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const { expect } = require('chai');
const Parser = require('../src/parser').default
describe('athena', () => {
const parser = new Parser();
const DEFAULT_OPT = { database: 'athena' }
function getParsedSql(sql, opt = DEFAULT_OPT) {
const ast = parser.astify(sql, opt);
return parser.sqlify(ast, opt);
}
it('should support array data type', () => {
const sql = `SELECT
sessionId session_id,
organizationName organization,
appMode note_type,
rating note_rating,
therapistId therapist_id,
distinct_id email,
FROM_UNIXTIME((mp_processing_time_ms / 1000)) last_updated_ts,
source,
CAST(json_parse (selectedSubTags) AS ARRAY (varchar)) note_rating_tags,
description rating_description
FROM
events_mp_master_event
WHERE
(mp_event_name = 'submit feedback clicked')`
expect(getParsedSql(sql)).to.be.equal(`SELECT "sessionId" AS "session_id", "organizationName" AS "organization", "appMode" AS "note_type", "rating" AS "note_rating", "therapistId" AS "therapist_id", "distinct_id" AS "email", FROM_UNIXTIME(("mp_processing_time_ms" / 1000)) AS "last_updated_ts", "source", CAST(json_parse("selectedSubTags") AS ARRAY(VARCHAR)) AS "note_rating_tags", "description" AS "rating_description" FROM "events_mp_master_event" WHERE ("mp_event_name" = 'submit feedback clicked')`)
})
it('should support over partition and extract', () => {
this.maxDiffSize = '900000000'
const sql = `WITH weekly_data AS (
SELECT
LOWER(m.distinct_id) as therapist,
SPLIT_PART(SPLIT_PART(distinct_id, '@', 2), '.', 1) AS eleos_organization,
DATE_TRUNC('week', timestamp_event) AS week_start,
MIN(DATE_TRUNC('week', timestamp_event)) OVER (PARTITION BY LOWER(m.distinct_id)) AS first_note_week
FROM
bronze_prod.outreach_mixpanel_events m
WHERE
SPLIT_PART(SPLIT_PART(distinct_id, '@', 2), '.', 1) in ('thresholds','trilogyinc', 'zepfcenter')
AND timestamp_event IS NOT NULL
AND event LIKE '%outreach - note saved%'
),
weekly_totals as (
SELECT
wd.eleos_organization,
wd.week_start,
COUNT(DISTINCT wd.therapist) active_therapists,
COUNT(DISTINCT CASE WHEN wd.first_note_week = wd.week_start THEN wd.therapist END) AS activated_therapists,
SUM(COUNT(DISTINCT CASE WHEN wd.first_note_week = wd.week_start THEN wd.therapist END))
OVER (PARTITION by wd.eleos_organization ORDER BY wd.week_start ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS accumulated_therapists
FROM weekly_data wd
GROUP BY wd.week_start, wd.eleos_organization
ORDER BY wd.week_start
),
weekly_data_with_namber as (
Select distinct
eleos_organization,
therapist,
week_start,
first_note_week,
EXTRACT(DAY FROM week_start - first_note_week)/7 as week_number
from weekly_data
order by first_note_week
)
Select
wd.eleos_organization,
wd.first_note_week,
wt.active_therapists,
wt.activated_therapists,
wt.accumulated_therapists,
SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END) AS week_0,
SUM(CASE WHEN week_number = 1 THEN 1 ELSE 0 END) AS week_1,
SUM(CASE WHEN week_number = 2 THEN 1 ELSE 0 END) AS week_2,
SUM(CASE WHEN week_number = 3 THEN 1 ELSE 0 END) AS week_3,
SUM(CASE WHEN week_number = 4 THEN 1 ELSE 0 END) AS week_4,
SUM(CASE WHEN week_number = 5 THEN 1 ELSE 0 END) AS week_5,
SUM(CASE WHEN week_number = 6 THEN 1 ELSE 0 END) AS week_6,
SUM(CASE WHEN week_number = 7 THEN 1 ELSE 0 END) AS week_7,
SUM(CASE WHEN week_number = 8 THEN 1 ELSE 0 END) AS week_8,
SUM(CASE WHEN week_number = 9 THEN 1 ELSE 0 END) AS week_9,
SUM(CASE WHEN week_number = 10 THEN 1 ELSE 0 END) AS week_10,
SUM(CASE WHEN week_number = 11 THEN 1 ELSE 0 END) AS week_11,
SUM(CASE WHEN week_number = 12 THEN 1 ELSE 0 END) AS week_12,
SUM(CASE WHEN week_number = 13 THEN 1 ELSE 0 END) AS week_13,
SUM(CASE WHEN week_number = 14 THEN 1 ELSE 0 END) AS week_14,
SUM(CASE WHEN week_number = 15 THEN 1 ELSE 0 END) AS week_15,
SUM(CASE WHEN week_number = 16 THEN 1 ELSE 0 END) AS week_16,
SUM(CASE WHEN week_number = 17 THEN 1 ELSE 0 END) AS week_17,
SUM(CASE WHEN week_number = 18 THEN 1 ELSE 0 END) AS week_18,
SUM(CASE WHEN week_number = 19 THEN 1 ELSE 0 END) AS week_19,
SUM(CASE WHEN week_number = 20 THEN 1 ELSE 0 END) AS week_20,
SUM(CASE WHEN week_number = 21 THEN 1 ELSE 0 END) AS week_21,
SUM(CASE WHEN week_number = 22 THEN 1 ELSE 0 END) AS week_22,
SUM(CASE WHEN week_number = 23 THEN 1 ELSE 0 END) AS week_23,
SUM(CASE WHEN week_number = 24 THEN 1 ELSE 0 END) AS week_24,
SUM(CASE WHEN week_number = 25 THEN 1 ELSE 0 END) AS week_25,
SUM(CASE WHEN week_number = 26 THEN 1 ELSE 0 END) AS week_26,
SUM(CASE WHEN week_number = 27 THEN 1 ELSE 0 END) AS week_27,
SUM(CASE WHEN week_number = 28 THEN 1 ELSE 0 END) AS week_28,
SUM(CASE WHEN week_number = 29 THEN 1 ELSE 0 END) AS week_29,
ROUND(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_0_percentage,
ROUND(SUM(CASE WHEN week_number = 1 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_1_percentage,
ROUND(SUM(CASE WHEN week_number = 2 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_2_percentage,
ROUND(SUM(CASE WHEN week_number = 3 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_3_percentage,
ROUND(SUM(CASE WHEN week_number = 4 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_4_percentage,
ROUND(SUM(CASE WHEN week_number = 5 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_5_percentage,
ROUND(SUM(CASE WHEN week_number = 6 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_6_percentage,
ROUND(SUM(CASE WHEN week_number = 7 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_7_percentage,
ROUND(SUM(CASE WHEN week_number = 8 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_8_percentage,
ROUND(SUM(CASE WHEN week_number = 9 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_9_percentage,
ROUND(SUM(CASE WHEN week_number = 10 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_10_percentage,
ROUND(SUM(CASE WHEN week_number = 11 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_11_percentage,
ROUND(SUM(CASE WHEN week_number = 12 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_12_percentage,
ROUND(SUM(CASE WHEN week_number = 13 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_13_percentage,
ROUND(SUM(CASE WHEN week_number = 14 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_14_percentage,
ROUND(SUM(CASE WHEN week_number = 15 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_15_percentage,
ROUND(SUM(CASE WHEN week_number = 16 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_16_percentage,
ROUND(SUM(CASE WHEN week_number = 17 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_17_percentage,
ROUND(SUM(CASE WHEN week_number = 18 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_18_percentage,
ROUND(SUM(CASE WHEN week_number = 19 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_19_percentage,
ROUND(SUM(CASE WHEN week_number = 20 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_20_percentage,
ROUND(SUM(CASE WHEN week_number = 21 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_21_percentage,
ROUND(SUM(CASE WHEN week_number = 22 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_22_percentage,
ROUND(SUM(CASE WHEN week_number = 23 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_23_percentage,
ROUND(SUM(CASE WHEN week_number = 24 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_24_percentage,
ROUND(SUM(CASE WHEN week_number = 25 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_25_percentage,
ROUND(SUM(CASE WHEN week_number = 26 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_26_percentage,
ROUND(SUM(CASE WHEN week_number = 27 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_27_percentage,
ROUND(SUM(CASE WHEN week_number = 28 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_28_percentage,
ROUND(SUM(CASE WHEN week_number = 29 THEN 1 ELSE 0 END)*1.0 / NULLIF(SUM(CASE WHEN week_number = 0 THEN 1 ELSE 0 END), 0)*1.0 * 100, 2) AS week_29_percentage
from weekly_data_with_namber wd
join weekly_totals wt on wd.eleos_organization = wt.eleos_organization and wd.first_note_week = wt.week_start
group by
wd.eleos_organization,
wd.first_note_week,
wt.active_therapists,
wt.activated_therapists,
wt.accumulated_therapists
order by first_note_week`
expect(getParsedSql(sql)).to.be.equal(`WITH "weekly_data" AS (SELECT LOWER("m"."distinct_id") AS "therapist", SPLIT_PART(SPLIT_PART("distinct_id", '@', 2), '.', 1) AS "eleos_organization", DATE_TRUNC('week', "timestamp_event") AS "week_start", MIN(DATE_TRUNC('week', "timestamp_event")) OVER (PARTITION BY LOWER("m"."distinct_id")) AS "first_note_week" FROM "bronze_prod"."outreach_mixpanel_events" AS "m" WHERE SPLIT_PART(SPLIT_PART("distinct_id", '@', 2), '.', 1) IN ('thresholds', 'trilogyinc', 'zepfcenter') AND "timestamp_event" IS NOT NULL AND "event" LIKE '%outreach - note saved%'), "weekly_totals" AS (SELECT "wd"."eleos_organization", "wd"."week_start", COUNT(DISTINCT "wd"."therapist") AS "active_therapists", COUNT(DISTINCT CASE WHEN "wd"."first_note_week" = "wd"."week_start" THEN "wd"."therapist" END) AS "activated_therapists", SUM(COUNT(DISTINCT CASE WHEN "wd"."first_note_week" = "wd"."week_start" THEN "wd"."therapist" END)) OVER (PARTITION BY "wd"."eleos_organization" ORDER BY "wd"."week_start" ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS "accumulated_therapists" FROM "weekly_data" AS "wd" GROUP BY "wd"."week_start", "wd"."eleos_organization" ORDER BY "wd"."week_start" ASC), "weekly_data_with_namber" AS (SELECT DISTINCT "eleos_organization", "therapist", "week_start", "first_note_week", EXTRACT(DAY FROM "week_start" - "first_note_week") / 7 AS "week_number" FROM "weekly_data" ORDER BY "first_note_week" ASC) SELECT "wd"."eleos_organization", "wd"."first_note_week", "wt"."active_therapists", "wt"."activated_therapists", "wt"."accumulated_therapists", SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END) AS "week_0", SUM(CASE WHEN "week_number" = 1 THEN 1 ELSE 0 END) AS "week_1", SUM(CASE WHEN "week_number" = 2 THEN 1 ELSE 0 END) AS "week_2", SUM(CASE WHEN "week_number" = 3 THEN 1 ELSE 0 END) AS "week_3", SUM(CASE WHEN "week_number" = 4 THEN 1 ELSE 0 END) AS "week_4", SUM(CASE WHEN "week_number" = 5 THEN 1 ELSE 0 END) AS "week_5", SUM(CASE WHEN "week_number" = 6 THEN 1 ELSE 0 END) AS "week_6", SUM(CASE WHEN "week_number" = 7 THEN 1 ELSE 0 END) AS "week_7", SUM(CASE WHEN "week_number" = 8 THEN 1 ELSE 0 END) AS "week_8", SUM(CASE WHEN "week_number" = 9 THEN 1 ELSE 0 END) AS "week_9", SUM(CASE WHEN "week_number" = 10 THEN 1 ELSE 0 END) AS "week_10", SUM(CASE WHEN "week_number" = 11 THEN 1 ELSE 0 END) AS "week_11", SUM(CASE WHEN "week_number" = 12 THEN 1 ELSE 0 END) AS "week_12", SUM(CASE WHEN "week_number" = 13 THEN 1 ELSE 0 END) AS "week_13", SUM(CASE WHEN "week_number" = 14 THEN 1 ELSE 0 END) AS "week_14", SUM(CASE WHEN "week_number" = 15 THEN 1 ELSE 0 END) AS "week_15", SUM(CASE WHEN "week_number" = 16 THEN 1 ELSE 0 END) AS "week_16", SUM(CASE WHEN "week_number" = 17 THEN 1 ELSE 0 END) AS "week_17", SUM(CASE WHEN "week_number" = 18 THEN 1 ELSE 0 END) AS "week_18", SUM(CASE WHEN "week_number" = 19 THEN 1 ELSE 0 END) AS "week_19", SUM(CASE WHEN "week_number" = 20 THEN 1 ELSE 0 END) AS "week_20", SUM(CASE WHEN "week_number" = 21 THEN 1 ELSE 0 END) AS "week_21", SUM(CASE WHEN "week_number" = 22 THEN 1 ELSE 0 END) AS "week_22", SUM(CASE WHEN "week_number" = 23 THEN 1 ELSE 0 END) AS "week_23", SUM(CASE WHEN "week_number" = 24 THEN 1 ELSE 0 END) AS "week_24", SUM(CASE WHEN "week_number" = 25 THEN 1 ELSE 0 END) AS "week_25", SUM(CASE WHEN "week_number" = 26 THEN 1 ELSE 0 END) AS "week_26", SUM(CASE WHEN "week_number" = 27 THEN 1 ELSE 0 END) AS "week_27", SUM(CASE WHEN "week_number" = 28 THEN 1 ELSE 0 END) AS "week_28", SUM(CASE WHEN "week_number" = 29 THEN 1 ELSE 0 END) AS "week_29", ROUND(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_0_percentage", ROUND(SUM(CASE WHEN "week_number" = 1 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_1_percentage", ROUND(SUM(CASE WHEN "week_number" = 2 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_2_percentage", ROUND(SUM(CASE WHEN "week_number" = 3 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_3_percentage", ROUND(SUM(CASE WHEN "week_number" = 4 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_4_percentage", ROUND(SUM(CASE WHEN "week_number" = 5 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_5_percentage", ROUND(SUM(CASE WHEN "week_number" = 6 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_6_percentage", ROUND(SUM(CASE WHEN "week_number" = 7 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_7_percentage", ROUND(SUM(CASE WHEN "week_number" = 8 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_8_percentage", ROUND(SUM(CASE WHEN "week_number" = 9 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_9_percentage", ROUND(SUM(CASE WHEN "week_number" = 10 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_10_percentage", ROUND(SUM(CASE WHEN "week_number" = 11 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_11_percentage", ROUND(SUM(CASE WHEN "week_number" = 12 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_12_percentage", ROUND(SUM(CASE WHEN "week_number" = 13 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_13_percentage", ROUND(SUM(CASE WHEN "week_number" = 14 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_14_percentage", ROUND(SUM(CASE WHEN "week_number" = 15 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_15_percentage", ROUND(SUM(CASE WHEN "week_number" = 16 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_16_percentage", ROUND(SUM(CASE WHEN "week_number" = 17 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_17_percentage", ROUND(SUM(CASE WHEN "week_number" = 18 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_18_percentage", ROUND(SUM(CASE WHEN "week_number" = 19 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_19_percentage", ROUND(SUM(CASE WHEN "week_number" = 20 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_20_percentage", ROUND(SUM(CASE WHEN "week_number" = 21 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_21_percentage", ROUND(SUM(CASE WHEN "week_number" = 22 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_22_percentage", ROUND(SUM(CASE WHEN "week_number" = 23 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_23_percentage", ROUND(SUM(CASE WHEN "week_number" = 24 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_24_percentage", ROUND(SUM(CASE WHEN "week_number" = 25 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_25_percentage", ROUND(SUM(CASE WHEN "week_number" = 26 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_26_percentage", ROUND(SUM(CASE WHEN "week_number" = 27 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_27_percentage", ROUND(SUM(CASE WHEN "week_number" = 28 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_28_percentage", ROUND(SUM(CASE WHEN "week_number" = 29 THEN 1 ELSE 0 END) * 1.0 / NULLIF(SUM(CASE WHEN "week_number" = 0 THEN 1 ELSE 0 END), 0) * 1.0 * 100, 2) AS "week_29_percentage" FROM "weekly_data_with_namber" AS "wd" INNER JOIN "weekly_totals" AS "wt" ON "wd"."eleos_organization" = "wt"."eleos_organization" AND "wd"."first_note_week" = "wt"."week_start" GROUP BY "wd"."eleos_organization", "wd"."first_note_week", "wt"."active_therapists", "wt"."activated_therapists", "wt"."accumulated_therapists" ORDER BY "first_note_week" ASC`)
})
it('should support from unnest', () => {
const sql = `with org_mapping AS (
select * from (
select trim(lower(name)) as name, organization_name,
ROW_NUMBER() OVER (PARTITION BY trim(lower(name))) as rn
from bronze_sales_prod.drive_organization_mapping)
where rn = 1
)
,orgs as (
SELECT trim(COALESCE(om.organization_name, custom_attributes.Account)) AS organization,
*
FROM "bronze_sales_prod"."intercom_all_conversations"
JOIN org_mapping om ON trim(lower(custom_attributes.Account)) = trim(lower(om.name))
)
, orgs_dates_metrics as (
select organization,
cast(from_unixtime(created_at) as date) AS conversation_ctreated_date,
array_agg(custom_attributes) custom_attributes_array,
approx_percentile(case when state = 'closed' then date_diff('minute', from_unixtime(created_at), from_unixtime(updated_at)) else 0 end,0.5) as median_resolution_time_minutes,
sum(case when state IN ('open', 'snoozed') then 1 else 0 end) open_conversations_count,
count(*) overall_conversations_count
from orgs
where organization is not null
group by 1,2
)
,last_year_org_dates as
(
SELECT distinct om.organization_name as organization,
date_add('day', -sequence, current_date) AS date
FROM UNNEST(sequence(1, 365)) AS t
join org_mapping om on 1=1
WHERE
date_add('day', -sequence, current_date) >= date_add('year', -1, current_date)
), counted_tags as (
SELECT organization
,cast(from_unixtime(created_at) as date) AS conversation_ctreated_date
,tag.name as tag
, json_extract_scalar(cast(source AS json), '$.author.email') AS author_email
,COUNT(*) AS count
FROM orgs
CROSS JOIN UNNEST(tags.tags) AS t(tag)
group by 1,2,3,4
), counted_tags_map as (
SELECT organization, conversation_ctreated_date, author_email,
MAP (
ARRAY_AGG(tag),
ARRAY_AGG(count)
) AS tags_counts
FROM
counted_tags
group by 1,2,3
)
select od.organization as organization_name
, od.date
, t.author_email
, coalesce(t.tags_counts, MAP()) as tags_counts
, coalesce(o.custom_attributes_array, ARRAY[]) as custom_attributes_array
, COALESCE(o.median_resolution_time_minutes,0) as median_resolution_time_minutes
, COALESCE(o.open_conversations_count,0) as open_conversations_count
, COALESCE(o.overall_conversations_count,0) as overall_conversations_count
, cast(current_timestamp as timestamp(6)) as dbt_insert_time
from last_year_org_dates od
left join orgs_dates_metrics o on od.organization = o.organization and od.date = o.conversation_ctreated_date
left join counted_tags_map t on od.organization = t.organization and od.date = t.conversation_ctreated_date`
expect(getParsedSql(sql)).to.be.equal(`WITH "org_mapping" AS (SELECT * FROM (SELECT TRIM(lower("name")) AS "name", "organization_name", ROW_NUMBER() OVER (PARTITION BY TRIM(lower("name"))) AS "rn" FROM "bronze_sales_prod"."drive_organization_mapping") WHERE "rn" = 1), "orgs" AS (SELECT TRIM(COALESCE("om"."organization_name", "custom_attributes"."Account")) AS "organization", * FROM "bronze_sales_prod"."intercom_all_conversations" INNER JOIN "org_mapping" AS "om" ON TRIM(lower("custom_attributes"."Account")) = TRIM(lower("om"."name"))), "orgs_dates_metrics" AS (SELECT "organization", CAST(from_unixtime("created_at") AS DATE) AS "conversation_ctreated_date", ARRAY_AGG("custom_attributes") AS "custom_attributes_array", approx_percentile(CASE WHEN "state" = 'closed' THEN date_diff('minute', from_unixtime("created_at"), from_unixtime("updated_at")) ELSE 0 END, 0.5) AS "median_resolution_time_minutes", SUM(CASE WHEN "state" IN ('open', 'snoozed') THEN 1 ELSE 0 END) AS "open_conversations_count", COUNT(*) OVER all_conversations_count FROM "orgs" WHERE "organization" IS NOT NULL GROUP BY 1, 2), "last_year_org_dates" AS (SELECT DISTINCT "om"."organization_name" AS "organization", date_add('day', -"sequence", CURRENT_DATE) AS DATE FROM UNNEST(sequence(1, 365)) AS "t" INNER JOIN "org_mapping" AS "om" ON 1 = 1 WHERE date_add('day', -"sequence", CURRENT_DATE) >= date_add('year', -1, CURRENT_DATE)), "counted_tags" AS (SELECT "organization", CAST(from_unixtime("created_at") AS DATE) AS "conversation_ctreated_date", "tag"."name" AS "tag", json_extract_scalar(CAST("source" AS JSON), '$.author.email') AS "author_email", COUNT(*) AS "count" FROM "orgs" CROSS JOIN UNNEST("tags"."tags") AS t("tag") GROUP BY 1, 2, 3, 4), "counted_tags_map" AS (SELECT "organization", "conversation_ctreated_date", "author_email", MAP(ARRAY_AGG("tag"), ARRAY_AGG("count")) AS "tags_counts" FROM "counted_tags" GROUP BY 1, 2, 3) SELECT "od"."organization" AS "organization_name", "od"."date", "t"."author_email", coalesce("t"."tags_counts", MAP()) AS "tags_counts", coalesce("o"."custom_attributes_array", ARRAY[]) AS "custom_attributes_array", COALESCE("o"."median_resolution_time_minutes", 0) AS "median_resolution_time_minutes", COALESCE("o"."open_conversations_count", 0) AS "open_conversations_count", COALESCE("o"."overall_conversations_count", 0) AS "overall_conversations_count", CAST(CURRENT_TIMESTAMP AS TIMESTAMP(6)) AS "dbt_insert_time" FROM "last_year_org_dates" AS "od" LEFT JOIN "orgs_dates_metrics" AS "o" ON "od"."organization" = "o"."organization" AND "od"."date" = "o"."conversation_ctreated_date" LEFT JOIN "counted_tags_map" AS "t" ON "od"."organization" = "t"."organization" AND "od"."date" = "t"."conversation_ctreated_date"`)
})
it('should parse with clause', () => {
const sql = `WITH user_logins as (
SELECT user_id, event, dttm, dashboard_id, slice_id
FROM (
SELECT l.user_id, 'login' AS event, l.dttm, CAST(NULL as bigint) AS dashboard_id, CAST(NULL as bigint) AS slice_id,
LAG(l.dttm) OVER (PARTITION BY l.user_id ORDER BY l.dttm) AS previous_dttm
FROM "bronze_prod"."superset_logs" l
WHERE l.action = 'welcome'
)
WHERE previous_dttm IS NULL -- Keep the first record
OR dttm > previous_dttm + INTERVAL '1' HOUR -- Only keep records that are more than 1 hour apart
ORDER BY user_id, dttm
),
user_events as (
SELECT l.user_id, json_extract_scalar(l."json", '$.event_name') AS event, l.dttm,
NULLIF(COALESCE(cast(json_extract_scalar(l."json", '$.source_id') as bigint), l.dashboard_id), 0) AS dashboard_id,
NULLIF(COALESCE(cast(json_extract_scalar(l."json", '$.slice_id') as bigint), cast(json_extract_scalar(l."json", '$.chartId') as bigint), l.slice_id), 0) AS slice_id
FROM "bronze_prod"."superset_logs" l
WHERE json_extract_scalar("json", '$.event_name') IN (
'spa_navigation',
'mount_dashboard',
'export_csv_dashboard_chart',
'chart_download_as_image',
'export_xlsx_dashboard_chart',
'change_dashboard_filter'
)
),
export_dashboard_logs as (
SELECT user_id, event, dttm,
CAST(json_extract_scalar(json_array_element, '$.value') as bigint) AS dashboard_id,
CAST(NULL as bigint) as slice_id
FROM (
SELECT user_id, event, dttm,
json_array_element
FROM (
SELECT l.user_id, 'export_dashboard' AS event, l.dttm,
json_extract(l."json", '$.rison.filters') AS filters_array
FROM
"bronze_prod"."superset_logs" l
WHERE action = 'ReportScheduleRestApi.get_list'
)
CROSS JOIN UNNEST(CAST(filters_array AS ARRAY<json>)) AS t (json_array_element)
WHERE
json_extract_scalar(json_array_element, '$.col') = 'dashboard_id'
)
),
relevant_logs as (
SELECT *, ROW_NUMBER() OVER(PARTITION BY user_id, dttm ORDER BY dashboard_id) as RN
FROM(
SELECT user_id, dttm, event, max(dashboard_id) as dashboard_id, max(slice_id) as slice_id
FROM (
SELECT *
FROM user_logins
UNION ALL
SELECT *
FROM user_events
UNION ALL
SELECT *
FROM export_dashboard_logs
)
GROUP BY user_id, dttm, event
)
),
organizational_domains as (
SELECT lower(split_part(split_part(therapist_mail, '@', 2), '.', 1)) AS organization_domain, max(therapist_organization_name) as organization
from "silver_prod"."eleos_full_therapist_info"
group by 1
)
SELECT l.user_id, l.dttm, l.event, l.dashboard_id, l.slice_id, u.last_name, u.email, o.organization, d.dashboard_title, s.slice_name, 'Client Facing' as superset_instance
FROM relevant_logs l
JOIN "bronze_prod"."superset_ab_user" u ON l.user_id = u.id
LEFT JOIN "bronze_prod"."superset_dashboards" d ON l.dashboard_id = d.id
LEFT JOIN "bronze_prod"."superset_slices" s ON l.slice_id = s.id
LEFT JOIN organizational_domains o ON lower(split_part(split_part(u.email, '@', 2), '.', 1)) = o.organization_domain
WHERE RN = 1 AND lower(u.email) NOT LIKE '%eleos%'
AND lower(u.email) NOT LIKE '%test%'
AND lower(u.username) NOT LIKE '%eleos%'
AND lower(u.username) NOT LIKE '%test%'
AND lower(u.username) NOT LIKE '%admin%'`
expect(getParsedSql(sql)).to.be.equal(`WITH "user_logins" AS (SELECT "user_id", "event", "dttm", "dashboard_id", "slice_id" FROM (SELECT "l"."user_id", 'login' AS "event", "l"."dttm", CAST(NULL AS BIGINT) AS "dashboard_id", CAST(NULL AS BIGINT) AS "slice_id", LAG("l"."dttm") OVER (PARTITION BY "l"."user_id" ORDER BY "l"."dttm" ASC) AS "previous_dttm" FROM "bronze_prod"."superset_logs" AS "l" WHERE "l"."action" = 'welcome') WHERE "previous_dttm" IS NULL OR "dttm" > "previous_dttm" + INTERVAL '1' HOUR ORDER BY "user_id" ASC, "dttm" ASC), "user_events" AS (SELECT "l"."user_id", json_extract_scalar("l"."json", '$.event_name') AS "event", "l"."dttm", NULLIF(COALESCE(CAST(json_extract_scalar("l"."json", '$.source_id') AS BIGINT), "l"."dashboard_id"), 0) AS "dashboard_id", NULLIF(COALESCE(CAST(json_extract_scalar("l"."json", '$.slice_id') AS BIGINT), CAST(json_extract_scalar("l"."json", '$.chartId') AS BIGINT), "l"."slice_id"), 0) AS "slice_id" FROM "bronze_prod"."superset_logs" AS "l" WHERE json_extract_scalar("json", '$.event_name') IN ('spa_navigation', 'mount_dashboard', 'export_csv_dashboard_chart', 'chart_download_as_image', 'export_xlsx_dashboard_chart', 'change_dashboard_filter')), "export_dashboard_logs" AS (SELECT "user_id", "event", "dttm", CAST(json_extract_scalar("json_array_element", '$.value') AS BIGINT) AS "dashboard_id", CAST(NULL AS BIGINT) AS "slice_id" FROM (SELECT "user_id", "event", "dttm", "json_array_element" FROM (SELECT "l"."user_id", 'export_dashboard' AS "event", "l"."dttm", json_extract("l"."json", '$.rison.filters') AS "filters_array" FROM "bronze_prod"."superset_logs" AS "l" WHERE "action" = 'ReportScheduleRestApi.get_list') CROSS JOIN UNNEST(CAST("filters_array" AS ARRAY<JSON>)) AS t("json_array_element") WHERE json_extract_scalar("json_array_element", '$.col') = 'dashboard_id')), "relevant_logs" AS (SELECT *, ROW_NUMBER() OVER (PARTITION BY "user_id", "dttm" ORDER BY "dashboard_id" ASC) AS "RN" FROM (SELECT "user_id", "dttm", "event", MAX("dashboard_id") AS "dashboard_id", MAX("slice_id") AS "slice_id" FROM (SELECT * FROM "user_logins" UNION ALL SELECT * FROM "user_events" UNION ALL SELECT * FROM "export_dashboard_logs") GROUP BY "user_id", "dttm", "event")), "organizational_domains" AS (SELECT lower(split_part(split_part("therapist_mail", '@', 2), '.', 1)) AS "organization_domain", MAX("therapist_organization_name") AS "organization" FROM "silver_prod"."eleos_full_therapist_info" GROUP BY 1) SELECT "l"."user_id", "l"."dttm", "l"."event", "l"."dashboard_id", "l"."slice_id", "u"."last_name", "u"."email", "o"."organization", "d"."dashboard_title", "s"."slice_name", 'Client Facing' AS "superset_instance" FROM "relevant_logs" AS "l" INNER JOIN "bronze_prod"."superset_ab_user" AS "u" ON "l"."user_id" = "u"."id" LEFT JOIN "bronze_prod"."superset_dashboards" AS "d" ON "l"."dashboard_id" = "d"."id" LEFT JOIN "bronze_prod"."superset_slices" AS "s" ON "l"."slice_id" = "s"."id" LEFT JOIN "organizational_domains" AS "o" ON lower(split_part(split_part("u"."email", '@', 2), '.', 1)) = "o"."organization_domain" WHERE "RN" = 1 AND lower("u"."email") NOT LIKE '%eleos%' AND lower("u"."email") NOT LIKE '%test%' AND lower("u"."username") NOT LIKE '%eleos%' AND lower("u"."username") NOT LIKE '%test%' AND lower("u"."username") NOT LIKE '%admin%'`)
})
it('should support filter function', () => {
let sql = `SELECT
id,
CAST(CURRENT_TIMESTAMP AS TIMESTAMP(6)) AS dbt_insert_time
FROM
some_table
WHERE
cardinality(
filter(map_values(note), VALUE -> VALUE IS NOT NULL)
) = 0;`
expect(getParsedSql(sql)).to.be.equal('SELECT "id", CAST(CURRENT_TIMESTAMP AS TIMESTAMP(6)) AS "dbt_insert_time" FROM "some_table" WHERE cardinality(FILTER(map_values("note"), VALUE -> "VALUE" IS NOT NULL)) = 0')
sql = `WITH
base_data AS (
SELECT
*
FROM
(
abc sqls
FULL JOIN def mqls ON (sqls.lead_id = mqls.id)
)
ORDER BY
COALESCE(opp_created_date, CAST(mqls.mql_date AS date)) ASC
),
layer_1 AS (
SELECT
*
FROM
base_data
ORDER BY
xxx_date DESC
)
SELECT
*,
(
CASE
WHEN (
opp_closed_quarter_start_date > opp_created_quarter_start_date
) THEN ARRAY_JOIN (
TRANSFORM (
FILTER (
SEQUENCE (
DATE_ADD('month', 3, opp_created_quarter_start_date),
DATE_ADD(
'month',
(
3 * (
date_diff (
'month',
opp_created_quarter_start_date,
opp_closed_quarter_start_date
) / 3
)
),
opp_created_quarter_start_date
),
INTERVAL '3' MONTH
),
(x) -> (
(x > opp_created_quarter_start_date)
AND (x < opp_closed_quarter_start_date)
)
),
(x) -> date_format(x, '%Y-%m-%d')
),
', '
)
ELSE null
END
) active_q
FROM
layer_1`
expect(getParsedSql(sql)).to.be.equal(`WITH "base_data" AS (SELECT * FROM ("abc" AS "sqls" FULL JOIN "def" AS "mqls" ON ("sqls"."lead_id" = "mqls"."id")) ORDER BY COALESCE("opp_created_date", CAST("mqls"."mql_date" AS DATE)) ASC), "layer_1" AS (SELECT * FROM "base_data" ORDER BY "xxx_date" DESC) SELECT *, (CASE WHEN ("opp_closed_quarter_start_date" > "opp_created_quarter_start_date") THEN ARRAY_JOIN(TRANSFORM(FILTER(SEQUENCE(DATE_ADD('month', 3, "opp_created_quarter_start_date"), DATE_ADD('month', (3 * (date_diff('month', "opp_created_quarter_start_date", "opp_closed_quarter_start_date") / 3)), "opp_created_quarter_start_date"), INTERVAL '3' MONTH), ("x") -> (("x" > "opp_created_quarter_start_date") AND ("x" < "opp_closed_quarter_start_date"))), ("x") -> date_format("x", '%Y-%m-%d')), ', ') ELSE NULL END) AS "active_q" FROM "layer_1"`)
})
it('should support key as column name', () => {
let sql = `WITH CTE AS (
SELECT * FROM test_cte
)
SELECT
organization,
date,
author_email,
t.key AS tag,
t.value AS count
FROM CTE
CROSS JOIN UNNEST(tags_counts) AS t(key, value)
ORDER BY 1, 2`;
expect(getParsedSql(sql)).to.be.equal('WITH "CTE" AS (SELECT * FROM "test_cte") SELECT "organization", DATE , "author_email", "t"."key" AS "tag", "t"."value" AS "count" FROM "CTE" CROSS JOIN UNNEST("tags_counts") AS t("key", "value") ORDER BY 1 ASC, 2 ASC')
sql = `SELECT
j.id,
h.created AS change_time,
i.fromstring AS from_status,
i.tostring AS to_status
FROM
"bronze_prod"."jira_issues" j
CROSS JOIN
UNNEST(j.changelog.histories) AS T (h)
CROSS JOIN
UNNEST(h.items) AS T (i)
WHERE
i.field = 'status'`
expect(getParsedSql(sql)).to.be.equal(`SELECT "j"."id", "h"."created" AS "change_time", "i"."fromstring" AS "from_status", "i"."tostring" AS "to_status" FROM "bronze_prod"."jira_issues" AS "j" CROSS JOIN UNNEST("j"."changelog"."histories") AS T("h") CROSS JOIN UNNEST("h"."items") AS T("i") WHERE "i"."field" = 'status'`)
sql = `SELECT id, array_agg(json_extract_scalar(elem, '$.value')) er_teams
FROM "bronze_prod"."jira_issues"
CROSS JOIN UNNEST(cast(json_extract(json_parse(fields), '$.customfield_10100') AS array(json))) AS t(elem)
GROUP BY id`
expect(getParsedSql(sql)).to.be.equal(`SELECT "id", array_agg(json_extract_scalar("elem", '$.value')) AS "er_teams" FROM "bronze_prod"."jira_issues" CROSS JOIN UNNEST(CAST(json_extract(json_parse("fields"), '$.customfield_10100') AS ARRAY(JSON))) AS t("elem") GROUP BY "id"`)
})
it('should support double quoted table mentions', () => {
const sql = `SELECT
"my_table"."my_column"
FROM
"my_table"`
expect(getParsedSql(sql)).to.be.equal('SELECT "my_table"."my_column" FROM "my_table"')
})
it('should over partition by', () => {
const sql = `select count(renewals_effective_date) OVER (PARTITION BY customer_id ORDER BY update_date ASC, renewals_effective_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) helper
from some_table`
expect(getParsedSql(sql)).to.be.equal('SELECT COUNT("renewals_effective_date") OVER (PARTITION BY "customer_id" ORDER BY "update_date" ASC, "renewals_effective_date" ASC ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) AS "helper" FROM "some_table"')
})
it('quoted function name, from multiple parentheses', () => {
const sql = 'SELECT "concat"(name, name) from ((raw_listings))'
expect(getParsedSql(sql)).to.be.equal('SELECT "concat"("name", "name") FROM (("raw_listings"))')
})
it('array_agg and trim function', () => {
let sql = "select ARRAY_AGG(comments.real_name || ':' || comments.text ORDER BY comments.comment_ts) AS comments FROM some_table"
expect(getParsedSql(sql)).to.be.equal(`SELECT ARRAY_AGG("comments"."real_name" || ':' || "comments"."text" ORDER BY "comments"."comment_ts" ASC) AS "comments" FROM "some_table"`)
sql = `select trim(BOTH FROM split("a,b", ',')[1]) from model_a`
expect(getParsedSql(sql)).to.be.equal(`SELECT TRIM(BOTH FROM split("a,b", ',')[1]) FROM "model_a"`)
})
it('from values clause', () => {
let sql = `SELECT
CAST(date_column AS date) update_date
FROM
(
(
VALUES
"sequence" (
"date"('2022-10-01'),
current_date,
INTERVAL '1' DAY
)
) t1 (date_array)
CROSS JOIN UNNEST (date_array) t2 (date_column)
)`
expect(getParsedSql(sql)).to.be.equal(`SELECT CAST("date_column" AS DATE) AS "update_date" FROM ((VALUES ("sequence"("date"('2022-10-01'), CURRENT_DATE, INTERVAL '1' DAY))) AS t1("date_array") CROSS JOIN UNNEST("date_array") AS t2("date_column"))`)
sql = `SELECT
leads.*
, from_unixtime(CAST(substrinfg(leads.demo_meeting_date_c, 1, 10) AS double)) demo_meeting_date_correct
, CAST(first_conversion_date_c AS date) first_conversion_date_correct
, aes.ae_owner
, created.created_by
FROM
(((("salesforce_leads" leads
LEFT JOIN salesforce_opportunitys opps ON (leads.converted_opportunity_id = opps.id))
LEFT JOIN (
SELECT
id user_id
, name lead_owner
FROM
salesforce_users
) owners ON (leads.owner_id = owners.user_id))
LEFT JOIN (
SELECT
id user_id
, name ae_owner
FROM
salesforce_users
) aes ON (leads.a_e_owner_c = aes.user_id))
LEFT JOIN (
SELECT
id user_id
, name created_by
FROM
salesforce_users
) created ON (leads.created_by_id = created.user_id))
WHERE (((NOT (leads.company LIKE '%test%')) AND (NOT (leads.company LIKE '%Test%'))) AND ((NOT (leads.name LIKE '%test%')) AND (NOT (leads.name LIKE '%Test%'))))`
expect(getParsedSql(sql)).to.be.equal(`SELECT "leads".*, from_unixtime(CAST(substrinfg("leads"."demo_meeting_date_c", 1, 10) AS DOUBLE)) AS "demo_meeting_date_correct", CAST("first_conversion_date_c" AS DATE) AS "first_conversion_date_correct", "aes"."ae_owner", "created"."created_by" FROM (((("salesforce_leads" AS "leads" LEFT JOIN "salesforce_opportunitys" AS "opps" ON ("leads"."converted_opportunity_id" = "opps"."id")) LEFT JOIN (SELECT "id" AS "user_id", "name" AS "lead_owner" FROM "salesforce_users") AS "owners" ON ("leads"."owner_id" = "owners"."user_id")) LEFT JOIN (SELECT "id" AS "user_id", "name" AS "ae_owner" FROM "salesforce_users") AS "aes" ON ("leads"."a_e_owner_c" = "aes"."user_id")) LEFT JOIN (SELECT "id" AS "user_id", "name" AS "created_by" FROM "salesforce_users") AS "created" ON ("leads"."created_by_id" = "created"."user_id")) WHERE (((NOT("leads"."company" LIKE '%test%')) AND (NOT("leads"."company" LIKE '%Test%'))) AND ((NOT("leads"."name" LIKE '%test%')) AND (NOT("leads"."name" LIKE '%Test%'))))`)
})
it('should support boolean type', () => {
const sql = 'SELECT * from table_name WHERE CAST ( foo.bar.baz as boolean) = false'
expect(getParsedSql(sql)).to.be.equal('SELECT * FROM "table_name" WHERE CAST("foo"."bar"."baz" AS BOOLEAN) = FALSE')
})
})