SQL Query Optimization Using TimescaleDB Continuous Aggregates

This is the first half of the videos on continuous aggregates. Learn the basics of continuous aggregates, and how to create them. But, note that this is only half of what you need to do to get your continuous aggregates up and running.

Make sure to check out the next video on how to set up your continuous aggregate refresh policy! Goodbye, long-running PostgreSQL queries 👋

🛠 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀

🐯 𝗔𝗯𝗼𝘂𝘁 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲

💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲!

📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀:
⏱ 0:00 ⇒ Introduction
⏱ 0:59 ⇒ Understanding continuous aggregates
⏱ 6:22 ⇒ Create a continuous aggregate query
⏱ 9:49 ⇒ Create continuous aggregate
⏱ 11:58 ⇒ Outro

[AlexxxeyS]

Thank you for your detailed and clear explanation.

[vimalaathythan]

my table has june 1 and 2 data. but when i create a materialized view it generates may 31 itself.
CREATE MATERIALIZED VIEW daily_avg_temperature
WITH (timescaledb.continuous) AS
SELECT time_bucket(INTERVAL '1 day', time_column) AS day,
device,
AVG(max_temperature)
FROM pandas
GROUP BY day, device;
what is the issue

[servercookie]

Dear TimescaledB, does TimescaleDB support joined function when it comes to continuous agreegates ?