2022-08-28 06:38:35 +02:00
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import prisma from 'lib/prisma';
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2022-08-26 07:04:32 +02:00
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import clickhouse from 'lib/clickhouse';
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2022-08-28 06:38:35 +02:00
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import { runQuery, CLICKHOUSE, PRISMA } from 'lib/db';
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2023-01-11 20:01:44 +01:00
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import { EVENT_TYPE } from 'lib/constants';
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2023-04-02 02:38:35 +02:00
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import { loadWebsite } from 'lib/query';
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2022-07-12 23:14:36 +02:00
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2022-12-13 04:45:38 +01:00
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export async function getWebsiteStats(
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...args: [
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websiteId: string,
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data: { startDate: Date; endDate: Date; type?: string; filters: object },
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]
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) {
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2022-08-28 06:38:35 +02:00
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return runQuery({
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[PRISMA]: () => relationalQuery(...args),
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2022-07-25 18:47:11 +02:00
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[CLICKHOUSE]: () => clickhouseQuery(...args),
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2022-07-21 06:31:26 +02:00
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});
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}
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2022-12-13 04:45:38 +01:00
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async function relationalQuery(
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websiteId: string,
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criteria: { startDate: Date; endDate: Date; filters: object },
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) {
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2023-04-02 00:44:30 +02:00
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const { startDate, endDate, filters = {} } = criteria;
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2023-01-11 20:01:44 +01:00
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const { toUuid, getDateQuery, getTimestampInterval, parseFilters, rawQuery } = prisma;
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2023-04-02 02:38:35 +02:00
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const website = await loadWebsite(websiteId);
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2023-04-20 06:08:52 +02:00
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const resetDate = new Date(website?.resetAt || website?.createdAt);
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2023-03-27 20:25:16 +02:00
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const params: any = [websiteId, resetDate, startDate, endDate];
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2022-12-13 04:45:38 +01:00
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const { filterQuery, joinSession } = parseFilters(filters, params);
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2022-07-12 23:14:36 +02:00
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return rawQuery(
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`select sum(t.c) as "pageviews",
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count(distinct t.session_id) as "uniques",
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sum(case when t.c = 1 then 1 else 0 end) as "bounces",
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2022-07-23 07:42:01 +02:00
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sum(t.time) as "totaltime"
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from (
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2023-01-11 20:01:44 +01:00
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select website_event.session_id,
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${getDateQuery('website_event.created_at', 'hour')},
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count(*) c,
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${getTimestampInterval('website_event.created_at')} as "time"
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from website_event
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2022-10-12 04:37:38 +02:00
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join website
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on website_event.website_id = website.website_id
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${joinSession}
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2023-04-19 18:13:16 +02:00
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where event_type = ${EVENT_TYPE.pageView}
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and website.website_id = $1${toUuid()}
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2023-03-27 20:25:16 +02:00
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and website_event.created_at >= $2
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and website_event.created_at between $3 and $4
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${filterQuery}
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group by 1, 2
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) t`,
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params,
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);
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}
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2022-07-21 06:31:26 +02:00
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2022-12-13 04:45:38 +01:00
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async function clickhouseQuery(
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websiteId: string,
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criteria: { startDate: Date; endDate: Date; filters: object },
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2022-12-13 04:45:38 +01:00
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) {
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const { startDate, endDate, filters = {} } = criteria;
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const { rawQuery, getDateFormat, getDateQuery, getBetweenDates, parseFilters } = clickhouse;
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const website = await loadWebsite(websiteId);
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2023-04-20 06:16:56 +02:00
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const resetDate = new Date(website?.resetAt || website?.createdAt);
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const params = { websiteId };
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const { filterQuery } = parseFilters(filters, params);
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2022-08-28 06:38:35 +02:00
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return rawQuery(
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`select
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sum(t.c) as "pageviews",
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count(distinct t.session_id) as "uniques",
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sum(if(t.c = 1, 1, 0)) as "bounces",
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sum(if(max_time < min_time + interval 1 hour, max_time-min_time, 0)) as "totaltime"
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from (
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select session_id,
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${getDateQuery('created_at', 'day')} time_series,
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count(*) c,
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min(created_at) min_time,
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max(created_at) max_time
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2023-03-29 20:06:12 +02:00
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from website_event
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where event_type = ${EVENT_TYPE.pageView}
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2023-01-12 09:02:12 +01:00
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and website_id = {websiteId:UUID}
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and created_at >= ${getDateFormat(resetDate)}
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and ${getBetweenDates('created_at', startDate, endDate)}
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${filterQuery}
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group by session_id, time_series
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) t;`,
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params,
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);
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}
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