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A few weeks ago, we massively improved the performance of the dashboard & website by optimizing some of our SQL queries. In this post, we'll share how we identified the queries that needed work. In the next post, we'll explore how we fixed each of them. We'll cover the basics and gradually work our way up to the more advanced/complex ways of identifying slow queries. In this post, you'll see: Let's go! What these results look like # As a reminder, this is the resulting performance gain for
Where This Story Begins In 2022, we had three different query interfaces. Logs had a custom search syntax with no autocomplete. Traces only had predefined filters - no query builder at all. Metrics had a raw PromQL input box where you'd paste queries from somewhere else and hope they worked. Each system spoke a different language. An engineer debugging a production issue had to context-switch not just between data types, but between entirely different mental models of how to query data. When
Windows-Use is a powerful automation agent that interact directly with the Windows at GUI layer. It bridges the gap between AI Agents and the Windows OS to perform tasks such as opening apps, clicking buttons, typing, executing shell commands, and capturing UI state all without relying on traditional computer vision models. Enabling any LLM to perform computer automation instead of relying on specific models for it. 🛠️Installation Guide Prerequisites Python 3.12 or higher UV (or pip ) ) Win
Features Installation To use Ark in a Go project, run: go get github.com/mlange-42/ark Usage Below is the classical Position/Velocity example that every ECS shows in the docs. See the User Guide, API docs and examples for details. package main import ( "math/rand/v2" "github.com/mlange-42/ark/ecs" ) // Position component type Position struct { X float64 Y float64 } // Velocity component type Velocity struct { X float64 Y float64 } func main () { // Create a new World world := ecs . NewWor
This article was written as part of my services Querying billions of weather records and getting results in under 200 milliseconds isn’t theory; it’s what real-time analytics solutions provide. Processing streaming IoT data from thousands of sensors while delivering real-time dashboards with no lag is what certain business domains need. That’s what you’ll learn at the end of this guide through building a ClickHouse-modeled analytics use case. You’ll learn how to land data in ClickHouse that is
This article was written as part of my services Querying billions of weather records and getting results in under 200 milliseconds isn’t theory; it’s what real-time analytics solutions provide. Processing streaming IoT data from thousands of sensors while delivering real-time dashboards with no lag is what certain business domains need. That’s what you’ll learn at the end of this guide through building a ClickHouse-modeled analytics use case. You’ll learn how to land data in ClickHouse that is
森人 - Mori-bito (forest-person) A terminal-based LDAP server explorer built with Go and BubbleTea, providing an interactive interface for browsing LDAP directory trees, viewing records, and executing custom queries. Features 🌲 Interactive Tree Navigation : Browse LDAP directory structure with keyboard/mouse : Browse LDAP directory structure with keyboard/mouse 📄 Record Viewer : View detailed LDAP entry attributes : View detailed LDAP entry attributes 📋 Clipboard Integration : Copy attribute
From Multi-Head to Latent Attention: The Evolution of Attention Mechanisms Vinithavn 7 min read · 15 hours ago 15 hours ago -- Listen Share Press enter or click to view image in full size What is attention? In any autoregressive model, the prediction of the future tokens is based on some preceding context. However, not all the tokens within this context equally contribute to the prediction, because some tokens might be more relevant than others. The attention mechanism addresses this by allow
From Multi-Head to Latent Attention: The Evolution of Attention Mechanisms Vinithavn 7 min read · 15 hours ago 15 hours ago -- Listen Share Press enter or click to view image in full size What is attention? In any autoregressive model, the prediction of the future tokens is based on some preceding context. However, not all the tokens within this context equally contribute to the prediction, because some tokens might be more relevant than others. The attention mechanism addresses this by allow
Benchmarks for Golang SQLite Drivers Note This work is sponsored by Monibot - Website, Server and Application Monitoring. Try out Monibot for free at https://monibot.io. For benchmarks I used the following libraries: bvinc, github.com/bvinc/go-sqlite-lite, a CGO-based solution. This is not a database/sql driver. craw, github.com/crawshaw/sqlite, a CGO-based solution. This is not a database/sql driver. eaton, github.com/eatonphil/gosqlite, a CGO-based solution. This is not a database/sql dri
At Radar, performance is a feature. Our platform processes over 1 billion API calls per day from hundreds of millions of devices worldwide. We provide geolocation infrastructure and solutions, including APIs for: Geocoding : Forward geocoding, reverse geocoding, and IP geocoding APIs with global coverage. : Forward geocoding, reverse geocoding, and IP geocoding APIs with global coverage. Search : Address autocomplete, address validation, and places search APIs. Address autocomplete, address v
At Radar, performance is a feature. Our platform processes over 1 billion API calls per day from hundreds of millions of devices worldwide. We provide geolocation infrastructure and solutions, including APIs for: Geocoding : Forward geocoding, reverse geocoding, and IP geocoding APIs with global coverage. : Forward geocoding, reverse geocoding, and IP geocoding APIs with global coverage. Search : Address autocomplete, address validation, and places search APIs. Address autocomplete, address v
SQLx 🧰 The Rust SQL Toolkit SQLx is an async, pure Rust † SQL crate featuring compile-time checked queries without a DSL. † The SQLite driver uses the libsqlite3 C library as SQLite is an embedded database (the only way we could be pure Rust for SQLite is by porting all of SQLite to Rust). †† SQLx uses #![forbid(unsafe_code)] unless the sqlite feature is enabled. The SQLite driver directly invokes the SQLite3 API via libsqlite3-sys , which requires unsafe . Cross-platform. Being native Rust
SQLx 🧰 The Rust SQL Toolkit SQLx is an async, pure Rust † SQL crate featuring compile-time checked queries without a DSL. † The SQLite driver uses the libsqlite3 C library as SQLite is an embedded database (the only way we could be pure Rust for SQLite is by porting all of SQLite to Rust). †† SQLx uses #![forbid(unsafe_code)] unless the sqlite feature is enabled. The SQLite driver directly invokes the SQLite3 API via libsqlite3-sys , which requires unsafe . Cross-platform. Being native Rust
BloomSearch Keyword search engine with hierarchical bloom filters for massive datasets BloomSearch provides extremely low memory usage and low cold-start searches through pluggable storage interfaces. Memory efficient : Bloom filters have constant size regardless of data volume : Bloom filters have constant size regardless of data volume Pluggable storage : DataStore and MetaStore interfaces for any backend (can be same or separate) : DataStore and MetaStore interfaces for any backend (can
BloomSearch Keyword search engine with hierarchical bloom filters for massive datasets BloomSearch provides extremely low memory usage and low cold-start searches through pluggable storage interfaces. Memory efficient : Bloom filters have constant size regardless of data volume : Bloom filters have constant size regardless of data volume Pluggable storage : DataStore and MetaStore interfaces for any backend (can be same or separate) : DataStore and MetaStore interfaces for any backend (can
This is a brief write-up of a trick I learned that helps me write code faster and more accurately. I say "trick", but it's really something I started to do without noticing as I moved further into my career. When you're working on something difficult, sketch a proof in your head as you go that your code will actually do what you want it to do. A simple idea, but easier said than done: doing this "online" without interrupting your flow takes a lot of practice. But once you get really good at it,
What is Context Engineering? A few years ago, many, even top AI researchers, claimed that prompt engineering would be dead by now. Obviously, they were very wrong, and in fact, prompt engineering is now even more important than ever. It is so important that it is now being rebranded as context engineering. Yes, another fancy term to describe the important process of tuning the instructions and relevant context that an LLM needs to perform its tasks effectively. Much has been written already
Abstraction boundaries are optimization boundaries The N+1 query problem occurs when your application code sends one SQL query per element in a collection. The N queries are redundant; since all of the data is in the database already, a single query should be enough. This problem is usually caused by a leaky abstraction; the ORM, or whatever database abstraction you are using, can’t anticipate that it would need to send N queries, so it can’t automatically optimize this down to a single query.
TLDR # Observability at scale: Our internal system grew from 19 PiB to 100 PB of uncompressed logs and from ~40 trillion to 500 trillion rows. Efficiency breakthrough: We absorbed a 20× surge in event volume using under 10% of the CPU previously needed. OTel pitfalls: The required parsing and marshalling of events in OpenTelemetry proved a bottleneck and didn’t scale - our custom pipeline addressed this. Introducing HyperDX: ClickHouse-native observability UI for seamless exploration, correlatio
TLDR # Observability at scale: Our internal system grew from 19 PiB to 100 PB of uncompressed logs and from ~40 trillion to 500 trillion rows. Efficiency breakthrough: We absorbed a 20× surge in event volume using under 10% of the CPU previously needed. OTel pitfalls: The required parsing and marshalling of events in OpenTelemetry proved a bottleneck and didn’t scale - our custom pipeline addressed this. Introducing HyperDX: ClickHouse-native observability UI for seamless exploration, correlatio
Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Gaining visibility — and, ultimately, insights — into enterprise cloud assets is growing ever more challenging. Cloud estates are sprawling and fragmented, and inventory capabilities in existing tools can be narrow and unintuitive, separating elements like cost and security data into disconnected platforms with limited flexibility. Cloud