LynxDB - I wanted Splunk's query language without Splunk

Evgenii Orlov — Tue, 26 May 2026 14:04:59 +0000

I've spent years staring at access and application logs, and the tool I missed most was Splunk's SPL - piping raw logs through transforms and aggregations and just exploring. But Splunk is heavy and expensive, and most lightweight alternatives optimize storage and search rather than the "sit in the data and keep re-asking the question" workflow.

So I built LynxDB: a single Go binary with a pipe-based, SPL-style query language (Lynx Flow). Two things I care about:

The same binary is a grep-like pipe filter and a server. cat app.log | lynxdb query '...' needs no server or config; lynxdb server adds indexing, retention and materialized views.
Logs stay raw and you parse/aggregate at read time, so you can turn messy semi-structured text into metrics in a couple of lines.

To be upfront about alternatives: VictoriaLogs is more mature and more resource-efficient at scale; ClickHouse will beat it on heavy analytical queries. LynxDB's bet is the exploratory workflow and the zero-infrastructure pipe mode, not out-performing those.

It's early v0.2.5, solo, not production-ready. Honest criticism very welcome, especially on the query language.

https://github.com/lynxbase/lynxdb

I built pq - the jq of Parquet. Here's why data engineers need a better CLI

Evgenii Orlov — Wed, 25 Mar 2026 14:45:09 +0000

I got tired of spinning up DuckDB or writing throwaway Python just to peek inside a Parquet file. So I built pq - a single binary CLI (Rust) that handles the full Parquet workflow from your terminal

Quick taste:

pq data.parquet — metadata, schema, compression, row groups at a glance
pq head -n 5 -c id,name s3://bucket/data.parquet — preview specific columns directly from S3
pq schema extract --ddl postgres data.parquet — generate CREATE TABLE (supports Postgres, ClickHouse, DuckDB, Spark, BigQuery, Snowflake, Redshift, MySQL)
pq check --contract contract.toml data/ — validate file structure and data contracts in CI
pq schema diff a.parquet b.parquet — catch schema drift between files
pq compact data/ -o s3://bucket/compacted/ — merge small files into optimal sizes
pq convert raw/*.csv -o parquet/ — batch convert CSV/JSON to Parquet

It auto-detects output format (table on TTY, JSON when piped), supports glob patterns, and works with S3, GCS, Azure Blob, and Cloudflare R2.

Install: brew install OrlovEvgeny/pq/pq or cargo install pq-parquet

What I'd love feedback on: What's your current Parquet inspection workflow? What commands would make this indispensable for your day-to-day?

GitHub: https://github.com/OrlovEvgeny/pq

DEV Community: Evgenii Orlov

LynxDB - I wanted Splunk's query language without Splunk

I built pq - the jq of Parquet. Here's why data engineers need a better CLI