SELECT
  CARDINALITY(recipients) AS recipient_count
FROM
  emails

SELECT
  ARRAY_POSITION(recipients, 'hello@example.com') AS position
FROM
  emails

SELECT
  STRING_TO_ARRAY('a,b,c,d,e,f', ',') AS parts

SELECT
  ARRAY_TO_STRING(ARRAY['a', 'b', 'c'], ',') AS joined_string

SELECT
  sender,
  ARRAY_AGG(subject) AS subjects,
  ARRAY_AGG(DISTINCT subject) AS distinct_subjects
FROM
  emails
GROUP BY
  sender

SELECT
  sender,
  recipient
FROM
  emails,
  UNNEST(recipients) AS recipient

SELECT MIN(response_time) AS min_response_time
FROM logs
WHERE status_code = 200

SELECT MAX(response_time) AS max_response_time
FROM logs
WHERE status_code = 200

SELECT COUNT(request_id) AS total_requests
FROM logs
WHERE status_code = 200 

SELECT SUM(bytes_transferred) AS total_bytes
FROM logs
GROUP BY service_name

SELECT AVG(response_time)
AS avg_response_time
FROM logs
WHERE status_code = 200
GROUP BY service_name

SELECT BOOL_AND(status_code = 200) AS all_success
FROM logs

SELECT BOOL_OR(status_code = 200) AS some_success
FROM logs

SELECT CEIL(price) AS rounded_price
FROM products

SELECT FLOOR(price) AS floored_price
FROM products

SELECT ROUND(price) AS rounded_price
FROM products

SELECT POWER(response_time, 2) AS squared_response_time
FROM logs

SELECT LOWER(customer_name) AS lowercase_name
FROM customers

SELECT UPPER(customer_name) AS uppercase_name
FROM customers

SELECT ABS(balance) AS absolute_balance
FROM accounts

SELECT COALESCE(phone_number, email) AS contact_info
FROM users

SELECT
  CAST(order_id AS VARCHAR) AS order_id_string,
  'Order-' || CAST(order_id AS VARCHAR) AS order_label
FROM
  orders

SELECT
  customer_name,
  LENGTH(customer_name) AS name_length
FROM
  customers

SELECT
  TIMESTAMP '2023-10-01 10:00:00' + INTERVAL '30 days' AS future_date,
  INTERVAL '1 MILLISECOND 2 SECONDS 3 MINUTES 4 HOURS 5 DAYS'

SELECT
  TRIM(name) AS trimmed_name
FROM
  users

SELECT
  REPLACE(description, 'old', 'new') AS updated_description
FROM
  products

SELECT
  SUBSTRING(title, 1, 10) AS short_title
FROM
  books

SELECT
  REVERSE(username) AS reversed_username
FROM
  users
LIMIT 5

SELECT
  STRPOS('foobar', 'bar')

SELECT
  SPLIT_PART('aaa-bbb-ccc', '-', 2)

SELECT
  EXTRACT(year FROM purchase_date) AS purchase_year
FROM
  sales

-- Get the Unix epoch of a timestamp
SELECT EXTRACT(epoch FROM TIMESTAMP '2021-01-01 00:00:00+00')
-- Returns: 1609459200

-- Get the total seconds in an interval
SELECT EXTRACT(epoch FROM INTERVAL '1 day 2 hours')
-- Returns: 93600

-- Calculate how many seconds ago each event occurred
SELECT
  event_time,
  EXTRACT(epoch FROM now()) - EXTRACT(epoch FROM event_time) AS seconds_ago
FROM
  events

SELECT
  TO_TIMESTAMP('25/12/2025 04:23 pm', 'DD/MM/YYYY HH:MI am') AS ts

SELECT
  TO_TIMESTAMP(1735142580) AS ts_from_epoch

SELECT
  TO_CHAR(order_date, 'MM-DD-YYYY') AS formatted_date
FROM
  orders

SELECT DATE_BIN('15 minutes', TIMESTAMP '2025-09-15 12:34:56', TIMESTAMP '2025-01-01')
-- Returns 2025-09-15 12:30:00

SELECT DATE_BIN('1 day', TIMESTAMP '2025-09-15 12:34:56', TIMESTAMP '2025-01-01')
-- Returns 2025-09-15 00:00:00

SELECT
  DATE_TRUNC('month', event_time) AS month_start
FROM
  events

-- Define the current analysis window
WITH bounds AS (
  SELECT CAST(CURRENT_SETTING('dd.time_frame_start') AS TIMESTAMP) AS time_frame_start,
         CAST(CURRENT_SETTING('dd.time_frame_end')   AS TIMESTAMP) AS time_frame_end
),
-- Define the immediately preceding window of equal length
     previous_bounds AS (
  SELECT time_frame_start - (time_frame_end - time_frame_start) AS prev_time_frame_start,
         time_frame_start                                       AS prev_time_frame_end
  FROM bounds
)
SELECT * FROM bounds, previous_bounds

SELECT
  *
FROM
  sales
WHERE
  purchase_date > NOW() - INTERVAL '1 hour'

-- Calculate the median (50th percentile) response time
SELECT
  APPROX_PERCENTILE(0.5) WITHIN GROUP (ORDER BY response_time) AS median_response_time
FROM
  logs

-- Calculate 95th and 99th response time percentiles by service
SELECT
  service_name,
  APPROX_PERCENTILE(0.95) WITHIN GROUP (ORDER BY response_time) AS p95_response_time,
  APPROX_PERCENTILE(0.99) WITHIN GROUP (ORDER BY response_time) AS p99_response_time
FROM
  logs
GROUP BY
  service_name

SELECT
  *
FROM
  emails
WHERE
  REGEXP_LIKE(email_address, '@example\.com$')

SELECT regexp_match('foobarbequebaz', '(bar)(beque)');
-- {bar,beque}

SELECT regexp_match('foobarbequebaz', 'barbeque');
-- {barbeque}

SELECT regexp_match('abc123xyz', '([a-z]+)(\d+)(x(.)z)');
-- {abc,123,xyz,y}

SELECT regexp_replace('Auth success token=abc123XYZ789', 'token=\w+', 'token=***');
-- Auth success token=***

SELECT regexp_replace('status=200 method=GET', 'status=(\d+) method=(\w+)', '$2: $1');
-- GET: 200

SELECT regexp_replace('INFO INFO INFO', 'INFO', 'DEBUG', 1, 2);
-- INFO DEBUG INFO

SELECT regexp_match('INFO', 'info')
-- NULL

SELECT regexp_match('INFO', 'info', 'i')
-- ['INFO']

SELECT regexp_match('a
b', '^b');
-- NULL

SELECT regexp_match('a
b', '^b', 'n');
-- ['b']