What is an ASOF Join?

Time series data is not always perfectly aligned. Timestamps may be slightly off, or there may be a delay between cause and effect. This can make connecting two sets of ordered data challenging. ASOF joins are a tool for solving this and other similar problems.

One of the problems that ASOF joins are used to solve is finding the value of a varying property at a specific point in time.

More generally, however, ASOF joins embody some common temporal analytic semantics, which can be cumbersome and slow to implement in standard SQL.

Stock Portfolio Example Data Set

Let’s start with a Stock Portofolio example.

We have a table stock_prices of stock prices with timestamps:

stock_prices

SYMBOL PRICE STOCK_PRICE_ASOF
TSLA 9.93 2024-02-02 14:29:10.753
TSLA 9.62 2024-02-02 14:29:52.806
TSLA 9.61 2024-02-02 14:30:43.379
TSLA 9.93 2024-02-02 14:32:35.397
TSLA 9.68 2024-02-02 14:41:38.263
TSLA 9.05 2024-02-02 14:45:00.480
TSLA 9.06 2024-02-02 14:45:32.556
TSLA 9.88 2024-02-02 15:04:29.979
TSLA 9.72 2024-02-02 15:06:40.404
TSLA 9.44 2024-02-02 15:23:07.893
TSLA 9.13 2024-02-02 15:23:21.083

We have another table stock_portfolio containing portfolio holdings at various points in time:

stock_portfolio

SYMBOL NUMBER_OF_SHARES TRANSACTION_TIMESTAMP
TSLA 100.00 2024-02-02 14:29:20.264
TSLA 77.00 2024-02-02 14:30:51.040
TSLA 56.00 2024-02-02 14:45:41.192
TSLA 86.00 2024-02-02 15:06:48.475
TSLA 60.00 2024-02-02 15:23:12.587

Note that the transaction_timestamp from stock_portfolio does not align with the stock_price_asof in the stock_prices table.

ASOF Joins in Snowflake

We can compute the value of each holding at that point in time by finding the most recent price before the holding’s timestamp by using an ASOF join.

SQL Query

select 
  stock_portfolio.symbol
  , price as stock_price
  , number_of_shares
  , transaction_timestamp
  , stock_price_asof  
  , price * number_of_shares as value
from stock_portfolio
asof join stock_prices
  match_condition (stock_portfolio.transaction_timestamp >= stock_prices.stock_price_asof)
  on stock_prices.symbol = stock_portfolio.symbol ;

This attaches the value of the holding at that time to each row.

Query Output

SYMBOL STOCK_PRICE NUMBER_OF_SHARES TRANSACTION_TIMESTAMP STOCK_PRICE_ASOF VALUE
TSLA 9.93 100.00 2024-02-02 14:29:20.264 2024-02-02 14:29:10.753 993.0000
TSLA 9.61 77.00 2024-02-02 14:30:51.040 2024-02-02 14:30:43.379 739.9700
TSLA 9.06 56.00 2024-02-02 14:45:41.192 2024-02-02 14:45:32.556 507.3600
TSLA 9.72 86.00 2024-02-02 15:06:48.475 2024-02-02 15:06:40.404 835.9200
TSLA 9.44 60.00 2024-02-02 15:23:12.587 2024-02-02 15:23:07.893 566.4000

It essentially executes a function defined by looking up nearby values in the stock_prices table.

Windowing Alternative

Standard SQL can implement this kind of join, but you need to use an effectivity aka state table using a windowing function as following:

with stock_price_effectivity as (
  select 
    symbol
    , stock_price_asof as start_time
    , lead (stock_price_asof) over (partition by symbol order by start_time asc) as end_time 
    , price
  from stock_prices
)   

select 
  stock_portfolio.symbol
  , price
  , number_of_shares * price as value
from state
inner join stock_portfolio on state.symbol = stock_portfolio.symbol
  and stock_portfolio.transaction_timestamp between start_time and end_time;

Note that we are first calculating the effectivity or state of stock price at each of the interval using the stock_price_asof timestamp. This is an expensive operation.

Here is what the output of the stock_price_effectivity will look like:

SYMBOL START_TIME END_TIME PRICE
TSLA 2024-02-02 14:29:10.753 2024-02-02 14:29:52.806 9.93
TSLA 2024-02-02 14:29:52.806 2024-02-02 14:30:43.379 9.62
TSLA 2024-02-02 14:30:43.379 2024-02-02 14:32:35.397 9.61
TSLA 2024-02-02 14:32:35.397 2024-02-02 14:41:38.263 9.93
TSLA 2024-02-02 14:41:38.263 2024-02-02 14:45:00.480 9.68
TSLA 2024-02-02 14:45:00.480 2024-02-02 14:45:32.556 9.05
TSLA 2024-02-02 14:45:32.556 2024-02-02 15:04:29.979 9.06
TSLA 2024-02-02 15:04:29.979 2024-02-02 15:06:40.404 9.88
TSLA 2024-02-02 15:06:40.404 2024-02-02 15:23:07.893 9.72
TSLA 2024-02-02 15:23:07.893 2024-02-02 15:23:21.083 9.44
TSLA 2024-02-02 15:23:21.083   9.13

See Also