Aggregating Time-Series Data: 5-Minute Intervals in SQL
Analyzing time-series data often requires grouping data into specific intervals. This guide demonstrates how to group data into 5-minute intervals using SQL, addressing a scenario where data needs to be aggregated within a defined timeframe. The example uses data from 'time' and 'id' tables, counting occurrences of the name 'John'. The challenge lies in moving from individual timestamp grouping to 5-minute interval aggregation.
Solutions for Different Database Systems
The optimal approach varies depending on your database system. Here are solutions for PostgreSQL and MySQL:
PostgreSQL
PostgreSQL offers a flexible approach using extract('epoch')
to get the Unix timestamp (seconds since epoch) and INTERVAL
:
SELECT date_trunc('minute', timestamp) + INTERVAL '5 minutes' * (extract(minute from timestamp)::int / 5) AS five_minute_interval, name, COUNT(b.name) FROM time a, id b WHERE ... -- Your WHERE clause here GROUP BY five_minute_interval, name ORDER BY five_minute_interval;
This query first truncates the timestamp to the minute using date_trunc
. Then, it calculates the 5-minute interval by adding multiples of 5 minutes based on the minute of the original timestamp.
MySQL
MySQL provides a simpler solution using UNIX_TIMESTAMP()
and integer division:
SELECT FROM_UNIXTIME(FLOOR(UNIX_TIMESTAMP(timestamp) / 300) * 300) AS five_minute_interval, name, COUNT(b.name) FROM time a, id b WHERE ... -- Your WHERE clause here GROUP BY five_minute_interval, name ORDER BY five_minute_interval;
This query converts the timestamp to a Unix timestamp, performs integer division by 300 (seconds in 5 minutes), and then converts the result back to a timestamp using FROM_UNIXTIME()
.
Both queries group the results by the calculated 5-minute interval and the name, providing the desired aggregated output. Remember to replace ...
with your specific WHERE
clause. The ORDER BY
clause ensures chronological presentation of results.
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