Va

Vaero – Build fast log pipelines in Python

Hacker News

Vaero – Build fast log pipelines in Python

We’ve spent the last few months building Vaero and are now sharing the beta for community feedback: https://github.com/vaerohq/vaero . Vaero is the first log shipper for developers to write log pipelines with code. You write pipelines in Python, and the pipelines are converted into task graphs that run super fast in Go. Why we built this: - Current log shippers like Fluentd, Logstash, and Vector.dev only let you specify log pipelines with config files (YAML, JSON, TOML) or domain specific languages (VRL) - This works for simple pipelines but is unwieldy for even mildly complex use cases - It’s annoying for someone on the team to have to learn the specific config file format or DSL of the tool - You can’t use the code-based development tools you’re used to, like IDEs, version control, git, and so on Our beta version lets you: - Write log pipelines in Python using a Spark-like syntax - Pipelines run blazing fast in Go - Native integrations to collect from log sources like HTTP, Okta, and more and write to destinations like S3 - Built-in transformation functions to reshape and normalize log data - Single binary running on a Docker - Open source We’d love to hear your feedback and opinions. Feel free to try us out for free - and if there’s a specific integration you want, let us know. Github: https://github.com/vaerohq/vaero

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, single, using · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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