AI · alternatives · 2026
24 alternatives to LLM Based Spark Profiler
Hey HN, Spark event logs run into 100s of MBs and offer a wealth of insight into your workloads but making sense of them has always been quite a bit prohibitive. We’ve recently built a lightweight tool that automatically parses Spark event logs and surfaces targeted insights to help you optimize your data jobs. Whether you’re chasing down a bottleneck or balancing performance vs. cost, the profiler got you covered with real-time configuration recommendations, data skew analysis, and more. Curious how it works in action? Check out this quick Loom video for a walk-through:…
LLM Based Spark Profiler is a tool that automatically analyzes Spark event logs to help data engineers optimize their jobs. It parses large event logs and provides targeted insights including data skew analysis and… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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LLM Spark▲365Dev platform for building production ready LLM apps
2023 · yourgpt.ai · its alternatives →
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[I'm the author] Spall is a web-accessible profiler that I made to help my web-dev friends load gigabyte+ JSON traces without lunch-break-long load times. Recently, Spall got experimental support for auto-tracing with binary traces (along with an in-progress native-port, to give it more memory headroom), which was used to help track down and fix some hard-to-spot lock contention issues in the Odin-language compiler. I demoed it at the Handmade Seattle conference in October, https://guide.handmade-seattle.com/c/2022/spall/, with a head-to-head against Perfetto,…
2023 · gravitymoth.com · its alternatives →
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LLM Stats▲293I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Oct 2025 · llm-stats.com · its alternatives →
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Hey HN! We are happy to share Perforator – our internal cluster-wide profiler with great support for native languages and a built-in AutoFDO pipeline to simplify sPGO builds. Perforator allows you to profile most binaries without having to recompile or adjust the build process. We use it at Yandex to profile each pod inside a large cluster at modest speed (99Hz), collecting petabytes of profiles every day. There's a blog post about it at https://medium.com/yandex/yandexs-high-performance-profiler-.... Inspired by Google-Wide Profiling, we started continuous profiling…
2025 · github.com · its alternatives →
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Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024 · its alternatives →
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2013 · static.sparkinspector.com · its alternatives →
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Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
2024 · getindexify.ai · its alternatives →
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Hi HN, My name is JR and I had a need for a simple analytics solution that allowed me to store (timestamp, json) logs and run SQL over them. It was hard to find the right solution. Solutions like Mixpanel and Amplitude optimized for particular report types. Whereas solutions like Snowflake, BigQuery, etc. required a lot of setup. I built GraphJSON to fit in the middle. I strived for the ease of use of tools like Mixpanel and Amplitude, but wanted to ensure affordances were built to support use cases that big data warehouses enable. Under the hood, GraphJSON is powered by ClickHouse. This…
2022 · graphjson.com · its alternatives →
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Hey HN. We’re Jay and Vadim from Highlight.io (https://highlight.io). We’re building an open source [1] monitoring platform for web applications. Today we’re excited to be sharing a performance tool we’ve been working on, which helps you inspect the latency of code execution from the client to the server. As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had…
2024 · github.com · its alternatives →
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2015 · blog.memsql.com · its alternatives →
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DEPRECATED StackImpact Python Profiler - Production-Grade Performance Profiler: CPU, memory allocations, blocking calls, exceptions, metrics, and more - stackimpact/stackimpact-python
2017 · github.com · its alternatives →
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Easy to use GraphQL performance analysis utility for tracing resolver execution time - Econify/graphql-request-profiler
2022 · github.com · its alternatives →
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2011 · github.com · its alternatives →
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2021 · prodfiler.com · its alternatives →
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Hi Hacker News! We’re Vadim and Chris from Highlight.io [1]. We do web app monitoring and are working on using LLMs/embeddings to add new functionality to our error monitoring product. Given that there’s a lot of founders/engineers using LLMs in their products, we figured we’d share how we built the new functionality, their impact on our workflows, and how you can try it out. Our goal was to build two features: (1) tagging errors (e.g. deeming an error as “authentication error” or a “database error”); and (2) grouping similar errors together (e.g. two errors that have a different…
2023 · github.com · its alternatives →
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A lightweight engine for durable execution / deterministic workflows I built with Rust, wasmtime and the WASM Component Model. Its main use is running reliable, long-running workflows that can automatically resume after failures. Looking for feedback on the approach and potential use cases!
2025 · obeli.sk · its alternatives →
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LogStitch▲84Find AWS Lambda failures fast, right on your Mac
Jun 2026 · logstitch.app · its alternatives →
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Blunders is a web-based profiler of JVM applications. Understand and fix performance problems, faster.
2020 · blunders.io · its alternatives →
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Note: this is an update to [this](https://news.ycombinator.com/item?id=44568529) "Show HN" post. timep is a state-of-the-art [debug-]trap-based bash profiler that is efficient and extremely accurate. Unlike other profilers, timep records: 1. per-command wall-clock time 2. per-command CPU time, and 3. the hierarchy of parent function calls /subshells for each command the wall-clock + CPU time combination allows you to determine if a particular command is CPU-bound or IO-bound, and the hierarchical logging gives you a map of how the code actually executed. The standout…
2025 · github.com · its alternatives →
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We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com · its alternatives →
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An open source Next app template to monitor your AI apps
2025 · github.com · its alternatives →
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2016 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →