# Compression Compression is the reduction of digital data size by encoding it to eliminate redundancy, including methods such as run-length encoding and Lempel-Ziv compression. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Libreboot 20140811 release DevFeed: [Libreboot 20140811 release]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: release Language: en Sources: [News about Libreboot releases and development]() Topics: [libreboot](), [coreboot](), [LVM](), [Compression]() Tags: [article](), [bios](), [canoeboot](), [compression](), [coreboot](), [free-software](), [libre](), [libreboot](), [lvm](), [opensource](), [release](), [uefi](), [xz]() ### AI overview Libreboot 20140811 is the project's fifth beta release. The release notes describe corrections and changes to GRUB modules, documentation, ROM checking guidance, patch organization, bucts files, binary archives, and archive compression. ### Source excerpt Article: Libreboot 20140811 release Web link: https://libreboot.org/news/libreboot20140811.html ## pgBackRest Compression: How Much CPU Is a Smaller Backup Worth? DevFeed: [pgBackRest Compression: How Much CPU Is a Smaller Backup Worth?]() Original publisher: [Read original article]() Author: Agustín Gallego Published: 2026-09-16T21:43:06Z Content type: comparison Language: en Sources: [Blog - Percona]() Topics: [Compression](), [Algorithms](), [PostgreSQL](), [postgresql 18](), [backups](), [Percona]() Tags: [algorithms](), [backup](), [compare](), [compression](), [cpu](), [percona](), [postgresql](), [postgresql-18](), [uncategorized]() ### AI overview This comparison measures pgBackRest compression algorithms and levels on Percona Distribution for PostgreSQL 18.4. It finds that low-level Zstandard offers a favorable balance between CPU use and backup size, with zst(3) near the efficiency curve's shoulder. ### Source excerpt In this blog post, we'll compare pgBackRest's compression algorithms and levels to find where spending more CPU stops buying a meaningfully smaller backup. The short version of the answer, which we'll build up to with real numbers, is that Zstandard at a low level is the sweet spot, and its default (zst(3)) already sits right ... Continued The post pgBackRest Compression: How Much CPU Is a Smaller Backup Worth? appeared first on Percona. ## Understanding W8A8 INT8 LLM quantization: Accuracy and performance results DevFeed: [Understanding W8A8 INT8 LLM quantization: Accuracy and performance results]() Original publisher: [Read original article]() Author: Sana Fayyaz Published: 2026-09-14T13:01:43Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Large Language Model](), [quantization](), [llama](), [Compression](), [Benchmark](), [dataset](), [Algorithms]() Tags: [ai-inference](), [artificial-intelligence](), [benchmark](), [benchmarks](), [compression](), [evaluation](), [hardware](), [llama](), [llm](), [model](), [models](), [performance](), [quantization](), [reasoning](), [zero-shot]() ### AI overview The article evaluates W8A8 INT8 quantization of a Llama 3.1 8B Instruct model. It describes reducing the model from 14.9 GB to 8.0 GB with SmoothQuant and GPTQ, then compares the base and compressed models on four benchmarks to assess accuracy and performance. ### Source excerpt In Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy, we compressed a Llama 3.1 8B Instruct model from 14.9 GB to 8.0 GB using 8-bit integer (INT8) W8A8 quantization with SmoothQuant and Generative Pre-trained Transformer Quantization (GPTQ). The post Understanding W8A8 INT8 LLM quantization: Accuracy and performance results appeared first on Red Hat Developer. ## Zstd Improvement For Linux 7.4 To Avoid Redundant Initialization DevFeed: [Zstd Improvement For Linux 7.4 To Avoid Redundant Initialization]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-13T14:04:23Z Content type: news Language: en Sources: [Phoronix]() Topics: [Compression](), [Linux](), [Benchmark](), [Cryptography]() Tags: [benchmark](), [compression](), [crypto](), [desktop-linux](), [development](), [kernel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [speed](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview The article reports Linux 7.4 patches that defer Zstd stream initialization until the first walk iteration, eliminating redundant initialization. Benchmarks show single-digit compression speed improvements and decompression speedups of 13% on bare metal or 35% in a virtual machine. ### Source excerpt In addition to Usama Arif's recent Linux patches for addressing a major inefficiency within the Linux kernel's Zstd compression code, he also has a separate patch series destined for Linux 7.4 to further enhance the Zstd compression/decompression performance by avoiding redundant initialization... ## Architecting for 6 Billion Daily Requests: Inside Wix's Media Platform DevFeed: [Architecting for 6 Billion Daily Requests: Inside Wix's Media Platform]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-09-08T08:24:05Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [Caching](), [Go Language](), [Latency](), [Compression](), [data](), [C]() Tags: [c](), [cache](), [caching](), [compression](), [cpu](), [data](), [go](), [latency](), [technical]() ### AI overview Wix describes engineering challenges in its media platform, which processes images, video, and audio at large scale. The supplied text explains how the team used unsafe pointers to reduce Go/C memory-copy overhead for AVIF encoding and a tiered Masters cache to reduce CPU work during image resizing. ### Source excerpt At Wix, "Media" isn't just about storing files. It's about processing streams of information - images, video, and audio - at an immense scale. We host over 300 million websites and serve more than 6 billion media requests every single day. To handle dozens of petabytes of data while keeping costs low and latency minimal, we had to treat media handling as a complex engineering discipline. Below are five specific technical challenges we faced and the architectural solutions we implemented to... ## 5 Embedding Compression Techniques DevFeed: [5 Embedding Compression Techniques]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-04T20:51:57Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Compression](), [Embeddings](), [quantization](), [Inference]() Tags: [compression](), [embedding](), [inference](), [quantization](), [vector]() ### AI overview A tutorial explaining five embedding compression techniques: PCA, Matryoshka Representation Learning, scalar quantization, binary quantization, and Product Quantization. It describes how they reduce dimensions or per-value precision and how rescoring can improve ranking after compressed retrieval. ### Source excerpt ...explained visually. ## Parquet File Write Support, Bloom Filters, Improved Performance: Hardwood 1.1.0.Beta1 Is Out DevFeed: [Parquet File Write Support, Bloom Filters, Improved Performance: Hardwood 1.1.0.Beta1 Is Out]() Original publisher: [Read original article]() Published: 2026-08-31T19:36:00Z Content type: release Language: en Sources: [Gunnar Morling]() Topics: [parquet](), [Library](), [Parser](), [Command-line interface](), [Compression]() Tags: [apache-parquet](), [api](), [cli](), [compression](), [library](), [parquet](), [performance](), [release]() ### AI overview The first beta of Hardwood 1.1 introduces initial Parquet file-writing support through record-based and batch-oriented APIs. The release also adds Bloom filters, dictionary-based row-group pruning, performance improvements, and CLI enhancements. ### Source excerpt Table of Contents Write Support Query Evaluation: Bloom Filters and Dictionary-Based Row-Group Pruning Performance Improvements Hardwood CLI Closing Thoughts "When is write support gonna land in Hardwood?" That's probably the most common question I got over the last few months. As of today, I am very happy to share that the answer has changed from "It's coming soon" to "A first cut is there, give it a try" -- the first Beta of Hardwood 1.1 is out! This is a major milestone for the project, marking the first step in evolving Hardwood from being solely a Parquet parser to a complete library for this widely used columnar file format. But there's more. This release also comes with significant enhancements to the query layer (Bloom filters, dictionary-based row-group pruning), many performance improvements such as a fast path for effectively fixed-length list columns, an even snappier CLI, and much more. Let's dig into some of the new features and changes! ## Paperless-ngx 3.1.0 Adds AI Workflow Actions and Document Versioning DevFeed: [Paperless-ngx 3.1.0 Adds AI Workflow Actions and Document Versioning]() Original publisher: [Read original article]() Author: Christian Rakoot Published: 2026-08-31T06:39:07Z Content type: news Language: en Sources: [Self Host Lab]() Topics: [Artificial Intelligence](), [OpenID connect (OIDC)](), [Compression](), [Open Source](), [Authorization]() Tags: [ai](), [bug](), [changelog](), [compression](), [identity](), [news](), [news-personal-cloud](), [ocr](), [open-source](), [paperless-ngx](), [personal-cloud](), [release](), [self-hosted](), [updates](), [workflow]() ### AI overview Paperless-ngx 3.1.0 adds automatic AI-based workflow classification, per-document remote OCR selection, OIDC group synchronization, document versioning, configurable ZIP compression, and smaller fixes. ### Source excerpt Paperless-ngx 3.1.0 landed on August 27, 2026, adding a workflow action that applies AI-generated tag and correspondent suggestions automatically, per-document selective remote OCR, OIDC group sync mapping identity-provider groups to superuser and staff roles, a new versioning system for merging documents into successive versions, configurable ZIP export compression, and numerous smaller UI bug fixes sitewide. ## Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost DevFeed: [Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost]() Original publisher: [Read original article]() Author: Cody Mazza-Anthony Published: 2026-08-19T14:32:58Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Compression](), [Large Language Model](), [Embeddings](), [Latency](), [Inference](), [Inference Performance](), [Low-Latency Inference](), [Post-training optimization](), [GraphQL](), [model-deployment]() Tags: [compression](), [embedding](), [inference](), [latency](), [llm](), [model](), [tokens]() ### AI overview Gisting compresses an LLM agent's system prompt into learned gist tokens, preserving prediction quality while reducing inference latency, increasing throughput, and lowering GPU requirements. ### Source excerpt Gisting compresses context into a set of learned tokens, preserving its quality while making the model faster and cheaper. ## Scale pgvector with binary quantization on Amazon Aurora PostgreSQL DevFeed: [Scale pgvector with binary quantization on Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Steve Dille Published: 2026-08-18T16:37:22Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [quantization](), [AI search](), [Compression](), [Embeddings](), [Cache](), [Latency](), [datasets](), [configuration](), [postgresql clusters](), [Database]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [cache](), [compression](), [datasets](), [embeddings](), [latency](), [postgresql](), [postgresql-compatible](), [quantization](), [rds-for-postgresql](), [search](), [technical-how-to](), [validation](), [vector]() ### AI overview This practical guide explains how to use binary quantization with reranking in pgvector to scale HNSW vector search on Amazon Aurora PostgreSQL. It covers index-size reduction, performance and recall tradeoffs, sizing, validation, and suitable operating conditions for datasets ranging from 5 million to 100 million vectors. ### Source excerpt Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best. ## MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12 DevFeed: [MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12]() Original publisher: [Read original article]() Author: Takahiro Harada; Sho Ikeda Published: 2026-08-13T14:30:00Z Content type: release Language: en Sources: [AMD GPUOpen]() Topics: [Compression](), [GPU](), [Inference](), [mlp](), [Machine learning](), [GUI](), [shaders](), [Open Source]() Tags: [agility-sdk](), [announcement](), [compression](), [directx](), [driver](), [getting-started](), [gpu](), [gpu-open-sdks](), [gpu-open-tools](), [gpuopen-sdks](), [gpuopen-tools](), [graphics-apis](), [gui](), [inference](), [maths](), [memory](), [microsoft](), [microsoft-agility-sdk](), [microsoft-directx](), [ml](), [mlp](), [neural](), [open-source](), [product-release](), [quick-start](), [real-time](), [shaders](), [technical-article](), [technical-articles](), [tools]() ### AI overview MiniDXNN v0.4.0 is an open-source library for GPU-accelerated MLP inference and training on DirectX 12. The release adds D3D12 Linear Algebra support, input encoding for neural texture compression, and a real-time GUI application for training and visualizing texture representations. ### Source excerpt MiniDXNN v0.4.0 introduces D3D12 Linear Algebra (SM 6.10) support, input encodings and neural texture compression, plus a real-time GUI app that trains and visualizes GPU-accelerated MLPs on DirectX® 12. ## Inside Appwrite's new build cache: 4x faster dependency installs DevFeed: [Inside Appwrite's new build cache: 4x faster dependency installs]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-08-13T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Caching](), [Cache](), [Deployment](), [Next.js](), [Package manager](), [Compression](), [cloud-infrastructure]() Tags: [cache](), [compression](), [dependencies](), [deployment](), [engineering](), [filesystem](), [next-js](), [object-storage](), [pnpm](), [production]() ### AI overview Appwrite's build network caches dependencies between builds for sites and functions. The cache is restored before installation and saved after successful builds, with snapshots isolated per resource. The article explains how Appwrite uses SquashFS images to efficiently store and unpack package-manager data, reporting faster installs for example Next.js applications. ### Source excerpt A deep dive into how Appwrite now caches your dependencies between builds, and what it means for your deployment times. ## The 2-Second Rule: How to make your website feel like magic in 2026 DevFeed: [The 2-Second Rule: How to make your website feel like magic in 2026]() Original publisher: [Read original article]() Author: Louise North Published: 2026-08-04T11:43:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Web](), [Latency](), [browser](), [Workers](), [Compression](), [Artificial Intelligence](), [Edge]() Tags: [architecture](), [avif-images](), [browser](), [compression](), [core-web-vitals](), [developers](), [digital-hospitality](), [edge](), [edge-computing](), [frontend-development](), [guide](), [http3](), [inclusive-design](), [interaction-to-next-paint](), [javascript-optimization](), [mobile-optimization](), [page-speed-optimization](), [performance](), [predictive-ai](), [site-reliability](), [speed](), [technical-seo](), [user-experience](), [ux-design-2026](), [web-development](), [web-development-trends](), [web-performance](), [website-usability](), [workers](), [zero-latency-ux]() ### AI overview A guide to making websites feel faster and more responsive by focusing on interaction performance, Web Workers, AVIF images, and responsive image delivery. It presents speed as an important part of user experience in 2026. ### Source excerpt In 2026, a "pretty" website is no longer enough--if it doesn't feel instant, it's invisible. This guide reveals how the world's top developers are using predictive AI and "Edge" architecture to kill the loading bar for good. ## Postgres 19 Compression: from pglz to LZ4 DevFeed: [Postgres 19 Compression: from pglz to LZ4]() Original publisher: [Read original article]() Author: Christopher Winslett Published: 2026-07-16T12:00:00Z Content type: article Language: en Sources: [CrunchyData Blog]() Topics: [Compression](), [Databases](), [Open Source]() Tags: [algorithm](), [automatic](), [compression](), [open-source](), [postgres](), [postgres-19](), [production-postgres](), [speed](), [storage]() ### AI overview This article explains how PostgreSQL compresses table, TOAST, and index data, and examines the planned Postgres 19 change from pglz to LZ4 as the default TOAST compression algorithm. ### Source excerpt With Postgres 19 switching the default from pglz to LZ4, this post walks through the compression decision path, storage strategies, and index-size limits that shape real-world behavior. ## How to improve website performance: Best practices for faster sites DevFeed: [How to improve website performance: Best practices for faster sites]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-07-10T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [Web](), [User experience (UX)](), [Optimization](), [Caching](), [Compression](), [Search engine optimization (SEO)](), [Google]() Tags: [best-practices](), [browser](), [caching](), [code](), [compression](), [conversion](), [core-web-vitals](), [development](), [ecommerce](), [how-to](), [optimization](), [performance](), [seo](), [ux](), [web]() ### AI overview This article explains why website performance matters for user experience, customer satisfaction, bounce rates, conversions, and search rankings. It introduces performance metrics and seven optimization methods, including code minification, browser caching, and compression, while also covering common mistakes. ### Source excerpt Learn why a fast-loading website is so crucial for UX, and pick up seven common strategies for improving website performance as you grow your site. ## Designing GPU-Accelerated Query Engines with NVIDIA GQE DevFeed: [Designing GPU-Accelerated Query Engines with NVIDIA GQE]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-06-30T17:36:43Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Databases](), [NCCL](), [CUDA](), [NVLink](), [Compression](), [IO](), [SQL](), [Parser](), [Streaming](), [Hardware](), [Open Source]() Tags: [accelerate](), [compression](), [cpu](), [cuda](), [cuda-x](), [data](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [databases](), [featured](), [gpu](), [hardware](), [memory](), [nvidia](), [nvlink](), [performance](), [sql]() ### AI overview This article presents GQE, a reference architecture for executing SQL queries on GPUs. It explains how NVIDIA hardware and CUDA-X libraries address memory, I/O, data movement, decompression, and end-to-end performance challenges for large datasets. ### Source excerpt GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advances--including high bandwidth memory (HBM), NVIDIA... ## How Jua delivers the world's most accurate physics simulations 3x faster with ClickHouse Cloud DevFeed: [How Jua delivers the world's most accurate physics simulations 3x faster with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [data](), [foundation-models](), [World models](), [Artificial Intelligence](), [Compression](), [Fine-tuning](), [Google](), [Microsoft](), [Nvidia]() Tags: [ai](), [architecture](), [benchmarks](), [clickhouse](), [cloud](), [compression](), [compute](), [cost](), [data](), [efficiency](), [fine-tuning](), [global](), [google](), [microsoft](), [net-11](), [nvidia](), [renewables](), [speed](), [world-model]() ### AI overview Jua uses ClickHouse Cloud to deliver physics simulation data for energy forecasting faster and more efficiently. The platform reduced forecast delivery time from one hour to 20 minutes, cut compute costs by a third, and reduced historical query times from hours to seconds. Jua's EPT-2 physics foundation model learns atmospheric physics from observational data and can transfer to other fluid-dynamics problems with minimal fine-tuning. ### Source excerpt Jua replaced a file-based forecast pipeline with ClickHouse Cloud, cutting data delivery time from one hour to 20 minutes and historical query times from hours to seconds -- giving energy traders a faster edge. ## Discord Update: June 25, 2026 Changelog DevFeed: [Discord Update: June 25, 2026 Changelog]() Original publisher: [Read original article]() Author: Clyde Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Discord Blog]() Topics: [changelog](), [Discord](), [App](), [iOS](), [Compression](), [Latency](), [Emoji](), [Streaming]() Tags: [app](), [blog](), [changelog](), [compression](), [discord](), [identity](), [ios](), [latency](), [mobile](), [server](), [shop](), [streaming](), [update](), [updates](), [voice]() ### AI overview Discord's June 25, 2026 changelog highlights a Trending Games page, mobile navigation and messaging updates, faster iOS photo uploads, wishlist support, Discord account linking with League of Legends and VALORANT, chat and channel pinning, voice invite previews, and upgraded game profile pages. ### Source excerpt Here's the Discord Changelog from June 25, 2026, so you can stay informed on what's new in recent app updates! ## What's New in pg\_clickhouse v0.3.2: Postgres 19, TLS, Regex, and Memory DevFeed: [What's New in pg\_clickhouse v0.3.2: Postgres 19, TLS, Regex, and Memory]() Original publisher: [Read original article]() Author: David Wheeler Published: 2026-06-23T11:25:40Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [releases](), [clickhouse](), [PostgreSQL](), [TLS (Transport Layer Security)](), [Regular expression](), [Compression](), [Database]() Tags: [clickhouse](), [compression](), [http](), [memory](), [postgres](), [postgresql](), [releases](), [tls]() ### AI overview pg_clickhouse v0.3.2 adds PostgreSQL 19 Beta 1 support, new TLS connection options, corrected regular-expression pushdown behavior, and fixes for memory-consumption issues. The release also adds ClickHouse native protocol compression for query results and data, plus additional pushdown improvements. ### Source excerpt The latest pg_clickhouse releases bring JSONB, date/time, and array function pushdown, plus HTTP result set streaming for lower memory usage. ## Bridge Queries in Redpanda SQL DevFeed: [Bridge Queries in Redpanda SQL]() Original publisher: [Read original article]() Author: Paul Wilkinson Published: 2026-06-23T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [SQL](), [Streaming](), [parquet](), [Compression](), [real-time](), [data](), [dashboards]() Tags: [analytics](), [compression](), [data](), [engineering](), [parquet](), [real-time](), [sql](), [storage](), [streaming]() ### AI overview This article introduces bridge queries in Redpanda SQL, which combine historical data from Iceberg tables with recent messages from Redpanda topics through a single virtual SQL table. By reading the freshness gap directly from the topic, teams can flush data to Iceberg less frequently, producing better-sized Parquet files while retaining near-real-time query results and reducing compaction overhead. ### Source excerpt Stop choosing between fresh data and robust Parquet files. Redpanda SQL bridge queries let you query live streaming topics and historical Iceberg tables together, without the compaction overhead. ## Blob Direct Write With Partitioned Blob Files DevFeed: [Blob Direct Write With Partitioned Blob Files]() Original publisher: [Read original article]() Author: Xingbo Wang Published: 2026-06-20T00:00:00Z Content type: article Language: en Sources: [RocksDB]() Topics: [rocksdb](), [Compression]() Tags: [blog](), [compression](), [rocksdb]() ### AI overview This RocksDB article explains Blob Direct Write, which externalizes qualifying large values to blob files earlier in the write path while storing compact BlobIndex references in the WAL and memtable. It also describes partitioning support that lets applications select blob-file destinations, including grouping values with similar TTLs. ### Source excerpt TL;DR Blob Direct Write moves large-value separation earlier in RocksDB's write path. When enable_blob_files and enable_blob_direct_write are enabled, values at or above min_blob_size can be written directly to blob files during a write, while the WAL and memtable store a compact BlobIndex reference instead of the full value. The companion partitioning support makes this more than a write-path optimization. A column family can have multiple direct-write blob partitions, and applications can provide a BlobFilePartitionStrategy to choose where each large value goes. That turns blob files into a policy-controlled grouping unit. For example, an application can route values with similar TTLs into the same set of blob files while using Universal Compaction for the key and metadata part of the LSM. The reduced-scope v1 implementation landed in pull request #14535, and custom partition selection was added in pull request #14565. Background Integrated BlobDB already separates large values from the LSM tree. The LSM stores keys plus blob references, and blob files store the large value bytes. This reduces compaction write amplification because compaction can rewrite keys and references without repeatedly copying large values. Before Blob Direct Write, however, large values still entered RocksDB through the normal write path first. They were serialized into a write batch, written to the WAL, inserted into the memtable, and later extracted into blob files during flush or compaction. That design is simple and broadly compatible, but it means large values still consume WAL bandwidth and memtable memory before they become out-of-line blobs. Blob Direct Write changes that placement point. The write path can externalize a large value immediately, then publish a BlobIndex through the normal WAL and memtable machinery. Write Path The core write-path logic lives in BlobWriteBatchTransformer and BlobFilePartitionManager. For a regular Put inside a WriteBatch, the transformer does the fo ## Less Is More: Why Audio on SoundCloud Looks Different DevFeed: [Less Is More: Why Audio on SoundCloud Looks Different]() Original publisher: [Read original article]() Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [SoundCloud Backstage Blog]() Topics: [Compression](), [Optimization]() Tags: [audio](), [compression](), [engineering](), [optimization](), [streaming](), [transcodings]() ### AI overview SoundCloud explains why its upgraded AAC encoder applies a low-pass filter around 17 kHz. By discarding frequencies that most listeners barely perceive, the encoder can allocate more bits to audible content and improve overall perceptual quality, even though spectrograms appear to show a loss. ### Source excerpt Last year, SoundCloud upgraded its AAC encoder for the first time in over a decade. The new one (Fraunhofer's libfdk_aac) delivers higher... ## How LivePerson optimized Logstash and Kafka performance on Google Cloud through benchmarking DevFeed: [How LivePerson optimized Logstash and Kafka performance on Google Cloud through benchmarking]() Original publisher: [Read original article]() Author: Emily Chioconi,Kiril Karamanolev,Strahil Nikolov Published: 2026-06-16T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [benchmarking](), [Kafka](), [Cloud](), [Optimization](), [Compression](), [cpu](), [log management](), [observability](), [DevOps](), [Kubernetes]() Tags: [amd](), [benchmarking](), [cloud](), [compression](), [cost](), [cpu](), [customer-story-logs](), [devops](), [google](), [google-cloud](), [infrastructure](), [kafka](), [kubernetes](), [logging](), [observability](), [optimization](), [performance](), [software-technology]() ### AI overview LivePerson benchmarked five Google Cloud machine types for a high-volume Logstash and Kafka logging pipeline. AMD Milan instances delivered substantially higher throughput and a lower cost per event, while Kafka compression codec selection produced additional performance gains and reduced cluster overhead. ### Source excerpt LivePerson found that benchmarking Google Cloud machine types cut Logstash costs by over half using AMD Milan instances, while Kafka compression codec selection significantly boosted throughput. ## ClickHouse achieves AWS Retail Competency DevFeed: [ClickHouse achieves AWS Retail Competency]() Original publisher: [Read original article]() Author: Aditya Chidurala Published: 2026-06-12T21:17:44Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services](), [real-time](), [data](), [Amazon S3](), [Compression](), [Kafka](), [Microservices](), [observability](), [Traces](), [Cloud](), [networking]() Tags: [amazon-s3](), [aws](), [batch](), [business](), [clickhouse](), [cloud](), [compression](), [data](), [e-commerce](), [kafka](), [microservices](), [observability](), [personalization](), [real-time](), [retail](), [s3](), [storage](), [traces]() ### AI overview ClickHouse has achieved the AWS Retail Competency in the Advanced Data Insights category, recognizing its validated expertise in real-time retail analytics and customer success on AWS. ### Source excerpt ClickHouse has achieved the AWS Retail Competency, joining a select group of AWS Partners recognized for deep expertise in helping retailers turn live operational data into real-time decisions. 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