Implementing a Custom date_bucket() Function in PostgreSQL for Timestamp Bucketing

The date_bucket() function in PostgreSQL is a powerful tool for time series analysis. It allows you to group timestamps into fixed-size intervals, often referred to as “buckets.” This grouping is useful for aggregating and analyzing data over consistent time periods, such as hours, days, or weeks.


The primary purpose of date_bucket() is to partition a sequence of timestamp values into regular intervals. This helps in:

  • Time Series Analysis: This involves detecting trends, patterns, and anomalies over time.
  • Data Aggregation: This involves summarizing data points within specified time periods for reporting or visualization purposes.
  • Performance Monitoring: This involves tracking metrics over fixed intervals to monitor and enhance performance.system performance.

How date_bucket() Works

Conceptually, here’s how date_bucket() operates:

  1. Input Parameters:
  • Interval (bucket_width): This represents the duration of each bucket (for instance, ‘1 hour’, ‘1 day’).
  • Timestamp (ts): This is the timestamp that will be grouped into a bucket.
  1. Conversion to Epoch Time:
  • The function initially converts the timestamp to the number of seconds since the Unix epoch (1970-01-01 00:00:00 UTC), also known as epoch time.
  1. Bucket Calculation:
  • The epoch time is divided by the length of the interval to determine the bucket into which the timestamp falls.
  • The division result is floored, or rounded down, to ensure the timestamp is assigned to the start of the interval.
  1. Conversion Back to Timestamp:
  • The result, once floored, is multiplied by the interval length and then converted back to a timestamp. This provides the starting time of the bucket.

date_bucket() implementation with example

Create the custom date_bucket() function:

Create the events table:

Insert sample data:

Query using the custom date_bucket() function:


date_bucket() in PostgreSQL is conceptually a tool for grouping timestamps into fixed-size intervals. This function is invaluable for time series analysis, allowing for efficient data aggregation, consistent bucketing, and simplified query writing. By transforming raw timestamps into regular intervals, date_bucket() enables clearer insights and more effective data management.

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About Shiv Iyer 455 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.