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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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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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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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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PIVOT (FROM v_power_per_day)
ON year(day)
USING sum(kWh);
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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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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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-- 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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ATTACH 'md:_share/stackoverflow/6c318917-6888-425a-bea1-5860c29947e5' AS stackoverflow;
USE stackoverflow;
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SELECT Title, CommentCount
FROM posts
WHERE PostTypeId = 1
ORDER BY CommentCount DESC
LIMIT 5;
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SELECT name, reputation, 
       round(reputation/day(today()-createdAt)) as rate, 
       day(today()-createdAt) as days, 
       createdAt
FROM users WHERE reputation > 1000000 ORDER BY rate DESC;
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WITH top_users as (
  SELECT name, reputation, 
       round(reputation/day(today()-createdAt)) as rate, day(today()-createdAt) as days, 
       createdAt
       FROM users WHERE reputation > 1000000
)
SELECT name, reputation, rate, bar(rate,150,300,35) AS bar FROM top_users;
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SELECT  year(CreationDate) AS year, count(*), 
        round(avg(ViewCount)), max(AnswerCount)
FROM posts 
GROUP BY year 
ORDER BY year DESC LIMIT 10;
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SELECT count(*) as freq, dayname(CreationDate) AS day, 
       bar(freq, 0, 150000,20) AS plot
FROM posts WHERE posttypeid = 1 
AND tags LIKE '%<sql>%'
GROUP BY all 
ORDER BY freq DESC;
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SELECT count(*) as freq, dayname(CreationDate) AS day, 
       bar(freq, 0, 10000,20) AS plot
FROM posts WHERE posttypeid = 1 
AND tags LIKE '%<rust>%'
GROUP BY all 
ORDER BY freq DESC;
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