---
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

### [Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform](https://yomu.fyi/post/intelligent-security-at-clickhouse-speed-how-cogent-security-built-an.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 24, 2026
- Type: Problem & solution

Enterprise security defenders face shrinking exploitation windows alongside massive volumes of vulnerability findings across disconnected tools. To overcome query bottlenecks from traditional relational databases, Cogent Security designed a lakehouse serving architecture using Apache Iceberg and per-customer ClickHouse instances. The architecture relies on query-driven denormalized tables, custom compression codecs, and pre-computed projections to serve hundreds of millions of rows with sub-second latency. On top of this data layer, Cogent implemented an agentic text-to-SQL system that iteratively queries live data, references an in-house Ontology Service for semantic context, and applies majority-voted evaluations. This combination improved text-to-SQL accuracy from 40% to 94% while providing sub-three-second response times across 98.7% of queries at 100 million rows.


### [A New Framework for Evaluating Voice Agents (EVA)](https://yomu.fyi/post/a-new-framework-for-evaluating-voice-agents-eva.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tara Bogavelli, Gabrielle Gauthier Melancon, Katrina Stankiewicz, Nifemi Bamgbose, Hoang Nguyen, Raghav Mehndiratta, Hari Subramani, Fanny Riols
- Published: Mar 24, 2026
- Type: Benchmark

EVA is an end-to-end framework for evaluating conversational voice agents across task accuracy and spoken conversational experience. It simulates multi-turn live-audio conversations with a user simulator, the evaluated voice agent, deterministic tool execution, validation, and metrics derived from recordings, transcripts, and tool-call logs. The framework reports EVA-A for Accuracy and EVA-X for Experience, combines code-based and LLM- or LALM-as-Judge metrics, and uses pass@3 and pass^3 across three trials to capture peak performance and consistency. Its initial release contains 50 reproducible English-language airline scenarios and 15 tools covering rebooking, itinerary changes, cancellations, standby, and vouchers. Results for 20 cascade and audio-native systems show a consistent tradeoff: agents stronger at task completion tend to provide worse experiences, while a substantial gap between pass@3 and pass^3 indicates inconsistent success.


### [Smarter Auto-Scaling for ClickHouse: The Two-Window Approach](https://yomu.fyi/post/smarter-auto-scaling-for-clickhouse-the-two-window-approach.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Ashwath Singh, Manas Alekar
- Published: Mar 24, 2026
- Type: Problem & solution

ClickHouse Cloud required a vertical auto-scaling mechanism that preserves rapid scale-ups while avoiding prolonged over-provisioning after traffic drops. Operating on a single 30-hour lookback window forced scale-downs to take up to 30 hours, whereas an earlier fixed-factor CPU algorithm caused severe oscillations. The engineering team deployed a dual-window recommender pairing a 3-hour window for responsive scale-downs with a 30-hour window for single-step scale-ups. Alongside a trend-based hunting check to resolve conflicting window recommendations, the team introduced target-tracking CPU scaling using geometric mean watermarks and 10-minute median smoothing. This dual-window architecture reduced scale-down latency to 3 hours, prevented scaling oscillations, and significantly reduced infrastructure costs for variable database workloads.


### [Building high-performance full-text search for object storage](https://yomu.fyi/post/building-high-performance-full-text-search-for-object-storage.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Elmi Ahmadov, Jimmy Aguilar, and George Larionov
- Published: Mar 24, 2026
- Type: Problem & solution

ClickHouse redesigned its native full-text index to address latency bottlenecks caused by random read patterns on remote object storage. Instead of using a Finite State Transducer dictionary, the new architecture organizes indexed tokens into fixed-size sorted blocks compressed with front-coding. Each data part separates index data across a small in-memory sparse index file, a sequential dictionary file, and a posting list file. This layout allows the query engine to pinpoint token locations with minimal random I/O and resolve multiple full-text functions directly from index files. Consequently, ClickHouse Cloud maintains predictable search latency across shared object storage while matching local disk performance and scaling through parallel query execution.


### [Fast regex search: indexing text for agent tools](https://yomu.fyi/post/fast-regex-search-indexing-text-for-agent-tools.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Vicent Marti
- Published: Mar 23, 2026
- Type: Explainer

Agentic coding has revived grep as a critical way to gather source-code context, but ripgrep can stall on large monorepos because it scans every file. The proposed remedy is a local inverted index of overlapping character trigrams, with regular expressions parsed into required and alternative trigram constraints that narrow candidate files before exact matching. The index stores posting lists in one disk file and a sorted hash-to-offset lookup table in another, memory-mapping only the table for binary-search lookups; hash collisions can broaden results but cannot produce incorrect ones. Freshness is prioritized because agents often search for text they have just written, and the client index layers user and agent changes over a Git commit. The reported conclusion is that instant local search creates qualitative workflow improvements, especially for bug investigation and large Enterprise repositories.


### [Structured Logging in .NET with Serilog and ClickHouse](https://yomu.fyi/post/structured-logging-in-net-with-serilog-and-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alex Soffronow Pagonidis
- Published: Mar 23, 2026
- Type: Tutorial

Traditional logging solutions like Seq, ELK, and cloud platforms often face scaling bottlenecks, including slower queries, higher operational costs, and complex pipeline management. To address this, an ASP.NET service can write structured logs directly to ClickHouse using Serilog and the Serilog.Sinks.ClickHouse library. The sink transmits data over HTTP and provides a fluent C# builder to configure table schemas, typed JSON columns, full-text indexes, and MergeTree engine parameters. ClickHouse stores log data in a columnar format that delivers 10 to 20 times compression while enabling direct SQL queries, JSON property filtering, and real-time aggregations through materialized views. Furthermore, the sink includes an in-memory buffer to handle transient network drops with configurable batching, retry mechanisms, and callback hooks for failure alerting.


### [Build a Domain-Specific Embedding Model in Under a Day](https://yomu.fyi/post/build-a-domain-specific-embedding-model-in-under-a-day.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steve Han, Rucha Apte, Sean Sodha, Oliver Holworthy
- Published: Mar 20, 2026
- Type: Tutorial

The tutorial presents a pipeline for adapting a general-purpose embedding model when it misses fine-grained distinctions in domain documents and labeled query–document data is unavailable. It generates synthetic question–answer pairs with NeMo Data Designer, filters them for quality, mines hard negatives with a 95% positive-score margin ceiling, and unrolls multi-hop queries for contrastive training. The recipe fine-tunes Llama-Nemotron-Embed-1B-v2, evaluates base and adapted models with BEIR metrics, and exports the result to ONNX or TensorRT. On the reported datasets, it improved Recall@10 and NDCG@10 by more than 10%; Atlassian increased Recall@60 from 0.751 to 0.951 on JIRA data. The complete workflow can run in roughly 2–3 hours for about 500 documents, using a single 80GB Ampere-or-newer GPU.


### [Introducing Composer 2](https://yomu.fyi/post/introducing-composer-2.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 19, 2026
- Type: Announcement

Cursor has made Composer 2 available as a coding model in Cursor. It is priced at $0.50/M input and $2.50/M output tokens, with a faster variant priced at $1.50/M input and $7.50/M output tokens. The release reports higher scores across CursorBench, Terminal-Bench 2.0, and SWE-bench Multilingual, with Composer 2 scoring 61.3, 61.7, and 73.7, respectively. Its training combines a first continued pretraining run with reinforcement learning on long-horizon coding tasks, allowing it to handle challenges requiring hundreds of actions. Composer 2's standard pricing and included usage pool on individual plans are presented alongside the faster option, which is set as the default.


### [March 2026 newsletter](https://yomu.fyi/post/march-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Mar 19, 2026
- Type: Announcement

The March 2026 ClickHouse newsletter details major platform updates, community deployments, and architectural practices for high-throughput analytics. Recent product announcements include ClickHouse 26.2 featuring time-based block flushing for streaming feeds, general availability of native inverted indexes for full-text search, and the release of chDB 4 with a Pandas-compatible DataStore API. Community implementations show Cloudflare managing 1.61 quadrillion daily events, while Trigger.dev isolated multi-tenant SQL execution using a domain-specific query language that compiles safe ClickHouse queries. In data optimization techniques, Hookdeck accelerated webhook payload search from over 30 seconds to under 400 milliseconds by partitioning payload hashes into typed bucket columns and scanning iterative time windows. Additional operational guidance emphasizes setting the ttl\_only\_drop\_parts parameter and aligning table partitioning with time units so background merges drop entire data parts cleanly without triggering mutation rewrites.


### [How ClickStack makes ClickHouse faster for observability](https://yomu.fyi/post/how-clickstack-makes-clickhouse-faster-for-observability.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mike Shi
- Published: Mar 18, 2026
- Type: Problem & solution

ClickHouse delivers high performance for telemetry data, but unoptimized queries on high-cardinality observability workloads can bypass pruning, inflate intermediate state, and exhaust cluster resources. ClickStack addresses this challenge by embedding engine-specific optimization best practices directly into how queries are generated, structured, and executed. For search workflows, the platform executes progressive queries across bounded time windows using optimize\_read\_in\_order to return initial rows rapidly without scanning entire historical ranges. ClickStack also segments large chart aggregations into parallel bucket-aligned queries, automatically redirects map attribute filters to pre-materialized columns, and extends lazy materialization thresholds to minimize runtime input-output overhead. These query formulation strategies are being packaged into specialized analytical endpoints to power notebooks and external tools without requiring manual SQL tuning or exposing raw database interfaces.


### [How we optimized Dash's relevance judge with DSPy](https://yomu.fyi/post/how-we-optimized-dash-s-relevance-judge-with-dspy.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra,Eric Wang,Dmitriy Meyerzon
- Published: Mar 17, 2026

Dropbox Dash optimized its LLM-as-a-judge relevance scoring system using DSPy to migrate from expensive proprietary models to cheaper open-weight alternatives. By establishing automated feedback loops based on human agreement and strict JSON format validation, the team systematically generated robust prompts for new models. This reduced human-score disagreement by 45% and slashed model adaptation time from weeks to days while enabling 10x to 100x more data labeling.


### [Holotron-12B - High Throughput Computer Use Agent](https://yomu.fyi/post/holotron-12b-high-throughput-computer-use-agent.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Pierre-Louis Cedoz, Hamza Benchekroun, Aurélien Lac, delfosse, Tony Wu, Antoine Bonnet, Kai Yuan, Aleix Cambray (H-AI), Alexandra, Axel Moyal
- Published: Mar 17, 2026
- Type: Announcement

Holotron-12B is a multimodal computer-use model released by H Company and available on Hugging Face. Post-trained from NVIDIA’s Nemotron-Nano-12B-v2-VL-BF16 on proprietary data, it targets agents that must perceive, decide, and act in interactive environments while handling long contexts with multiple images. Its hybrid State-Space Model and attention architecture stores a constant state per layer per generated sequence instead of attention’s per-token key and value activations, reducing memory demands for long-context inference. In a WebVoyager evaluation on one H100 with vLLM, it reached 8.9k tokens/s at concurrency 100 versus 5.1k for Holo2-8B, and its benchmark score rose from 35.1% to 80.5% after training on localization and navigation data.


### [How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse](https://yomu.fyi/post/how-socialpruf-built-a-faster-more-reliable-data-stack-by-replacing-ne.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 17, 2026
- Type: Problem & solution

Socialpruf, a social media performance tracking platform ingesting hundreds of posts per second, experienced analytics latency, connection dropouts, and high network transfer costs while running Postgres on Neon alongside ClickHouse Cloud. To resolve these issues, the team migrated their transactional system of record to NVMe-backed Postgres managed by ClickHouse, colocating transactional and analytical workloads. Although standard Postgres logical replication failed on their 0.5 TB dataset, they achieved continuous synchronization using PeerDB before conducting a production cutover. The migration eliminated connection dropouts, reduced network transfer fees, and improved overall Postgres query performance by approximately 30 percent. To manage thousands of concurrent connections generated by ingestion workers, Socialpruf implemented ClickHouse's bundled, horizontally scalable PgBouncer instances.


### [Training Composer for longer horizons](https://yomu.fyi/post/training-composer-for-longer-horizons.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Federico Cassano & Sasha Rush
- Published: Mar 17, 2026
- Type: Explainer

Composer is trained for long-horizon coding tasks using self-summarization, allowing reinforcement-learning trajectories to exceed the model’s context window. When a fixed token trigger is reached, Composer receives a synthetic summarization query, uses scratch space to produce a condensed context, and resumes with the summary plus conversation state; training rewards every token in the chained rollout with the final outcome. Against a tuned prompt-based compaction baseline, self-summary reduced compaction-related error by 50% on CursorBench at both 80k and 40k triggers, while summaries averaged about 1,000 tokens instead of more than 5,000 and reused the KV cache. An early Composer checkpoint solved Terminal-Bench 2.0’s make-doom-for-mips after 170 turns, summarizing more than 100,000 tokens into roughly 1,000 tokens of retained information.


### [Designing the new async-native ClickHouse Python client](https://yomu.fyi/post/designing-the-new-async-native-clickhouse-python-client.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Joe Spadola
- Published: Mar 16, 2026
- Type: Problem & solution

The official ClickHouse Python client, clickhouse-connect, originally addressed asynchronous execution by wrapping its synchronous HTTP client in a thread pool executor. However, this executor model experienced thread exhaustion, GIL contention, and severe tail latency variance under heavy concurrent workloads. To resolve these scalability bottlenecks, the developers redesigned the client around a half-sync/half-async pattern utilizing aiohttp for async network I/O. A custom bounded queue bridges streaming socket reads on the event loop with synchronous, CPU-intensive binary parsing running inside background threads. Benchmarks demonstrate that this architecture stabilizes tail latencies, delivering an average P95 latency of 556 milliseconds compared to 869 milliseconds for the legacy executor client.


### [ClickHouse Release 26.2](https://yomu.fyi/post/clickhouse-release-26-2.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 16, 2026
- Type: Announcement

ClickHouse version 26.2 introduces twenty-five new features, forty-three performance optimizations, and one hundred eighty-three bug fixes across the database engine. This release graduates both the text-index and the QBit vector embedding data type to production-ready status for search and retrieval workloads. For streaming ingestion pipelines where row arrival rates are low, the engine adds time-based block flushing configurations to write data parts before hitting default row or byte thresholds. The update also embeds the ClickStack observability user interface directly into the standard binary distribution, accessible through the local HTTP server port. Further enhancements include automatic min-max indexing for temporal columns in MergeTree tables, interactive time-based one-time password authentication in the client, and a high-performance prime number generator function.


### [AI doesn’t always generate perfect ClickHouse schemas (yet)](https://yomu.fyi/post/ai-doesn-t-always-generate-perfect-clickhouse-schemas-yet.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Mar 13, 2026
- Type: Explainer

LLMs can generate syntactically valid ClickHouse schemas whose advanced choices are poorly matched to a workload. The post examines recurring pitfalls including query-driven partitioning, per-column codecs, premature projections, materialized-view sprawl, JSON configurations, and forced deduplication with OPTIMIZE TABLE ... FINAL. It explains that partitions primarily support data management, incremental materialized views add work to every insert, and ReplacingMergeTree deduplicates during background merges rather than at insertion. The recommended method is to begin with a simple MergeTree or ReplacingMergeTree, a well-chosen ORDER BY, default compression, and no optional structures, then measure production behavior before adding complexity or seeking human expertise for high-scale, interconnected designs.


### [What's new in ClickStack. February '26.](https://yomu.fyi/post/what-s-new-in-clickstack-february-26.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Mar 12, 2026
- Type: Announcement

ClickStack introduced several platform updates and query performance enhancements across both open-source and Cloud environments for February 2026. ClickHouse Cloud OpenAPI now exposes ClickStack resources including dashboards, alerts, sources, and webhooks to support automated configuration-as-code workflows. In addition, ClickHouse 26.2 embeds the ClickStack UI directly into the database binary with a 4.1 MB footprint for local exploration and diagnostics. Visualizations now support native SQL mode for tables, allowing users to join disparate datasets like logs and traces across injected dynamic time variables. Finally, query execution speeds improve through streaming skip indexes, index pruning on disjunctions, and an expanded 100,000-row lazy materialization limit alongside new threshold alerting on number charts.


### [Enabling R8 optimization at scale with AI-assisted debugging](https://yomu.fyi/post/enabling-r8-optimization-at-scale-with-ai-assisted-debugging.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Nguyen Van Minh
- Published: Mar 12, 2026
- Type: Problem & solution

Grab enabled R8 optimization for its Android app after repeated attempts were blocked by debugging difficulty, organization-wide testing demands, and slow build feedback. The effort targeted a roughly 10-year-old codebase exceeding 9 million lines and combined AI-assisted debugging, a pragmatic testing strategy, and infrastructure investment. Its QA build was configured to match production R8 processing. Beyond existing shrinking, optimization added techniques such as method inlining, class merging, constant folding, dead-code elimination, and devirtualization. After about 10 weeks with one engineer coordinating across teams, the project reported around 25% lower ANR rates, a 16% reduction in reference-device download size, and nearly 27% faster startup; the startup result combined R8 with corrected Baseline and Startup Profiles.


### [Shopify’s journey to faster breadth-first GraphQL execution](https://yomu.fyi/post/shopify-s-journey-to-faster-breadth-first-graphql-execution.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Mar 12, 2026
- Type: Explainer

Shopify describes how high-cardinality, deeply nested GraphQL queries exposed execution overhead beyond data loading, with field resolvers assembling responses consuming much of request time. Its GraphQL Cardinal engine replaces conventional depth-first traversal with breadth-first execution, resolving each selected field once across an aggregated set of objects and reducing repeated field-level work. For large list queries, Shopify reports up to 15x faster execution, 90% less memory use, and reductions of many seconds in P50 latency. The migration included a tuned interpreter, cheaper tracer behavior, shadow verification, benchmarks, and ongoing conversion of tens of thousands of legacy resolvers, while future work includes asynchronous patterns and lower-level C bindings.


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