ClickHouse's new "lazy materialization" feature improves query performance by deferring the calculation of intermediate result sets until absolutely necessary. Instead of eagerly computing and storing each step of a complex query, ClickHouse now analyzes the entire query plan and identifies opportunities to skip or combine calculations, especially when dealing with filtering conditions or aggregations. This leads to significant reductions in memory usage and processing time, particularly for queries involving large intermediate data sets that are subsequently filtered down to a smaller final result. The blog post highlights performance improvements of up to 10x, and this optimization is automatically applied without any user intervention.
The post "Everyone knows all the apps on your phone" argues that the extensive data collection practices of mobile advertising networks effectively reveal which apps individuals use, even without explicit permission. Through deterministic and probabilistic methods linking device IDs, IP addresses, and other signals, these networks can create detailed profiles of app usage across devices. This information is then packaged and sold to advertisers, data brokers, and even governments, allowing them to infer sensitive information about users, from their political affiliations and health concerns to their financial status and personal relationships. The post emphasizes the illusion of privacy in the mobile ecosystem, suggesting that the current opt-out model is inadequate and calls for a more robust approach to data protection.
Hacker News users discussed the privacy implications of app usage data being readily available to mobile carriers and how this data can be used for targeted advertising and even more nefarious purposes. Some commenters highlighted the ease with which this data can be accessed, not just by corporations but also by individuals with basic technical skills. The discussion also touched upon the ineffectiveness of current privacy regulations and the lack of real control users have over their data. A few users pointed out the potential for this data to reveal sensitive information like health conditions or financial status based on app usage patterns. Several commenters expressed a sense of resignation and apathy, suggesting the fight for data privacy is already lost, while others advocated for stronger regulations and user control over data sharing.
DuckDB now offers preview support for querying data directly in Amazon S3 via a new extension. This allows users to create and query tables stored as Parquet, CSV, or JSON files on S3 without downloading data, leveraging S3's scalability and DuckDB's analytical capabilities. The extension utilizes the httpfs extension for access and supports various S3-specific features like AWS credentials and different regions. While still experimental, this functionality opens the door to building efficient "lakehouse" architectures directly on S3 using DuckDB.
Hacker News commenters generally expressed excitement about DuckDB's new S3 integration, praising its speed, simplicity, and potential to disrupt the data lakehouse space. Several users shared their positive experiences using DuckDB, highlighting its performance advantages compared to other query engines like Presto and Athena. Some raised concerns about the potential vendor lock-in with S3, suggesting that supporting alternative storage solutions would be beneficial. Others discussed the limitations of Parquet files for analytical workloads, and how DuckDB might address those issues. A few commenters pointed out the importance of robust schema evolution and data governance features for enterprise adoption. The overall sentiment was very positive, with many seeing this as a significant step forward for data analysis on cloud storage.
Umami is a self-hosted, open-source web analytics alternative to Google Analytics that prioritizes simplicity, speed, and privacy. It provides a clean, minimal interface for tracking website metrics like page views, unique visitors, bounce rate, and session duration, without collecting any personally identifiable information. Umami is designed to be lightweight and fast, minimizing its impact on website performance, and offers a straightforward setup process.
HN commenters largely praise Umami's simplicity, self-hostability, and privacy focus as a welcome alternative to Google Analytics. Several users share their positive experiences using it, highlighting its ease of setup and lightweight resource usage. Some discuss the trade-offs compared to more feature-rich analytics platforms, acknowledging Umami's limitations in advanced analysis and segmentation. A few commenters express interest in specific features like custom event tracking and improved dashboarding. There's also discussion around alternative self-hosted analytics solutions like Plausible and Ackee, with comparisons to their respective features and performance. Overall, the sentiment is positive, with many users appreciating Umami's minimalist approach and alignment with privacy-conscious web analytics.
The Asurion article outlines how to manage various Apple "intelligence" features, which personalize and improve user experience but also collect data. It explains how to disable Siri suggestions, location tracking for specific apps or entirely, personalized ads, sharing analytics with Apple, and features like Significant Locations and personalized recommendations in apps like Music and TV. The article emphasizes that disabling these features may impact the functionality of certain apps and services, and offers steps for both iPhone and Mac devices.
HN commenters largely express skepticism and distrust of Apple's "intelligence" features, viewing them as data collection tools rather than genuinely helpful features. Several comments highlight the difficulty in truly disabling these features, pointing out that Apple often re-enables them with software updates or buries the relevant settings deep within menus. Some users suggest that these "intelligent" features primarily serve to train Apple's machine learning models, with little tangible benefit to the end user. A few comments discuss specific examples of unwanted behavior, like personalized ads appearing based on captured data. Overall, the sentiment is one of caution and a preference for maintaining privacy over utilizing these features.
Summary of Comments ( 8 )
https://news.ycombinator.com/item?id=43763688
HN commenters generally praised ClickHouse's lazy materialization feature. Several noted the cleverness of deferring calculations until absolutely necessary, highlighting potential performance gains, especially with larger datasets. Some questioned the practical impact compared to existing optimizations, wondering about specific scenarios where it shines. Others pointed out similarities to other database systems and languages like SQL Server and Haskell, suggesting that this approach, while not entirely novel, is a valuable addition to ClickHouse. One commenter expressed concern about potential debugging complexity introduced by this lazy evaluation model.
The Hacker News post discussing ClickHouse's lazy materialization feature has a moderate number of comments, mostly focusing on the technical implications and potential benefits of this new functionality.
Several commenters express enthusiasm for the performance improvements promised by lazy materialization, particularly in scenarios involving complex queries and large datasets. They appreciate the ability to defer computations until absolutely necessary, avoiding unnecessary work and potentially speeding up query execution. The concept of pushing projections down the query plan is also highlighted as a key advantage, optimizing data processing by only calculating the necessary columns.
Some users delve deeper into the technical details, discussing how lazy materialization interacts with other database features like vectorized execution and query optimization. They speculate about the potential impact on memory usage and execution time, noting the trade-offs involved in deferring computations. One commenter mentions the potential for further optimization by intelligently deciding which parts of the query to materialize eagerly versus lazily, hinting at the complexity of implementing such a feature effectively.
A few comments touch on the broader implications of lazy materialization for database design and query writing. They suggest that this feature could encourage users to write more complex queries without worrying as much about performance penalties, potentially leading to more sophisticated data analysis. However, there's also some caution expressed about the potential for unexpected behavior or performance regressions if lazy materialization isn't handled carefully.
Some users share their experience with similar features in other database systems, drawing comparisons and contrasting the approaches taken by different vendors. This provides valuable context and helps to understand the unique aspects of ClickHouse's implementation.
While there isn't overwhelming discussion, the existing comments demonstrate a clear interest in the technical aspects of lazy materialization and its potential impact on ClickHouse's performance and usability. They highlight the trade-offs involved in this optimization technique and offer insightful perspectives on its potential benefits and drawbacks.