Columnar store for fast, lightweight logging

Hacker News

Founder here, Parseable is a lightweight log ingestion and query engine written in Rust. Parseable can ingest data from existing logging agents (FluentBit, LogStash, Vector, syslog-ng and more) using HTTP + JSON output. Ingested logs are stored as semi-indexed Parquet files (on disk or S3). You can query the data with builtin query engine using SQL or use a query engines of choice like Spark, Presto, Trino and so on. We also developed a Grafana data source plugin that lets you visualise log data via Grafana. Sample dashboard link in readme. As log data grew, our industry has responded with SaaS and fully managed offerings, but if you're looking for freedom, interoperability and full control over your data - there are no great options. With Parseable, we are looking to provide an strong, viable and FOSS alternative.

Visit product View on Hacker NewsJan 18, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, visual · Missing: mac, macos, cursor

78%78% 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: exist, existing, ide · Missing: https docs, excited, just released

63%63% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% 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

45%45% 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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

20%20% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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