GraphJSON – Easily log and analyze events using ClickHouse

Hacker News

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 enables really efficient disk compression and fast queries. In many ways, you can think of GraphJSON as an easy way to explore ClickHouse without having to run and maintain your own clusters. I'd love for you to give it a try. You can generally start logging your data in under a minute. From there, you can either use the UI tooling to create graphs in a no-code way. Or if you're more advanced, you can use the SQL editor to do any query you can think of!

Visit product View on Hacker NewsAug 17, 2022

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

78%78% predicted probability of success on Indie Hackers, 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: clickhouse, io · Missing: https docs, excited, just released

68%68% 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: using, code · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

10%10% 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.

Correct prediction on native model

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