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VictoriaLogs v1.0.0 Release

Hacker News

VictoriaLogs v1.0.0 Release

Hi, HN friends! I'm pleased to announce production-ready release of VictoriaLogs - open source database for logs! It has the following features: - It is easy to manage - just a single small executable without external dependencies, which just works. There is no need in complex configuration and tuning - it works optimally with any logs - structured and/or unstructured (aka plaintext logs) - out of the box. - It accepts logs from all the popular log collectors and shippers - see https://docs.victoriametrics.com/victorialogs/data-ingestion... , so you can try it alongside the existing database for logs, without significant changes in your setup. - It needs up to 30x less RAM than Elasticsearch for the same amounts of logs, while providing the same performance for full-text search. - It needs up to 15x less disk space for storing the ingested logs than Elasticsearch. - It provides easy yet powerful and fast query language for typical log analysis tasks, including analytical tasks - LogsQL. See https://docs.victoriametrics.com/victorialogs/logsql/ - It is optimized for storing and querying wide events (aka logs with hundreds of high-cardinality fields). - It provides an excellent integration with traditional command-line tools such as grep, awk, less, tail, etc. See https://docs.victoriametrics.com/victorialogs/querying/#comm... - It provides tools for generating metrics and alerts from the ingested logs - see https://docs.victoriametrics.com/victorialogs/vmalert/ - It is optimized for efficient storing and querying large volumes of logs (e.g. tens of terabytes and more). Try VictoriaLogs alongside your existing solution and then decide whether it is worth switching to VictoriaLogs!

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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, tasks, open · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
41%41% 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
11%11% 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.

Incorrect prediction on native model

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