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ASOF JOIN docs: update example data, simplify queries #116

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Expand Up @@ -61,169 +61,272 @@ Read more about execution order in the

## ASOF JOIN

`ASOF JOIN` joins two different time-series measured.
`ASOF JOIN` joins two time-series on their timestamp, using the following
logic: for each row in the first time-series,

For each row in the first time-series, the `ASOF JOIN` takes from the second
time-series a timestamp that meets both of the following criteria:

- The timestamp is the closest to the first timestamp.
- The timestamp is **strictly prior or equal to** the first timestamp.
1. consider all timestamps in the second time-series **earlier or equal to**
the first one
2. choose **the latest** such timestamp

### Example

Given the following tables:
Let's use an example with two tables:

Table `buy` (the left table):
- `trades`: trade events on a single stock
- `order_book`: level-1 order book snapshots for that stock

<div className="pink-table">
`trades` data:

<div className="blue-table">

| timestamp | price |
| --------------------------- | -------- |
| 2024-06-22T00:00:00.039906Z | 0.092014 |
| 2024-06-22T00:00:00.343909Z | 9.805 |
| 2024-06-22T00:00:00.349387Z | 134.56 |
| 2024-06-22T00:00:00.349387Z | 134.56 |
| 2024-06-22T00:00:00.446196Z | 9.805 |
| timestamp | price | size |
| --------------- | ------ | ---- |
| 08:00:00.007140 | 175.97 | 400 |
| 08:00:00.609618 | 178.55 | 400 |
| 08:00:00.672131 | 176.09 | 400 |
| 08:00:00.672147 | 176.03 | 400 |
| 08:00:01.146931 | 175.45 | 400 |
| 08:00:01.495188 | 177.90 | 400 |
| 08:00:01.991977 | 175.35 | 400 |
| 08:00:01.991991 | 175.36 | 400 |
| 08:00:02.039451 | 175.36 | 400 |
| 08:00:02.836413 | 175.55 | 400 |
| 08:00:03.447858 | 176.79 | 400 |
| 08:00:04.782191 | 181.00 | 15 |
| 08:00:05.408871 | 175.77 | 400 |
| 08:00:06.007145 | 176.52 | 400 |
| 08:00:06.740159 | 184.00 | 1 |
| 08:00:07.593841 | 175.75 | 400 |
| 08:00:10.310291 | 176.38 | 29 |
| 08:00:10.550535 | 175.86 | 400 |
| 08:00:10.761790 | 175.94 | 400 |
| 08:00:12.046660 | 176.15 | 400 |
| 08:00:12.897624 | 176.62 | 400 |
| 08:00:13.838193 | 176.51 | 25 |
| 08:00:15.125509 | 176.17 | 400 |
| 08:00:16.727077 | 176.48 | 400 |
| 08:00:18.813886 | 176.68 | 400 |
| 08:00:22.180535 | 176.05 | 400 |
| 08:00:25.125634 | 176.16 | 400 |
| 08:00:26.117889 | 176.33 | 1 |
| 08:00:26.184839 | 176.52 | 400 |
| 08:00:26.185102 | 176.41 | 25 |

</div>

The `sell` table (the right table):
`order_book` data:

<div className="blue-table">
<div className="pink-table">

| timestamp | price |
| --------------------------- | -------- |
| 2024-06-22T00:00:00.222534Z | 64120.28 |
| 2024-06-22T00:00:00.222534Z | 64120.28 |
| 2024-06-22T00:00:00.222534Z | 64116.74 |
| 2024-06-22T00:00:00.222534Z | 64116.5 |
| 2024-06-22T00:00:00.543826Z | 134.56 |
| timestamp | bid_price | bid_size | ask_price | ask_size |
| --------- | --------- | -------- | --------- | -------- |
| 08:00:00 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:01 | 176.33 | 4744 | 176.6 | 8404 |
| 08:00:02 | 176.07 | 136 | 176.76 | 4946 |
| 08:00:03 | 176.07 | 84 | 176.75 | 2182 |
| 08:00:04 | 176.07 | 112 | 176.59 | 2734 |
| 08:00:05 | 176.38 | 212 | 176.5 | 6966 |
| 08:00:06 | 176.33 | 176 | 176.52 | 8174 |
| 08:00:07 | 176.33 | 276 | 176.67 | 7345 |
| 08:00:08 | 176.33 | 48 | 176.67 | 1600 |
| 08:00:09 | 176.35 | 66 | 176.67 | 2400 |
| 08:00:10 | 176.36 | 695 | 176.38 | 20698 |
| 08:00:11 | 176.35 | 98 | 176.59 | 2800 |
| 08:00:12 | 176.48 | 104 | 176.59 | 4040 |
| 08:00:13 | 176.48 | 165 | 176.38 | 6035 |
| 08:00:14 | 176.35 | 56 | 176.38 | 720 |
| 08:00:15 | 176.35 | 119 | 176.38 | 1530 |
| 08:00:16 | 176.35 | 133 | 176.38 | 3710 |
| 08:00:18 | 176.35 | 84 | 176.38 | 1880 |
| 08:00:19 | 176.35 | 14 | 176.38 | 180 |
| 08:00:20 | 176.35 | 14 | 176.38 | 180 |
| 08:00:21 | 176.35 | 112 | 176.38 | 1440 |
| 08:00:22 | 176.35 | 133 | 176.38 | 1710 |
| 08:00:25 | 176.35 | 122 | 176.38 | 3929 |
| 08:00:26 | 176.35 | 300 | 176.37 | 6952 |
| 08:00:28 | 176.07 | 28 | 176.37 | 496 |

</div>

An `ASOF JOIN` query can look like the following:
We want to join each trade event to the relevant order book snapshot. All
we have to write is

```questdb-sql
WITH
buy AS ( -- select the first 5 buys in June 22
SELECT timestamp, price FROM trades
WHERE timestamp = '2024-06-22' AND side = 'buy' LIMIT 5
),
sell AS ( -- select the first 5 sells in June 22
SELECT timestamp, price FROM trades
WHERE timestamp = '2024-06-22' AND side = 'sell' LIMIT 5
)
SELECT
buy.timestamp, sell.timestamp, buy.price, sell.price
FROM buy ASOF JOIN sell;
```questdb-sql title="A basic ASOF JOIN example" demo
trades ASOF JOIN order_book
```

This is the JOIN result:
and we get this result:

<div className="table-alternate">

| timestamp | timestamp1 | price | price1 |
| --------------------------- | --------------------------- | -------- | ------- |
| 2024-06-22T00:00:00.039906Z | NULL | 0.092014 | NULL |
| 2024-06-22T00:00:00.343909Z | 2024-06-22T00:00:00.222534Z | 9.805 | 64116.5 |
| 2024-06-22T00:00:00.349387Z | 2024-06-22T00:00:00.222534Z | 134.56 | 64116.5 |
| 2024-06-22T00:00:00.349387Z | 2024-06-22T00:00:00.222534Z | 134.56 | 64116.5 |
| 2024-06-22T00:00:00.446196Z | 2024-06-22T00:00:00.222534Z | 9.805 | 64116.5 |
| timestamp | price | size | timestamp1 | bid_price | bid_size | ask_price | ask_size |
| --------------- | ------ | ---- | ---------- | --------- | -------- | --------- | -------- |
| 08:00:00.007140 | 175.97 | 400 | 08:00:00 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:00.609618 | 178.55 | 400 | 08:00:00 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:00.672131 | 176.09 | 400 | 08:00:00 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:00.672147 | 176.03 | 400 | 08:00:00 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:01.146931 | 175.45 | 400 | 08:00:01 | 176.33 | 4744 | 176.60 | 8404 |
| 08:00:01.495188 | 177.90 | 400 | 08:00:01 | 176.33 | 4744 | 176.60 | 8404 |
| 08:00:01.991977 | 175.35 | 400 | 08:00:01 | 176.33 | 4744 | 176.60 | 8404 |
| 08:00:01.991991 | 175.36 | 400 | 08:00:01 | 176.33 | 4744 | 176.60 | 8404 |
| 08:00:02.039451 | 175.36 | 400 | 08:00:02 | 176.07 | 136 | 176.76 | 4946 |
| 08:00:02.836413 | 175.55 | 400 | 08:00:02 | 176.07 | 136 | 176.76 | 4946 |
| 08:00:03.447858 | 176.79 | 400 | 08:00:03 | 176.07 | 84 | 176.75 | 2182 |
| 08:00:04.782191 | 181.00 | 15 | 08:00:04 | 176.07 | 112 | 176.59 | 2734 |
| 08:00:05.408871 | 175.77 | 400 | 08:00:05 | 176.38 | 212 | 176.50 | 6966 |
| 08:00:06.007145 | 176.52 | 400 | 08:00:06 | 176.33 | 176 | 176.52 | 8174 |
| 08:00:06.740159 | 184.00 | 1 | 08:00:06 | 176.33 | 176 | 176.52 | 8174 |
| 08:00:07.593841 | 175.75 | 400 | 08:00:07 | 176.33 | 276 | 176.67 | 7345 |
| 08:00:10.310291 | 176.38 | 29 | 08:00:10 | 176.36 | 695 | 176.38 | 20698 |
| 08:00:10.550535 | 175.86 | 400 | 08:00:10 | 176.36 | 695 | 176.38 | 20698 |
| 08:00:10.761790 | 175.94 | 400 | 08:00:10 | 176.36 | 695 | 176.38 | 20698 |
| 08:00:12.046660 | 176.15 | 400 | 08:00:12 | 176.48 | 104 | 176.59 | 4040 |
| 08:00:12.897624 | 176.62 | 400 | 08:00:12 | 176.48 | 104 | 176.59 | 4040 |
| 08:00:13.838193 | 176.51 | 25 | 08:00:13 | 176.48 | 165 | 176.38 | 6035 |
| 08:00:15.125509 | 176.17 | 400 | 08:00:15 | 176.35 | 119 | 176.38 | 1530 |
| 08:00:16.727077 | 176.48 | 400 | 08:00:16 | 176.35 | 133 | 176.38 | 3710 |
| 08:00:18.813886 | 176.68 | 400 | 08:00:18 | 176.35 | 84 | 176.38 | 1880 |
| 08:00:22.180535 | 176.05 | 400 | 08:00:22 | 176.35 | 133 | 176.38 | 1710 |
| 08:00:25.125634 | 176.16 | 400 | 08:00:25 | 176.35 | 122 | 176.38 | 3929 |
| 08:00:26.117889 | 176.33 | 1 | 08:00:26 | 176.35 | 300 | 176.37 | 6952 |
| 08:00:26.184839 | 176.52 | 400 | 08:00:26 | 176.35 | 300 | 176.37 | 6952 |
| 08:00:26.185102 | 176.41 | 25 | 08:00:26 | 176.35 | 300 | 176.37 | 6952 |

</div>

The result has all rows from the `buys` table joined with rows from the `sells`
table. For each timestamp from the `buys` table, the query looks for a timestamp
that is equal or prior to it from the `sells` table. If no matching timestamp is
found, NULL is inserted.

### Using `ON` for matching column value

An additional `ON` clause can be used to join the tables based on the value of a
selected column.
The tables in the above example are just about one stock; in reality the same
table covers many stocks, and you want the results not to get mixed between
them. This is what the `ON` clause is for -- you can point out the key (ticker)
column and get results separate for each key.

The query above does not use the optional `ON` clause. If both tables store data
for multiple symbols, `ON` clause provides a way to find sells for buys with
matching symbol value.

Table `buy` (the left table):
Here's the trades table expanded to include two stocks, and a new `symbol` column:

<div className="pink-table">

| timestamp | symbol | price |
| --------------------------- | ------- | -------- |
| 2024-06-22T00:00:00.039906Z | XLM-USD | 0.092014 |
| 2024-06-22T00:00:00.039906Z | UNI-USD | 9.805 |
| 2024-06-22T00:00:00.349387Z | SOL-USD | 134.56 |
| 2024-06-22T00:00:00.349387Z | SOL-USD | 134.56 |
| 2024-06-22T00:00:00.446196Z | UNI-USD | 9.805 |
| timestamp | symbol | price | size |
| --------------- | ------ | ------ | ---- |
| 08:00:00.007168 | AAPL | 176.91 | 400 |
| 08:00:00.834205 | AAPL | 175.93 | 400 |
| 08:00:00.988111 | AAPL | 176.47 | 100 |
| 08:00:01.199577 | AAPL | 175.46 | 400 |
| 08:00:01.495172 | AAPL | 177.95 | 400 |
| 08:00:01.538683 | GOOG | 175.82 | 400 |
| 08:00:01.555565 | AAPL | 176.33 | 25 |
| 08:00:02.006636 | GOOG | 150.0 | 10 |
| 08:00:02.039451 | AAPL | 175.36 | 400 |
| 08:00:02.460454 | GOOG | 175.45 | 400 |
| 08:00:03.012909 | GOOG | 175.5 | 1 |
| 08:00:03.494927 | GOOG | 185.0 | 5 |
| 08:00:03.524212 | AAPL | 175.48 | 400 |
| 08:00:04.648333 | AAPL | 175.66 | 400 |
| 08:00:04.943421 | GOOG | 175.48 | 400 |
| 08:00:05.884890 | AAPL | 176.54 | 28 |
| 08:00:05.961856 | GOOG | 175.66 | 400 |
| 08:00:06.589806 | GOOG | 175.65 | 400 |
| 08:00:06.740159 | AAPL | 184.0 | 1 |
| 08:00:07.342978 | GOOG | 176.55 | 400 |
| 08:00:07.345877 | AAPL | 176.73 | 400 |
| 08:00:10.419065 | AAPL | 176.41 | 400 |
| 08:00:11.636237 | AAPL | 176.69 | 400 |
| 08:00:11.683078 | GOOG | 176.67 | 400 |
| 08:00:13.650868 | AAPL | 176.52 | 124 |
| 08:00:13.650880 | AAPL | 176.59 | 124 |
| 08:00:14.055762 | AAPL | 176.66 | 400 |
| 08:00:14.083022 | GOOG | 176.81 | 400 |
| 08:00:15.088091 | GOOG | 176.52 | 400 |
| 08:00:15.125494 | AAPL | 176.12 | 400 |
| 08:00:15.147691 | GOOG | 176.54 | 400 |

</div>

The `sell` table (the right table):
Order book, similarly extended with the `symbol` column:

<div className="blue-table">

| timestamp | symbol | price |
| --------------------------- | ------- | -------- |
| 2024-06-21T23:59:59.187884Z | SOL-USD | 134.54 |
| 2024-06-21T23:59:59.878276Z | UNI-USD | 9.804 |
| 2024-06-22T00:00:00.222534Z | BTC-USD | 64120.28 |
| 2024-06-22T00:00:00.543826Z | SOL-USD | 134.56 |
| 2024-06-22T00:00:00.644399Z | SOL-USD | 134.56 |
| timestamp | symbol | bid_price | bid_size | ask_price | ask_size |
| --------- | ------ | --------- | -------- | --------- | -------- |
| 08:00:00 | AAPL | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:01 | GOOG | 130.32 | 7516 | 130.9 | 25652 |
| 08:00:01 | AAPL | 176.33 | 4744 | 176.6 | 8404 |
| 08:00:02 | GOOG | 130.59 | 9046 | 130.68 | 9264 |
| 08:00:02 | AAPL | 176.07 | 136 | 176.76 | 4946 |
| 08:00:03 | GOOG | 130.34 | 4086 | 130.82 | 12676 |
| 08:00:03 | AAPL | 176.07 | 84 | 176.75 | 2182 |
| 08:00:04 | GOOG | 130.29 | 350 | 130.79 | 8780 |
| 08:00:04 | AAPL | 176.07 | 112 | 176.59 | 2734 |
| 08:00:05 | GOOG | 130.29 | 182 | 130.68 | 6060 |
| 08:00:05 | AAPL | 176.38 | 212 | 176.5 | 6966 |
| 08:00:06 | GOOG | 130.48 | 394 | 130.65 | 6828 |
| 08:00:06 | AAPL | 176.33 | 176 | 176.52 | 8174 |
| 08:00:07 | GOOG | 130.52 | 366 | 130.61 | 21260 |
| 08:00:07 | AAPL | 176.33 | 276 | 176.67 | 7345 |
| 08:00:08 | GOOG | 130.48 | 480 | 130.76 | 13032 |
| 08:00:08 | AAPL | 176.33 | 48 | 176.67 | 1600 |
| 08:00:09 | GOOG | 130.48 | 216 | 130.74 | 6458 |
| 08:00:09 | AAPL | 176.35 | 66 | 176.67 | 2400 |
| 08:00:10 | GOOG | 130.48 | 72 | 130.74 | 2400 |
| 08:00:10 | AAPL | 176.36 | 695 | 176.38 | 20698 |
| 08:00:11 | GOOG | 130.51 | 1236 | 130.52 | 26596 |
| 08:00:11 | AAPL | 176.35 | 98 | 176.59 | 2800 |
| 08:00:12 | GOOG | 130.5 | 378 | 130.68 | 22000 |
| 08:00:12 | AAPL | 176.48 | 104 | 176.59 | 4040 |
| 08:00:13 | GOOG | 130.6 | 174 | 130.68 | 5200 |
| 08:00:13 | AAPL | 176.48 | 165 | 176.38 | 6035 |
| 08:00:14 | GOOG | 130.6 | 138 | 130.62 | 8616 |
| 08:00:14 | AAPL | 176.35 | 56 | 176.38 | 720 |
| 08:00:15 | GOOG | 130.6 | 394 | 130.52 | 9374 |

</div>

Notice how both tables have a new column `symbol` that stores the stock name.

The `ON` clause allows you to match the value of the `symbol` column in the
`buys` table with that in the `sells` table:

```questdb-sql
WITH
buy AS ( -- select the first 5 buys in June 22
SELECT * FROM trades
WHERE timestamp IN '2024-06-22' AND side = 'buy' LIMIT 5
),
sell AS ( -- sells in the last second of June 21 and 1 second later
SELECT * FROM trades
WHERE timestamp IN '2024-06-21T23:59:59;1s' AND side = 'sell'
)
SELECT
buy.timestamp, sell.timestamp, buy.symbol,
(buy.price - sell.price) spread
FROM buy ASOF JOIN sell ON (symbol);
And here's the ASOF JOIN query with the `ON` clause added:

```questdb-sql title="ASOF JOIN with symbol matching" demo
SELECT t.timestamp, t.symbol, price, size, bid_price, bid_size, ask_price, ask_size
FROM trades t ASOF JOIN order_book ON (symbol);
```

The above query returns these results:
Result:

<div className="table-alternate">

| timestamp | timestamp1 | symbol | spread |
| --------------------------- | --------------------------- | ------- | ------ |
| 2024-06-22T00:00:00.039906Z | NULL | XLM-USD | NULL |
| 2024-06-22T00:00:00.343909Z | 2024-06-21T23:59:59.878276Z | UNI-USD | 0.0009 |
| 2024-06-22T00:00:00.349387Z | 2024-06-21T23:59:59.187884Z | SOL-USD | 0.02 |
| 2024-06-22T00:00:00.349387Z | 2024-06-21T23:59:59.187884Z | SOL-USD | 0.02 |
| 2024-06-22T00:00:00.446196Z | 2024-06-21T23:59:59.878276Z | UNI-USD | 0.0009 |
| timestamp | symbol | price | size | bid_price | bid_size | ask_price | ask_size |
| --------------- | ------ | ------ | ---- | --------- | -------- | --------- | -------- |
| 08:00:00.007168 | AAPL | 176.91 | 400 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:00.834205 | AAPL | 175.93 | 400 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:00.988111 | AAPL | 176.47 | 100 | 176.47 | 5542 | 176.82 | 13054 |
| 08:00:01.199577 | AAPL | 175.46 | 400 | 176.33 | 4744 | 176.60 | 8404 |
| 08:00:01.495172 | AAPL | 177.95 | 400 | 176.33 | 4744 | 176.60 | 8404 |
| 08:00:01.538683 | GOOG | 175.82 | 400 | 130.32 | 7516 | 130.90 | 25652 |
| 08:00:01.555565 | AAPL | 176.33 | 25 | 176.33 | 4744 | 176.60 | 8404 |
| 08:00:02.006636 | GOOG | 150.00 | 10 | 130.59 | 9046 | 130.68 | 9264 |
| 08:00:02.039451 | AAPL | 175.36 | 400 | 176.07 | 136 | 176.76 | 4946 |
| 08:00:02.460454 | GOOG | 175.45 | 400 | 130.59 | 9046 | 130.68 | 9264 |
| 08:00:03.012909 | GOOG | 175.50 | 1 | 130.34 | 4086 | 130.82 | 12676 |
| 08:00:03.494927 | GOOG | 185.00 | 5 | 130.34 | 4086 | 130.82 | 12676 |
| 08:00:03.524212 | AAPL | 175.48 | 400 | 176.07 | 84 | 176.75 | 2182 |
| 08:00:04.648333 | AAPL | 175.66 | 400 | 176.07 | 112 | 176.59 | 2734 |
| 08:00:04.943421 | GOOG | 175.48 | 400 | 130.29 | 350 | 130.79 | 8780 |
| 08:00:05.884890 | AAPL | 176.54 | 28 | 176.38 | 212 | 176.50 | 6966 |
| 08:00:05.961856 | GOOG | 175.66 | 400 | 130.29 | 182 | 130.68 | 6060 |
| 08:00:06.589806 | GOOG | 175.65 | 400 | 130.48 | 394 | 130.65 | 6828 |
| 08:00:06.740159 | AAPL | 184.00 | 1 | 176.33 | 176 | 176.52 | 8174 |
| 08:00:07.342978 | GOOG | 176.55 | 400 | 130.52 | 366 | 130.61 | 21260 |
| 08:00:07.345877 | AAPL | 176.73 | 400 | 176.33 | 276 | 176.67 | 7345 |
| 08:00:10.419065 | AAPL | 176.41 | 400 | 176.36 | 695 | 176.38 | 20698 |
| 08:00:11.636237 | AAPL | 176.69 | 400 | 176.35 | 98 | 176.59 | 2800 |
| 08:00:11.683078 | GOOG | 176.67 | 400 | 130.51 | 1236 | 130.52 | 26596 |
| 08:00:13.650868 | AAPL | 176.52 | 124 | 176.48 | 165 | 176.38 | 6035 |
| 08:00:13.650880 | AAPL | 176.59 | 124 | 176.48 | 165 | 176.38 | 6035 |
| 08:00:14.055762 | AAPL | 176.66 | 400 | 176.35 | 56 | 176.38 | 720 |
| 08:00:14.083022 | GOOG | 176.81 | 400 | 130.60 | 138 | 130.62 | 8616 |
| 08:00:15.088091 | GOOG | 176.52 | 400 | 130.60 | 394 | 130.52 | 9374 |
| 08:00:15.125494 | AAPL | 176.12 | 400 | 176.35 | 56 | 176.38 | 720 |
| 08:00:15.147691 | GOOG | 176.54 | 400 | 130.60 | 394 | 130.52 | 9374 |

</div>

This query returns all rows from the `buy` table joined with records from the
`sell` table that meet both the following criterion:

- The `symbol` column of the two tables has the same value
- The timestamp of the `sell` record is prior to or equal to the timestamp of
the `buy` record.

The XLM-USD record in the `buy` table is not joined with any record in the
`sell` table because there is no record in the `sell` table with the same stock
name and a timestamp prior to or equal to the timestamp of the XLM-USD record.
Note how the `sell` table has three rows with the SOL-USD symbol, but both of
the SOL-USD in the `buy` table are matching to the first entry, as it is the
only timestamp which is equal or prior.

### Timestamp considerations

`ASOF` join can be performed only on tables or result sets that are ordered by
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