SQL, Python & More for DuckDB - Page 6

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Parquet to ExcelSQL

Editor's note: although not very intuitive, format for MS Excel XLSX files is provided by the spatial extension. While other snippets show you how to read Excel files, this shows you how to write data from a named Parquet file (or any DuckDB resultset) to Excel.

Execute this SQL

INSTALL spatial;
LOAD spatial;
COPY (SELECT * FROM 'source.parquet') TO 'target.XLSX' WITH (FORMAT GDAL, DRIVER 'XLSX');
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Simon Müller

Edited 02/23/24

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DuckDB in Action, Examples from chapters 3 and 4: Having fun with power production measurements

Editor's note: the Manning book "DuckDB in Action" has tons of great SQL exercises, including a few chapters which work on power production data. Michael, one of the authors, shares some of these queries here [and on other snippets published on this site].

Attach and select MotherDuck database
Data shared/available on MotherDuck

ATTACH 'md:_share/duckdb_in_action_ch3_4/d0c08584-1d33-491c-8db7-cf9c6910eceb' AS duckdb_in_action_ch3_4;
USE duckdb_in_action_ch3_4;
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3.17 Using arg_max to find sibling values of aggregated values computed in a common table expressionSQL

WITH per_hour AS (
    SELECT system_id,
           date_trunc('hour', read_on) AS read_on,
           avg(power) / 1000 AS kWh
    FROM readings
    GROUP BY ALL
)
SELECT name,
       max(kWh),
       arg_max(read_on, kWh) AS 'Read on'
FROM per_hour
   JOIN systems s ON s.id = per_hour.system_id
WHERE system_id = 34
GROUP by s.name;
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4.18 Using named windows defined with a partition and a range to compute several aggregates at onceSQL

SELECT system_id,
       day,
       min(kWh) OVER seven_days AS "7-day min",
       quantile(kWh, [0.25, 0.5, 0.75])
         OVER seven_days AS "kWh 7-day quartile",
       max(kWh) OVER seven_days AS "7-day max",
FROM v_power_per_day
WINDOW
    seven_days AS (
        PARTITION BY system_id, month(day)
        ORDER BY day ASC
        RANGE BETWEEN INTERVAL 3 Days PRECEDING
                  AND INTERVAL 3 Days FOLLOWING
    )
ORDER BY system_id, day;
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4.23 Filtering outside the where clause to find a time period with a high production over 7 daysSQL

SELECT system_id,
       day,
       avg(kWh) OVER (
            PARTITION BY system_id
            ORDER BY day ASC
            RANGE BETWEEN INTERVAL 3 Days PRECEDING
                      AND INTERVAL 3 Days FOLLOWING
       ) AS "kWh 7-day moving average"
FROM v_power_per_day
QUALIFY "kWh 7-day moving average" > 875 
ORDER BY system_id, day;
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4.26 Easily pivoting a result set (around a year)SQL

PIVOT (FROM v_power_per_day)
ON year(day)
USING sum(kWh);
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4.28 Using the ASOF join to pick prices that have been valid up to the point in time of sellingSQL

WITH prices AS (
  SELECT range AS valid_at,
         random()*10 AS price
  FROM range(
       '2023-01-01 01:00:00'::timestamp,
       '2023-01-01 02:00:00'::timestamp, INTERVAL '15 minutes')
),
sales AS (
  SELECT range AS sold_at,
         random()*10 AS num
  FROM range(
      '2023-01-01 01:00:00'::timestamp,
      '2023-01-01 02:00:00'::timestamp, INTERVAL '5 minutes')
)
SELECT sold_at, valid_at AS 'with_price_at', round(num * price,2) as price
FROM sales
ASOF JOIN prices
    ON prices.valid_at <= sales.sold_at;
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4.29 Using the ASOF join together with a window function to compute accumulated earningsSQL

SELECT power.day,
       power.kWh,
       prices.value as 'ct/kWh',
       round(sum(prices.value * power.kWh)
           OVER (ORDER BY power.day ASC) / 100, 2)
           AS 'Accumulated earnings in EUR'
FROM v_power_per_day power
    ASOF JOIN prices
    ON prices.valid_from <= power.day
WHERE system_id = 34
ORDER BY day;
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4.33 Using a LATERAL Join for looping through a JSON array returned by a solar radiation APISQL

-- The below query generates a series of 7 days, 
-- joins those with the hours 8, 13 and 19 (7pm) to create indexes.
-- Those indexes are the day number * 24 plus the desired hour of the
-- day to find the value in the JSON array.
-- That index is the lateral driver for the sub-query:
--
-- It then calls Open Meteo for Offenbach, apparently the city with 
-- the most sunshine in germany to get shortwave radition 
-- at those times
WITH days AS (
  SELECT generate_series AS value FROM generate_series(7)
), hours AS (
  SELECT unnest([8, 13, 18]) AS value
), indexes AS (
  SELECT days.value * 24 + hours.value AS i
  FROM days, hours
)
SELECT date_trunc('day', now()) - INTERVAL '7 days' +
         INTERVAL (indexes.i || ' hours') AS ts,
       ghi.v AS 'GHI in W/m^2'
FROM indexes,
LATERAL (
  SELECT hourly.shortwave_radiation_instant[i+1] AS v
  FROM read_json_auto('https://api.open-meteo.com/v1/forecast?latitude=48.47377&longitude=7.94495&hourly=shortwave_radiation_instant&past_days=7')
) AS ghi
ORDER BY ts;
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Michael Simons