Terraform's lifecycle can sometimes lead to unexpected changes in attributes managed by providers, particularly when external factors modify them. This blog post explores strategies to prevent Terraform from reverting these intentional external modifications. It focuses on using ignore_changes within a resource's lifecycle block to specify the attributes to disregard during the plan and apply phases. The post demonstrates this with an AWS security group example, where an external tool might add ingress rules that Terraform shouldn't overwrite. It emphasizes the importance of carefully choosing which attributes to ignore, as it can mask legitimate changes and potentially introduce drift. The author recommends using ignore_changes sparingly and considering alternative solutions like null_resource or data sources to manage externally controlled resources when possible.
CEO Simulator: Startup Edition is a browser-based simulation game where players take on the role of a startup CEO. You manage resources like cash, morale, and ideas, making decisions across departments such as marketing, engineering, and sales. The goal is to navigate the challenges of running a startup, balancing competing priorities and striving for a successful exit, either through acquisition or an IPO. The game features randomized events that force quick thinking and strategic adaptation, offering a simplified but engaging experience of the pressures and triumphs of the startup world.
HN commenters generally found the CEO Simulator simplistic but fun for a short time. Several pointed out the unrealistic aspects of the game, like instantly hiring hundreds of engineers and the limited scope of decisions. Some suggested improvements, including more complex financial modeling, competitive dynamics, and varied employee personalities. A common sentiment was that the game captured the "feeling" of being overwhelmed as a CEO, even if the mechanics were shallow. A few users compared it favorably to other similar games and praised its clean UI. There was also a brief discussion about the challenges of representing startup life accurately in a game format.
ClickHouse excels at ingesting large volumes of data, but improper bulk insertion can overwhelm the system. To optimize performance, prioritize using the native clickhouse-client with the INSERT INTO ... FORMAT command and appropriate formatting like CSV or JSONEachRow. Tune max_insert_threads and max_insert_block_size to control resource consumption during insertion. Consider pre-sorting data and utilizing clickhouse-local for larger datasets, especially when dealing with multiple files. Finally, merging small inserted parts using optimize table after the bulk insert completes significantly improves query performance by reducing fragmentation.
HN users generally agree that ClickHouse excels at ingesting large volumes of data. Several commenters caution against using clickhouse-client for bulk inserts due to its single-threaded nature and recommend using a client library or the HTTP interface for better performance. One user highlights the importance of adjusting max_insert_block_size for optimal throughput. Another points out that ClickHouse's performance can vary drastically based on hardware and schema design, suggesting careful benchmarking. The discussion also touches upon alternative tools like DuckDB for smaller datasets and the benefit of using a message queue like Kafka for asynchronous ingestion. A few users share their positive experiences with ClickHouse's performance and ease of use, even with massive datasets.
Enterprises adopting AI face significant, often underestimated, power and cooling challenges. Training and running large language models (LLMs) requires substantial energy consumption, impacting data center infrastructure. This surge in demand necessitates upgrades to power distribution, cooling systems, and even physical space, potentially catching unprepared organizations off guard and leading to costly retrofits or performance limitations. The article highlights the increasing power density of AI hardware and the strain it puts on existing facilities, emphasizing the need for careful planning and investment in infrastructure to support AI initiatives effectively.
HN commenters generally agree that the article's power consumption estimates for AI are realistic, and many express concern about the increasing energy demands of large language models (LLMs). Some point out the hidden costs of cooling, which often surpasses the power draw of the hardware itself. Several discuss the potential for optimization, including more efficient hardware and algorithms, as well as right-sizing models to specific tasks. Others note the irony of AI being used for energy efficiency while simultaneously driving up consumption, and some speculate about the long-term implications for sustainability and the electrical grid. A few commenters are skeptical, suggesting the article overstates the problem or that the market will adapt.
The "World Grid" concept proposes a globally interconnected network for resource sharing, focusing on energy, logistics, and data. This interconnectedness would foster greater cooperation and resource optimization across geopolitical boundaries, enabling nations to collaborate on solutions for climate change, resource scarcity, and economic development. By pooling resources and expertise, the World Grid aims to increase efficiency and resilience while addressing global challenges more effectively than isolated national efforts. This framework challenges traditional geopolitical divisions, suggesting a more integrated and collaborative future.
Hacker News users generally reacted to "The World Grid" proposal with skepticism. Several commenters questioned the political and logistical feasibility of such a massive undertaking, citing issues like land rights, international cooperation, and maintenance across diverse geopolitical landscapes. Others pointed to the intermittent nature of renewable energy sources and the challenges of long-distance transmission, suggesting that distributed generation and storage might be more practical. Some argued that the focus should be on reducing energy consumption rather than building massive new infrastructure. A few commenters expressed interest in the concept but acknowledged the immense hurdles involved in its realization. Several users also debated the economic incentives and potential benefits of such a grid, with some highlighting the possibility of arbitrage and others questioning the overall cost-effectiveness.
bpftune is a new open-source tool from Oracle that leverages eBPF (extended Berkeley Packet Filter) to automatically tune Linux system parameters. It dynamically adjusts settings related to networking, memory management, and other kernel subsystems based on real-time workload characteristics and system performance. The goal is to optimize performance and resource utilization without requiring manual intervention or system-specific expertise, making it easier to adapt to changing workloads and achieve optimal system behavior.
Hacker News commenters generally expressed interest in bpftune and its potential. Some questioned the overhead of constantly monitoring and tuning, while others highlighted the benefits for dynamic workloads. A few users pointed out existing tools like tuned-adm, expressing curiosity about bpftune's advantages over them. The project's novelty and use of eBPF were appreciated, with some anticipating its integration into existing performance tuning workflows. A desire for clear documentation and examples of real-world usage was also expressed. Several commenters were specifically intrigued by the network latency use case, hoping for more details and benchmarks.
Summary of Comments ( 11 )
https://news.ycombinator.com/item?id=43454642
The Hacker News comments discuss practical approaches to the problem of Terraform providers sometimes changing attributes unexpectedly. Several users suggest using
ignore_changeslifecycle arguments within Terraform configurations, emphasizing its utility but also cautioning about potential risks if misused. Others propose leveraging thenullprovider or generating local values to manage these situations, offering specific code examples. The discussion touches on the complexities of state management and the potential for drift, with recommendations for robust testing and careful planning. Some commenters highlight the importance of understanding why the provider is making changes, advocating for addressing the root cause rather than simply ignoring the symptoms. The thread also features a brief exchange on the benefits and drawbacks of the presentedignore_changessolution versus simply overriding the changed value every time, with arguments made for both sides.The Hacker News post "Ignoring unwanted Terraform attribute changes" discussing the linked blog post has generated several comments. Many revolve around the complexities and frustrations of managing Terraform state, particularly when dealing with external forces modifying resources managed by Terraform.
One commenter highlights the common scenario where a provider might default a value that the user didn't explicitly set, leading to Terraform wanting to revert it on the next apply. They suggest this is especially problematic when combined with
for_eachresources. They appreciate the blog post's solution usingignore_changeslifecycle meta-argument but express a desire for Terraform to handle this more elegantly by default. This sentiment of wishing for better default behavior from Terraform echoes through other comments as well.Another user mentions the struggles of managing resources where underlying providers or external systems might alter values outside of Terraform's purview. They describe their current strategy of manually editing the state file which, while functional, is clearly not ideal. They see the
ignore_changesapproach as a much cleaner and more maintainable way to handle these situations.The discussion then delves into the nuances of when to utilize
ignore_changes. One participant cautions against overusing it as a catch-all solution. They emphasize the importance of understanding why a value is drifting and whether ignoring the change is truly the appropriate course of action. They suggest investigating if a provider's default behavior can be configured or if the external system modifying the resource can be adjusted. Ignoring changes should be a conscious decision made with full awareness of the potential implications.Another commenter reiterates this caution, pointing out that blindly using
ignore_changescould mask legitimate problems and create unexpected side effects down the line. They suggest treating it as a temporary fix while a more robust solution is investigated.Some users suggest alternative approaches like using
nullvalues for certain attributes to avoid conflicts or leveraging theprevent_destroylifecycle argument to prevent accidental deletion of resources. These suggestions highlight the various tools available in Terraform for managing state drift, but also reinforce the complexity of choosing the right approach for a given scenario.Finally, a commenter touches upon the broader issue of state management in Infrastructure-as-Code and expresses hope for future improvements in Terraform that could simplify these kinds of challenges.