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Description

The If combinator can be applied to the sum function to calculate the sum of values for rows where the condition is true, using the sumIf aggregate combinator function.

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Example usage

In this example, we’ll create a table that stores sales data with success flags, and we’ll use sumIf to calculate the total sales amount for successful transactions.
Query
CREATE TABLE sales(
    transaction_id UInt32,
    amount Decimal(10,2),
    is_successful UInt8
) ENGINE = MergeTree
ORDER BY ();

INSERT INTO sales VALUES
    (1, 100.50, 1),
    (2, 200.75, 1),
    (3, 150.25, 0),
    (4, 300.00, 1),
    (5, 250.50, 0),
    (6, 175.25, 1);

SELECT
    sumIf(amount, is_successful = 1) AS total_successful_sales
FROM sales;
The sumIf function will sum only the amounts where is_successful = 1. In this case, it will sum: 100.50 + 200.75 + 300.00 + 175.25.
Response
   ┌─total_successful_sales─┐
1. │                  776.50 │
   └───────────────────────┘

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Calculate trading volume by price direction

In this example we’ll use the stock table available at ClickHouse playground to calculate trading volume by price direction in the first half of the year 2002.
Query
SELECT 
    toStartOfMonth(date) AS month,
    formatReadableQuantity(sumIf(volume, price > open)) AS volume_on_up_days,
    formatReadableQuantity(sumIf(volume, price < open)) AS volume_on_down_days,
    formatReadableQuantity(sumIf(volume, price = open)) AS volume_on_neutral_days,
    formatReadableQuantity(sum(volume)) AS total_volume
FROM stock.stock
WHERE date BETWEEN '2002-01-01' AND '2002-12-31'
GROUP BY month
ORDER BY month;

    ┌──────month─┬─volume_on_up_days─┬─volume_on_down_days─┬─volume_on_neutral_days─┬─total_volume──┐
 1. │ 2002-01-01 │ 26.07 billion     │ 30.74 billion       │ 781.80 million         │ 57.59 billion │
 2. │ 2002-02-01 │ 20.84 billion     │ 29.60 billion       │ 642.36 million         │ 51.09 billion │
 3. │ 2002-03-01 │ 28.81 billion     │ 23.57 billion       │ 762.60 million         │ 53.14 billion │
 4. │ 2002-04-01 │ 24.72 billion     │ 30.99 billion       │ 763.92 million         │ 56.47 billion │
 5. │ 2002-05-01 │ 25.09 billion     │ 30.57 billion       │ 858.57 million         │ 56.52 billion │
 6. │ 2002-06-01 │ 29.10 billion     │ 30.88 billion       │ 875.71 million         │ 60.86 billion │
 7. │ 2002-07-01 │ 32.27 billion     │ 41.73 billion       │ 747.32 million         │ 74.75 billion │
 8. │ 2002-08-01 │ 28.57 billion     │ 27.49 billion       │ 1.17 billion           │ 57.24 billion │
 9. │ 2002-09-01 │ 23.37 billion     │ 31.02 billion       │ 775.66 million         │ 55.17 billion │
10. │ 2002-10-01 │ 38.57 billion     │ 34.05 billion       │ 956.48 million         │ 73.57 billion │
11. │ 2002-11-01 │ 34.90 billion     │ 25.47 billion       │ 998.34 million         │ 61.37 billion │
12. │ 2002-12-01 │ 22.99 billion     │ 28.65 billion       │ 1.14 billion           │ 52.79 billion │
    └────────────┴───────────────────┴─────────────────────┴────────────────────────┴───────────────┘

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Calculate trading volume by stock symbol

In this example we’ll use the stock table available at ClickHouse playground to calculate trading volume by stock symbol in 2006 for three of the largest tech companies at the time.
Query
SELECT 
    toStartOfMonth(date) AS month,
    formatReadableQuantity(sumIf(volume, symbol = 'AAPL')) AS apple_volume,
    formatReadableQuantity(sumIf(volume, symbol = 'MSFT')) AS microsoft_volume,
    formatReadableQuantity(sumIf(volume, symbol = 'GOOG')) AS google_volume,
    sum(volume) AS total_volume,
    round(sumIf(volume, symbol IN ('AAPL', 'MSFT', 'GOOG')) / sum(volume) * 100, 2) AS major_tech_percentage
FROM stock.stock
WHERE date BETWEEN '2006-01-01' AND '2006-12-31'
GROUP BY month
ORDER BY month;
Response
    ┌──────month─┬─apple_volume───┬─microsoft_volume─┬─google_volume──┬─total_volume─┬─major_tech_percentage─┐
 1. │ 2006-01-01 │ 782.21 million │ 1.39 billion     │ 299.69 million │  84343937700 │                  2.93 │
 2. │ 2006-02-01 │ 670.38 million │ 1.05 billion     │ 297.65 million │  73524748600 │                  2.74 │
 3. │ 2006-03-01 │ 744.85 million │ 1.39 billion     │ 288.36 million │  87960830800 │                  2.75 │
 4. │ 2006-04-01 │ 718.97 million │ 1.45 billion     │ 185.65 million │  78031719800 │                  3.02 │
 5. │ 2006-05-01 │ 557.89 million │ 2.32 billion     │ 174.94 million │  97096584100 │                  3.14 │
 6. │ 2006-06-01 │ 641.48 million │ 1.98 billion     │ 142.55 million │  96304086800 │                  2.87 │
 7. │ 2006-07-01 │ 624.93 million │ 1.33 billion     │ 127.74 million │  79940921800 │                  2.61 │
 8. │ 2006-08-01 │ 639.35 million │ 1.13 billion     │ 107.16 million │  84251753200 │                  2.23 │
 9. │ 2006-09-01 │ 633.45 million │ 1.10 billion     │ 121.72 million │  82775234300 │                  2.24 │
10. │ 2006-10-01 │ 514.82 million │ 1.29 billion     │ 158.90 million │  93406712600 │                   2.1 │
11. │ 2006-11-01 │ 494.37 million │ 1.24 billion     │ 118.49 million │  90177365500 │                  2.06 │
12. │ 2006-12-01 │ 603.95 million │ 1.14 billion     │ 91.77 million  │  80499584100 │                  2.28 │
    └────────────┴────────────────┴──────────────────┴────────────────┴──────────────┴───────────────────────┘

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See also