---
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

### [Scaling developer experience: How we improved Android Studio in a large monorepo](https://yomu.fyi/post/scaling-developer-experience-how-we-improved-android-studio-in-a-large.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Rifqi Fahmi
- Published: May 15, 2026
- Type: Problem & solution

Grab’s passenger Android monorepo contains roughly 2,000 modules and 11 million lines of code, making Gradle-based Android Studio syncs increasingly slow and memory-intensive even though Bazel handles builds. A cold full sync could exceed 35 minutes, and a survey of 55 developers found that 82% wanted an option to exclude modules from syncing. The team built a custom Focus plugin that uses a dependency graph generated during migrateToBazel, avoids repeating Gradle’s configuration phase, and integrates with Android Studio through a one-click Select → Focus → Sync flow; a Groovy include-shadow technique avoids requiring teams to reorganize settings files. Typical focused syncs fell below two minutes, while reported results show reductions from 35 minutes to under one minute depending on module size, memory usage from 10 GB to 2 GB, and improved Compose preview updates, with broader IDE operations requiring expanded focus or full sync.


### [How the D. E. Shaw group powers high-cardinality observability at scale with ClickHouse](https://yomu.fyi/post/how-the-d-e-shaw-group-powers-high-cardinality-observability-at-scale.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: May 15, 2026
- Type: Explainer

Inside D. E. Shaw group, a global investment and technology development firm, millions of compute workloads run monthly across its high-performance computing infrastructure. To support capacity planning and resource allocation, it collects task-level metrics for requested and utilized compute, producing millions of distinct time series that its previous observability platform could not accommodate. Engineers benchmarked databases on identical hardware and datasets; ClickHouse ingested approximately 3.5 million samples per second on one node versus roughly 480,000 for a competing solution, while similar queries completed in seconds instead of timing out. They retained InfluxDB line protocol, batched messages into ClickHouse, and built disk-based tooling to transform and stream historical data; the resulting four-server NVMe cluster stores about 68 TB compressed and sustains 530k records per second on average. Materialized views support capacity-planning dashboards, while OpenTelemetry tracing extends the platform to other observability workloads, with SQL migration and data modeling requiring engineering effort.


### [ClickHouse vs Prometheus for High Cardinality, Part 2: Cardinality in ClickHouse](https://yomu.fyi/post/clickhouse-vs-prometheus-for-high-cardinality-part-2-cardinality-in-cl.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Rory Crispin, Dale McDiarmid
- Published: May 15, 2026
- Type: Explainer

Traditional time-series databases like Prometheus experience write amplification, memory overhead, and operational challenges when high-cardinality label combinations churn rapidly. ClickHouse alters these economics by abandoning dedicated series objects in favor of a wide events model, storing telemetry as rows in columnar tables with dynamic Map attributes and metric measurements. Dynamic attributes can be handled using sharded Map types alongside materialized columns and text indexes for frequently queried labels. This design trades ingestion overhead for query-time processing, relying on columnar compression, partition pruning, and parallel scans to compute aggregations on demand. ClickHouse avoids the ingest-side bottlenecks of high cardinality, though Prometheus remains preferable for moderate cardinality, native PromQL semantics, and single-series lookups.


### [Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality](https://yomu.fyi/post/granite-embedding-multilingual-r2-open-apache-2-0-multilingual-embeddi.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Radu Florian, Parul Awasthy, Aashka Trivedi, Madison Lee
- Published: May 14, 2026
- Type: Announcement

Granite Embedding Multilingual R2 introduces two Apache 2.0 multilingual embedding models built on ModernBERT: a 97M-parameter compact model and a 311M full-size model. Both cover more than 200 languages, receive enhanced retrieval training for 52 languages and nine programming languages, and support context lengths up to 32,768 tokens, compared with 512 for the R1 models. The compact model scores 60.3 on MTEB Multilingual Retrieval, while the full-size model scores 65.2; the reported results place them among the strongest open models in their parameter ranges. R2 replaces the XLM-RoBERTa architecture with alternating attention lengths, rotary position embeddings, newer multilingual and code tokenizers, and Flash Attention 2.0 support, while the 311M model adds Matryoshka embeddings and both models provide ONNX and OpenVINO weights.


### [From latency to instant: Modernizing GitHub Issues navigation performance](https://yomu.fyi/post/from-latency-to-instant-modernizing-github-issues-navigation-performan.md)
- Company: [Github](https://yomu.fyi/company/github.md)
- Author: Natalie Guevara
- Published: May 14, 2026
- Type: Problem & solution

GitHub Issues addressed navigation latency by shifting workloads to the client using a local-first, stale-while-revalidate architecture. To evaluate perceived delays, the team tracked user transitions through Highest Priority Content thresholds, aiming for instant rendering in under 200 milliseconds. The core implementation added a persistent client-side cache using IndexedDB, paired with a synchronous in-memory tier to serve hot issue payloads without asynchronous overhead. A selective preheating mechanism resolves missing cache entries for high-intent links without overburdening backend capacity with redundant requests. Following broad rollout, the proportion of instant React soft navigations increased from 4% to approximately 22%, yielding an overall cache-hit ratio of around 33%.


### [ClickHouse vs Prometheus for High Cardinality, Part 1: Understanding the Problem](https://yomu.fyi/post/clickhouse-vs-prometheus-for-high-cardinality-part-1-understanding-the.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Rory Crispin, Dale McDiarmid
- Published: May 14, 2026
- Type: Explainer

Observability systems frequently encounter high cardinality when metrics contain numerous unique label combinations across multidimensional cloud infrastructure. In Prometheus, every unique label combination instantiates an independent time series that incurs metadata overhead, requires in-memory memSeries registration, and continually expands inverted index posting lists. The ingestion engine stores samples in two-hour Head blocks using compressed 120-sample chunks with XOR-based encodings, but rapid series creation and elevated scrape frequencies multiply chunk allocations and memory consumption. When executing read operations, broad aggregations and regular expressions require intersecting expansive posting lists and decoding entire chunks sequentially because the engine cannot push value predicates directly into compressed storage blocks. Consequently, managing high-cardinality workloads in series-oriented architectures forces operational trade-offs between system stability and granular visibility into ephemeral components.


### [Unlocking asynchronicity in continuous batching](https://yomu.fyi/post/unlocking-asynchronicity-in-continuous-batching.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Rémi Ouazan Reboul, Pedro Cuenca, Aritra Roy Gosthipaty
- Published: May 14, 2026
- Type: Tutorial

Continuous batching improves GPU utilization with tightly packed batches, but its synchronous loop makes the CPU and GPU take turns, leaving the GPU idle while the CPU prepares each next batch. Profiling 8K-token generation with a batch size of 32 and an 8B model recorded 300.6 seconds, with the GPU idle for 24.0% of runtime. The post builds asynchronous batching by overlapping CPU preparation with GPU computation, using non-default CUDA streams, non-blocking transfers, and synchronization events to coordinate dependencies. In the same experiment, GPU activity rose to 99.4% and total generation time fell to 234.5 seconds, a 22% speedup, without new kernels or model changes while maintaining model accuracy.


### [Postgres FDW: Pushdown is a negotiation](https://yomu.fyi/post/postgres-fdw-pushdown-is-a-negotiation.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Kaushik Iska, David Wheeler, Philip Dubé
- Published: May 14, 2026
- Type: Explainer

Postgres Foreign Data Wrapper extensions allow PostgreSQL to query external datastores like ClickHouse by delegating execution to remote engines. Engineering pg\_clickhouse centers on pushdown decisions that determine whether SQL expressions run remotely or stream raw data back across the wire. Because pushdown depends on planner callbacks, deparser translations, and ClickHouse semantic compatibility, a single untranslated clause can block upper-level query pushdown entirely. Resolving pushdown barriers enables complex analytic queries to return hundreds of rows in milliseconds rather than pulling tens of millions of rows for local processing. Ultimately, pushdown development requires an iterative negotiation across differing SQL grammars, occasionally requiring the revocation of translations that fail to maintain strict equivalence.


### [ClickHouse Cloud: Fast, Updatable Lookups with the Join Table Engine](https://yomu.fyi/post/clickhouse-cloud-fast-updatable-lookups-with-the-join-table-engine.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Hellmar Becker
- Published: May 12, 2026
- Type: Explainer

ClickHouse Cloud provides an in-memory Join table engine for precomputed hash-map lookups and joins, aimed at enriching append-only fact data with dimensions that change occasionally. With Join(ANY, LEFT, key), inserting another row for a key acts as an upsert, while joinGet and matching JOIN clauses use the in-memory structure. Open-source Join tables are not distributed and persist each INSERT batch as separate .bin files without automatic compaction, but ClickHouse Cloud transparently uses SharedJoin with MergeTree for ALL or ReplacingMergeTree for ANY. In the demonstrated enrichment query, duplicate inserts for id 1 leave value 42 visible, while the underlying table retains duplicates until merging; the in-memory load applies FINAL and always selects the latest entry, making ANY LEFT suitable for frequently updated dimensions.


### [Faster Queries and New Capabilities with the Open-Source Databricks JDBC Driver](https://yomu.fyi/post/faster-queries-and-new-capabilities-with-the-open-source-databricks-jd.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Toussaint Webb, Gopal Lal, Kaitlin Baumgardner
- Published: May 12, 2026
- Type: Announcement

The open-source Databricks JDBC driver 3.x and later releases address connectivity needs affecting dashboards, spreadsheets, applications, and other data workflows. Compared with the legacy 2.x driver, they deliver up to 30% faster retrieval of large query results while preserving backward compatibility. Architectural changes add full Arrow-based transfer for JDK 16+, a first-class asynchronous execution API, stream-based ingestion into Databricks Volumes, and integration with the Statement Execution API. The driver also supports stored procedures, multi-statement transactions, Unity Catalog metric views, query tags, geospatial and complex data types, plus built-in client telemetry for query latency, metrics, and errors. Its open-source ownership and Databricks maintenance are presented as enabling faster fixes, new features, transparency, contributions, and tighter platform integration.


### [We replaced Redis with MySQL for inventory reservations—and it scaled](https://yomu.fyi/post/we-replaced-redis-with-mysql-for-inventory-reservations-and-it-scaled.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: May 12, 2026
- Type: Problem & solution

Shopify replaced its Redis-based inventory reservation system with MySQL to unify reservations with the inventory ledger and provide ACID coordination between payment and inventory updates. The new design stores one row per sellable unit in a bounded pool of up to 1,000 rows per item/location combination, uses MySQL 8's SKIP LOCKED to avoid waiting on locked units, and replenishes depleted pools. Composite primary keys reduced row locks, READ COMMITTED avoided gap locks that interfered with replenishment, and consistent lock ordering prevented deadlocks. The investigation found connection-pool usage elsewhere in checkout, rather than reservation query speed alone, was the immediate ceiling; cleanup removed 50% of primary-database reads and 33% of transactions, while configuration changes added headroom. Shopify validated the design in shadow mode, then rolled it out gradually with a Redis rollback path, meeting high-throughput targets during peak 2025 traffic.


### [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.


### [From vibe code to production-ready: observability for Next.js and Supabase apps](https://yomu.fyi/post/from-vibe-code-to-production-ready-observability-for-next-js-and-supab.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Sergiy Dybskiy
- Published: May 11, 2026
- Type: Problem & solution

AI-assisted software development often generates functional code that lacks proper instrumentation, leading to unindexed database queries, N+1 patterns, and fragmented connection strategies. While Supabase provides built-in query analytics, security advisors, and subsystem logs, it cannot trace distributed requests across the entire application stack. Integrating Sentry bridges this visibility gap by establishing distributed traces across Next.js frontends, Deno-based Supabase Edge Functions, and Postgres databases. Teams can forward Supabase logs into dedicated Sentry projects and configure runtime integrations to automatically detect performance bottlenecks such as slow spans and Web Vitals regressions. Furthermore, integrating Model Context Protocol servers allows Sentry's Seer debugger to identify root causes and generate automated fixes for emerging issues.


### [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.


### [ClickHouse Release 26.4](https://yomu.fyi/post/clickhouse-release-26-4.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: May 8, 2026
- Type: Announcement

ClickHouse Release 26.4 introduces 39 new features, 45 performance optimizations, and 238 bug fixes, focusing on broader SQL compatibility and indexing improvements. The update expands standard SQL support by allowing VALUES clauses as table expressions with column aliases, PostgreSQL-style EXTRACT units, NATURAL JOIN, and SET TIME ZONE commands. For text search, LIKE and ILIKE pattern queries using splitByNonAlpha tokenizers scan inverted index dictionaries instead of executing full-table scans when the dedicated evaluation setting is active. The release also provides the JSONAllValues function to extract all leaf values from a JSON column into a string array, enabling text skip indexes that significantly reduce scanned data and improve query latency.


### [Monitor Unreal Engine Game Performance with Application Metrics](https://yomu.fyi/post/monitor-unreal-engine-game-performance-with-application-metrics.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Ivan Tustanivskyi
- Published: May 8, 2026
- Type: Announcement

Unreal Engine games frequently experience performance issues such as frame-rate drops, hitches, and network degradation that do not register as application crashes. To address the lack of live production telemetry, the Sentry Unreal SDK introduces automatic performance metrics. The integration periodically samples frame times, network health, active UObject counts, physical memory usage, and garbage collection pause durations across desktop, console, and Android platforms. Telemetry entries attach contextual hardware and environment attributes alongside trace IDs to connect aggregate latency spikes directly to underlying traces and spans. Enabled by default in Unreal SDK version 1.11.0, this telemetry provides developers with fleet-wide visibility into hardware-specific bottlenecks across game releases.


### [Zero-Growth Stack, Real Gains: How Stack Allocation Can Save 10% CPU in Go](https://yomu.fyi/post/zero-growth-stack-real-gains-how-stack-allocation-can-save-10-cpu-in-g.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: May 7, 2026
- Type: Benchmark

At Uber, repeated Go goroutine stack expansion consumed substantial CPU in a service whose stack-memory usage remained low, making memory a viable trade-off. Go starts goroutines with 2KB stacks and doubles them when needed, copying the previous stack; the post also notes that adaptive stack sizing and stack shrinking can add runtime cost. Uber patched the runtime, disabled adaptive sizing and shrinking, and used profile-guided analysis of pprof traces plus assembly-derived function stack usage to choose a static power-of-two starting size. For the profiled service, the analyzer identified about 19KB of required stack usage, leading to a 32KB setting instead of 2KB. After deployment, stack-growth CPU fell from close to 10% to below 1%, while most instances used about 50MB of stack and a few reached 200MB.


### [Introducing Postgres Query Insights in ClickHouse Cloud](https://yomu.fyi/post/introducing-postgres-query-insights-in-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Amog Iska
- Published: May 7, 2026
- Type: Announcement

ClickHouse Cloud introduced Query Insights in preview for Managed Postgres to help teams diagnose slow database queries. Powered by the open-source extension pg\_stat\_ch, the tool streams per-statement telemetry directly into ClickHouse to enable fast aggregations across billions of execution rows. Postgres normalizes query literals into placeholders during the parse-analyze phase before telemetry leaves the database, preventing sensitive data exposure. Telemetry emission maintains around three percent producer overhead by utilizing a non-blocking try-lock on a shared-memory ring buffer. Users can examine high-level database health metrics, rank query patterns by runtime or latency percentiles, and inspect execution flyouts detailing disk spills, cache hits, and parallel worker behavior.


### [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.


### [How lakebase architecture delivers 5x faster Postgres writes](https://yomu.fyi/post/how-lakebase-architecture-delivers-5x-faster-postgres-writes.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Wein, Vlad Lazar
- Published: May 7, 2026
- Type: Benchmark

Lakebase’s separation of Postgres compute and distributed storage removes the local-disk torn-page risk that necessitates full page writes (FPW) in traditional deployments. The change disables FPW on compute and moves full page image generation into the pageserver, which creates reset points after a configured number of WAL deltas while preserving bounded read reconstruction. In HammerDB TPROC-C tests, 32-vCPU throughput rose from 95,686 to 439,300 new orders per minute, while average WAL per transaction fell from 58KB to under 4KB, a 94% reduction. Production validation reported WAL reductions from 30MB/s to 1MB/s on a 56-vCPU project, p99 read latency improvements of 30% to 50%, and up to threefold regional storage p99 improvement. The rollout reached all Lakebase Serverless and Neon databases without restarts or customer interruption, using the existing XLOG\_FPW\_CHANGE mechanism.


[Newer posts](https://yomu.fyi/topic/performance/page/19.md) · [Older posts](https://yomu.fyi/topic/performance/page/21.md)