Software development dynamics are shifting as code generation costs decrease, requiring engineering organizations to adapt their leadership and processes. Snowflake addressed this transformation across its software lifecycle by first encouraging widespread, unrestricted experimentation with coding agents before formalizing successful practices. The engineering organization codified these practices into fourteen distinct AI design patterns, including strategies such as planning in markdown and isolating parallel agents. This structured approach, combined with dedicated focus weeks and a progress scale, allowed engineering teams to accelerate both inner and outer loops of development. Consequently, a team of three engineers leveraged coding agents to deliver a 40x improvement to Snowflake's query compiler while significantly shortening release validation times.


### [ClickHouse Agents is now available for Managed Postgres](https://yomu.fyi/post/clickhouse-agents-is-now-available-for-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 2, 2026
- Type: Announcement

ClickHouse Agents is now available for all Managed Postgres services in ClickHouse Cloud, extending its plain-English agent interface to operational data. The beta service, powered by Claude and built on LibreChat, combines an agent builder and chat interface with a sandboxed code interpreter; it is read-only by default and supports MCP-compatible systems. Agents can inspect Postgres schemas, run read-only queries, return the SQL used, and reason across Postgres and ClickHouse by querying each engine independently before merging results. Additional skills guide migration from Postgres to ClickHouse through discovery, access-pattern analysis, schema design, type mapping, CDC ingestion, and validation, while other workflows use metrics, Query Insights, and EXPLAIN to rank performance fixes. Planned extensions include scheduled recommendations and safe automated remediation, but the current release focuses on exploration, analysis, monitoring, and guided database work.


### [How we keep GPUs reliable across Databricks AI](https://yomu.fyi/post/how-we-keep-gpus-reliable-across-databricks-ai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Steven Chen, Feng Wang, Bhavik Soni, Chengguang Yang, Albert Zhong, Naren Loganathan, Harsh Panchal, Jianwei Xie
- Published: Jul 1, 2026
- Type: Explainer

Databricks AI describes GPU reliability as a fleet-scale problem involving crashed jobs, silent slowdowns, and numerical corruption during distributed training. Its approach combines stress testing with demanding reinforcement learning, agentic coding, and document intelligence workloads, plus gpu-monitor, a multi-stage service that checks nodes during bootstrap, continuously while active, and periodically between workloads. The checks cover compute burn-in, peer connectivity, NCCL correctness and bandwidth, RDMA, ECC and HBM memory, PCIe integrity, throttling, thermal conditions, XID errors, and fabric health. A production incident showed why cumulative InfiniBand port downtime matters: a single outage exceeded the lower-level NCCL\_IB\_TIMEOUT, killing a collective before the longer PyTorch watchdog timeout. Nodes that fail checks are quarantined, while inter-node probes sweep message sizes from 8 bytes to 2 GiB and apply latency or BusBW criteria; the system is continuously expanded as new failure modes emerge.


### [Meta’s AI Storage Blueprint at Scale](https://yomu.fyi/post/meta-s-ai-storage-blueprint-at-scale.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Sidharth Bajaj, Venkatraghavan Srinivasan
- Published: Jul 1, 2026
- Type: Explainer

Meta describes how its BLOB-storage architecture evolved to address AI workloads' storage bottlenecks, which can stall GPUs and slow research iteration as datasets and GPU deployments expand. Legacy requests traversed multiple stateful metadata layers, sometimes across regions, producing latencies of hundreds of milliseconds even when flash made millisecond access possible. The redesign consolidates metadata into a flat schema backed by ZippyDB for O(1) path resolution, removes the dataplane proxy in favor of a fat client SDK, and deploys regional BLOB-storage stacks beside GPUs. For data loading, Meta combines host memory and flash with regional flash and global HDD-backed storage as tiered caches, using ordinary and deep prefetch, on-demand hydration, and TTL or LRU eviction policies. Production rollout saw rapid adoption, while both data-loading paradigms remain supported.


### [How we scale PgBouncer in ClickHouse Managed Postgres](https://yomu.fyi/post/how-we-scale-pgbouncer-in-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 1, 2026
- Type: Benchmark

PgBouncer is single-threaded, so a single process can bottleneck connection pooling on multi-vCPU machines even when Postgres has capacity. ClickHouse Managed Postgres runs a fleet of PgBouncer processes sized to available cores, sharing one port through so\_reuseport; peering forwards query-cancellation requests to the process that owns the session, while transaction pooling and per-process connection-budget splits limit Postgres connections. In tests on identical AWS EC2 c7i.4xlarge pooler instances, a 16-process fleet reached roughly 336,000 transactions per second at 256 clients, compared with 76,893 for one process, while using about 49% versus 8% in-guest CPU. The fleet's advantage emerged under concurrency, whereas the single process was slightly faster at eight clients, and the described setup ships by default with every ClickHouse Managed Postgres server.


### [How Artemis Security runs 69x faster detection queries with ClickHouse Cloud](https://yomu.fyi/post/how-artemis-security-runs-69x-faster-detection-queries-with-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 1, 2026
- Type: Benchmark

Artemis Security uses ClickHouse Cloud on AWS for real-time threat detection across many terabytes of compressed logs, tens of billions of daily rows, and queries over trillions of rows. To reduce repeated scans, its engineers coalesce hundreds of detection-rule predicates into one query, tagging matching rows by rule ID; batches of more than 100 rules now run in under a second, with a measured 69x speedup. A Claude Code skill called /poke connects ClickHouse system tables, CloudWatch, Datadog, application data, and source code to investigate infrastructure issues through natural-language prompts. For investigative queries over semi-structured JSON, Artemis extracts frequently queried fields into a parallel materialized table and uses a hybrid two-step lookup, producing 30–60x faster queries and up to 400x lower CPU consumption.


### [Designing GPU-Accelerated Query Engines with NVIDIA GQE](https://yomu.fyi/post/designing-gpu-accelerated-query-engines-with-nvidia-gqe.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jun 30, 2026
- Type: Explainer

GPU-accelerated query engines encounter performance constraints stemming from memory and I/O bandwidth limitations. NVIDIA introduces GQE, a reference architecture using NVIDIA cuDF, nvCOMP, and other CUDA-X libraries to accelerate SQL query execution across modern hardware like the NVIDIA GB200 NVL4. The architecture coordinates three distinct layers: a query layer consuming Substrait plans, a data layer managing in-memory table transfers, and an execution layer executing relational operator task graphs. GQE employs pipeline parallelism, partition pruning, hybrid compression, and batched asynchronous transfers to overlap data movement with GPU compute. On a non-standard 1 TB TPC-H benchmark, GQE on a single B200 GPU achieved a 7.5x total execution time speedup over dual-socket CPU configurations running DuckDB.


### [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools](https://yomu.fyi/post/optimizing-a-neural-reconstruction-pipeline-using-nvidia-nsight-develo.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jun 30, 2026
- Type: Problem & solution

NVIDIA Omniverse NuRec reconstructs dynamic 3D environments from camera and lidar sensor data for autonomous vehicle simulation workflows, but high computational demands historically caused long reconstruction times. Engineers used NVIDIA Nsight Systems and Nsight Compute to systematically profile the PyTorch-based training pipeline and optimize its underlying CUDA kernels. Nsight Systems revealed GPU underutilization caused by numerous small kernels and blocking synchronization calls, leading to a fused interpolate kernel that accelerated execution from 4.184 milliseconds to 83.81 microseconds. Profiling with Nsight Compute uncovered resource mismatch in the renderBackward kernel, prompting the team to split it into dedicated camera and lidar implementations with tuned register and shared memory allocations. These adjustments raised kernel occupancy from approximately 15% to between 30% and 50% while decreasing the longest lidar kernel runtime from 31 milliseconds to 18 milliseconds.


### [A Quadrillion Rows across three Clouds: scaling LogHouse](https://yomu.fyi/post/a-quadrillion-rows-across-three-clouds-scaling-loghouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 30, 2026
- Type: Problem & solution

LogHouse now stores 431 PiB of uncompressed data across 1.59 quadrillion rows, spans more than 30 regions across three cloud providers, and serves typical queries in a few hundred milliseconds. The platform grew 23x from 19 PiB in two years, while peak ingestion reached 80 GiB/s and 190 million rows per second. Its write architecture uses local geosharding, multiple cells in busy regions, and ClickHouse Async Inserts to combine small incoming writes into larger parts without overwhelming merges. On the read side, a single Distributed table hides the cell topology, while unavailable shards remain visible by default rather than producing silently incomplete results. Current work includes reducing index and metadata memory, improving async-insert durability, collecting system data from object storage, and expanding OpenTelemetry traces and metrics.


### [Clever ingests 200x more logs at the same cost with ClickHouse Cloud](https://yomu.fyi/post/clever-ingests-200x-more-logs-at-the-same-cost-with-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 30, 2026
- Type: Problem & solution

Clever, an identity platform for K-12 education, faced a fragmented logging setup as its 400 services and 200 AWS Lambdas generated about 150 TB of uncompressed logs monthly. Datadog indexed only about 10% and retained it for three days, while the remainder was in Athena and required SQL. Clever migrated to ClickHouse Cloud on AWS, indexing all logs with 60-day retention and reporting 200× more searchable log volume at no additional cost, with 10× compression. Its design extracts frequently queried JSON fields into columns, stores remaining attributes in a map alongside raw JSON, uses skipping indexes, date partitions, a 60-day TTL, and reverse Unix timestamp ordering; this ordering produced an 8–10× query speedup. A custom query layer translates LogQL to SQL, while separate development and production services and multi-region deployment address isolation and availability.


### [How Jua delivers the world’s most accurate physics simulations 3x faster with ClickHouse Cloud](https://yomu.fyi/post/how-jua-delivers-the-world-s-most-accurate-physics-simulations-3x-fast.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 30, 2026
- Type: Problem & solution

Jua uses ClickHouse Cloud to deliver data from EPT-2, its physics foundation model, to energy traders and grid operators faster than its previous file-based pipeline. Jua’s forecasts combine outputs from multiple models and add roughly four terabytes of new data daily, producing about one petabyte compressed and over 10–12 petabytes uncompressed. Replacing Zarr and other file-based storage plus manual pre-fetching with ClickHouse Cloud reduced flagship forecast delivery from one hour to 20 minutes, with about 20 seconds of upload overhead. The platform also cut compute costs by roughly a third, reduced two-year single-location historical queries from hours to seconds, and supports both point lookups and regional aggregations; Jua selected the managed service for operational simplicity, separated storage and compute, and cost efficiency.


### [GenPage: Towards End-to-End Generative Homepage Construction at Netflix](https://yomu.fyi/post/genpage-towards-end-to-end-generative-homepage-construction-at-netflix.md)
- Company: [Netflix](https://yomu.fyi/company/netflix.md)
- Author: Netflix Technology Blog
- Published: Jun 29, 2026
- Type: Problem & solution

Netflix traditionally constructs its structured two-dimensional homepage through a complex, multi-stage recommender pipeline that separates candidate generation and ranking across rows and entities. To simplify this architecture and optimize directly for whole-page user satisfaction, Netflix developed GenPage, an end-to-end generative transformer model that autoregressively builds the entire homepage from raw tokenized context. The system relies on a domain-specific tokenizer to compress engagement history, context injection for cold start, hybrid row decoding to minimize decoding steps, and reinforcement learning post-training. In online A/B testing against the production baseline, GenPage delivered statistically significant gains in core user engagement metrics while cutting end-to-end serving latency by 20 percent. Offline evaluations further showed that enriching context prompts improved recommendation quality more effectively than increasing model capacity in the current operating regime.


### [Next.js already traces your requests. Here's how to export them with OpenTelemetry.](https://yomu.fyi/post/next-js-already-traces-your-requests-here-s-how-to-export-them-with-op.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Kyle Tryon
- Published: Jun 29, 2026
- Type: Tutorial

Next.js provides out-of-the-box tracing for incoming requests, fetch calls, middleware, and server-side rendering, but traces remain invisible without a configured exporter. Developers can use the @vercel/otel package inside an instrumentation file to initialize the OpenTelemetry SDK and transmit traces to any OTLP-compatible destination. While Next.js automatically creates spans for standard request lifecycles, developers can define custom active spans in the Node runtime to capture specific domain operations and contextual attributes. Exporting traces to backends like Sentry requires configuring standard OTLP endpoint and authentication environment variables. Choosing between direct OTLP export via @vercel/otel and the dedicated Sentry SDK depends on requirements, as direct export lacks browser tracing, error monitoring, and Edge custom spans.


### [Benchmarks and Obscurantism: A “red” line that should not be crossed](https://yomu.fyi/post/benchmarks-and-obscurantism-a-red-line-that-should-not-be-crossed.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 29, 2026
- Type: Benchmark

Melvyn Peignon examines a Databricks keynote benchmark in which ClickHouse was shown “crashing” under workloads associated with Reyden, Databricks’ gated low-latency compute product. The post argues that the presentation omitted hardware, cost, configuration, cache settings, deployment details, and sufficient methodology for independent validation. Using the disclosed TPC-H SF1 dataset and Q6 query, ClickHouse was tested on a 30 vCPU, 120 GB node without tuning; it did not crash and sustained about 420 QPS at sub-second P90 latency. The post reports that reaching 15,000 QPS required straightforward sizing to roughly 30–40 untuned nodes, while further gains could come from settings such as thread counts, query cache, and projections. It concludes that benchmarks should be open, reproducible, and detailed enough to inspect rather than accepted as keynote claims.


### [Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer](https://yomu.fyi/post/creating-the-nvidia-nemotron-3-ultra-nvfp4-checkpoint-with-nvidia-mode.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jun 26, 2026
- Type: Problem & solution

Moving large model weights efficiently requires quantization, but mapping weights to NVFP4's eight positive values risks severe precision loss or outlier-induced underflow. To address this, NVIDIA quantized the 550B Nemotron 3 Ultra model using NVIDIA Model Optimizer with a multi-precision strategy rather than quantizing all layers uniformly. The recipe applies four-over-six adaptive block scaling to Mixture of Experts routed weights, while assigning FP8 to shared experts and retaining BF16 for sensitive attention linears and embeddings. This process reduced the checkpoint size from 1,121 GB down to 352.3 GB and enabled single-checkpoint execution across Hopper using W4A16 and Blackwell using native W4A4. As a result, the model achieves up to 5.9x higher inference throughput than the GLM-5.1 754B FP4 model on decode-heavy workloads while matching BF16 benchmark accuracy.


### [ClickHouse achieves AWS Cloud Operations Competency](https://yomu.fyi/post/clickhouse-achieves-aws-cloud-operations-competency.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 26, 2026
- Type: Announcement

ClickHouse has achieved the AWS Cloud Operations Competency in the Monitoring and Observability category, recognizing validated expertise in operating reliable AWS systems with logs, metrics, and traces. The post describes observability as a high-volume, high-cardinality, high-concurrency workload where legacy systems may require sampling, field reduction, or short retention. ClickHouse Cloud uses columnar storage, compression, parallel queries, compute-compute separation, and ClickPipes integrations with Amazon Kinesis, Amazon MSK, and Amazon S3 to support full-fidelity telemetry. Customer examples include Modal’s 1 to 2 million events per minute, Exabeam’s 1.2 million events per second per region, and Qonto’s reduction of 231 TB to 376 GB. The company also describes MCP servers, AI Notebooks, and managed ClickStack as investments for collaborative incident investigation.


### [Streamlining Resource Binding with End-to-End Support for Vulkan Descriptor Heaps](https://yomu.fyi/post/streamlining-resource-binding-with-end-to-end-support-for-vulkan-descr.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jun 25, 2026
- Type: Announcement

Vulkan introduces the VK\_EXT\_descriptor\_heap extension to refactor GPU resource binding, replacing set-based layouts and pool allocations with application-managed descriptor memory heaps. This approach aligns Vulkan closer to Direct3D 12 and benefits workloads like dynamic texture indexing and complex ray tracing shaders. Developers manage memory directly, binding a single resource heap and a separate sampler heap globally to avoid high rebinding overhead. Shaders can access descriptors either through mapping structures using constant offsets or push constants, or directly via untyped pointers enabled by VK\_KHR\_shader\_untyped\_pointers. Support is integrated into NVIDIA drivers 610, Vulkan Headers 1.4.340, and Nsight Graphics 2026.2 for frame capture and inspection.


### [Announcing Silk: a silky smooth fiber runtime for ClickHouse](https://yomu.fyi/post/announcing-silk-a-silky-smooth-fiber-runtime-for-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 25, 2026
- Type: Announcement

Silk is a C++ stackful-fiber library and scheduler created for ClickHouse’s highly concurrent, I/O-bound components, including object-storage I/O, distributed cache lookups, replica coordination, and HTTP fan-out. Its design combines one pinned OS scheduler thread per CPU, NUMA-aware work stealing, io\_uring for asynchronous I/O and timers, and zero heap allocation in the steady-state hot path; fiber stacks are mmap’d from per-fiber pools with guard pages, while runtime containers are intrusive. The post reports benchmarks including roughly 3.6-nanosecond fiber yields with cross-CPU stealing, 5.9 million file IOPS in a working configuration, and about fifteen times boost::asio’s throughput at one connection. At 10,000 concurrent S3-style requests, internal measurements showed a roughly 65% improvement in 99.9th-percentile latency versus an equivalent thread-pool executor, with identical median throughput; distributed cache is the first intended ClickHouse integration.


[Newer posts](https://yomu.fyi/topic/performance/page/13.md) · [Older posts](https://yomu.fyi/topic/performance/page/15.md)