---
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

### [Frontend Engineering at Palantir: Plotlines in Three.js](https://yomu.fyi/post/frontend-engineering-at-palantir-plotlines-in-three-js.md)
- Company: [Palantir](https://yomu.fyi/company/palantir.md)
- Author: Palantir
- Published: Apr 7, 2026
- Type: Explainer

Palantir’s frontend engineers are building a 3D implementation of Gaia to visualize Foundry Ontology objects with geospatial properties, including ground locations, satellite positions, and paths through space. The system uses Zodiac, a Three.js library backed by Foundry-based APIs: the backend stores, filters, and computes data, while the frontend receives simplified geometry and style information for rendering. Performance techniques include converting latitude and longitude to Earth-fixed coordinates once, batching similarly styled lines into single draw calls, and using instanced meshes plus batched updates for dense satellite scenes; orbital propagation uses TLEs client-side, while heavier analysis remains server-side. The article also describes frame-synchronized animation callbacks that update material properties without rebuilding geometry, while noting that 3D APIs continue to iterate across usability, platform integration, and performance as new data sources and workflows are added.


### [ClickHouse Release 26.3](https://yomu.fyi/post/clickhouse-release-26-3.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 7, 2026
- Type: Announcement

ClickHouse 26.3 delivers twenty-seven new features, forty performance optimizations, and over two hundred bug fixes. Common table expressions can now be materialized into temporary tables via the MATERIALIZED clause when enabled, evaluating subqueries once to reduce query execution times and peak memory usage. The query optimizer expands join reordering beyond inner and left or right joins to include anti, semi, and full joins when table statistics are available. In addition, user-defined functions can run compiled WebAssembly code inside sandboxed Wasmtime runtimes. Storage for the Map data type also introduces hash-based bucketing during background merges, accelerating single-key lookups by two to forty-nine times without slowing initial inserts.


### [Zero downtime Upgrade: Yelp’s Cassandra 4.x Upgrade Story](https://yomu.fyi/post/zero-downtime-upgrade-yelp-s-cassandra-4-x-upgrade-story.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Mark Surnin and Muhammad Junaid Muzammil, Software Engineer
- Published: Apr 7, 2026
- Type: Problem & solution

Yelp's Database Reliability Engineering team upgraded over a thousand Cassandra nodes from version 3.11 to 4.1 without downtime. The upgrade delivered reliability, observability, and performance improvements, including Java 11 support, configurable guardrails, and fixed incremental repairs. To avoid the high costs, consistency compromises, and data streaming delays of standing up separate datacenters, the team selected an in-place rolling upgrade strategy. The rollout orchestrated staged node updates alongside compatible versions of the Stargate proxy and custom data pipeline connectors without requiring client code modifications. Although Stargate 2.x introduced regressions on range and multi-partition queries that required downgrading to 1.x, the finished migration achieved up to a 58% reduction in p99 latency across key clusters.


### [Better MoE model inference with warp decode](https://yomu.fyi/post/better-moe-model-inference-with-warp-decode.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Less Wright, Federico Cassano & Zhiyuan Zhang
- Published: Apr 6, 2026
- Type: Benchmark

MoE inference commonly organizes decode around experts, but the post describes “warp decode,” an output-centric kernel for small-batch generation on NVIDIA Blackwell GPUs. Instead of staging tokens into expert-major buffers, each warp owns one output scalar, streams the required weight rows, accumulates contributions from all eight routed experts in FP32, and uses warp-level reductions; the gate/up and down projections are compressed into two kernels. This removes padding, scatter, combine, activation gather, and per-expert output buffers, while keeping BF16 activations and avoiding intermediate quantization. On B200s, the approach delivered 1.84x higher decode throughput, sustained 3.95 TB/s at batch size 32, and produced outputs 1.4x closer to a full FP32 reference.


### [Improving storage efficiency in Magic Pocket, our immutable blob store](https://yomu.fyi/post/improving-storage-efficiency-in-magic-pocket-our-immutable-blob-store.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
- Published: Apr 2, 2026

After introducing a direct erasure-coding service, Dropbox encountered severe storage fragmentation in its immutable blob store, Magic Pocket, due to a large number of under-filled volumes. Because their legacy compaction strategy was only designed to top off near-full volumes, the team created new multi-strategy compaction methods to aggressively consolidate sparse volumes. These approaches leverage dynamic programming and streaming pipelines to rapidly reclaim unused disk capacity at exabyte scale.


### [Welcome Gemma 4: Frontier multimodal intelligence on device](https://yomu.fyi/post/welcome-gemma-4-frontier-multimodal-intelligence-on-device.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: merve, Pedro Cuenca, Sergio Paniego, ben burtenshaw, Steven Zheng, Alvaro Bartolome, Nathan Habib
- Published: Apr 2, 2026
- Type: Announcement

Google DeepMind’s Gemma 4 family is available on Hugging Face as Apache 2-licensed, multimodal models intended for use across agents, inference engines, fine-tuning libraries, and on-device deployments. The family accepts image and text inputs across five sizes, while E2B, E4B, and 12B Unified also support audio; context windows range from 128k to 256k tokens. Its design combines alternating local and global attention, dual RoPE configurations, Per-Layer Embeddings, shared KV caches, and model-specific vision and audio handling, including an encoder-free unified 12B variant. The post documents integrations for Transformers, llama.cpp, MLX, WebGPU, Rust, and other tools, alongside fine-tuning examples and demos. Reported instruction-tuned results include a 1452 estimated text-only LMArena score for 31B and benchmark results spanning reasoning, coding, vision, audio, and long-context tasks.


### [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.


### [Rill and ClickHouse: real-time operational BI for a metered world](https://yomu.fyi/post/rill-and-clickhouse-real-time-operational-bi-for-a-metered-world.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 1, 2026
- Type: Problem & solution

Granular operations across cloud computing, payment platforms, and artificial intelligence workloads produce high-volume event streams that traditional business intelligence tools struggle to unify and aggregate. Operational BI platform Rill addresses this challenge by combining declarative data loading through the dlt framework with ClickHouse's high-performance analytical aggregation engine. Data transformations, SQL-based measure expressions, and dashboard configurations are defined entirely as code using SQL and YAML. This declarative, metrics-first architecture allows developers to validate analytics locally, track changes in Git, and eliminate intermediate caching layers when querying live data. By querying ClickHouse directly and linking AI-generated insights back to underlying queries, the system provides traceable, real-time operational analytics for enterprise environments.


### [Falcon Perception](https://yomu.fyi/post/falcon-perception.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: FalconPerception
- Published: Apr 1, 2026
- Type: Announcement

Falcon Perception is a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding and segmentation from natural-language prompts. It processes image patches, text, and task tokens in one sequence, using hybrid attention so image tokens receive bidirectional context while text and task tokens decode causally. Its Chain-of-Perception interface predicts each instance as coordinate, size, and segmentation tokens, with specialized Fourier-feature and dot-product heads for dense outputs. The post also introduces PBench, which profiles attributes, OCR-guided identification, spatial understanding, relations, and crowded scenes. Falcon Perception reaches 68.0 Macro-F1 on SA-Co versus 62.3 for SAM 3, while presence calibration remains weaker at MCC 0.64 versus 0.82; the 0.3B Falcon OCR model scores 80.3 on olmOCR and 88.6 on OmniDocBench.


### [Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents](https://yomu.fyi/post/granite-4-0-3b-vision-compact-multimodal-intelligence-for-enterprise-d.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Madison Lee, Rogerio Feris, Eli Schwartz, Dhiraj Joshi, Pengyuan Li, Isaac Sanchez
- Published: Mar 31, 2026
- Type: Announcement

Granite 4.0 3B Vision is a compact vision-language model designed to extract information from enterprise documents, including tables, charts, forms, and semantically meaningful key-value pairs. It combines a million-scale ChartNet dataset with a code-guided synthesis pipeline, DeepStack Injection for routing semantic and high-resolution visual features to different layers, and a LoRA adapter architecture built on Granite 4.0 Micro. The modular design supports standalone image processing, text-only fallback, and integration with Docling for multi-page document pipelines. Reported evaluations show 86.4% on Chart2Summary, leading the evaluated models, second place on Chart2CSV at 62.1%, leading scores on the listed table benchmarks, and 85.5% zero-shot exact-match accuracy on VAREX.


### [Training mRNA Language Models Across 25 Species for $165](https://yomu.fyi/post/training-mrna-language-models-across-25-species-for-165.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Maziyar Panahi
- Published: Mar 31, 2026
- Type: Benchmark

OpenMed describes an end-to-end protein engineering pipeline that connects ESMFold structure prediction, ProteinMPNN sequence design, and custom mRNA codon optimization. The codon-language-model study compares CodonBERT, ModernBERT, and several CodonRoBERTa variants using 250,000 E. coli coding sequences, a 69-token codon vocabulary, masked-language modeling, and training on four 80GB A100 GPUs. CodonRoBERTa-large-v2 achieved perplexity 4.10, CAI Spearman correlation 0.404, and 7.7% synonymous recovery, while ModernBERT-base reached perplexity 26.24 and correlation 0.070. The work then trained a multi-species suite on 381,283 sequences spanning 25 species, producing four production models in 55 GPU-hours and documenting planned extensions such as larger training data and additional prediction heads.


### [Boost Training Goodput: How Continuous Checkpointing Optimizes Reliability in Orbax and MaxText](https://yomu.fyi/post/boost-training-goodput-how-continuous-checkpointing-optimizes-reliabil.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Shutong Li, Colin Gaffney
- Published: Mar 31, 2026
- Type: Benchmark

Orbax and MaxText introduce continuous checkpointing to balance training reliability and performance when hardware failures or preemption events interrupt long-running jobs. Fixed checkpoint intervals create a trade-off: infrequent saves risk substantial lost work, while frequent saves can block or bottleneck training, particularly over unstable networks. MaxText enables asynchronous checkpointing that attempts a new save only after the previous background request is fulfilled; its continuous mode overrides checkpoint\_period, and max\_num\_checkpoints\_to\_keep can limit storage use. A llama-3.1-70B benchmark on two v5p-128 slices found smaller P50 checkpoint intervals, but average training step time increased because of more frequent device-to-host transfers; the analysis estimated resource conservation against mean-time-between-failure assumptions. Orbax also supports minimum intervals and customizable preservation policies, while co-locating storage with training is recommended because cross-metro bandwidth can reduce checkpointing speed and reliability.


### [TRL v1.0: Post-Training Library Built to Move with the Field](https://yomu.fyi/post/trl-v1-0-post-training-library-built-to-move-with-the-field.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Quentin Gallouédec, Steven Liu, Pedro Cuenca, Sergio Paniego
- Published: Mar 31, 2026
- Type: Announcement

TRL v1.0 marks a transition from research codebase to a library intended for production systems, with clearer stability expectations. It implements more than 75 post-training methods, but its design prioritizes trying, comparing, and using them over coverage alone. The library responds to a field in which PPO, preference optimization methods such as DPO, ORPO, and KTO, and RLVR-style approaches such as GRPO repeatedly changed which components of the training stack appear essential. TRL addresses that volatility with a stable core under semantic versioning, an experimental surface for faster-moving methods, and deliberately limited abstractions that favor explicit implementations over generic hierarchies. v1.0 also follows distributed breaking changes across the 0.x releases, so migration from the last 0.x version is described as minimal, while future work targets asynchronous GRPO, scaling, and actionable training diagnostics.


### [Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees](https://yomu.fyi/post/predicting-rider-conversion-in-sparse-data-environments-with-bayesian.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Zammit Alban
- Published: Mar 30, 2026
- Type: Problem & solution

Lyft requires real-time predictions of whether a rider will request a ride after viewing price and ETA details to balance supply and demand and personalize user experiences. Traditional machine learning models overfit on high-cardinality, sparse context intersections, whereas deep neural networks introduce unacceptable inference latency during live user sessions. To address this, engineers created a hierarchical Bayesian tree framework that organizes session data into increasingly granular context partitions. Each node houses a parametric model trained top-down, applying Gaussian Bayesian priors and L2 penalties relative to parent parameters to smooth predictions in data-rare child segments. Using simple parametric models at each node also permits monotonic constraints, guaranteeing logically consistent predictions aligned with domain expectations.


### [How Padlet uses ClickHouse Cloud to power real-time classroom analytics](https://yomu.fyi/post/how-padlet-uses-clickhouse-cloud-to-power-real-time-classroom-analytic.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 30, 2026
- Type: Problem & solution

Padlet needed a real-time analytics system to provide teachers with immediate insights into student engagement across 40 million monthly unique users. To avoid the operational overhead of managing database clusters with a small engineering team, the company adopted ClickHouse Cloud and ClickPipes for managed data ingestion. Active user sessions emit 30-second heartbeat events that stream alongside page views into ClickHouse. Materialized views pre-aggregate engagement duration and view counts by date, while native HyperLogLog sketches process unique visitor calculations efficiently across arbitrary time ranges. In production, this architecture ingested roughly eight billion events in a single month and delivered analytical query results with a 45-millisecond median latency.


### [Top 10 best practices tips for ClickHouse](https://yomu.fyi/post/top-10-best-practices-tips-for-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Yonatan Dolan
- Published: Mar 26, 2026
- Type: Explainer

ClickHouse delivers rapid analytical queries across massive datasets, but suboptimal schemas and queries can significantly reduce performance and storage efficiency. Aligning table definitions with ClickHouse storage and execution mechanics resolves these bottlenecks. Key practices include placing low-cardinality columns first in the ORDER BY clause to enable granule pruning, and selecting optimal data types while avoiding Nullable wrappers to cut memory overhead. Developers should treat partitioning as a data management tool for TTL drops or merge engines rather than a generic speed booster, preventing fragmented parts from slowing query execution. Recent improvements like the Analyzer query planner have also made joins viable, though dictionaries remain faster for static lookups.


### [More Visibility, Less Guesswork: ClickHouse Cloud's New Monitoring Capabilities](https://yomu.fyi/post/more-visibility-less-guesswork-clickhouse-cloud-s-new-monitoring-capab.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mihir Gokhale
- Published: Mar 25, 2026
- Type: Announcement

ClickHouse Cloud released new monitoring capabilities and dashboards to give platform administrators deeper visibility into deployment health and server behavior. A newly introduced Overview page consolidates critical signals into a unified view for rapid health checks. Additionally, an updated Infrastructure page tracks service scaling over time, presenting CPU and memory utilization with new aggregation options alongside modals explaining automatic scaling decisions. The platform now sends automated email and Slack notifications when deployments encounter risks such as excessive parts, failed mutations, or query concurrency saturation. These additions allow administrators to proactively identify operational bottlenecks and configure scaling limits without spending excessive time searching for telemetry data.


### [Reducing our monorepo size to improve developer velocity](https://yomu.fyi/post/reducing-our-monorepo-size-to-improve-developer-velocity.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
- Published: Mar 25, 2026

Dropbox reduced its core server monorepo size from 87GB to 20GB after approaching GitHub's 100GB limit and suffering from hour-long clone times. The excessive bloat was traced to Git's delta compression heuristics interacting poorly with internationalization directory paths, generating inefficient diffs across different languages. By collaborating with GitHub to run an aggressive server-side repack with tuned window and depth settings, Dropbox cut clone times to under 15 minutes.


### [Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces](https://yomu.fyi/post/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-t.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Iraklikhorguani
- Published: Mar 25, 2026
- Type: Explainer

Lyft describes a framework for estimating long-term effects of pricing and incentive decisions in its two-sided marketplace, where direct and market-mediated consequences can diverge. The method first uses residualized observational regressions to estimate how policy changes alter distributions of negative experiences such as wait times, surge, cancellations, idleness, and hourly earnings, validating those responses with switch-back experiments. It then applies Augmented Inverse Probability Weighting to estimate how these experiences affect future outcomes, summarizes the relationship with a surrogacy index, and checks calibration using user-split experiments. Finally, Lyft combines mediated and separately estimated direct effects and uses region-split experiments for end-to-end validation, selecting treated and control regions with a forward-selection algorithm inspired by FDiD. The framework is intended to support scenario planning, budget allocation, and continuous calibration of long-run marketplace forecasts.


### [ClickHouse is data lake ready](https://yomu.fyi/post/clickhouse-is-data-lake-ready.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Karolina Ruiz Rogelj, Melvyn Peignon
- Published: Mar 25, 2026
- Type: Announcement

ClickHouse announced expanded data lake capabilities, enabling direct querying, accelerated ingestion into MergeTree, and writes across Apache Iceberg and Delta Lake tables. Open lake architectures provide storage portability across cloud providers and catalogs, but standard query engines often struggle with latency for real-time workloads. Over two years of development, ClickHouse engineered a native Parquet reader with page-level parallelism, integrated the Delta Rust Kernel, and introduced support for schema evolution, time travel, and DML operations. The platform functions across AWS Glue, Unity Catalog, Polaris, and Iceberg REST Catalog on storage systems including Amazon S3, Google Cloud Storage, and Azure Blob Storage. These enhancements allow organizations to execute federated, high-concurrency SQL queries across multiple catalogs or write processed analytical results back to their lakes without vendor lock-in.


[Newer posts](https://yomu.fyi/topic/performance/page/22.md) · [Older posts](https://yomu.fyi/topic/performance/page/24.md)