"ASOF JOIN: Bridging the Time Gap in Data Analysis"

You must have been hearing more and more about a new type of JOIN called ASOF JOIN. Modern databases are adding this feature to their systems. All time series databases definitely have it. If the database you're using has it, you can directly use it; if it doesn't, there are query patterns to achieve the same result. Below is an ELI5 explanation to build the concept, and then we'll move on to real-world examples.

Picture this: You and your best friend each have a photo album. Your album is full of snapshots from your daily life, while your friend's album captures their own adventures. Every photo has a date scribbled underneath. One day, you get curious. You want to see what your friend was up to on the days you took your pictures. But there's a catch – your friend didn't snap photos on the exact same days as you. This is where ASOF JOIN comes in. It's a smart helper that looks at your photo and then finds the most recent picture of your friend before or on that same date.

Now, expand this idea to time series, databases, and SQL. ASOF JOIN allows you to combine two datasets based on timestamps, matching records from one dataset with the most recent corresponding records from another dataset, even when the timestamps don't align perfectly.

Why is it important for time series data?

• Handling real-world data inconsistencies: Time series data often comes from different sources with varying update frequencies. ASOF JOIN helps reconcile these differences by finding the most relevant matches based on time.
• Enabling point-in-time analysis: It allows analysts to reconstruct the state of data at any given point in time, which is crucial for accurate historical analysis and backtesting.

How does ASOF JOIN work?

• It takes two datasets, each with timestamp columns.
• For each record in the first dataset, it finds the most recent matching record in the second dataset that occurred at or before that time.
• It then combines the information from both datasets based on these matches.

In SQL, the traditional ways to achieve this were:

• Ranking Records Method: This method uses window functions to rank records based on their timestamps and then selects the most relevant record for each event.
• Range-based Method: This method defines a range for each record in one dataset and joins records from the second dataset that fall within this range. Here, we have to build an end date on the dataset to be joined first.

But the join keyword 'ASOF JOIN' takes it to the next level. For example, consider DuckDB:

SELECT *
FROM table1
ASOF JOIN table2
USING (timestamp_column, matching_column);        

It does a lot of magic and heavy lifting behind the scenes to make it work with an intuitive syntax. There are different variants of syntax in different databases, but the goal is to work with time series data without putting in too much effort.

Some practical scenarios where it's useful:

• Stock Market Analysis: Match trades with the most recent stock price quotes at the time of each trade.
• Weather Data Analysis: Associate hourly weather readings with the latest weather alerts for each station.
• E-commerce Order Processing: Link customer orders with the most recent applicable promotions based on order dates.
• IoT Sensor Calibration: Connect sensor readings to the latest calibration data to ensure accuracy.
• Financial Risk Assessment: Match portfolio positions with the latest risk factor data for accurate valuation.
• Supply Chain Management: Associate shipments with the most recent inventory levels to manage stock effectively.
• Healthcare Patient Monitoring: Link patient vital signs with the latest medication administration records for better care tracking.