greptimedb

Hi HN — I’m the creator of GreptimeDB. We just shipped v1.0.0-rc.1 (our first 1.0 release candidate / feature-freeze milestone). Highlights: Online region repartition (SPLIT/MERGE) to adjust partitions at runtime (distributed clusters; shared object storage + GC). Faster Metric Engine PK filters via memcomparable byte comparisons (microbench: ~20–90× on dense codec). PromQL planner prefers TSID (skips unnecessary label columns). Would love feedback from folks running observability/time-series workloads, especially on upgrade paths + repartition behavior in real clusters. GitHub releases/changelog: https://github.com/GreptimeTeam/greptimedb/releases

We also started with the typical kube-prometheus-stack, but we don’t like Prometheus/PromQL. Moreover, it only solves the „metrics“ part - to handle logs and traces, more quite heavy and complex components have to be added to the observability stack. This didn‘t feel right, so we looked around and found greptimedb https://github.com/GreptimeTeam/greptimedb, which simplifies the whole stack. It‘s designed to handle metrics, logs, and traces. We collect metrics and logs via OpenTelemetry, and visualize them with Grafana. It provides endpoints for Postgres, MySQL, PromQL; we‘re happy to be able to build dashboards using SQL as that’s where we have the most knowledge. The benchmarks look promising, but our k8s clusters aren’t huge anyway. As a platform engineer, we appreciate the simplicity of our observability stack. Any other happy greptime

would love to see what Andy's take on GreptimeDB https://github.com/GreptimeTeam/greptimedb

was hoping to see https://github.com/GreptimeTeam/greptimedb on the list maybe 2026, or some bonus content for 2025 :) https://www.greptime.com/blogs/2024-09-09-report-summary

I believe the primary issue with using full-text search in PostgreSQL is its performance and scalability limitations. Elasticsearch/OpenSearch offers more than just search functionality; they come with extensive ecosystems, including ELK for logging and processors for ETL pipelines. These platforms are powerful and provide out-of-the-box solutions for developers. However, as the article mentioned, scaling them up or out can be costly. ParadeDB looks like is based on tantivy(https://github.com/quickwit-oss/tantivy) which is an impressive project. We leverage it to implement full-text indexing for GreptimeDB too (https://github.com/GreptimeTeam/greptimedb). Nevertheless, building full-text indexes with Tantivy is still resource-intensive. This is why Grafana Loki, which only indexes tags instead of building full-text indexes, is popular in

Thanks for your sharing. I was ever working actively on Apache Kvrocks, an alternative for Redis on Flash. And we benefit a lot from Redis docs to catch up its command. In comparison, Kvrocks docs is clearly "less than awesome". In my $DAYJOB, I convince my boss that the docs, at least the README is important, as you show the esbuild example here. Here are two patches to improve the README of it: * https://github.com/GreptimeTeam/greptimedb/pull/3528 * https://github.com/GreptimeTeam/greptimedb/pull/3522 That in #3522 firstly, I tried to reduce the content and "offload" the detailed docs into the doc site. And in #3528, I found that heading elements center align is still fancy and we need some short, clear introduction and advantages. Anyway, a good product is the leading 1 and docs is the following 0. Without a good product, no good docs

If I get "local cache layer" correctly, it's possible. And it's even desired if you want to reduce the deployment burden. Here are some related codes on how we implement such a layer in GreptimeDB: * https://github.com/GreptimeTeam/greptimedb/blob/v0.7.0/src/o... * https://github.com/GreptimeTeam/greptimedb/blob/v0.7.0/src/m...

This is truly a fantastic combination -- implement the logic in Rust and use it in Python. GreptimeDB also implements a similar functionality that allows writing Python script to do post-process of SQL query results, with the help of RustPython and Arrow. Maybe this combination can bring a sweet point between performance and efficiency. docs: https://docs.greptime.com/user-guide/coprocessor-and-scripti... code: https://github.com/GreptimeTeam/greptimedb/tree/develop/src/...