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812 posts about Performance. Every summary links to the original.
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Bare metal gives engineers direct control over GPU, network, and virtualization layers, but it shifts elasticity and lifecycle work onto the team. This trade-off map shows when that bargain pays off.
Bun packs a runtime, package manager, bundler, and test runner into one binary. Here is how those four parts fit together and where the seams still show.
A five-step how-to for deciding when an AMD Instinct GPU should stay dedicated and when SR-IOV virtual functions, VMs, or sidecar isolation can safely share it.
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The tutorial builds a .NET distributed application around a YARP API gateway, two backend APIs, an analytics API, a Blazor dashboard, a load generator, and ClickHouse. Aspire’s AppHost coordinates these resources, injects connection strings and service endpoints, orders startup with dependency health checks, and exposes selected services through stable or external endpoints. The gateway records proxied requests in ClickHouse while emitting OpenTelemetry metrics for request counts, duration, and backend failures. A bounded queue and background ingestion keep request logging off the hot path, while a narrow schema and materialized view precompute minute-level aggregates for volume, p95 latency, error rates, and route comparisons. Aspire supplies live telemetry, whereas ClickHouse retains request history for analytical queries and drill-down into slow requests.
ClickHouse Cloud is benchmarked against Snowflake, Databricks, BigQuery, and Redshift on the join-heavy TPC-H workload, with the main comparison using SF100: 100 GB, 866M rows, and 22 queries. Using a single 59-core AWS compute node, the benchmark measures fastest hot runs from three executions with result caching disabled, applies public billing models and comparable Enterprise-tier regional pricing, and publishes scripts, queries, and result files for reproduction. ClickHouse completed SF100 in 19.8 seconds for $0.063 and ranked first on the cost-performance score, while Snowflake Large was faster at 15.9 seconds but cost $0.143; at SF10, ClickHouse ran the workload in 2.9 seconds for $0.009. The results are attributed to two years of join engineering, including faster hash joins, improved planning, correlated subquery support, lazy column replication, runtime filters, and statistics-based join reordering.
ClickHouse 26.5 is a monthly release containing 38 new features, 51 performance optimizations, and 224 bug fixes. It reports a record number of performance optimizations and introduces a filesystem table function for running SQL against a local file system. Its main query-planning examples push ORDER BY … LIMIT through eligible LEFT or RIGHT JOINs when the sort key depends only on the preserved side, allowing ClickHouse to limit rows before joining. In a TPC-H benchmark at scale factor 100, enabling this optimization reduced fastest runtime from 1.878 seconds to 0.092 seconds, peak memory from 1.88 GiB to 10.98 MiB, and data read by 1.5×. Additional changes include a GROUP BY … LIMIT shortcut for eligible unordered queries, an experimental in-browser clickhouse-client, and per-subquery query-cache controls.
JetBrains is releasing Mellum2, a 12B-parameter Mixture-of-Experts model trained from scratch on natural language and code. It targets latency-sensitive, high-throughput workloads including routing, retrieval-augmented generation (RAG), summarization, sub-agents, and coding features. The model has 12B total parameters but activates 2.5B per token, with its architecture keeping total capacity high while making inference more efficient for real-time use. Released under the Apache 2.0 license, Mellum2 is described as competitive with similarly sized open models on code generation, reasoning, science, and math benchmarks while delivering more than 2x faster inference. Its text-and-code focus supports high-throughput production workloads and private deployments where proprietary code or internal data may be involved.
Enterprise workflows are dynamic, long-running, API- and data-rich, and constrained by policies or regulations, creating challenges that an LLM alone may not handle efficiently. The article defines agent logic as software primitives—including knowledge graphs, algorithms, and program-analysis libraries—within an agent harness that steer models toward relevant workflow context. Examples span legacy COBOL and PL/1 understanding, automated test generation, incident investigation and code remediation, and compliance modernization; reported results include ~30× lower token consumption for application understanding, 20–45% higher test coverage, and up to 4.0× better incident-investigation performance than a ReAct baseline. A Condition Insights example reports 57% fewer unsupported claims and 77% lower average token usage, supporting the conclusion that structured guidance can improve quality, cost, and rule adherence.
Databricks says its work on Spark Declarative Pipelines will be featured at SIGMOD 2026, where it received an honorable mention award, and that it will attend June 1–5 in Bangalore as a Platinum Sponsor. The announcement describes two mix-and-match incremental programming paths in SDP: materialized views maintained by the Enzyme engine and streaming APIs with stateful operators and watermarks. Enzyme applies incremental view maintenance to ETL, supporting production materialized views with joins, window functions, aggregations, combinations of these patterns, non-deterministic functions such as current_date(), and AI-specific functions. It also supports Python as well as SQL, detects changes in materialized-view definitions, chooses partition- or row-level updates, selectively caches intermediate results, and uses a cost model based on plan information and prior executions; the post says it outperforms an anonymized competing solution.
The post introduces torch.profiler through a beginner-oriented walkthrough of profiling a matrix multiplication and addition, explaining how to configure the profiler and interpret its outputs. It uses a small function with torch.matmul and torch.add, annotates it with record_function, captures CPU and CUDA activities after warm-up iterations, and exports a profiler table plus a Chrome trace. The table summarizes event counts and CPU or GPU time, while the trace shows execution order, call hierarchy, CPU-GPU gaps, kernel launches, and overlap. Comparing 64x64 and 4096x4096 matrices demonstrates a shift from overhead-bound behavior to compute-bound behavior; the guide also covers dispatch chains, hidden device-to-device copies, profiler buffer activity, and torch.compile's runtime and caching behavior.
Tax ID collection at checkout supports compliant tax invoices and tax determination, including reverse-charge treatment for qualifying cross-border business transactions. Validation can be performed synchronously before purchase completion or asynchronously after checkout: synchronous checks reduce correction and audit risk but may increase abandonment and expose conversion to government-service downtime, while asynchronous checks preserve completion but require follow-up on failures. The implementation discussion covers direct national-registry integrations, third-party validation services, and Stripe’s CheckoutSessions and PaymentIntents integrations, where the Tax ID Element uses verification modes of if_supported or none. Stripe first applies a country-specific format check, routes validly formatted IDs to government APIs, may fall back to asynchronous processing after four seconds, and exposes status through customer.tax_id.updated and the customer object.
amazon.science
Traditional data center networks rely on hierarchical fat-tree topologies, which introduce overhead, upper-layer congestion, and vulnerability to router failures. Although flat random networks theoretically maximize route diversity and resilience, routing computation and cabling complexity historically made them impractical for physical data centers. AWS addressed these constraints by introducing Resilient Network Graphs, a quasi-random architecture powered by a passive optical device called ShuffleBox and a routing algorithm named Spraypoint. Spraypoint sprays packets across random neighbors toward designated waypoints, unlocking twice as many independent paths while operating within commodity router memory limits. Adopted as the default architecture across new AWS facilities, this design reduces router counts by 69%, lowers projected network power consumption by 40%, and increases throughput by up to 33%.
Sentry rebuilt its marketing site and blog of roughly 2,500 pages, replacing a legacy Gatsby setup and traditional headless CMS with Astro, Markdown, Frontmatter, and AI-driven automation. The previous architecture suffered from 14-minute build times, restrictive CMS schema limits, and frequent build failures caused by external API dependencies. A team of 2.5 developers executed the migration in two months using Claude Code agents for data extraction, template translation, and testing. To manage content without a CMS dashboard, Sentry built Claude Skills to guide updates, run local previews, and draft GitHub pull requests. The migration reduced average build times to under 4 minutes, cut broken staging builds by 95 percent, and improved Web Vitals scores from 89 to 97.
Open House 2026 Day 2 recaps ClickHouse’s tenth year as an open-source project and the ecosystem forming around its data platform. The event reported more than 2,600 contributors, nearly 48,000 GitHub stars, over 200 integrations, and the launch of the House Mates partner program. Announcements included Fivetran’s generally available ClickHouse Cloud destination, a Google Cloud Dataflow Pub/Sub template, Sigma’s public-beta connector and ETL Cache Layer, and a ClickHouse dbt Fusion adapter available in alpha through the CLI. The recap also describes MCP and agent-oriented observability work, including a ClickStack MCP server that translates investigations into optimized ClickHouse queries. Internal testing reported up to 25% fewer tool calls, 2.5 times greater consistency, and nearly 20% better evaluation results.
BI dashboards can become slow and expensive when teams respond to latency with separate aggregate tables, refresh pipelines, extracts, and tool-specific semantic layers. The post presents Databricks’ BI serving stack from physical storage through Unity Catalog’s governed semantic layer, recommending Gold-layer star schemas, managed tables, liquid clustering, and Predictive Optimization to reduce scanned data and improve query plans. Metric Views centralize KPI definitions and semantic metadata for dashboards, Genie, SQL notebooks, third-party BI tools, and AI agents, while materialization automatically maintains incremental pre-aggregations and routes queries transparently. Additional TCO guidance covers serverless SQL warehouse autoscaling, DBSQL disk and query-result caching, direct lakehouse connections, and system-table monitoring. The stated outcome is compounded lower latency and compute cost, including an observed average 22% performance improvement from Predictive Optimization and sub-second performance from materialized metrics.
At its 2026 user conference in San Francisco, ClickHouse reports surpassing 4,000 customers and $250 million in annual run-rate revenue, while marking nearly ten years since Alexey Milovidov open-sourced the database. The company attributes its growth to a community of contributors and describes use across AI products, financial services, observability, analytics, and infrastructure. New offerings include ClickHouse Agents, a fully managed agentic analytics service powered by Anthropic’s Claude; CostBench, an open benchmark using each warehouse’s real billing model; and House Mates, a partner program launching with more than 50 partners across six continents. Additional updates include Managed Postgres in public beta, Managed ClickStack and Langfuse for AI-era observability, generally available full-text search, and automatic JOIN optimization, with cost-performance identified as the central constraint for high-concurrency, low-latency workloads.
At Open House 2026, ClickHouse announced that its serverless cloud offering exceeded $250 million in annual run-rate revenue and reached 4,000 customers, with ARR more than tripling year-over-year and more than 1,000 net new customers added since January. The company launched ClickHouse Agents, a fully managed, no-code agentic analytics service powered by Anthropic’s Claude, with a chat interface, sandboxed code interpreter, artifacts, skills, memory, multi-agent workflows, and MCP-compatible integrations. It also published CostBench, an open benchmark applying each vendor’s real compute billing model to the same workload; ClickHouse Cloud was reported to have 23x better cost-performance than the nearest cloud data warehouse. Other announcements included Managed Postgres in public beta, managed ClickStack, generally available full-text search, cross-region replication, and the House Mates partner program.
Reachy Mini’s conversation app can now run its speech stack locally instead of sending audio to a server, avoiding cloud, API keys, and data leaving the machine. The tutorial uses speech-to-speech as a cascaded VAD → STT → LLM → TTS pipeline exposed through a Realtime API-compatible /v1/realtime WebSocket, with Silero VAD v5, Parakeet-TDT 0.6B v3, Qwen3-TTS, and a selectable LLM backend. It shows llama.cpp serving Gemma 4 E4B with parallel slots, 64k context, flash attention, and full sliding-window attention, then covers vLLM, MLX, Transformers, hosted Responses API endpoints, and network connection to a wireless robot. Once the backend and app are connected, conversations run through the local loop, while individual stages can be exchanged to trade latency, quality, language coverage, and hardware choices.
Async RL weight synchronization currently requires the trainer to send a complete model to the inference engine after each optimizer step, creating a critical-path transfer that can reach roughly a terabyte for a frontier 1T-parameter checkpoint. The article attributes the opportunity for reduction to bf16 arithmetic: at RL learning rates, many updates fall below the bf16 visibility threshold, leaving about 99% of weights bit-identical between steps and more than 98% even in the reported worst case. TRL's delta-weight-sync implementation records changed elements around the optimizer step, encodes them in sparse safetensors, uploads each delta to a Hugging Face Bucket, and lets vLLM fetch and apply it. On Qwen3-0.6B, this reduces each per-step payload from 1.2 GB to 20–35 MB, while a demonstrated setup connected separate trainer, vLLM, and Wordle Spaces through one bucket. Open issues include extra CPU bf16 snapshots, fixed anchor cadence, and unmeasured multi-node FSDP2 support.
ClickHouse CTO and creator Alexey Milovidov completed a 12-day APJ tour spanning India, Singapore, Indonesia, South Korea, and Japan, with customer meetings, industry events, executive sessions, press interviews, and community meetups. The itinerary covered six cities and included three Confluent Data Streaming World Tour events, four ClickHouse meetups, and more than 10 enterprise customer meetings across varied industries and workloads. Discussions centered on real-time analytics, connected products, and analytics at industrial scale, while in-person conversations supplied feedback that the post says helps improve ClickHouse. The trip concluded with the ClickHouse Japan Launch Event on April 27, marking the company’s official entry into that market. The account presents physical presence as a way to extend ClickHouse’s open-building culture beyond GitHub and strengthen its feedback loop with APJ customers and communities.
ClickHouse’s Open House 2026 Day 1 announced updates spanning ClickHouse Cloud, Postgres, distributed query execution, observability, AI agents, and lakehouse interoperability. ClickHouse Postgres entered public beta with local NVMe storage, high availability, S3-backed point-in-time recovery, and a native CDC path into ClickHouse; early benchmarks reported more than 5x the transactions per second of AWS RDS. Multi-stage distributed queries repartition intermediate data between stages to scale joins and high-cardinality aggregations, reducing TPC-H SF100 runtime from 117.6 seconds on one node to 54.7 seconds on eight nodes. Additional launches included a Claude-powered ClickHouse Agents public beta, an AI-enhanced query builder, Monitoring v2, and Langfuse V4’s reported 200x query performance improvement, while the wrap-up framed the releases around real-time systems without lock-in.
ClickStack Cloud is entering private preview as a fully managed, serverless observability service built on ClickHouse for teams that want ClickHouse-powered logs, metrics, and traces without operating the underlying infrastructure. Users send OpenTelemetry data to a managed OTLP endpoint, while the service handles ingestion, durable buffering through an object-storage-backed event queue, scaling, storage, and query serving, then exposes telemetry in the ClickStack UI with integrated authentication and RBAC. The preview separates ingestion and query resources and is developing automatic tuning based on query patterns, including materialized fields, primary-key adjustments, materialized views, and indexes. Dedicated query compute for agentic and high-volume analytical workloads is planned after the preview, while pricing remains unfinished and the existing Managed ClickStack offering remains intended for teams requiring direct infrastructure and schema control.
Traditional data warehouse benchmarks measure query execution speed without accounting for the varying compute costs across platforms. CostBench introduces an open benchmarking framework designed to evaluate data warehouses on a performance-per-dollar metric across both read and write workloads. Its initial release evaluates ClickHouse Cloud, Snowflake, Databricks, BigQuery, and Redshift using 43 production-derived analytical queries on an anonymized dataset combined with each vendor's compute billing model. In these read-side tests, ClickHouse Cloud maintained high speed at lower costs as data scaled, outperforming the nearest competitor by 23 times in cost-performance. The benchmark repository openly publishes its scripts, configurations, pricing assumptions, and raw JSON results to ensure all comparative metrics remain fully inspectable and reproducible.
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