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Dev tools · September 23, 2021

Logtail

Query your logs like you query your database

Visit site ↗Alternativestop 8% of September 2021

What it does

ClickHouse-based SQL-compatible log management. Log more, pay less.

Does the same job

all alternatives →
  • LC
    Logtail – ClickHouse-Based Log Management2021 · logtail.com · ▲43
  • GE
    GraphJSON – Easily log and analyze events using ClickHouse2022 · graphjson.com · ▲128

    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…

  • FS
    FSQL – Search through your file system with SQL-esque queries2017 · github.com · ▲341
  • CL
    ClickHouse-local – a small tool for serverless data analytics2023 · clickhouse.com · ▲289

    Me with my friend Vitaly Ludvichenko made an experiment to combine ClickHouse server and client to make a self-contained program running a database engine and processing data without a server: https://github.com/ClickHouse/ClickHouse/pull/150 Development continued in the past 6 years, and now clickhouse-local becomes a swiss-army knife for data processing. Say "ffmpeg" for datasets and more. It can resemble textql, octosql, dsq, duckdb, trdsql, q, datafusion-cli, spyql, but has better capabilities and performance. Here is a tutorial:…

  • WB
    We built a ClickHouse-based logging service2023 · github.com · ▲192

    Hey hn! I'm one of the co-founders of highlight.io, an open source monitoring tool. Today we're sharing a ClickHouse-based logging solution we've been working on. We wanted to showcase how we built it and share how you could try it out to give feedback. Since we started working on highlight.io, we've been hyper-focused on "cohesion", or ensuring that when you install your monitoring stack, all of the resources in that stack (user interactions, requests, traces, logs, etc.) are connected in a consumable way. We've written up more about our philosophy on this here [1]. We started building…

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