samanthasu

That's why the whole server is under attack??

any details?

Great blog! I was encountering this issue with the buffer limits. Will try it later.

Vector, a Rust-based observability data pipeline, faces challenges with mismatched upstream and downstream processing rates, often exceeding the capacity of databases like Elasticsearch or ClickHouse. Traditional static rate limiting fails to adapt to dynamic system conditions, leading to resource underutilization or overload. This blog introduces how Vector adjusts the request rate and maximizes resource utilization

great one! and i would recommnd this hands-on guide for diagnosing memory leaks in Rust applications. it explains how to enable heap profiling in 'jemalloc', collect memory allocation data, and generate flame graphs for analysis. https://greptime.com/blogs/2024-01-18-memory-leak#diagnosing...

This seminar talks about: - Extending Apache DataFusion: PromQL & Elasticsearch query string support - Performance extensions: Windowed Sort - Architectural extensions: Distributed queries - Query type extensions: Geo / Vector / JSON / and more

this one explains why certain libraries perform better and offers practical advice for choosing the right tool for your use case

Superficial vs. Insightful Synthesizing research findings into actionable insights is profoundly shaped by the researcher’s domain knowledge and perspective, determining if the reports are merely superficial or truly insightful. Tip: Deepen your domain knowledge and develop strategic thinking skills to enhance the quality of your insights.

Researcher vs. AI Tool Researchers will be easily replaced by AI if they cannot analyze the reasons behind the data and provide valuable recommendations. Tip: Leverage AI for fundamental research tasks, focus on strategic thinking and actionable insights that impact decision-making within your organization.

Data-Driven vs. Personal Interest People often accept research findings only when they match their beliefs, resulting in decisions swayed by stakeholders' interests rather than actual data. Tip: Align your research with business goals and use storytelling to connect findings and insights to these goals with concrete evidence.

Cost vs. Value Research can be expensive, and its value is often indirect and unrecognized, resulting in a low priority for organizations. Tip: Set measurable metrics for your research and demonstrate its impact, such as # unmet needs identified, # recommendations implemented, and # new features or product changes based on research, NPS, revenue increase, etc.

Mine is this one https://greptime.com/blogs/2024-05-07-error-rust Quite informative

same same

looking forward to the bonus content!

I’ve been thinking a lot about the challenges involved in scaling systems for observability (metrics, logs, and traces). Many of the existing solutions for time-series storage, while powerful, still leave a lot to be desired when it comes to combining high performance with low operational overhead

would love to see what Andy's take on GreptimeDB https://github.com/GreptimeTeam/greptimedb

That's quite tricky to describe because it's just always different in everyone's experience. I would say it's a good start if you can get your first deal from your network or friends. And then you need some pilot cases to support your product success. And you need a long run in building your product reputation, your SEO ranking, etc. Then it seems that it can be a bit easier to get customers...

still on this way as well :)

This is the version without any post editing...Super

I am Sam. And I wanna say 2024 is definitely a challenging year to me as I was laid off and a lot of changes in this year. My fav memory from Xmas would be the year of 2019, celebrating with my bff in winter wonderland, drinking, laughing, and we were really happy since we were still students with no worries at all. Wish time could go back

I guess it would be a great combo of speed and quality. Alrealy applied

If the battery can work so long, I'd love to use it for my quartz watch!!

Lilian's latest blog about the reward hacking in reinforcement learning. It's more about the practical solutions research instead of how to define reward hacking.

That is excellent visualization!

A good error report is not only about how it gets constructed, but what is more important, to tell what human can understand from its cause and trace. In this example, we analyzed and showed how to design stacked errors and what should be considered in this process.