Victor FeyensandThibault Delavallee
3f6625ef13
PURPOSE Several counter fields are performed inefficiently. They use compute methods using length of 2many fields instead of making use of more efficient ORM methods like read_group. SPECIFICATIONS There may be a lot of clicks linked to a given link tracker. Compute the count of clicks in batch with a read_group to improve performance. Same for utm campaign mailing count performances. Some missing indexes are added to speedup queries. LINKS Task ID 2196182 PR #44662 Co-Authored-By: Victor Feyens <vfe@odoo.com> Co-Authored-By: Thibault Delavallee <tde@odoo.com>
95 lines
3.9 KiB
Python
95 lines
3.9 KiB
Python
# -*- coding: utf-8 -*-
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# Part of Odoo. See LICENSE file for full copyright and licensing details.
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from odoo import api, fields, models
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class UtmCampaign(models.Model):
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_inherit = 'utm.campaign'
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mailing_mail_ids = fields.One2many(
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'mailing.mailing', 'campaign_id',
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domain=[('mailing_type', '=', 'mail')],
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string='Mass Mailings')
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mailing_mail_count = fields.Integer('Number of Mass Mailing', compute="_compute_mailing_mail_count")
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# stat fields
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received_ratio = fields.Integer(compute="_compute_statistics", string='Received Ratio')
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opened_ratio = fields.Integer(compute="_compute_statistics", string='Opened Ratio')
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replied_ratio = fields.Integer(compute="_compute_statistics", string='Replied Ratio')
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bounced_ratio = fields.Integer(compute="_compute_statistics", string='Bounced Ratio')
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@api.depends('mailing_mail_ids')
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def _compute_mailing_mail_count(self):
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if self.ids:
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mailing_data = self.env['mailing.mailing'].read_group(
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[('campaign_id', 'in', self.ids)],
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['campaign_id'],
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['campaign_id']
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)
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mapped_data = {m['campaign_id'][0]: m['campaign_id_count'] for m in mailing_data}
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else:
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mapped_data = dict()
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for campaign in self:
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campaign.mailing_mail_count = mapped_data.get(campaign.id, 0)
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def _compute_statistics(self):
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""" Compute statistics of the mass mailing campaign """
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self.env.cr.execute("""
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SELECT
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c.id as campaign_id,
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COUNT(s.id) AS expected,
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COUNT(CASE WHEN s.sent is not null THEN 1 ELSE null END) AS sent,
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COUNT(CASE WHEN s.scheduled is not null AND s.sent is null AND s.exception is null AND s.ignored is not null THEN 1 ELSE null END) AS ignored,
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COUNT(CASE WHEN s.id is not null AND s.bounced is null THEN 1 ELSE null END) AS delivered,
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COUNT(CASE WHEN s.opened is not null THEN 1 ELSE null END) AS opened,
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COUNT(CASE WHEN s.replied is not null THEN 1 ELSE null END) AS replied,
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COUNT(CASE WHEN s.bounced is not null THEN 1 ELSE null END) AS bounced
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FROM
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mailing_trace s
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RIGHT JOIN
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utm_campaign c
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ON (c.id = s.campaign_id)
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WHERE
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c.id IN %s
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GROUP BY
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c.id
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""", (tuple(self.ids), ))
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all_stats = self.env.cr.dictfetchall()
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stats_per_campaign = {
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stats['campaign_id']: stats
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for stats in all_stats
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}
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for campaign in self:
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stats = stats_per_campaign.get(campaign.id)
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if not stats:
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vals = {
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'received_ratio': 0,
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'opened_ratio': 0,
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'replied_ratio': 0,
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'bounced_ratio': 0
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}
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else:
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total = (stats['expected'] - stats['ignored']) or 1
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delivered = stats['sent'] - stats['bounced']
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vals = {
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'received_ratio': 100.0 * delivered / total,
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'opened_ratio': 100.0 * stats['opened'] / total,
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'replied_ratio': 100.0 * stats['replied'] / total,
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'bounced_ratio': 100.0 * stats['bounced'] / total
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}
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campaign.update(vals)
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def _get_mailing_recipients(self, model=None):
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"""Return the recipients of a mailing campaign. This is based on the statistics
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build for each mailing. """
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res = dict.fromkeys(self.ids, {})
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for campaign in self:
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domain = [('campaign_id', '=', campaign.id)]
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if model:
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domain += [('model', '=', model)]
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res[campaign.id] = set(self.env['mailing.trace'].search(domain).mapped('res_id'))
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return res
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