@questdb.bsky.social

Real-time analytics is still too complex—too many tools, too much glue. Are we close to a true all-in-one solution? #QuestDB 's Marko Topolnik explores how materialized views simplify things + compares QuestDB, ClickHouse, TimescaleDB & InfluxDB. questdb.com/blog/realtim...

Real-time analytics with an all-in-one system: Are we there yet? | QuestDB

Explore how modern databases are evolving into unified real-time analytics platforms. Compare TimescaleDB, ClickHouse, InfluxDB, and QuestDB's approaches to handling both historical and streaming…

questdb.com

Somewhere in a database... a lonely little row from 2017 still holds out for hope. QuestDB’s TTL helps old data move on. Listen, row, don't think of it as deletion... More like closure. We just need some time for ourselves (and our more recent data). questdb.com/docs/concept...

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MCP is shiny and new. Vector databases are shiny and new, too. What can a vector DB do that a high-performance DB + frontier model via MCP can’t? Genuine inquiry! — Please share your use case.

A common typo: WAL log (Write-Ahead Log log) Or is it?! Write-Ahead Logs (WAL) actually do log twice. First to the WAL. Then to the DB. The more you know! 🌈

New in QuestDB docs: full breakdown of our architecture. Learn how we achieve high-performance on time-series data — from storage and ingestion to the query engine. Built from scratch in low-latency Java, C++ & Rust. 📘 Explore it here: questdb.com/docs/guides/...

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Tired of heavy SAMPLE BY queries slowing down dashboards in QuestDB? Use materialized views to precompute OHLC & other aggregates. They auto-refresh on ingest for blazing-fast reads! ⚡ Learn how 👉 questdb.com/blog/how-to-... #SQL #QuestDB

How to create a materialized view | QuestDB

Learn what a materialized view is, its benefits and drawbacks, and how to quickly create materialized views in QuestDB to supercharge your aggregation queries with incremental refresh.

questdb.com

Asking an LLM for an exact price point is like asking a poet for your bank balance. You might get a moving response… But it won’t be right. “ETH price on March 8, 2022?” “Twas fair and volatile, sir.” Hmm... You'll need a database. questdb.com/blog/why-ai-...

Why AI needs a database | QuestDB

AI won't replace databases — but it will transform how we use them. This article explores why AI and databases work better together, breaking down LLM tokenization, real-time data access, and…

questdb.com

QuestDB now supports Oracle-style comments, so you can add helpful annotations… Or sneak messages to your coworkers. 👀 ☕️

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Structured data is very sneaky! Just ask your favourite LLM. 1GB of text = ~250M tokens 1GB of structured trades = ~1B tokens That’s 4× the token cost for less semantic richness. Our new agentic friends have many capabilities, chewing structured data not among them. questdb.com/blog/why-ai-...

Why AI needs a database | QuestDB

AI won't replace databases — but it will transform how we use them. This article explores why AI and databases work better together, breaking down LLM tokenization, real-time data access, and…

questdb.com

Hammer! Shooting Star! Bullish Engulfing! Piercing Line! While these sound like gnarly video game moves, they are in fact common patterns in OHLC candlestick charts. Learn more about them from our glossary: questdb.com/glossary/ohc... -- and checkout #QuestDB if your charts need a speed boost.

The Great Guide to OHLC Candlesticks | QuestDB

Clear visual examples and a comprehensive guide to OHLC Candlesticks. Learn about its sources, storage, processing, and applications in high-frequency trading and market analysis. All you need in…

questdb.com

One of the use cases materialized views unlock is faster Klines —candlestick— charts. In our latest blog post we show an example of ingesting market data from Polygon.io, creating cascading materialized views, then using Grafana.com to display a real-time dashboard. questdb.com/blog/kline-c...

Building K-line (Candlestick) Charts with QuestDB and Grafana | QuestDB

In this tutorial, we will stream real-time crypto data from polygon.io and utilize QuestDB’s new materialized view functionality to create aggregated OHLC tables efficiently. We will then visualize th...

questdb.com

And that, kids, is how PHP made it into our codebase. A user on PHP hit a QuestDB 8.2.2 issue. We gave an immediate workaround, then @jerrinot.bsky.social shipped a permanent fix 11 days later—with a new PHP test runner for coverage on par with Python or Rust. Community collaboration FTW!

Screenshot from slack, showing Jaromir saying:

That's excellent news :tada:
I included a PHP test runner in the fix. So from now on PHP PDO has the same test coverage as we have for Python, Rust or C#. This should prevent future regressions.
Many thanks for your help: The report, reproducer and now the confirmation - it all helped greatly.

Did you know you can orchestrate and schedule your QuestDB data workflows using Airflow with the PostgresOperator or Dagster with a resource using psycopg?

Airflow web console view showing a graph with four tasks: Convert_to_parquet (postgresOperator), detach_partition (postgresOperator), upload_to_s3 (python_operator), delete_folder (python_operator)Dagster web UI view showing a graph with five operations: get_execution_date, convert_to_parquet, detach_partition, upload_files_to_s3, and delete_folder