The blog post argues Apache Iceberg is poised to become a foundational technology in the modern data stack, similar to how Hadoop was for the previous generation. Iceberg provides a robust, open table format that addresses many shortcomings of directly querying data lake files. Its features, including schema evolution, hidden partitioning, and time travel, enable reliable and performant data analysis across various engines like Spark, Trino, and Flink. This standardization simplifies data management and facilitates better data governance, potentially unifying the currently fragmented modern data stack. Just as Hadoop provided a base layer for big data processing, Iceberg aims to be the underlying table format that different data tools can build upon.
The blog post argues that SQLite, often perceived as a lightweight embedded database, is surprisingly well-suited for large-scale server deployments, even outperforming traditional client-server databases in certain scenarios. It posits that SQLite's simplicity, file-based nature, and lack of a separate server process translate to reduced operational overhead, easier scaling through horizontal sharding, and superior performance for read-heavy workloads, especially when combined with efficient caching mechanisms. While acknowledging limitations for complex joins and write-heavy applications, the author contends that SQLite's strengths make it a compelling, often overlooked option for modern web backends, particularly those focusing on serving static content or leveraging serverless functions.
Hacker News users discussed the practicality and nuance of using SQLite as a server-side database, particularly at scale. Several commenters challenged the author's assertion that SQLite is better at hyper-scale than micro-scale, pointing out that its single-writer nature introduces bottlenecks in heavily write-intensive applications, precisely the kind often found at smaller scales. Some argued the benefits of SQLite, like simplicity and ease of deployment, are more valuable in microservices and serverless architectures, where scale is addressed through horizontal scaling and data sharding. The discussion also touched on the benefits of SQLite's reliability and its suitability for read-heavy workloads, with some users suggesting its effectiveness for data warehousing and analytics. Several commenters offered their own experiences, some highlighting successful use cases of SQLite at scale, while others pointed to limitations encountered in production environments.
A plasticizer called B2E, used in dampeners within vintage hard drives, is degrading and turning into a gooey substance. This "goo" can contaminate the platters and heads of the drive, rendering it unusable. While impacting mostly older Seagate SCSI drives from the late 90s and early 2000s, other manufacturers like Maxtor and Quantum also used similar dampeners, though failure rates seem lower. The degradation appears unavoidable due to B2E's chemical instability, posing a preservation risk for data stored on these drives.
Several Hacker News commenters corroborate the article's claims about degrading dampers in older hard drives, sharing personal experiences of encountering the issue and its resulting drive failures. Some discuss the chemical composition of the deteriorating material, suggesting it's likely a silicone-based polymer. Others offer potential solutions, like replacing the affected dampers, or using freezing temperatures to temporarily harden the material and allow data recovery. A few commenters note the planned obsolescence aspect, with manufacturers potentially using materials with known degradation timelines. There's also debate on the effectiveness of storing drives vertically versus horizontally, and the role of temperature and humidity in accelerating the decay. Finally, some users express frustration with the lack of readily available replacement dampers and the difficulty of the repair process.
DeepSeek's Fire-Flyer File System (3FS) is a high-performance, distributed file system designed for AI workloads. It boasts significantly faster performance than existing solutions like HDFS and Ceph, particularly for small files and random access patterns common in AI training. 3FS leverages RDMA and kernel bypass techniques for low latency and high throughput, while maintaining POSIX compatibility for ease of integration with existing applications. Its architecture emphasizes scalability and fault tolerance, allowing it to handle the massive datasets and demanding requirements of modern AI.
Hacker News users discussed the potential advantages and disadvantages of 3FS, DeepSeek's Fire-Flyer File System. Several commenters questioned the claimed performance benefits, particularly the "10x faster" assertion, asking for clarification on the specific benchmarks used and comparing it to existing solutions like Ceph and GlusterFS. Some expressed skepticism about the focus on NVMe over other storage technologies and the lack of detail regarding data consistency and durability. Others appreciated the open-sourcing of the project and the potential for innovation in the distributed file system space, but stressed the importance of rigorous testing and community feedback for wider adoption. Several commenters also pointed out the difficulty in evaluating the system without more readily available performance data and the lack of clear documentation on certain features.
This study demonstrates a significant advancement in magnetic random-access memory (MRAM) technology by leveraging the orbital Hall effect (OHE). Researchers fabricated a device using a topological insulator, Bi₂Se₃, as the OHE source, generating orbital currents that efficiently switch the magnetization of an adjacent ferromagnetic layer. This approach requires substantially lower current densities compared to conventional spin-orbit torque (SOT) MRAM, leading to improved energy efficiency and potentially faster switching speeds. The findings highlight the potential of OHE-based SOT-MRAM as a promising candidate for next-generation non-volatile memory applications.
Hacker News users discussed the potential impact of the research on MRAM technology, expressing excitement about its implications for lower power consumption and faster switching speeds. Some questioned the practicality due to the cryogenic temperatures required for the observed effect, while others pointed out that room-temperature operation might be achievable with further research and different materials. Several commenters delved into the technical details of the study, discussing the significance of the orbital Hall effect and its advantages over the spin Hall effect for generating spin currents. There was also discussion about the challenges of scaling this technology for mass production and the competitive landscape of next-generation memory technologies. A few users highlighted the complexity of the physics involved and the need for simplified explanations for a broader audience.
Storing and utilizing text embeddings efficiently for machine learning tasks can be challenging due to their large size and the need for portability across different systems. This post advocates for using Parquet files in conjunction with the Polars DataFrame library as a superior solution. Parquet's columnar storage format enables efficient filtering and retrieval of specific embeddings, while Polars provides fast data manipulation in Python. This combination outperforms traditional methods like storing embeddings in CSV or JSON, especially when dealing with millions of embeddings, by significantly reducing file size and processing time, leading to faster model training and inference. The author demonstrates this advantage by showcasing a practical example of similarity search within a large embedding dataset, highlighting the significant performance gains achieved with the Parquet/Polars approach.
Hacker News users discussed the benefits of using Parquet and Polars for storing and accessing text embeddings. Several commenters praised the combination, highlighting Parquet's efficiency for storing vector data and Polars' speed for querying and manipulating it. One commenter mentioned the ease of integration with tools like DuckDB for analytical queries. Others pointed out potential downsides, including Parquet's columnar storage being less ideal for retrieving entire embeddings and the relative immaturity of the Polars ecosystem compared to Pandas. The discussion also touched on alternative approaches like FAISS and LanceDB, acknowledging their strengths for similarity searches but emphasizing the advantages of Parquet/Polars for general-purpose data manipulation and analysis of embeddings. A few users questioned the focus on "portability," suggesting that cloud-based vector databases offer superior performance for most use cases.
Twitch is implementing a 100-hour upload limit per rolling 30-day period for most partners and affiliates, starting April 19, 2024. Content exceeding this limit will be progressively deleted, oldest first. This change aims to improve discoverability and performance, with VODs, Highlights, and Clips still permanently downloadable before deletion. Twitch promises more storage options in the future but offers no concrete details. Partners who require more than 100 hours can appeal for increased capacity.
HN commenters largely criticized Twitch's decision to limit past broadcast storage to 100 hours and delete excess content. Many saw this as a cost-cutting measure detrimental to creators, particularly smaller streamers who rely on VODs for growth and highlight reels. Some suggested alternative solutions like tiered storage options or allowing creators to download their content. The lack of prior notice and the short timeframe for downloading archives were also major points of concern, with users expressing frustration at the difficulty of downloading large amounts of data quickly. The potential loss of valuable content, including unique moments and historical records of streams, was lamented. Several commenters speculated on technical reasons behind the decision but ultimately viewed it negatively, impacting trust in the platform.
This blog post demonstrates how to build a flexible and cost-effective data lakehouse using AWS S3 for storage and leveraging the open-source Apache Iceberg table format. It walks through using Python and various open-source query engines like DuckDB, DataFusion, and Polars to interact with data directly on S3, bypassing the need for expensive data warehousing solutions. The post emphasizes the advantages of this approach, including open table formats, engine interchangeability, schema evolution, and cost optimization by separating compute and storage. It provides practical examples of data ingestion, querying, and schema management, showcasing the power and flexibility of this architecture for data analysis and exploration.
Hacker News users generally expressed skepticism towards the proposed "open" data lakehouse solution. Several commenters pointed out that while using open file formats like Parquet is a step in the right direction, true openness requires avoiding vendor lock-in with specific query engines like DuckDB. The reliance on custom Python tooling was also seen as a potential barrier to adoption and maintainability compared to established solutions. Some users questioned the overall benefit of this approach, particularly regarding cost-effectiveness and operational overhead compared to managed services. The perceived complexity and lack of clear advantages led to discussions about the practical applicability of this architecture for most users. A few commenters offered alternative approaches, including using managed services or simpler open-source tools.
This blog post from 2004 recounts the author's experience troubleshooting a customer's USB floppy drive issue. The customer reported their A: drive constantly seeking, even with no floppy inserted. After remote debugging revealed no software problems, the author deduced the issue stemmed from the drive itself. USB floppy drives, unlike internal ones, lack a physical switch to detect the presence of a disk. Instead, they rely on a light sensor which can malfunction, causing the drive to perpetually search for a non-existent disk. Replacing the faulty drive solved the problem, highlighting a subtle difference between USB and internal floppy drive technologies.
HN users discuss various aspects of USB floppy drives and the linked blog post. Some express nostalgia for the era of floppies and the challenges of driver compatibility. Several commenters delve into the technical details of how USB storage devices work, including the translation layers required for legacy devices like floppy drives and the differences between the "fixed" storage model of floppies versus other removable media. The complexities of the USB Mass Storage Class Bulk-Only Transport protocol are also mentioned. One compelling comment thread explores the idea that Microsoft's attempt to enforce the use of a particular class driver may have stifled innovation and created difficulties for users who needed specific functionality from their USB floppy drives. Another interesting point raised is how different vendors implemented USB floppy drives, with some integrating the controller into the drive and others requiring a separate controller in the cable.
The blog post details a teardown and analysis of a SanDisk High Endurance microSDXC card. The author physically de-caps the card to examine the controller and flash memory chips, identifying the controller as a SMI SM2703 and the NAND flash as likely Micron TLC. They then analyze the card's performance using various benchmarking tools, observing consistent write speeds around 30MB/s, significantly lower than the advertised 60MB/s. The author concludes that while the card may provide decent sustained write performance, the marketing claims are inflated and the "high endurance" aspect likely comes from over-provisioning rather than superior hardware. The post also speculates about the internal workings of the pSLC caching mechanism potentially responsible for the consistent write speeds.
Hacker News users discuss the intricacies of the SanDisk High Endurance card and the reverse-engineering process. Several commenters express admiration for the author's deep dive into the card's functionality, particularly the analysis of the wear-leveling algorithm and its pSLC mode. Some discuss the practical implications of the findings, including the limitations of endurance claims and the potential for data recovery even after the card is deemed "dead." One compelling exchange revolves around the trade-offs between endurance and capacity, and whether higher endurance necessitates lower overall storage. Another interesting thread explores the challenges of validating write endurance claims and the lack of standardized testing. A few commenters also share their own experiences with similar cards and offer additional insights into the complexities of flash memory technology.
German consumers are reporting that Seagate hard drives advertised and sold as new were actually refurbished drives with heavy prior usage. Some drives reportedly logged tens of thousands of power-on hours and possessed SMART data indicating significant wear, including reallocated sectors and high spin-retry counts. This affects several models, including IronWolf and Exos enterprise-grade drives purchased through various retailers. While Seagate has initiated replacements for some affected customers, the extent of the issue and the company's official response remain unclear. Concerns persist regarding the potential for widespread resale of used drives as new, raising questions about Seagate's quality control and refurbishment practices.
Hacker News commenters express skepticism and concern over the report of Seagate allegedly selling used hard drives as new in Germany. Several users doubt the veracity of the claims, suggesting the reported drive hours could be a SMART reporting error or a misunderstanding. Others point out the potential for refurbished drives to be sold unknowingly, highlighting the difficulty in distinguishing between genuinely new and refurbished drives. Some commenters call for more evidence, suggesting analysis of the drive's physical condition or firmware versions. A few users share anecdotes of similar experiences with Seagate drives failing prematurely. The overall sentiment is one of caution towards Seagate, with some users recommending alternative brands.
The blog post details how Definite integrated concurrent read/write functionality into DuckDB using Apache Arrow Flight. Previously, DuckDB only supported single-writer, multi-reader access. By leveraging Flight's DoPut and DoGet streams, they enabled multiple clients to simultaneously read and write to a DuckDB database. This involved creating a custom Flight server within DuckDB, utilizing transactions to manage concurrency and ensure data consistency. The post highlights performance improvements achieved through this integration, particularly for analytical workloads involving large datasets, and positions it as a key advancement for interactive data analysis and real-time applications. They open-sourced this integration, making concurrent DuckDB access available to a wider audience.
Hacker News users discussed DuckDB's new concurrent read/write feature via Arrow Flight. Several praised the project's rapid progress and innovative approach. Some questioned the performance implications of using Flight for this purpose, particularly regarding overhead. Others expressed interest in specific use cases, such as combining DuckDB with other data tools and querying across distributed datasets. The potential for improved performance with columnar data compared to row-based systems was also highlighted. A few users sought clarification on technical aspects, like the level of concurrency achieved and how it compares to other databases.
Kronotop is a new open-source database designed as a Redis-compatible, transactional document store built on top of FoundationDB. It aims to offer the familiar interface and ease-of-use of Redis, combined with the strong consistency, scalability, and fault tolerance provided by FoundationDB. Kronotop supports a subset of Redis commands, including string, list, set, hash, and sorted set data structures, along with multi-key transactions ensuring atomicity and isolation. This makes it suitable for applications needing both the flexible data modeling of a document store and the robust guarantees of a distributed transactional database. The project emphasizes performance and is actively under development.
HN commenters generally expressed interest in Kronotop, praising its use of FoundationDB for its robustness and the project's potential. Some questioned the need for another database when Redis already exists, suggesting the value proposition wasn't entirely clear. Others compared it favorably to Redis' JSON support, highlighting Kronotop's transactional nature and ACID compliance as significant advantages. Performance concerns were raised, with a desire for benchmarks to compare it to existing solutions. The project's early stage was acknowledged, leading to discussions about potential feature additions like secondary indexes and broader API compatibility. The choice of Rust was also lauded for its performance and safety characteristics.
Summary of Comments ( 30 )
https://news.ycombinator.com/item?id=43277214
HN users generally disagree with the premise that Iceberg is the "Hadoop of the modern data stack." Several commenters point out that Iceberg solves different problems than Hadoop, focusing on table formats and metadata management rather than distributed compute. Some suggest that tools like dbt are closer to filling the Hadoop role in orchestrating data transformations. Others argue that the modern data stack is too fragmented for any single tool to dominate like Hadoop once did. A few commenters express skepticism about Iceberg's long-term relevance, while others praise its capabilities and adoption by major companies. The comparison to Hadoop is largely seen as inaccurate and unhelpful.
The Hacker News post "Apache iceberg the Hadoop of the modern-data-stack?" generated a moderate number of comments, mostly discussing the merits and drawbacks of Iceberg, its comparison to Hadoop, and its role within the modern data stack. There isn't overwhelming engagement, but enough comments exist to provide some diverse perspectives.
Several commenters pushed back against the article's comparison of Iceberg to Hadoop. They argue that Hadoop is a complex ecosystem encompassing storage (HDFS), compute (MapReduce, YARN), and other tools, while Iceberg primarily focuses on table formats and metadata management. They see Iceberg as more analogous to Hive's metastore, offering a standardized way to interact with data lakehouse architectures, rather than being a complete platform like Hadoop. One commenter pointed out that drawing parallels solely based on potential "vendor lock-in" is superficial and doesn't reflect the fundamental differences in their scope.
Some commenters expressed appreciation for Iceberg's features, highlighting its schema evolution capabilities, ACID properties, and support for different query engines. They noted its usefulness in managing large datasets and its potential to improve the reliability and maintainability of data pipelines. However, other comments countered that Iceberg's complexity could introduce overhead and might not be necessary for all use cases.
A recurring theme in the comments is the evolving landscape of the data stack and the role of tools like Iceberg within it. Some users discussed their experiences with Iceberg, highlighting successful integrations and the benefits they've observed. Others expressed caution, emphasizing the need for careful evaluation before adopting new technologies. The "Hadoop of the modern data stack" analogy sparked debate about whether such a centralizing force is emerging or even desirable in the current, more modular and specialized data ecosystem. A few comments touched on alternative table formats like Delta Lake and Hudi, comparing their features and suitability for different scenarios.
In summary, the comments section provides a mixed bag of opinions on Iceberg. While some acknowledge its potential and benefits, others question the comparison to Hadoop and advocate for careful consideration of its complexity and suitability for specific use cases. The discussion reflects the ongoing evolution of the data stack and the search for effective tools and architectures to manage the increasing volume and complexity of data.