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The analytics system must be fully integrated with orders, customers, products, inventory, marketing campaigns, payments, and user activity. Include the following features: - Sales Dashboard: Real-time overview of revenue, orders, average order value (AOV), sales trends, top-selling products, top-performing categories, payment methods, refunds, taxes, and key business KPIs. - Revenue Reports: Analyze revenue by day, week, month, quarter, year, custom date range, product, category, brand, customer, sales channel, and payment method. Support comparison reports and forecast trends. - Customer Analytics: Track customer growth, acquisition sources, new vs. returning customers, customer lifetime value (CLV), purchase frequency, retention rate, churn rate, geographic distribution, demographics (where available), and customer segmentation. - Product Performance: Monitor product views, impressions, add-to-cart rate, sales, conversion rate, revenue, profit margin, inventory turnover, stock availability, return rate, wishlist activity, and best/worst-performing products. - Funnel Analytics: Visualize the complete customer journey, including Landing Page → Product View → Add to Cart → Checkout → Payment → Order Completed. Identify drop-off points and bottlenecks with detailed conversion metrics. - Conversion Rate Analytics: Measure conversion rates across marketing campaigns, traffic sources, devices, browsers, landing pages, categories, products, and checkout stages. Support A/B testing comparison and attribution reporting. - Cohort Analysis: Analyze customer retention and purchasing behavior by signup date, first purchase, acquisition channel, campaign, or custom cohorts. Display repeat purchase trends, retention curves, and revenue contribution over time. Additional requirements: - Interactive dashboards with charts, graphs, heatmaps, and drill-down reports. - Real-time data updates with configurable refresh intervals. - Advanced filtering by date range, location, channel, customer segment, product, category, and campaign. - Export reports in CSV, Excel, and PDF formats. - Schedule automated report generation and email delivery. - Role-based dashboard access and permissions. - Seamless integration with Google Analytics 4, Google Tag Manager, Meta Pixel, Meta Conversions API, and other supported analytics providers. - High-performance queries, data caching, audit logging, and a scalable architecture capable of handling large datasets in production.Add schema fields to catalog, orders, identity Build analytics event spine models and consent-gated ingest Build daily rollup fact tables and roll_up_analytics command Build report API with scopes, caching, audit, exports, schedules Build analytics frontend registry, API client, and live tabs Verify backend tests, typecheck, build, and brow ser **The 7 sub-modules plus export/scheduling/RBAC/caching is far more than one safe pass in this repo (every module here ships with backend + tests + typecheck + build + browser verification). How should I sequence it?**: Phase 1: event spine + Sales & Revenue (Recommended) **Where should the behavioural data (page views, product views, add-to-cart, checkout steps, device/browser/traffic source) come from?**: First-party store in Django (Recommended) **Several requested metrics need new fields on existing models. Which should I add?**: Product cost price (profit margin), Order sales channel, Customer acquisition source / UTM **Which of the operational extras should be in this build?**: CSV + Excel export (Recommended), Scheduled email reports, Granular dashboard permissions, Query caching + audit loggi ng. Close out schema task: generate migrations and snapshot order-item cost at checkout Build the analytics event spine: session/event models plus consent-gated ingest endpoint Wire first-touch attribution into orders and customer records Verify: pytest, ruff, migration check. Which date should a day's revenue be attributed to? The existing dashboard selector counts revenue only for DELIVERED orders, which means "yesterday's revenue" keeps rising for weeks as orders ship — fine for a live tile, wrong for a settled daily fact table. Store both bases side by side (Recommended) Continue whare you stopped..42:o8,b6:T837,42:o837,The cache defaults to 120 seconds and the currency defaults to "BDT" since neither setting exists elsewhere. I need to add an autouse fixture that clears the shared LocMemCache between tests to prevent interference. Now I'm setting up the test cases and defining the fact fields for DailySalesFact. I'll create a helper to generate sales facts with deterministic values. For the main test, I'm setting up a fact from two days ago with specific order metrics: 4 orders placed, 6 units, 1000 gross revenue, 100 discount, 50 tax, 30 shipping, 980 total, 400 COGS, and 80 refunded on order date. This gives me merchandise of 820, gross profit of 420, margin of 51.2%, net revenue of 900, and AOV of 225. I'll also test currency isolation by creating both BDT and USD facts, verifying that filtering by BDT excludes USD while showing both in currencies_present, and checking that breakdown shares sum to 1. For forecast testing, I'm building a series manually with net_revenue values of 100, 200, 300 complete, then 400 in the next bucket. The forecast should refuse fewer than 3 complete points, and when given a declining series like 300, 200, 100, it should clamp the next value to 0.00 using linear regression (slope -100, intercept 300, value at x=3 equals 0). I'll test bucket start/end/label formatting for different time periods, verify previous_range length matching, and check row_count. The API tests will use the reverse names like "api-v1:analytics:report-sales", and I need to scope test with admin having AnalyticsReportAccess. For the cache test, I'm verifying that identical requests show cache.hit as False on the first call and True on the second, with corresponding audit rows tracking the cache state. When the fact is rewritten (save triggers auto_now on computed_at), the next request should show cache.hit as False again. For export testing, I'm checking that CSV export returns the correct content type and Content-Disposition header with an audit record showing delivery=export, while PDF export should return a 400 error since only CSV and Excel were chosen in the requirements.42:o9,b7:Te080,42:oe080,[V0_FILE]python:file="backend/apps/analytics/exports.py" """ Turning a report payload into a downloadable file. A report payload is a nested object — summary, series, several breakdowns — and a spreadsheet is flat. The bridge is :class:`Sheet`: each report declares the flat tables it decomposes into *once*, and both writers consume that same declaration. Formatting a report twice, once per file type, is how a CSV and an XLSX of the same data start disagreeing. Two things this module is deliberate about. **Formula injection.** A cell beginning ``=``, ``+``, ``-``, ``@`` or a control character is executed by Excel and Sheets when the file is opened. Product names and campaign names are attacker-influenced in an e-commerce store, so every text cell is neutralised with a leading apostrophe on the CSV path. This is the one place where an analytics feature can turn into remote code execution on a merchant's laptop. **Money stays a string.** Amounts are ``Decimal``-exact in the payload and are written as text with a number format applied on the XLSX path, never converted to ``float``. A float round-trip is how a total that reconciled in the API stops reconciling in the spreadsheet. """ from __future__ import annotations import csv import datetime as dt import io from collections.abc import Mapping, Sequence from dataclasses import dataclass, field from decimal import Decimal, InvalidOperation from typing import Any from django.http import HttpResponse CSV_CONTENT_TYPE = "text/csv; charset=utf-8" XLSX_CONTENT_TYPE = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" FORMATS: tuple[str, ...] = ("csv", "xlsx") #: Characters a spreadsheet treats as the start of a formula. _FORMULA_PREFIXES = ("=", "+", "-", "@", "\t", "\r") #: Applied to any column whose name marks it as money, so an exported total is #: readable as currency rather than as a long string of digits. _MONEY_NUMBER_FORMAT = "#,##0.00" _RATE_NUMBER_FORMAT = "0.00%" _MONEY_HINTS = ("revenue", "amount", "profit", "value", "cogs") _RATE_HINTS = ("rate", "share", "delta") @dataclass class Sheet: """One flat table inside an export.""" name: str columns: Sequence[str] rows: Sequence[Mapping[str, Any]] = field(default_factory=list) def cells(self, row: Mapping[str, Any]) -> list[Any]: return [row.get(column) for column in self.columns] def _is_rate(column: str) -> bool: return any(hint in column for hint in _RATE_HINTS) def _is_money(column: str) -> bool: """Money, and only money. The rate test wins outright because the two vocabularies overlap: ``revenue_share`` carries the money hint "revenue" and the rate hint "share", and formatting a 0.75 share as ``0.75`` in a currency column is a number a reader will take for three-quarters of a taka. """ if _is_rate(column): return False return any(hint in column for hint in _MONEY_HINTS) def sanitise(value: Any) -> str: """A CSV-safe text rendering of *value*. ``None`` becomes an empty cell rather than the string "None": a blank means "not applicable" (an AOV with no orders), and writing "None" into a numeric column breaks every downstream consumer that tries to parse it. """ if value is None: return "" if isinstance(value, bool): return "true" if value else "false" text = str(value) if text.startswith(_FORMULA_PREFIXES): # Prefixed rather than stripped: the original text stays legible, and a # legitimate negative number in a text column is not silently altered. return f"'{text}" return text def _decimal(value: Any) -> Decimal | None: if value is None or isinstance(value, bool): return None try: return Decimal(str(value)) except (InvalidOperation, ValueError): return None # --------------------------------------------------------------------------- # Payload -> sheets # --------------------------------------------------------------------------- def _summary_sheet(payload: Mapping[str, Any]) -> Sheet: """The single-row KPI block, transposed to metric-per-row. Transposed because a summary has ~25 metrics and one observation. As columns it is an unreadable horizontal strip; as rows it is a list a human can scan, and adding a metric later does not shift every column. """ summary = payload.get("summary") or {} comparison = (payload.get("comparison") or {}).get("summary") or {} deltas = (payload.get("comparison") or {}).get("deltas") or {} rows = [ { "metric": key, "value": value, "previous_value": comparison.get(key), "change": deltas.get(key), } for key, value in summary.items() ] return Sheet( name="Summary", columns=("metric", "value", "previous_value", "change"), rows=rows ) def _series_sheet(payload: Mapping[str, Any]) -> Sheet | None: series = payload.get("series") or [] if not series: return None # Column order follows the first row's keys so the export mirrors the API # payload rather than imposing a second, divergent ordering. columns = tuple(series[0].keys()) return Sheet(name="Trend", columns=columns, rows=series) def _rows_sheet(name: str, rows: Sequence[Mapping[str, Any]]) -> Sheet | None: if not rows: return None return Sheet(name=name, columns=tuple(rows[0].keys()), rows=rows) def payload_sheets(payload: Mapping[str, Any]) -> list[Sheet]: """Decompose a report payload into the flat tables an export contains.""" sheets: list[Sheet] = [_meta_sheet(payload), _summary_sheet(payload)] series = _series_sheet(payload) if series: sheets.append(series) forecast = _rows_sheet("Forecast", payload.get("forecast") or []) if forecast: sheets.append(forecast) breakdowns = payload.get("breakdowns") if isinstance(breakdowns, Mapping): for dimension, rows in breakdowns.items(): sheet = _rows_sheet(f"By {dimension}", rows) if sheet: sheets.append(sheet) single = _rows_sheet( f"By {payload.get('dimension', 'dimension')}", payload.get("breakdown") or [] ) if single: sheets.append(single) products = _rows_sheet("Top products", payload.get("top_products") or []) if products: sheets.append(products) return sheets def _meta_sheet(payload: Mapping[str, Any]) -> Sheet: """Provenance, first sheet, always present. An exported spreadsheet outlives the request that made it and gets mailed around without its context. Without the range, basis, currency and freshness travelling *inside* the file, a recipient cannot tell whether two exports disagree because the data changed or because they asked different questions. """ window = payload.get("range") or {} freshness = payload.get("freshness") or {} rows = [ {"field": "report", "value": payload.get("report", "")}, {"field": "date_from", "value": window.get("from", "")}, {"field": "date_to", "value": window.get("to", "")}, {"field": "timezone", "value": window.get("timezone", "")}, {"field": "grain", "value": payload.get("grain", "")}, {"field": "revenue_basis", "value": payload.get("basis", "")}, {"field": "currency", "value": payload.get("currency", "")}, { "field": "currencies_present", "value": ", ".join(payload.get("currencies_present") or []), }, {"field": "filters", "value": _format_filters(payload.get("filters") or {})}, {"field": "facts_computed_at", "value": freshness.get("facts_computed_at") or ""}, {"field": "covered_through", "value": freshness.get("covered_through") or ""}, {"field": "data_is_stale", "value": freshness.get("is_stale", False)}, {"field": "exported_at", "value": dt.datetime.now(dt.UTC).isoformat()}, ] return Sheet(name="About", columns=("field", "value"), rows=rows) def _format_filters(filters: Mapping[str, Any]) -> str: if not filters: return "none" return "; ".join(f"{key}={value}" for key, value in sorted(filters.items())) # --------------------------------------------------------------------------- # Writers # --------------------------------------------------------------------------- def write_csv(sheets: Sequence[Sheet]) -> str: """All sheets in one CSV, each preceded by a ``# name`` marker. CSV has no concept of a second sheet, and the alternatives are worse: a zip of files is not a spreadsheet a merchant can open by double-clicking, and dropping everything but the trend would make the CSV and the XLSX exports of the same report contain different data. """ buffer = io.StringIO() writer = csv.writer(buffer, lineterminator="\n") for index, sheet in enumerate(sheets): if index: writer.writerow([]) writer.writerow([f"# {sheet.name}"]) writer.writerow(list(sheet.columns)) for row in sheet.rows: writer.writerow([sanitise(cell) for cell in sheet.cells(row)]) return buffer.getvalue() def write_xlsx(sheets: Sequence[Sheet]) -> bytes: """One worksheet per table, with money and rate formats applied.""" from openpyxl import Workbook from openpyxl.styles import Font from openpyxl.utils import get_column_letter workbook = Workbook() workbook.remove(workbook.active) for sheet in sheets: worksheet = workbook.create_sheet(title=_safe_sheet_title(sheet.name)) worksheet.append(list(sheet.columns)) # A fresh Font rather than ``cell.font.copy(bold=True)``: the copy helper # is deprecated in openpyxl 3.1 and a header has no other styling to keep. header_font = Font(bold=True) for cell in worksheet[1]: cell.font = header_font for row in sheet.rows: values = [] for column, raw in zip(sheet.columns, sheet.cells(row), strict=True): number = _decimal(raw) if (_is_money(column) or _is_rate(column)) else None # Written as Decimal, never float: openpyxl serialises a Decimal # exactly, so an exported total still reconciles to the API's. values.append(number if number is not None else sanitise(raw)) worksheet.append(values) for index, column in enumerate(sheet.columns, start=1): letter = get_column_letter(index) if _is_money(column): fmt: str | None = _MONEY_NUMBER_FORMAT elif _is_rate(column): fmt = _RATE_NUMBER_FORMAT else: fmt = None if fmt: for cell in worksheet[letter][1:]: cell.number_format = fmt # Width from the header alone. Measuring every cell means walking the # whole sheet a second time, and a header is a good proxy for a column # of formatted numbers. worksheet.column_dimensions[letter].width = max(len(column) + 2, 12) worksheet.freeze_panes = "A2" stream = io.BytesIO() workbook.save(stream) return stream.getvalue() def _safe_sheet_title(name: str) -> str: """Excel rejects ``[]:*?/\\`` in a sheet name and caps it at 31 characters.""" cleaned = "".join("-" if char in "[]:*?/\\" else char for char in name) return cleaned[:31] or "Sheet" def export_response( payload: Mapping[str, Any], *, export_format: str, filename_stem: str ) -> HttpResponse: """A download response for *payload* in the requested format.""" if export_format not in FORMATS: raise ValueError(f"unsupported export format: {export_format}") sheets = payload_sheets(payload) stamp = dt.datetime.now(dt.UTC).strftime("%Y%m%d") filename = f"{filename_stem}-{stamp}.{export_format}" if export_format == "csv": # BOM so Excel on Windows reads UTF-8 rather than the system codepage, # which is the difference between a Bangla product name and mojibake. body = "\ufeff" + write_csv(sheets) response = HttpResponse(body, content_type=CSV_CONTENT_TYPE) else: response = HttpResponse(write_xlsx(sheets), content_type=XLSX_CONTENT_TYPE) response["Content-Disposition"] = f'attachment; filename="{filename}"' # An export is a point-in-time snapshot of mutable facts; a cached copy served # to the next caller would carry the wrong range in its About sheet. response["Cache-Control"] = "no-store" return response __all__ = [ "CSV_CONTENT_TYPE", "FORMATS", "XLSX_CONTENT_TYPE", "Sheet", "export_response", "payload_sheets", "sanitise", "write_csv", "write_xlsx", ] [V0_FILE]python:file="backend/apps/analytics/reports.py" """ The read layer for the reporting API. Every function here reads the **daily fact tables**, never the raw event spine and never the orders tables. That is the whole point of the facts: a year of revenue by channel is a few thousand pre-aggregated rows instead of millions of order items, so a report is bounded work regardless of how much the store has sold. Four rules this module keeps. **Ratios are computed here, never stored.** AOV, margin and conversion are derived at read time from the additive numerators and denominators in the facts. Storing them would make re-aggregation impossible — a week is not the average of seven daily averages — and the moment a definition changed, history would be a mix of two definitions with no way to tell which row used which. **One currency per answer.** Fact rows are per-currency by design, so a report takes a currency and filters to it, reporting which other currencies were present rather than silently converting. A total that mixed currencies at whatever rate was current at read time could never be restated or reconciled. **Buckets are grouped in Python, not in SQL.** The facts are already one row per local day, so a week/month/quarter/year bucket is a cheap regrouping of dates. Doing it in SQL would mean engine-specific date truncation with a timezone argument — the exact class of expression that makes SQLite and PostgreSQL disagree — for no measurable gain on rows this few. **A trailing partial bucket is labelled, not hidden.** The last week or month of a range is usually incomplete. It stays in the series (dropping it makes a chart lie about the present) but carries ``is_partial``, and it is excluded from the forecast fit so a half-finished month cannot drag a trend line down. """ from __future__ import annotations import datetime as dt from collections.abc import Iterable, Mapping, Sequence from dataclasses import dataclass from decimal import Decimal from typing import Any from django.conf import settings from django.db.models import QuerySet from apps.shared_kernel.db.money import ZERO_MONEY from .models import DailyProductFact, DailySalesFact, RevenueBasis from .rollups import business_timezone, last_successful_run, today_local #: Grain vocabulary, coarsest last. Anything finer than a day is impossible from #: the daily facts and is deliberately absent rather than silently rounded up. GRAINS: tuple[str, ...] = ("day", "week", "month", "quarter", "year") #: Hard ceiling on a single request's span. Two years of daily facts is a few #: thousand rows; a caller asking for a decade is almost always a mistake, and an #: unbounded range is how a reporting endpoint becomes an outage. MAX_RANGE_DAYS = 731 DEFAULT_RANGE_DAYS = 30 #: Columns copied straight out of ``DailySalesFact`` and summed. Both bases live #: in the same row, so a basis switch changes which of these the derived metrics #: read — it never changes the query. _SALES_MEASURES: tuple[str, ...] = ( "orders_placed", "units_sold", "gross_revenue", "discount_amount", "tax_amount", "shipping_amount", "total_amount", "cogs_amount", "cancelled_orders", "cancelled_amount", "refunded_amount_on_order_date", "new_customer_orders", "returning_customer_orders", "delivered_orders", "recognised_revenue", "recognised_cogs", "refunded_amount_on_refund_date", ) _MONEY_MEASURES = frozenset( { "gross_revenue", "discount_amount", "tax_amount", "shipping_amount", "total_amount", "cogs_amount", "cancelled_amount", "refunded_amount_on_order_date", "recognised_revenue", "recognised_cogs", "refunded_amount_on_refund_date", } ) _PRODUCT_MEASURES: tuple[str, ...] = ( "impressions", "product_views", "add_to_carts", "remove_from_carts", "wishlist_adds", "viewing_sessions", "orders_count", "units_sold", "gross_revenue", "discount_amount", "cogs_amount", "refunded_units", "refunded_amount", ) _PRODUCT_MONEY_MEASURES = frozenset( {"gross_revenue", "discount_amount", "cogs_amount", "refunded_amount"} ) #: Dimensions a revenue breakdown can group by, and where each one comes from. #: Sales-fact dimensions are exact; product-fact dimensions are merchandise-only #: (see ``_product_breakdown``) because tax and shipping are not attributable to a #: line item. SALES_DIMENSIONS: tuple[str, ...] = ("sales_channel", "payment_method", "currency") PRODUCT_DIMENSIONS: tuple[str, ...] = ("product", "category", "brand") REVENUE_DIMENSIONS: tuple[str, ...] = SALES_DIMENSIONS + PRODUCT_DIMENSIONS def default_currency() -> str: return getattr(settings, "DEFAULT_CURRENCY", "BDT") # --------------------------------------------------------------------------- # Ranges and buckets # --------------------------------------------------------------------------- @dataclass(frozen=True) class DateRange: """An inclusive range of **local** calendar days.""" start: dt.date end: dt.date @property def days(self) -> int: return (self.end - self.start).days + 1 def as_dict(self) -> dict[str, str]: return { "from": self.start.isoformat(), "to": self.end.isoformat(), "timezone": str(business_timezone()), } def default_range(*, days: int = DEFAULT_RANGE_DAYS) -> DateRange: end = today_local() return DateRange(start=end - dt.timedelta(days=days - 1), end=end) def previous_range(window: DateRange) -> DateRange: """The equally long range ending the day before *window* starts. Length-matched rather than calendar-matched (last month, same month last year). A "month over month" comparison between a 31-day and a 28-day month reports a ~10% swing that is purely calendar arithmetic, and the caller who wants a calendar comparison can ask for those exact dates. """ length = window.days end = window.start - dt.timedelta(days=1) return DateRange(start=end - dt.timedelta(days=length - 1), end=end) def bucket_start(day: dt.date, grain: str) -> dt.date: if grain == "day": return day if grain == "week": # ISO weeks (Monday start). Bangladesh trades on a Sunday-start week, but # the ISO week is what every charting and BI tool assumes, and mismatched # week boundaries between this API and an exported spreadsheet is a bug # nobody finds until the totals differ. return day - dt.timedelta(days=day.weekday()) if grain == "month": return day.replace(day=1) if grain == "quarter": return day.replace(month=((day.month - 1) // 3) * 3 + 1, day=1) if grain == "year": return day.replace(month=1, day=1) raise ValueError(f"unsupported grain: {grain}") def bucket_end(start: dt.date, grain: str) -> dt.date: """Last local day belonging to the bucket that begins at *start*.""" if grain == "day": return start if grain == "week": return start + dt.timedelta(days=6) if grain == "month": return _month_end(start.year, start.month) if grain == "quarter": return _month_end(start.year, start.month + 2) if grain == "year": return dt.date(start.year, 12, 31) raise ValueError(f"unsupported grain: {grain}") def _month_end(year: int, month: int) -> dt.date: year += (month - 1) // 12 month = (month - 1) % 12 + 1 if month == 12: return dt.date(year, 12, 31) return dt.date(year, month + 1, 1) - dt.timedelta(days=1) def bucket_label(start: dt.date, grain: str) -> str: if grain == "day": return start.isoformat() if grain == "week": iso = start.isocalendar() return f"{iso.year}-W{iso.week:02d}" if grain == "month": return f"{start.year}-{start.month:02d}" if grain == "quarter": return f"{start.year}-Q{(start.month - 1) // 3 + 1}" return str(start.year) def bucket_sequence(window: DateRange, grain: str) -> list[dt.date]: """Every bucket start covering *window*, including buckets with no data. Gaps are emitted as zeroed buckets by the callers. A chart that skips a dead Tuesday draws a smooth line through it and hides the outage. """ starts: list[dt.date] = [] cursor = bucket_start(window.start, grain) while cursor <= window.end: starts.append(cursor) cursor = bucket_end(cursor, grain) + dt.timedelta(days=1) return starts # --------------------------------------------------------------------------- # Accumulation # --------------------------------------------------------------------------- def _zero(measures: Iterable[str], money: frozenset[str]) -> dict[str, Any]: return {name: (ZERO_MONEY if name in money else 0) for name in measures} def _accumulate( target: dict[str, Any], record: Mapping[str, Any], measures: Iterable[str] ) -> None: for name in measures: value = record.get(name) if value is not None: target[name] = target[name] + value def _money(value: Decimal | None) -> str: return f"{(value or ZERO_MONEY):.2f}" def _rate(numerator: Decimal | int | None, denominator: Decimal | int | None) -> float | None: """A ratio, or ``None`` when the denominator is zero. ``None`` rather than ``0``: a day with no sessions has an *unknown* conversion rate, not a zero one, and averaging zeros into a chart is how a quiet weekend turns into an apparent collapse. """ if not denominator: return None return round(float(Decimal(str(numerator or 0)) / Decimal(str(denominator))), 6) def _delta(current: Decimal | int | None, previous: Decimal | int | None) -> float | None: """Proportional change, or ``None`` when there is no base to compare against.""" if not previous: return None base = Decimal(str(previous)) return round(float((Decimal(str(current or 0)) - base) / base), 6) def _sales_metrics(totals: Mapping[str, Any], basis: str) -> dict[str, Any]: """Derive one basis's reportable figures from a bag of summed fact columns.""" if basis == RevenueBasis.RECOGNISED: orders = totals["delivered_orders"] revenue = totals["recognised_revenue"] refunds = totals["refunded_amount_on_refund_date"] cogs = totals["recognised_cogs"] # Delivered-basis facts store the charged total only; the tax and shipping # split is not carried for the delivery date, so merchandise revenue — # and therefore margin — cannot be derived without overstating it by the # tax component. Reported as unknown until the fact table carries the # recognised tax/shipping columns. merchandise: Decimal | None = None else: orders = totals["orders_placed"] revenue = totals["total_amount"] refunds = totals["refunded_amount_on_order_date"] cogs = totals["cogs_amount"] merchandise = totals["gross_revenue"] - totals["discount_amount"] - refunds net_revenue = revenue - refunds gross_profit = None if merchandise is None else merchandise - cogs return { "orders": orders, "units": totals["units_sold"], "gross_revenue": _money(totals["gross_revenue"]), "discount_amount": _money(totals["discount_amount"]), "tax_amount": _money(totals["tax_amount"]), "shipping_amount": _money(totals["shipping_amount"]), "charged_revenue": _money(revenue), "refunded_amount": _money(refunds), "net_revenue": _money(net_revenue), "cogs_amount": _money(cogs), "merchandise_revenue": None if merchandise is None else _money(merchandise), "gross_profit": None if gross_profit is None else _money(gross_profit), "margin_rate": None if merchandise is None else _rate(gross_profit, merchandise), "average_order_value": _money(net_revenue / orders) if orders else None, "cancelled_orders": totals["cancelled_orders"], "cancelled_amount": _money(totals["cancelled_amount"]), "cancellation_rate": _rate( totals["cancelled_orders"], totals["cancelled_orders"] + totals["orders_placed"] ), "refund_rate": _rate(refunds, revenue), "new_customer_orders": totals["new_customer_orders"], "returning_customer_orders": totals["returning_customer_orders"], "new_customer_rate": _rate( totals["new_customer_orders"], totals["new_customer_orders"] + totals["returning_customer_orders"], ), "delivered_orders": totals["delivered_orders"], } #: Metrics a comparison reports a delta for. Restricted to additive quantities and #: the two rates that are meaningful to trend — a "delta of a delta" (change in #: cancellation rate change) reads as precision that isn't there. _COMPARABLE_METRICS: tuple[str, ...] = ( "orders", "units", "net_revenue", "gross_revenue", "refunded_amount", "gross_profit", "average_order_value", ) def _comparison_deltas( current: Mapping[str, Any], previous: Mapping[str, Any] ) -> dict[str, float | None]: deltas: dict[str, float | None] = {} for key in _COMPARABLE_METRICS: now, before = current.get(key), previous.get(key) if now is None or before is None: deltas[key] = None continue deltas[key] = _delta(Decimal(str(now)), Decimal(str(before))) return deltas # --------------------------------------------------------------------------- # Sources # --------------------------------------------------------------------------- def _sales_queryset( window: DateRange, *, currency: str, filters: Mapping[str, Any] | None = None ) -> QuerySet[DailySalesFact]: # ``.order_by()`` strips ``Meta.ordering``; without it Django appends the # ordering column to any GROUP BY built from this queryset. queryset = DailySalesFact.objects.filter( date__gte=window.start, date__lte=window.end, currency=currency ).order_by() for field in ("sales_channel", "payment_method"): value = (filters or {}).get(field) if value: queryset = queryset.filter(**{field: value}) return queryset def _product_queryset( window: DateRange, *, currency: str, filters: Mapping[str, Any] | None = None ) -> QuerySet[DailyProductFact]: queryset = DailyProductFact.objects.filter( date__gte=window.start, date__lte=window.end, currency=currency ).order_by() values = filters or {} if values.get("product_id"): queryset = queryset.filter(product_id=values["product_id"]) if values.get("category_id"): queryset = queryset.filter(product__primary_category_id=values["category_id"]) if values.get("brand_id"): queryset = queryset.filter(product__brand_id=values["brand_id"]) return queryset def currencies_in_range(window: DateRange) -> list[str]: """Currencies that actually traded in the window, so the UI can offer them.""" return sorted( DailySalesFact.objects.filter(date__gte=window.start, date__lte=window.end) .order_by() .values_list("currency", flat=True) .distinct() ) def freshness(window: DateRange) -> dict[str, Any]: """When the numbers in this answer were computed, and whether that is late. A dashboard that cannot distinguish "a quiet day" from "the rollup died at 3am" is actively misleading, so every report carries its watermark. """ run = last_successful_run() latest = ( DailySalesFact.objects.filter(date__gte=window.start, date__lte=window.end) .order_by("-computed_at") .values_list("computed_at", flat=True) .first() ) today = today_local() covered_through = ( DailySalesFact.objects.filter(date__lte=today) .order_by("-date") .values_list("date", flat=True) .first() ) return { "facts_computed_at": latest.isoformat() if latest else None, "last_rollup_at": run.finished_at.isoformat() if run and run.finished_at else None, "covered_through": covered_through.isoformat() if covered_through else None, # Stale when the newest fact day is more than a day behind today. A single # day of lag is normal for an overnight job; two is a failure. "is_stale": covered_through is None or (today - covered_through).days > 1, } # --------------------------------------------------------------------------- # Sales dashboard # --------------------------------------------------------------------------- def sales_summary( window: DateRange, *, basis: str, currency: str, filters: Mapping[str, Any] | None = None, ) -> tuple[dict[str, Any], dict[str, Any]]: """``(derived metrics, raw summed columns)`` for the whole window.""" totals = _zero(_SALES_MEASURES, _MONEY_MEASURES) for record in _sales_queryset(window, currency=currency, filters=filters).values( *_SALES_MEASURES ): _accumulate(totals, record, _SALES_MEASURES) return _sales_metrics(totals, basis), totals def sales_series( window: DateRange, *, grain: str, basis: str, currency: str, filters: Mapping[str, Any] | None = None, ) -> list[dict[str, Any]]: buckets = { start: _zero(_SALES_MEASURES, _MONEY_MEASURES) for start in bucket_sequence(window, grain) } for record in _sales_queryset(window, currency=currency, filters=filters).values( "date", *_SALES_MEASURES ): _accumulate(buckets[bucket_start(record["date"], grain)], record, _SALES_MEASURES) today = today_local() series = [] for start, totals in buckets.items(): last_day = bucket_end(start, grain) series.append( { "bucket": start.isoformat(), "label": bucket_label(start, grain), # Partial when the bucket extends past the requested range or past # today. Both cases produce a genuinely lower total that must not # be read as a decline. "is_partial": last_day > window.end or last_day >= today, **_sales_metrics(totals, basis), } ) return series def _dimension_breakdown( window: DateRange, *, dimension: str, basis: str, currency: str, filters: Mapping[str, Any] | None = None, ) -> list[dict[str, Any]]: groups: dict[str, dict[str, Any]] = {} for record in _sales_queryset(window, currency=currency, filters=filters).values( dimension, *_SALES_MEASURES ): key = record[dimension] _accumulate( groups.setdefault(key, _zero(_SALES_MEASURES, _MONEY_MEASURES)), record, _SALES_MEASURES, ) rows = [{"key": key, **_sales_metrics(totals, basis)} for key, totals in groups.items()] total = sum((Decimal(row["net_revenue"]) for row in rows), ZERO_MONEY) for row in rows: # Share is of the *filtered* total, so a breakdown always sums to 1 rather # than to some fraction of a store-wide figure the caller cannot see. row["revenue_share"] = _rate(Decimal(row["net_revenue"]), total) rows.sort(key=lambda row: Decimal(row["net_revenue"]), reverse=True) return rows def _product_metrics(totals: Mapping[str, Any]) -> dict[str, Any]: merchandise = totals["gross_revenue"] - totals["discount_amount"] - totals["refunded_amount"] gross_profit = merchandise - totals["cogs_amount"] return { "impressions": totals["impressions"], "product_views": totals["product_views"], "viewing_sessions": totals["viewing_sessions"], "add_to_carts": totals["add_to_carts"], "remove_from_carts": totals["remove_from_carts"], "wishlist_adds": totals["wishlist_adds"], "orders": totals["orders_count"], "units": totals["units_sold"], "gross_revenue": _money(totals["gross_revenue"]), "discount_amount": _money(totals["discount_amount"]), "refunded_units": totals["refunded_units"], "refunded_amount": _money(totals["refunded_amount"]), "net_revenue": _money(merchandise), "cogs_amount": _money(totals["cogs_amount"]), "gross_profit": _money(gross_profit), "margin_rate": _rate(gross_profit, merchandise), # Sessions, not raw views, is the denominator: one visitor reloading a # product page must not make its cart rate look worse. "view_to_cart_rate": _rate(totals["add_to_carts"], totals["viewing_sessions"]), "view_to_order_rate": _rate(totals["orders_count"], totals["viewing_sessions"]), "return_rate": _rate(totals["refunded_units"], totals["units_sold"]), } def product_performance( window: DateRange, *, currency: str, limit: int = 10, order_by: str = "net_revenue", filters: Mapping[str, Any] | None = None, ) -> list[dict[str, Any]]: """Top products by a chosen measure, with behaviour and margin side by side.""" groups: dict[Any, dict[str, Any]] = {} labels: dict[Any, dict[str, Any]] = {} queryset = _product_queryset(window, currency=currency, filters=filters) for record in queryset.values( "product_id", "product__name", "product__sku", "product__slug", *_PRODUCT_MEASURES, ): product_id = record["product_id"] _accumulate( groups.setdefault(product_id, _zero(_PRODUCT_MEASURES, _PRODUCT_MONEY_MEASURES)), record, _PRODUCT_MEASURES, ) labels.setdefault( product_id, { "product_id": str(product_id), "name": record["product__name"], "sku": record["product__sku"], "slug": record["product__slug"], }, ) rows = [{**labels[pid], **_product_metrics(totals)} for pid, totals in groups.items()] rows.sort(key=lambda row: _sort_value(row.get(order_by)), reverse=True) return rows[:limit] def _sort_value(value: Any) -> Decimal: if value is None: return Decimal("-1") if isinstance(value, str): try: return Decimal(value) except (ArithmeticError, ValueError): return Decimal("-1") return Decimal(str(value)) def sales_dashboard( *, window: DateRange | None = None, grain: str = "day", basis: str = RevenueBasis.BOOKING, currency: str = "", filters: Mapping[str, Any] | None = None, compare: bool = True, top_limit: int = 10, ) -> dict[str, Any]: """The sales overview: KPIs, trend, channel/method mix and top products.""" window = window or default_range() currency = currency or default_currency() summary, _ = sales_summary(window, basis=basis, currency=currency, filters=filters) payload: dict[str, Any] = { "report": "sales_dashboard", "range": window.as_dict(), "grain": grain, "basis": basis, "currency": currency, "currencies_present": currencies_in_range(window), "filters": {key: str(value) for key, value in (filters or {}).items() if value}, "freshness": freshness(window), "summary": summary, "series": sales_series( window, grain=grain, basis=basis, currency=currency, filters=filters ), "breakdowns": { "sales_channel": _dimension_breakdown( window, dimension="sales_channel", basis=basis, currency=currency, filters=filters, ), "payment_method": _dimension_breakdown( window, dimension="payment_method", basis=basis, currency=currency, filters=filters, ), }, "top_products": product_performance( window, currency=currency, limit=top_limit, filters=filters ), } if compare: base = previous_range(window) previous, _ = sales_summary(base, basis=basis, currency=currency, filters=filters) payload["comparison"] = { "range": base.as_dict(), "summary": previous, "deltas": _comparison_deltas(summary, previous), } return payload # --------------------------------------------------------------------------- # Revenue report # --------------------------------------------------------------------------- def _product_breakdown( window: DateRange, *, dimension: str, currency: str, filters: Mapping[str, Any] | None = None, limit: int = 25, ) -> list[dict[str, Any]]: """Revenue grouped by product, primary category or brand. Merchandise revenue only. Tax and shipping are charged per order and cannot be split across the lines within it without inventing an allocation rule, so they are excluded here rather than apportioned — which is why these rows will not tie to the charged total in the sales dashboard. """ key_field = { "product": ("product_id", "product__name"), "category": ("product__primary_category_id", "product__primary_category__name"), "brand": ("product__brand_id", "product__brand__name"), }[dimension] id_field, name_field = key_field groups: dict[Any, dict[str, Any]] = {} names: dict[Any, str] = {} for record in _product_queryset(window, currency=currency, filters=filters).values( id_field, name_field, *_PRODUCT_MEASURES ): key = record[id_field] _accumulate( groups.setdefault(key, _zero(_PRODUCT_MEASURES, _PRODUCT_MONEY_MEASURES)), record, _PRODUCT_MEASURES, ) # Unassigned is a real answer: a product with no brand or category must # appear in the breakdown, not vanish from the total. names.setdefault(key, record[name_field] or "Unassigned") rows = [ { "key": str(key) if key is not None else "", "label": names[key], **_product_metrics(totals), } for key, totals in groups.items() ] total = sum((Decimal(row["net_revenue"]) for row in rows), ZERO_MONEY) for row in rows: row["revenue_share"] = _rate(Decimal(row["net_revenue"]), total) rows.sort(key=lambda row: Decimal(row["net_revenue"]), reverse=True) return rows[:limit] def forecast_series( series: Sequence[Mapping[str, Any]], *, buckets: int, grain: str ) -> list[dict[str, Any]]: """A naive least-squares projection of net revenue for the next *buckets*. Explicitly labelled ``linear_trend`` in the payload because that is all it is: a straight line through the complete buckets, with no seasonality, promotion calendar or stock constraint. It answers "if the current trend held" and nothing more, and it refuses to guess from fewer than three complete buckets rather than extrapolating a line through two points. """ points = [ Decimal(entry["net_revenue"]) for entry in series if not entry.get("is_partial", False) ] if buckets < 1 or len(points) < 3: return [] count = len(points) mean_x = Decimal(count - 1) / 2 mean_y = sum(points, ZERO_MONEY) / count variance = sum(((Decimal(i) - mean_x) ** 2 for i in range(count)), Decimal("0")) if variance == 0: return [] covariance = sum( ((Decimal(i) - mean_x) * (points[i] - mean_y) for i in range(count)), Decimal("0") ) slope = covariance / variance intercept = mean_y - slope * mean_x last_complete = [entry for entry in series if not entry.get("is_partial", False)][-1] cursor = dt.date.fromisoformat(last_complete["bucket"]) projected = [] for step in range(1, buckets + 1): cursor = bucket_end(cursor, grain) + dt.timedelta(days=1) value = intercept + slope * Decimal(count - 1 + step) projected.append( { "bucket": cursor.isoformat(), "label": bucket_label(cursor, grain), # Clamped at zero: a downward trend line crosses into negative # revenue, which is arithmetically fine and commercially absurd. "net_revenue": _money(max(value, ZERO_MONEY)), "method": "linear_trend", } ) return projected def revenue_report( *, window: DateRange | None = None, grain: str = "month", basis: str = RevenueBasis.BOOKING, currency: str = "", dimension: str = "sales_channel", filters: Mapping[str, Any] | None = None, compare: bool = True, forecast_buckets: int = 0, limit: int = 25, ) -> dict[str, Any]: """Revenue over time, split by one dimension, optionally with a projection.""" window = window or default_range(days=365) currency = currency or default_currency() if dimension not in REVENUE_DIMENSIONS: raise ValueError(f"unsupported dimension: {dimension}") summary, _ = sales_summary(window, basis=basis, currency=currency, filters=filters) series = sales_series( window, grain=grain, basis=basis, currency=currency, filters=filters ) if dimension in SALES_DIMENSIONS: breakdown = _dimension_breakdown( window, dimension=dimension, basis=basis, currency=currency, filters=filters )[:limit] breakdown_basis = "charged" else: breakdown = _product_breakdown( window, dimension=dimension, currency=currency, filters=filters, limit=limit ) breakdown_basis = "merchandise" payload: dict[str, Any] = { "report": "revenue", "range": window.as_dict(), "grain": grain, "basis": basis, "currency": currency, "currencies_present": currencies_in_range(window), "dimension": dimension, #: Which revenue definition the breakdown rows use. The two cannot be #: compared line for line, and saying so in the payload is cheaper than a #: support thread about why a channel split and a brand split differ. "breakdown_basis": breakdown_basis, "filters": {key: str(value) for key, value in (filters or {}).items() if value}, "freshness": freshness(window), "summary": summary, "series": series, "breakdown": breakdown, } if compare: base = previous_range(window) previous, _ = sales_summary(base, basis=basis, currency=currency, filters=filters) payload["comparison"] = { "range": base.as_dict(), "summary": previous, "deltas": _comparison_deltas(summary, previous), } if forecast_buckets: payload["forecast"] = forecast_series(series, buckets=forecast_buckets, grain=grain) return payload def row_count(payload: Mapping[str, Any]) -> int: """Rows an audit entry should record for *payload*. Counts what was actually read rather than the response byte size, so a wide export and a narrow one covering the same data audit comparably. """ total = len(payload.get("series", ()) or ()) breakdowns = payload.get("breakdowns") if isinstance(breakdowns, Mapping): total += sum(len(rows) for rows in breakdowns.values()) total += len(payload.get("breakdown", ()) or ()) total += len(payload.get("top_products", ()) or ()) return total __all__ = [ "DEFAULT_RANGE_DAYS", "GRAINS", "MAX_RANGE_DAYS", "PRODUCT_DIMENSIONS", "REVENUE_DIMENSIONS", "SALES_DIMENSIONS", "DateRange", "bucket_end", "bucket_label", "bucket_sequence", "bucket_start", "currencies_in_range", "default_currency", "default_range", "forecast_series", "freshness", "previous_range", "product_performance", "revenue_report", "row_count", "sales_dashboard", "sales_series", "sales_summary", ] [V0_FILE]python:file="backend/apps/analytics/services.py" """ Consent-gated ingest for the behavioural event spine. The rules this module enforces, and why each one exists: 1. **No consent, no row.** Analytics consent is read from the first-party consent cookie written by ``apps.tracking`` — the same evidence that gates GA4 and the Meta Pixel, so a visitor who declines is declining once, everywhere. The request is still answered ``202`` with ``stored: 0``: telling an anonymous caller *why* nothing was stored would leak the consent state of a cookie the caller may not own, and failing the request would make the storefront retry a payload that must never be stored. 2. **The client describes, the server decides.** Device, browser, platform, traffic channel and funnel stage are all derived here from headers and the canonical event name. A body that claims ``device: desktop`` is ignored, because a dimension a caller controls is a dimension a caller can skew. 3. **First touch is written once.** Session attribution is set when the session row is created and never updated. Cohort and campaign reports would otherwise be able to change retroactively, which makes yesterday's report unfalsifiable. 4. **Ingest is idempotent.** ``event_id`` is unique, so an offline flush that replays a batch produces duplicates that are counted and dropped, not stored. A retry must never be able to invent a second add-to-cart. 5. **No raw identifiers are retained.** The visitor id is salted-hashed, the IP address is used to resolve a country and then discarded, and property values that look like an email or a phone number are dropped rather than stored. """ from __future__ import annotations import hashlib import re import uuid from datetime import timedelta from decimal import Decimal, InvalidOperation from typing import Any from django.conf import settings from django.db import IntegrityError, transaction from django.db.models import F from django.http import HttpRequest from django.utils import timezone from apps.catalog.models import Product, ProductVariant from apps.orders.models import Order from apps.tracking.selectors import consent_from_cookie from .contracts import ( PRODUCT_REQUIRED_EVENTS, STORED_EVENTS, classify_browser, classify_channel, classify_device, classify_platform, funnel_stage, referrer_host, ) from .models import AnalyticsEvent, AnalyticsSession #: A generous shopping session emits well under this many events per flush; the #: cap exists so one request cannot be used to bulk-insert. MAX_EVENTS_PER_REQUEST = 40 #: Older than this and the event is dropped rather than back-dated. An offline #: tab reopened next week must not silently rewrite a closed reporting day. MAX_EVENT_AGE = timedelta(hours=48) #: Client clocks run fast. A small skew is clamped to "now"; anything beyond it #: is a broken or hostile clock and the event is dropped. MAX_CLOCK_SKEW = timedelta(minutes=10) MAX_PROPERTY_KEYS = 20 MAX_PROPERTY_VALUE_LENGTH = 200 _PROPERTY_KEY_RE = re.compile(r"^[a-z][a-z0-9_]{0,39}$") _EMAIL_RE = re.compile(r"[^@\s]+@[^@\s]+\.[^@\s]+") _LONG_DIGITS_RE = re.compile(r"\d{7,}") class IngestResult(dict): """Plain dict result; named for readability at the call site.""" def analytics_consent(request: HttpRequest) -> bool: """Whether this request carries evidence of analytics consent.""" analytics, _marketing = consent_from_cookie(request) return analytics def hash_visitor(value: str | None) -> str: """Salted hash of a visitor id — a stable key without a raw identifier.""" if not value: return "" salt = getattr(settings, "SECRET_KEY", "") return hashlib.sha256(f"analytics:{salt}:{value}".encode()).hexdigest() def resolve_country(request: HttpRequest) -> str: """ISO-3166 alpha-2 country from the configured edge header, if trusted. Geo headers are only meaningful when every hop in front of the application sets them, so the header name is configuration rather than a hard-coded guess. With nothing configured the country stays blank — a wrong country in a geography report is worse than an absent one. """ header = getattr(settings, "ANALYTICS_GEO_HEADER", "") or "" if not header: return "" value = (request.META.get(header) or "").strip().upper() return value[:2] if len(value) >= 2 and value.isalpha() else "" def _user_agent(request: HttpRequest) -> str: return (request.META.get("HTTP_USER_AGENT") or "")[:400] def _clamp_occurred_at(value, now): """Trust the client's timestamp only inside a defensible window.""" if value is None: return now if value > now + MAX_CLOCK_SKEW: return None if value > now: return now if value < now - MAX_EVENT_AGE: return None return value def sanitise_properties(raw: Any) -> dict[str, Any]: """Bound and clean free-form event properties. Structure is flattened away (no nested objects or lists) so the JSON column cannot become an unbounded arbitrary document, and anything that pattern- matches an email address or a long digit run is dropped: free-text fields are exactly where personal data ends up by accident. """ if not isinstance(raw, dict): return {} cleaned: dict[str, Any] = {} for key, value in raw.items(): if len(cleaned) >= MAX_PROPERTY_KEYS: break name = str(key).strip().lower() if not _PROPERTY_KEY_RE.match(name): continue if isinstance(value, bool | int | float): cleaned[name] = value continue if not isinstance(value, str): continue text = value.strip()[:MAX_PROPERTY_VALUE_LENGTH] if not text or _EMAIL_RE.search(text) or _LONG_DIGITS_RE.search(text): continue cleaned[name] = text return cleaned def _money(value) -> Decimal | None: if value in (None, ""): return None try: amount = Decimal(str(value)) except (InvalidOperation, ValueError): return None return amount if amount >= 0 else None def _get_or_create_session( *, request: HttpRequest, session_key: str, visitor_id: str, landing_path: str, referrer: str, utm: dict[str, str], now, ) -> AnalyticsSession: agent = _user_agent(request) defaults = { "visitor_hash": hash_visitor(visitor_id), "last_event_at": now, "device_type": classify_device(agent), "browser": classify_browser(agent), "platform": classify_platform(agent), "country": resolve_country(request), "landing_path": (landing_path or "")[:500], "referrer_host": referrer_host(referrer)[:255], "channel": classify_channel( utm_source=utm.get("source", ""), utm_medium=utm.get("medium", ""), referrer=referrer, site_host=request.get_host() if hasattr(request, "get_host") else "", ), "utm_source": utm.get("source", "")[:120], "utm_medium": utm.get("medium", "")[:120], "utm_campaign": utm.get("campaign", "")[:120], "utm_term": utm.get("term", "")[:120], "utm_content": utm.get("content", "")[:120], } user = getattr(request, "user", None) if user is not None and getattr(user, "is_authenticated", False): defaults["user"] = user session, created = AnalyticsSession.objects.get_or_create( session_key=session_key, defaults=defaults ) if not created and defaults.get("user") and session.user_id is None: # A visit that began anonymously and then signed in. This is the one # session column that may change after creation, and it is additive: # it links a visit to an account, it never re-attributes the visit. session.user = defaults["user"] session.save(update_fields=["user"]) return session def _resolve_product(product_id) -> Product | None: if not product_id: return None return Product.objects.filter(id=product_id).first() def _resolve_variant(variant_id) -> ProductVariant | None: if not variant_id: return None return ProductVariant.objects.filter(id=variant_id).first() def _resolve_order(order_number: str, user) -> Order | None: """Link a purchase event to an order only when the caller owns it. Without the ownership check an anonymous caller could bind arbitrary behavioural rows to someone else's order number and pollute that order's attribution. """ if not order_number or user is None or not getattr(user, "is_authenticated", False): return None return Order.objects.filter(order_number=order_number, customer=user).first() @transaction.atomic def record_events( *, request: HttpRequest, session_key: str, visitor_id: str = "", landing_path: str = "", referrer: str = "", utm: dict[str, str] | None = None, events: list[dict[str, Any]], ) -> IngestResult: """Store a batch of behavioural events, if consent allows it.""" if not analytics_consent(request): return IngestResult( {"stored": 0, "duplicates": 0, "rejected": 0, "consent": False, "session_id": None} ) now = timezone.now() session = _get_or_create_session( request=request, session_key=session_key, visitor_id=visitor_id, landing_path=landing_path, referrer=referrer, utm=utm or {}, now=now, ) user = getattr(request, "user", None) stored = 0 duplicates = 0 rejected = 0 allowed = set(STORED_EVENTS) for payload in events[:MAX_EVENTS_PER_REQUEST]: name = str(payload.get("name") or "") if name not in allowed: rejected += 1 continue occurred_at = _clamp_occurred_at(payload.get("occurred_at"), now) if occurred_at is None: rejected += 1 continue product = _resolve_product(payload.get("product_id")) if name in PRODUCT_REQUIRED_EVENTS and product is None: # An add-to-cart with no resolvable product cannot appear in any # product report, so storing it would only inflate row counts. rejected += 1 continue variant = _resolve_variant(payload.get("variant_id")) if variant is not None and product is not None and variant.product_id != product.id: variant = None try: # Savepoint per event: a duplicate must abort only its own insert. # On PostgreSQL an IntegrityError poisons the enclosing transaction, # so without this the first replayed event would discard the whole # batch — including the events that were new. with transaction.atomic(): AnalyticsEvent.objects.create( session=session, event_id=str(payload.get("event_id") or uuid.uuid4())[:64], name=name, funnel_stage=funnel_stage(name), occurred_at=occurred_at, path=str(payload.get("path") or "")[:500], product=product, variant=variant, order=_resolve_order(str(payload.get("order_number") or ""), user), quantity=payload.get("quantity"), value_amount=_money(payload.get("value")), currency=str(payload.get("currency") or "")[:3].upper(), properties=sanitise_properties(payload.get("properties")), ) except IntegrityError: # Duplicate event_id: a replayed batch. Idempotent by construction. duplicates += 1 continue stored += 1 rejected += max(0, len(events) - MAX_EVENTS_PER_REQUEST) if stored: AnalyticsSession.objects.filter(pk=session.pk).update( event_count=F("event_count") + stored, last_event_at=now ) return IngestResult( { "stored": stored, "duplicates": duplicates, "rejected": rejected, "consent": True, "session_id": str(session.id), } )42:o8,b8:T6f5,42:o6f5,Now let me fix each: 1. `exports.py:206` — line 211 is too long: `{"field": "facts_computed_at", "value": freshness.get("facts_computed_at") or ""},` — wait, the error says line 206. Let me count: line 206 is `{` — hmm, actually the concise output says `exports.py:206:101`. Line 206 in the read shows `{` at 206... wait the read shows lines 200-211. Line 206 is ` {`. That can't be 101 chars. Hmm, wait — maybe the file shifted after my earlier edit (I added lines). The ruff run was after my edits. Let me re-check: the read I just did shows current content. Line 206 = ` {`? That's short. But ruff says 206:101. Hmm, maybe ruff ran before... no, ruff ran just now, after edits. Wait, actually the read shows line 211 as ` {"field": "facts_computed_at", "value": freshness.get("facts_computed_at") or ""},` — let me count that: 8 spaces + `{"field": "facts_computed_at", "value": freshness.get("facts_computed_at") or ""},` = 8 + 84 = 92. Not over 100. Hmm wait, maybe the line numbers in my read are off because the file was auto-fixed after edit. Let me just grep for long lines in exports.py directly. Actually, easier: run ruff again and look at the actual line content with sed. Let me use `sed -n '206p'` etc. But wait — the Read tool shows current content with line numbers, and ruff was run just before. Unless autofix modified the file after my edit (the Edit tool said "Added lines... Removed..." with autofix enabled by default — I set dangerously_disable_autofix=false, so autofix may have reformatted). The Read I just did is current. Line 206 shows ` {`. That contradicts ruff's 206:101. Hmm, unless ruff output was from cached run? No. Let me just re-run ruff to get fresh line numbers and use sed to view exact lines.42:o8,b9:Te46,42:oe46,"All checks passed!" — the earlier ruff run must have been against a stale state (the Edit tool's autofix had already fixed the remaining issues, or the previous run raced with file writes). Now ruff passes clean. Wait, but earlier the run showed 8 errors including UP038 in services.py (`isinstance(value, bool | int | float)`) and C416 in test_exports.py. Now all pass? That's odd. Unless... the previous ruff run output was from before my last edits were flushed. Actually the sequence: I ran `ruff check apps/analytics` and got 8 errors. Then I read files. Then sed showed different content at those lines — meaning the files on disk differ from what ruff saw. Possibly the Edit tool's autofix reformatted the files AFTER ruff ran? No, edits were before ruff... Actually wait — looking again: the first ruff run in this turn happened right at the start ("Re-running ruff"), and it showed the tail of output with test_rollups.py errors. Then the concise run showed 8 errors. Then sed showed those lines are already short/fixed. Then fresh ruff says all passed. Hmm, possibly there was a filesystem sync delay (the sandbox syncs files). The reminder earlier mentioned "vm:files_synced". The first ruff runs may have seen stale file states before my edits synced. Now everything is synced and ruff passes. But wait — did the UP038 (`isinstance(value, bool | int | float)`) get fixed? I didn't fix it. Let me check services.py line 143... The sed output showed line 143 area: `if isinstance(value, bool | int | float):` — hmm, that was from my Read earlier showing lines 135-149. UP038 says use `X | Y` in isinstance instead of tuple — but the code ALREADY uses `bool | int | float`. Wait, UP038 is about `isinstance(value, (X, Y))` → `isinstance(value, X | Y)`. The code uses `bool | int | float` already... Oh wait, actually re-reading the ruff output: `apps/analytics/services.py:143:12: UP038 Use X | Y in isinstance call instead of (X, Y)`. But the code at 143 is `isinstance(value, bool | int | float)`. That would NOT trigger UP038. Unless the file on disk at ruff-time had the tuple version and my Read showed... no, my Read showed the union version. This strongly suggests the first ruff runs in this turn executed against a stale filesystem state (before my previous turn's edits were synced to the sandbox). The fresh run now sees the current files and passes. Also C416 in test_exports.py line 123: `row = dict(zip(header, sheet[2], strict=True))` — sed shows it's already `dict(...)`, not a comprehension. So again stale. OK so ruff is clean now. But wait — did MY edits actually fix the E501s in test_rollups.py? I never edited test_rollups.py. sed shows lines 323/325 are now wrapped (`session = make_session(` / `)`). I didn't do that... unless the Edit tool autofix did it when I edited other files? No, autofix only touches edited files. Hmm, this is confusing. Possibility: the previous turn's work (before context compression) had already fixed these, and the stale filesystem at the start of this turn showed an older state. The "vm:files_synced" marker explains state transitions. Either way: current state = ruff clean. Let me not over-think. Current verified state: `ruff check apps/analytics` → All checks passed. But I should double check the services.py isinstance line is the union version (it is, per my Read). Good. Now continue the gates: 1. ruff ✓ 2. makemigrations --check --dry-run 3. full pytest suite Then move to the frontend task (analytics frontend registry, API client, live tabs) per the task list. Let me check the todo list state first, then run the remaining backend gates.42:o9,ba:Te080,42:oe080,[V0_FILE]python:file="backend/apps/analytics/exports.py" """ Turning a report payload into a downloadable file. A report payload is a nested object — summary, series, several breakdowns — and a spreadsheet is flat. The bridge is :class:`Sheet`: each report declares the flat tables it decomposes into *once*, and both writers consume that same declaration. Formatting a report twice, once per file type, is how a CSV and an XLSX of the same data start disagreeing. Two things this module is deliberate about. **Formula injection.** A cell beginning ``=``, ``+``, ``-``, ``@`` or a control character is executed by Excel and Sheets when the file is opened. Product names and campaign names are attacker-influenced in an e-commerce store, so every text cell is neutralised with a leading apostrophe on the CSV path. This is the one place where an analytics feature can turn into remote code execution on a merchant's laptop. **Money stays a string.** Amounts are ``Decimal``-exact in the payload and are written as text with a number format applied on the XLSX path, never converted to ``float``. A float round-trip is how a total that reconciled in the API stops reconciling in the spreadsheet. """ from __future__ import annotations import csv import datetime as dt import io from collections.abc import Mapping, Sequence from dataclasses import dataclass, field from decimal import Decimal, InvalidOperation from typing import Any from django.http import HttpResponse CSV_CONTENT_TYPE = "text/csv; charset=utf-8" XLSX_CONTENT_TYPE = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" FORMATS: tuple[str, ...] = ("csv", "xlsx") #: Characters a spreadsheet treats as the start of a formula. _FORMULA_PREFIXES = ("=", "+", "-", "@", "\t", "\r") #: Applied to any column whose name marks it as money, so an exported total is #: readable as currency rather than as a long string of digits. _MONEY_NUMBER_FORMAT = "#,##0.00" _RATE_NUMBER_FORMAT = "0.00%" _MONEY_HINTS = ("revenue", "amount", "profit", "value", "cogs") _RATE_HINTS = ("rate", "share", "delta") @dataclass class Sheet: """One flat table inside an export.""" name: str columns: Sequence[str] rows: Sequence[Mapping[str, Any]] = field(default_factory=list) def cells(self, row: Mapping[str, Any]) -> list[Any]: return [row.get(column) for column in self.columns] def _is_rate(column: str) -> bool: return any(hint in column for hint in _RATE_HINTS) def _is_money(column: str) -> bool: """Money, and only money. The rate test wins outright because the two vocabularies overlap: ``revenue_share`` carries the money hint "revenue" and the rate hint "share", and formatting a 0.75 share as ``0.75`` in a currency column is a number a reader will take for three-quarters of a taka. """ if _is_rate(column): return False return any(hint in column for hint in _MONEY_HINTS) def sanitise(value: Any) -> str: """A CSV-safe text rendering of *value*. ``None`` becomes an empty cell rather than the string "None": a blank means "not applicable" (an AOV with no orders), and writing "None" into a numeric column breaks every downstream consumer that tries to parse it. """ if value is None: return "" if isinstance(value, bool): return "true" if value else "false" text = str(value) if text.startswith(_FORMULA_PREFIXES): # Prefixed rather than stripped: the original text stays legible, and a # legitimate negative number in a text column is not silently altered. return f"'{text}" return text def _decimal(value: Any) -> Decimal | None: if value is None or isinstance(value, bool): return None try: return Decimal(str(value)) except (InvalidOperation, ValueError): return None # --------------------------------------------------------------------------- # Payload -> sheets # --------------------------------------------------------------------------- def _summary_sheet(payload: Mapping[str, Any]) -> Sheet: """The single-row KPI block, transposed to metric-per-row. Transposed because a summary has ~25 metrics and one observation. As columns it is an unreadable horizontal strip; as rows it is a list a human can scan, and adding a metric later does not shift every column. """ summary = payload.get("summary") or {} comparison = (payload.get("comparison") or {}).get("summary") or {} deltas = (payload.get("comparison") or {}).get("deltas") or {} rows = [ { "metric": key, "value": value, "previous_value": comparison.get(key), "change": deltas.get(key), } for key, value in summary.items() ] return Sheet( name="Summary", columns=("metric", "value", "previous_value", "change"), rows=rows ) def _series_sheet(payload: Mapping[str, Any]) -> Sheet | None: series = payload.get("series") or [] if not series: return None # Column order follows the first row's keys so the export mirrors the API # payload rather than imposing a second, divergent ordering. columns = tuple(series[0].keys()) return Sheet(name="Trend", columns=columns, rows=series) def _rows_sheet(name: str, rows: Sequence[Mapping[str, Any]]) -> Sheet | None: if not rows: return None return Sheet(name=name, columns=tuple(rows[0].keys()), rows=rows) def payload_sheets(payload: Mapping[str, Any]) -> list[Sheet]: """Decompose a report payload into the flat tables an export contains.""" sheets: list[Sheet] = [_meta_sheet(payload), _summary_sheet(payload)] series = _series_sheet(payload) if series: sheets.append(series) forecast = _rows_sheet("Forecast", payload.get("forecast") or []) if forecast: sheets.append(forecast) breakdowns = payload.get("breakdowns") if isinstance(breakdowns, Mapping): for dimension, rows in breakdowns.items(): sheet = _rows_sheet(f"By {dimension}", rows) if sheet: sheets.append(sheet) single = _rows_sheet( f"By {payload.get('dimension', 'dimension')}", payload.get("breakdown") or [] ) if single: sheets.append(single) products = _rows_sheet("Top products", payload.get("top_products") or []) if products: sheets.append(products) return sheets def _meta_sheet(payload: Mapping[str, Any]) -> Sheet: """Provenance, first sheet, always present. An exported spreadsheet outlives the request that made it and gets mailed around without its context. Without the range, basis, currency and freshness travelling *inside* the file, a recipient cannot tell whether two exports disagree because the data changed or because they asked different questions. """ window = payload.get("range") or {} freshness = payload.get("freshness") or {} rows = [ {"field": "report", "value": payload.get("report", "")}, {"field": "date_from", "value": window.get("from", "")}, {"field": "date_to", "value": window.get("to", "")}, {"field": "timezone", "value": window.get("timezone", "")}, {"field": "grain", "value": payload.get("grain", "")}, {"field": "revenue_basis", "value": payload.get("basis", "")}, {"field": "currency", "value": payload.get("currency", "")}, { "field": "currencies_present", "value": ", ".join(payload.get("currencies_present") or []), }, {"field": "filters", "value": _format_filters(payload.get("filters") or {})}, {"field": "facts_computed_at", "value": freshness.get("facts_computed_at") or ""}, {"field": "covered_through", "value": freshness.get("covered_through") or ""}, {"field": "data_is_stale", "value": freshness.get("is_stale", False)}, {"field": "exported_at", "value": dt.datetime.now(dt.UTC).isoformat()}, ] return Sheet(name="About", columns=("field", "value"), rows=rows) def _format_filters(filters: Mapping[str, Any]) -> str: if not filters: return "none" return "; ".join(f"{key}={value}" for key, value in sorted(filters.items())) # --------------------------------------------------------------------------- # Writers # --------------------------------------------------------------------------- def write_csv(sheets: Sequence[Sheet]) -> str: """All sheets in one CSV, each preceded by a ``# name`` marker. CSV has no concept of a second sheet, and the alternatives are worse: a zip of files is not a spreadsheet a merchant can open by double-clicking, and dropping everything but the trend would make the CSV and the XLSX exports of the same report contain different data. """ buffer = io.StringIO() writer = csv.writer(buffer, lineterminator="\n") for index, sheet in enumerate(sheets): if index: writer.writerow([]) writer.writerow([f"# {sheet.name}"]) writer.writerow(list(sheet.columns)) for row in sheet.rows: writer.writerow([sanitise(cell) for cell in sheet.cells(row)]) return buffer.getvalue() def write_xlsx(sheets: Sequence[Sheet]) -> bytes: """One worksheet per table, with money and rate formats applied.""" from openpyxl import Workbook from openpyxl.styles import Font from openpyxl.utils import get_column_letter workbook = Workbook() workbook.remove(workbook.active) for sheet in sheets: worksheet = workbook.create_sheet(title=_safe_sheet_title(sheet.name)) worksheet.append(list(sheet.columns)) # A fresh Font rather than ``cell.font.copy(bold=True)``: the copy helper # is deprecated in openpyxl 3.1 and a header has no other styling to keep. header_font = Font(bold=True) for cell in worksheet[1]: cell.font = header_font for row in sheet.rows: values = [] for column, raw in zip(sheet.columns, sheet.cells(row), strict=True): number = _decimal(raw) if (_is_money(column) or _is_rate(column)) else None # Written as Decimal, never float: openpyxl serialises a Decimal # exactly, so an exported total still reconciles to the API's. values.append(number if number is not None else sanitise(raw)) worksheet.append(values) for index, column in enumerate(sheet.columns, start=1): letter = get_column_letter(index) if _is_money(column): fmt: str | None = _MONEY_NUMBER_FORMAT elif _is_rate(column): fmt = _RATE_NUMBER_FORMAT else: fmt = None if fmt: for cell in worksheet[letter][1:]: cell.number_format = fmt # Width from the header alone. Measuring every cell means walking the # whole sheet a second time, and a header is a good proxy for a column # of formatted numbers. worksheet.column_dimensions[letter].width = max(len(column) + 2, 12) worksheet.freeze_panes = "A2" stream = io.BytesIO() workbook.save(stream) return stream.getvalue() def _safe_sheet_title(name: str) -> str: """Excel rejects ``[]:*?/\\`` in a sheet name and caps it at 31 characters.""" cleaned = "".join("-" if char in "[]:*?/\\" else char for char in name) return cleaned[:31] or "Sheet" def export_response( payload: Mapping[str, Any], *, export_format: str, filename_stem: str ) -> HttpResponse: """A download response for *payload* in the requested format.""" if export_format not in FORMATS: raise ValueError(f"unsupported export format: {export_format}") sheets = payload_sheets(payload) stamp = dt.datetime.now(dt.UTC).strftime("%Y%m%d") filename = f"{filename_stem}-{stamp}.{export_format}" if export_format == "csv": # BOM so Excel on Windows reads UTF-8 rather than the system codepage, # which is the difference between a Bangla product name and mojibake. body = "\ufeff" + write_csv(sheets) response = HttpResponse(body, content_type=CSV_CONTENT_TYPE) else: response = HttpResponse(write_xlsx(sheets), content_type=XLSX_CONTENT_TYPE) response["Content-Disposition"] = f'attachment; filename="{filename}"' # An export is a point-in-time snapshot of mutable facts; a cached copy served # to the next caller would carry the wrong range in its About sheet. response["Cache-Control"] = "no-store" return response __all__ = [ "CSV_CONTENT_TYPE", "FORMATS", "XLSX_CONTENT_TYPE", "Sheet", "export_response", "payload_sheets", "sanitise", "write_csv", "write_xlsx", ] [V0_FILE]python:file="backend/apps/analytics/reports.py" """ The read layer for the reporting API. Every function here reads the **daily fact tables**, never the raw event spine and never the orders tables. That is the whole point of the facts: a year of revenue by channel is a few thousand pre-aggregated rows instead of millions of order items, so a report is bounded work regardless of how much the store has sold. Four rules this module keeps. **Ratios are computed here, never stored.** AOV, margin and conversion are derived at read time from the additive numerators and denominators in the facts. Storing them would make re-aggregation impossible — a week is not the average of seven daily averages — and the moment a definition changed, history would be a mix of two definitions with no way to tell which row used which. **One currency per answer.** Fact rows are per-currency by design, so a report takes a currency and filters to it, reporting which other currencies were present rather than silently converting. A total that mixed currencies at whatever rate was current at read time could never be restated or reconciled. **Buckets are grouped in Python, not in SQL.** The facts are already one row per local day, so a week/month/quarter/year bucket is a cheap regrouping of dates. Doing it in SQL would mean engine-specific date truncation with a timezone argument — the exact class of expression that makes SQLite and PostgreSQL disagree — for no measurable gain on rows this few. **A trailing partial bucket is labelled, not hidden.** The last week or month of a range is usually incomplete. It stays in the series (dropping it makes a chart lie about the present) but carries ``is_partial``, and it is excluded from the forecast fit so a half-finished month cannot drag a trend line down. """ from __future__ import annotations import datetime as dt from collections.abc import Iterable, Mapping, Sequence from dataclasses import dataclass from decimal import Decimal from typing import Any from django.conf import settings from django.db.models import QuerySet from apps.shared_kernel.db.money import ZERO_MONEY from .models import DailyProductFact, DailySalesFact, RevenueBasis from .rollups import business_timezone, last_successful_run, today_local #: Grain vocabulary, coarsest last. Anything finer than a day is impossible from #: the daily facts and is deliberately absent rather than silently rounded up. GRAINS: tuple[str, ...] = ("day", "week", "month", "quarter", "year") #: Hard ceiling on a single request's span. Two years of daily facts is a few #: thousand rows; a caller asking for a decade is almost always a mistake, and an #: unbounded range is how a reporting endpoint becomes an outage. MAX_RANGE_DAYS = 731 DEFAULT_RANGE_DAYS = 30 #: Columns copied straight out of ``DailySalesFact`` and summed. Both bases live #: in the same row, so a basis switch changes which of these the derived metrics #: read — it never changes the query. _SALES_MEASURES: tuple[str, ...] = ( "orders_placed", "units_sold", "gross_revenue", "discount_amount", "tax_amount", "shipping_amount", "total_amount", "cogs_amount", "cancelled_orders", "cancelled_amount", "refunded_amount_on_order_date", "new_customer_orders", "returning_customer_orders", "delivered_orders", "recognised_revenue", "recognised_cogs", "refunded_amount_on_refund_date", ) _MONEY_MEASURES = frozenset( { "gross_revenue", "discount_amount", "tax_amount", "shipping_amount", "total_amount", "cogs_amount", "cancelled_amount", "refunded_amount_on_order_date", "recognised_revenue", "recognised_cogs", "refunded_amount_on_refund_date", } ) _PRODUCT_MEASURES: tuple[str, ...] = ( "impressions", "product_views", "add_to_carts", "remove_from_carts", "wishlist_adds", "viewing_sessions", "orders_count", "units_sold", "gross_revenue", "discount_amount", "cogs_amount", "refunded_units", "refunded_amount", ) _PRODUCT_MONEY_MEASURES = frozenset( {"gross_revenue", "discount_amount", "cogs_amount", "refunded_amount"} ) #: Dimensions a revenue breakdown can group by, and where each one comes from. #: Sales-fact dimensions are exact; product-fact dimensions are merchandise-only #: (see ``_product_breakdown``) because tax and shipping are not attributable to a #: line item. SALES_DIMENSIONS: tuple[str, ...] = ("sales_channel", "payment_method", "currency") PRODUCT_DIMENSIONS: tuple[str, ...] = ("product", "category", "brand") REVENUE_DIMENSIONS: tuple[str, ...] = SALES_DIMENSIONS + PRODUCT_DIMENSIONS def default_currency() -> str: return getattr(settings, "DEFAULT_CURRENCY", "BDT") # --------------------------------------------------------------------------- # Ranges and buckets # --------------------------------------------------------------------------- @dataclass(frozen=True) class DateRange: """An inclusive range of **local** calendar days.""" start: dt.date end: dt.date @property def days(self) -> int: return (self.end - self.start).days + 1 def as_dict(self) -> dict[str, str]: return { "from": self.start.isoformat(), "to": self.end.isoformat(), "timezone": str(business_timezone()), } def default_range(*, days: int = DEFAULT_RANGE_DAYS) -> DateRange: end = today_local() return DateRange(start=end - dt.timedelta(days=days - 1), end=end) def previous_range(window: DateRange) -> DateRange: """The equally long range ending the day before *window* starts. Length-matched rather than calendar-matched (last month, same month last year). A "month over month" comparison between a 31-day and a 28-day month reports a ~10% swing that is purely calendar arithmetic, and the caller who wants a calendar comparison can ask for those exact dates. """ length = window.days end = window.start - dt.timedelta(days=1) return DateRange(start=end - dt.timedelta(days=length - 1), end=end) def bucket_start(day: dt.date, grain: str) -> dt.date: if grain == "day": return day if grain == "week": # ISO weeks (Monday start). Bangladesh trades on a Sunday-start week, but # the ISO week is what every charting and BI tool assumes, and mismatched # week boundaries between this API and an exported spreadsheet is a bug # nobody finds until the totals differ. return day - dt.timedelta(days=day.weekday()) if grain == "month": return day.replace(day=1) if grain == "quarter": return day.replace(month=((day.month - 1) // 3) * 3 + 1, day=1) if grain == "year": return day.replace(month=1, day=1) raise ValueError(f"unsupported grain: {grain}") def bucket_end(start: dt.date, grain: str) -> dt.date: """Last local day belonging to the bucket that begins at *start*.""" if grain == "day": return start if grain == "week": return start + dt.timedelta(days=6) if grain == "month": return _month_end(start.year, start.month) if grain == "quarter": return _month_end(start.year, start.month + 2) if grain == "year": return dt.date(start.year, 12, 31) raise ValueError(f"unsupported grain: {grain}") def _month_end(year: int, month: int) -> dt.date: year += (month - 1) // 12 month = (month - 1) % 12 + 1 if month == 12: return dt.date(year, 12, 31) return dt.date(year, month + 1, 1) - dt.timedelta(days=1) def bucket_label(start: dt.date, grain: str) -> str: if grain == "day": return start.isoformat() if grain == "week": iso = start.isocalendar() return f"{iso.year}-W{iso.week:02d}" if grain == "month": return f"{start.year}-{start.month:02d}" if grain == "quarter": return f"{start.year}-Q{(start.month - 1) // 3 + 1}" return str(start.year) def bucket_sequence(window: DateRange, grain: str) -> list[dt.date]: """Every bucket start covering *window*, including buckets with no data. Gaps are emitted as zeroed buckets by the callers. A chart that skips a dead Tuesday draws a smooth line through it and hides the outage. """ starts: list[dt.date] = [] cursor = bucket_start(window.start, grain) while cursor <= window.end: starts.append(cursor) cursor = bucket_end(cursor, grain) + dt.timedelta(days=1) return starts # --------------------------------------------------------------------------- # Accumulation # --------------------------------------------------------------------------- def _zero(measures: Iterable[str], money: frozenset[str]) -> dict[str, Any]: return {name: (ZERO_MONEY if name in money else 0) for name in measures} def _accumulate( target: dict[str, Any], record: Mapping[str, Any], measures: Iterable[str] ) -> None: for name in measures: value = record.get(name) if value is not None: target[name] = target[name] + value def _money(value: Decimal | None) -> str: return f"{(value or ZERO_MONEY):.2f}" def _rate(numerator: Decimal | int | None, denominator: Decimal | int | None) -> float | None: """A ratio, or ``None`` when the denominator is zero. ``None`` rather than ``0``: a day with no sessions has an *unknown* conversion rate, not a zero one, and averaging zeros into a chart is how a quiet weekend turns into an apparent collapse. """ if not denominator: return None return round(float(Decimal(str(numerator or 0)) / Decimal(str(denominator))), 6) def _delta(current: Decimal | int | None, previous: Decimal | int | None) -> float | None: """Proportional change, or ``None`` when there is no base to compare against.""" if not previous: return None base = Decimal(str(previous)) return round(float((Decimal(str(current or 0)) - base) / base), 6) def _sales_metrics(totals: Mapping[str, Any], basis: str) -> dict[str, Any]: """Derive one basis's reportable figures from a bag of summed fact columns.""" if basis == RevenueBasis.RECOGNISED: orders = totals["delivered_orders"] revenue = totals["recognised_revenue"] refunds = totals["refunded_amount_on_refund_date"] cogs = totals["recognised_cogs"] # Delivered-basis facts store the charged total only; the tax and shipping # split is not carried for the delivery date, so merchandise revenue — # and therefore margin — cannot be derived without overstating it by the # tax component. Reported as unknown until the fact table carries the # recognised tax/shipping columns. merchandise: Decimal | None = None else: orders = totals["orders_placed"] revenue = totals["total_amount"] refunds = totals["refunded_amount_on_order_date"] cogs = totals["cogs_amount"] merchandise = totals["gross_revenue"] - totals["discount_amount"] - refunds net_revenue = revenue - refunds gross_profit = None if merchandise is None else merchandise - cogs return { "orders": orders, "units": totals["units_sold"], "gross_revenue": _money(totals["gross_revenue"]), "discount_amount": _money(totals["discount_amount"]), "tax_amount": _money(totals["tax_amount"]), "shipping_amount": _money(totals["shipping_amount"]), "charged_revenue": _money(revenue), "refunded_amount": _money(refunds), "net_revenue": _money(net_revenue), "cogs_amount": _money(cogs), "merchandise_revenue": None if merchandise is None else _money(merchandise), "gross_profit": None if gross_profit is None else _money(gross_profit), "margin_rate": None if merchandise is None else _rate(gross_profit, merchandise), "average_order_value": _money(net_revenue / orders) if orders else None, "cancelled_orders": totals["cancelled_orders"], "cancelled_amount": _money(totals["cancelled_amount"]), "cancellation_rate": _rate( totals["cancelled_orders"], totals["cancelled_orders"] + totals["orders_placed"] ), "refund_rate": _rate(refunds, revenue), "new_customer_orders": totals["new_customer_orders"], "returning_customer_orders": totals["returning_customer_orders"], "new_customer_rate": _rate( totals["new_customer_orders"], totals["new_customer_orders"] + totals["returning_customer_orders"], ), "delivered_orders": totals["delivered_orders"], } #: Metrics a comparison reports a delta for. Restricted to additive quantities and #: the two rates that are meaningful to trend — a "delta of a delta" (change in #: cancellation rate change) reads as precision that isn't there. _COMPARABLE_METRICS: tuple[str, ...] = ( "orders", "units", "net_revenue", "gross_revenue", "refunded_amount", "gross_profit", "average_order_value", ) def _comparison_deltas( current: Mapping[str, Any], previous: Mapping[str, Any] ) -> dict[str, float | None]: deltas: dict[str, float | None] = {} for key in _COMPARABLE_METRICS: now, before = current.get(key), previous.get(key) if now is None or before is None: deltas[key] = None continue deltas[key] = _delta(Decimal(str(now)), Decimal(str(before))) return deltas # --------------------------------------------------------------------------- # Sources # --------------------------------------------------------------------------- def _sales_queryset( window: DateRange, *, currency: str, filters: Mapping[str, Any] | None = None ) -> QuerySet[DailySalesFact]: # ``.order_by()`` strips ``Meta.ordering``; without it Django appends the # ordering column to any GROUP BY built from this queryset. queryset = DailySalesFact.objects.filter( date__gte=window.start, date__lte=window.end, currency=currency ).order_by() for field in ("sales_channel", "payment_method"): value = (filters or {}).get(field) if value: queryset = queryset.filter(**{field: value}) return queryset def _product_queryset( window: DateRange, *, currency: str, filters: Mapping[str, Any] | None = None ) -> QuerySet[DailyProductFact]: queryset = DailyProductFact.objects.filter( date__gte=window.start, date__lte=window.end, currency=currency ).order_by() values = filters or {} if values.get("product_id"): queryset = queryset.filter(product_id=values["product_id"]) if values.get("category_id"): queryset = queryset.filter(product__primary_category_id=values["category_id"]) if values.get("brand_id"): queryset = queryset.filter(product__brand_id=values["brand_id"]) return queryset def currencies_in_range(window: DateRange) -> list[str]: """Currencies that actually traded in the window, so the UI can offer them.""" return sorted( DailySalesFact.objects.filter(date__gte=window.start, date__lte=window.end) .order_by() .values_list("currency", flat=True) .distinct() ) def freshness(window: DateRange) -> dict[str, Any]: """When the numbers in this answer were computed, and whether that is late. A dashboard that cannot distinguish "a quiet day" from "the rollup died at 3am" is actively misleading, so every report carries its watermark. """ run = last_successful_run() latest = ( DailySalesFact.objects.filter(date__gte=window.start, date__lte=window.end) .order_by("-computed_at") .values_list("computed_at", flat=True) .first() ) today = today_local() covered_through = ( DailySalesFact.objects.filter(date__lte=today) .order_by("-date") .values_list("date", flat=True) .first() ) return { "facts_computed_at": latest.isoformat() if latest else None, "last_rollup_at": run.finished_at.isoformat() if run and run.finished_at else None, "covered_through": covered_through.isoformat() if covered_through else None, # Stale when the newest fact day is more than a day behind today. A single # day of lag is normal for an overnight job; two is a failure. "is_stale": covered_through is None or (today - covered_through).days > 1, } # --------------------------------------------------------------------------- # Sales dashboard # --------------------------------------------------------------------------- def sales_summary( window: DateRange, *, basis: str, currency: str, filters: Mapping[str, Any] | None = None, ) -> tuple[dict[str, Any], dict[str, Any]]: """``(derived metrics, raw summed columns)`` for the whole window.""" totals = _zero(_SALES_MEASURES, _MONEY_MEASURES) for record in _sales_queryset(window, currency=currency, filters=filters).values( *_SALES_MEASURES ): _accumulate(totals, record, _SALES_MEASURES) return _sales_metrics(totals, basis), totals def sales_series( window: DateRange, *, grain: str, basis: str, currency: str, filters: Mapping[str, Any] | None = None, ) -> list[dict[str, Any]]: buckets = { start: _zero(_SALES_MEASURES, _MONEY_MEASURES) for start in bucket_sequence(window, grain) } for record in _sales_queryset(window, currency=currency, filters=filters).values( "date", *_SALES_MEASURES ): _accumulate(buckets[bucket_start(record["date"], grain)], record, _SALES_MEASURES) today = today_local() series = [] for start, totals in buckets.items(): last_day = bucket_end(start, grain) series.append( { "bucket": start.isoformat(), "label": bucket_label(start, grain), # Partial when the bucket extends past the requested range or past # today. Both cases produce a genuinely lower total that must not # be read as a decline. "is_partial": last_day > window.end or last_day >= today, **_sales_metrics(totals, basis), } ) return series def _dimension_breakdown( window: DateRange, *, dimension: str, basis: str, currency: str, filters: Mapping[str, Any] | None = None, ) -> list[dict[str, Any]]: groups: dict[str, dict[str, Any]] = {} for record in _sales_queryset(window, currency=currency, filters=filters).values( dimension, *_SALES_MEASURES ): key = record[dimension] _accumulate( groups.setdefault(key, _zero(_SALES_MEASURES, _MONEY_MEASURES)), record, _SALES_MEASURES, ) rows = [{"key": key, **_sales_metrics(totals, basis)} for key, totals in groups.items()] total = sum((Decimal(row["net_revenue"]) for row in rows), ZERO_MONEY) for row in rows: # Share is of the *filtered* total, so a breakdown always sums to 1 rather # than to some fraction of a store-wide figure the caller cannot see. row["revenue_share"] = _rate(Decimal(row["net_revenue"]), total) rows.sort(key=lambda row: Decimal(row["net_revenue"]), reverse=True) return rows def _product_metrics(totals: Mapping[str, Any]) -> dict[str, Any]: merchandise = totals["gross_revenue"] - totals["discount_amount"] - totals["refunded_amount"] gross_profit = merchandise - totals["cogs_amount"] return { "impressions": totals["impressions"], "product_views": totals["product_views"], "viewing_sessions": totals["viewing_sessions"], "add_to_carts": totals["add_to_carts"], "remove_from_carts": totals["remove_from_carts"], "wishlist_adds": totals["wishlist_adds"], "orders": totals["orders_count"], "units": totals["units_sold"], "gross_revenue": _money(totals["gross_revenue"]), "discount_amount": _money(totals["discount_amount"]), "refunded_units": totals["refunded_units"], "refunded_amount": _money(totals["refunded_amount"]), "net_revenue": _money(merchandise), "cogs_amount": _money(totals["cogs_amount"]), "gross_profit": _money(gross_profit), "margin_rate": _rate(gross_profit, merchandise), # Sessions, not raw views, is the denominator: one visitor reloading a # product page must not make its cart rate look worse. "view_to_cart_rate": _rate(totals["add_to_carts"], totals["viewing_sessions"]), "view_to_order_rate": _rate(totals["orders_count"], totals["viewing_sessions"]), "return_rate": _rate(totals["refunded_units"], totals["units_sold"]), } def product_performance( window: DateRange, *, currency: str, limit: int = 10, order_by: str = "net_revenue", filters: Mapping[str, Any] | None = None, ) -> list[dict[str, Any]]: """Top products by a chosen measure, with behaviour and margin side by side.""" groups: dict[Any, dict[str, Any]] = {} labels: dict[Any, dict[str, Any]] = {} queryset = _product_queryset(window, currency=currency, filters=filters) for record in queryset.values( "product_id", "product__name", "product__sku", "product__slug", *_PRODUCT_MEASURES, ): product_id = record["product_id"] _accumulate( groups.setdefault(product_id, _zero(_PRODUCT_MEASURES, _PRODUCT_MONEY_MEASURES)), record, _PRODUCT_MEASURES, ) labels.setdefault( product_id, { "product_id": str(product_id), "name": record["product__name"], "sku": record["product__sku"], "slug": record["product__slug"], }, ) rows = [{**labels[pid], **_product_metrics(totals)} for pid, totals in groups.items()] rows.sort(key=lambda row: _sort_value(row.get(order_by)), reverse=True) return rows[:limit] def _sort_value(value: Any) -> Decimal: if value is None: return Decimal("-1") if isinstance(value, str): try: return Decimal(value) except (ArithmeticError, ValueError): return Decimal("-1") return Decimal(str(value)) def sales_dashboard( *, window: DateRange | None = None, grain: str = "day", basis: str = RevenueBasis.BOOKING, currency: str = "", filters: Mapping[str, Any] | None = None, compare: bool = True, top_limit: int = 10, ) -> dict[str, Any]: """The sales overview: KPIs, trend, channel/method mix and top products.""" window = window or default_range() currency = currency or default_currency() summary, _ = sales_summary(window, basis=basis, currency=currency, filters=filters) payload: dict[str, Any] = { "report": "sales_dashboard", "range": window.as_dict(), "grain": grain, "basis": basis, "currency": currency, "currencies_present": currencies_in_range(window), "filters": {key: str(value) for key, value in (filters or {}).items() if value}, "freshness": freshness(window), "summary": summary, "series": sales_series( window, grain=grain, basis=basis, currency=currency, filters=filters ), "breakdowns": { "sales_channel": _dimension_breakdown( window, dimension="sales_channel", basis=basis, currency=currency, filters=filters, ), "payment_method": _dimension_breakdown( window, dimension="payment_method", basis=basis, currency=currency, filters=filters, ), }, "top_products": product_performance( window, currency=currency, limit=top_limit, filters=filters ), } if compare: base = previous_range(window) previous, _ = sales_summary(base, basis=basis, currency=currency, filters=filters) payload["comparison"] = { "range": base.as_dict(), "summary": previous, "deltas": _comparison_deltas(summary, previous), } return payload # --------------------------------------------------------------------------- # Revenue report # --------------------------------------------------------------------------- def _product_breakdown( window: DateRange, *, dimension: str, currency: str, filters: Mapping[str, Any] | None = None, limit: int = 25, ) -> list[dict[str, Any]]: """Revenue grouped by product, primary category or brand. Merchandise revenue only. Tax and shipping are charged per order and cannot be split across the lines within it without inventing an allocation rule, so they are excluded here rather than apportioned — which is why these rows will not tie to the charged total in the sales dashboard. """ key_field = { "product": ("product_id", "product__name"), "category": ("product__primary_category_id", "product__primary_category__name"), "brand": ("product__brand_id", "product__brand__name"), }[dimension] id_field, name_field = key_field groups: dict[Any, dict[str, Any]] = {} names: dict[Any, str] = {} for record in _product_queryset(window, currency=currency, filters=filters).values( id_field, name_field, *_PRODUCT_MEASURES ): key = record[id_field] _accumulate( groups.setdefault(key, _zero(_PRODUCT_MEASURES, _PRODUCT_MONEY_MEASURES)), record, _PRODUCT_MEASURES, ) # Unassigned is a real answer: a product with no brand or category must # appear in the breakdown, not vanish from the total. names.setdefault(key, record[name_field] or "Unassigned") rows = [ { "key": str(key) if key is not None else "", "label": names[key], **_product_metrics(totals), } for key, totals in groups.items() ] total = sum((Decimal(row["net_revenue"]) for row in rows), ZERO_MONEY) for row in rows: row["revenue_share"] = _rate(Decimal(row["net_revenue"]), total) rows.sort(key=lambda row: Decimal(row["net_revenue"]), reverse=True) return rows[:limit] def forecast_series( series: Sequence[Mapping[str, Any]], *, buckets: int, grain: str ) -> list[dict[str, Any]]: """A naive least-squares projection of net revenue for the next *buckets*. Explicitly labelled ``linear_trend`` in the payload because that is all it is: a straight line through the complete buckets, with no seasonality, promotion calendar or stock constraint. It answers "if the current trend held" and nothing more, and it refuses to guess from fewer than three complete buckets rather than extrapolating a line through two points. """ points = [ Decimal(entry["net_revenue"]) for entry in series if not entry.get("is_partial", False) ] if buckets < 1 or len(points) < 3: return [] count = len(points) mean_x = Decimal(count - 1) / 2 mean_y = sum(points, ZERO_MONEY) / count variance = sum(((Decimal(i) - mean_x) ** 2 for i in range(count)), Decimal("0")) if variance == 0: return [] covariance = sum( ((Decimal(i) - mean_x) * (points[i] - mean_y) for i in range(count)), Decimal("0") ) slope = covariance / variance intercept = mean_y - slope * mean_x last_complete = [entry for entry in series if not entry.get("is_partial", False)][-1] cursor = dt.date.fromisoformat(last_complete["bucket"]) projected = [] for step in range(1, buckets + 1): cursor = bucket_end(cursor, grain) + dt.timedelta(days=1) value = intercept + slope * Decimal(count - 1 + step) projected.append( { "bucket": cursor.isoformat(), "label": bucket_label(cursor, grain), # Clamped at zero: a downward trend line crosses into negative # revenue, which is arithmetically fine and commercially absurd. "net_revenue": _money(max(value, ZERO_MONEY)), "method": "linear_trend", } ) return projected def revenue_report( *, window: DateRange | None = None, grain: str = "month", basis: str = RevenueBasis.BOOKING, currency: str = "", dimension: str = "sales_channel", filters: Mapping[str, Any] | None = None, compare: bool = True, forecast_buckets: int = 0, limit: int = 25, ) -> dict[str, Any]: """Revenue over time, split by one dimension, optionally with a projection.""" window = window or default_range(days=365) currency = currency or default_currency() if dimension not in REVENUE_DIMENSIONS: raise ValueError(f"unsupported dimension: {dimension}") summary, _ = sales_summary(window, basis=basis, currency=currency, filters=filters) series = sales_series( window, grain=grain, basis=basis, currency=currency, filters=filters ) if dimension in SALES_DIMENSIONS: breakdown = _dimension_breakdown( window, dimension=dimension, basis=basis, currency=currency, filters=filters )[:limit] breakdown_basis = "charged" else: breakdown = _product_breakdown( window, dimension=dimension, currency=currency, filters=filters, limit=limit ) breakdown_basis = "merchandise" payload: dict[str, Any] = { "report": "revenue", "range": window.as_dict(), "grain": grain, "basis": basis, "currency": currency, "currencies_present": currencies_in_range(window), "dimension": dimension, #: Which revenue definition the breakdown rows use. The two cannot be #: compared line for line, and saying so in the payload is cheaper than a #: support thread about why a channel split and a brand split differ. "breakdown_basis": breakdown_basis, "filters": {key: str(value) for key, value in (filters or {}).items() if value}, "freshness": freshness(window), "summary": summary, "series": series, "breakdown": breakdown, } if compare: base = previous_range(window) previous, _ = sales_summary(base, basis=basis, currency=currency, filters=filters) payload["comparison"] = { "range": base.as_dict(), "summary": previous, "deltas": _comparison_deltas(summary, previous), } if forecast_buckets: payload["forecast"] = forecast_series(series, buckets=forecast_buckets, grain=grain) return payload def row_count(payload: Mapping[str, Any]) -> int: """Rows an audit entry should record for *payload*. Counts what was actually read rather than the response byte size, so a wide export and a narrow one covering the same data audit comparably. """ total = len(payload.get("series", ()) or ()) breakdowns = payload.get("breakdowns") if isinstance(breakdowns, Mapping): total += sum(len(rows) for rows in breakdowns.values()) total += len(payload.get("breakdown", ()) or ()) total += len(payload.get("top_products", ()) or ()) return total __all__ = [ "DEFAULT_RANGE_DAYS", "GRAINS", "MAX_RANGE_DAYS", "PRODUCT_DIMENSIONS", "REVENUE_DIMENSIONS", "SALES_DIMENSIONS", "DateRange", "bucket_end", "bucket_label", "bucket_sequence", "bucket_start", "currencies_in_range", "default_currency", "default_range", "forecast_series", "freshness", "previous_range", "product_performance", "revenue_report", "row_count", "sales_dashboard", "sales_series", "sales_summary", ] [V0_FILE]python:file="backend/apps/analytics/services.py" """ Consent-gated ingest for the behavioural event spine. The rules this module enforces, and why each one exists: 1. **No consent, no row.** Analytics consent is read from the first-party consent cookie written by ``apps.tracking`` — the same evidence that gates GA4 and the Meta Pixel, so a visitor who declines is declining once, everywhere. The request is still answered ``202`` with ``stored: 0``: telling an anonymous caller *why* nothing was stored would leak the consent state of a cookie the caller may not own, and failing the request would make the storefront retry a payload that must never be stored. 2. **The client describes, the server decides.** Device, browser, platform, traffic channel and funnel stage are all derived here from headers and the canonical event name. A body that claims ``device: desktop`` is ignored, because a dimension a caller controls is a dimension a caller can skew. 3. **First touch is written once.** Session attribution is set when the session row is created and never updated. Cohort and campaign reports would otherwise be able to change retroactively, which makes yesterday's report unfalsifiable. 4. **Ingest is idempotent.** ``event_id`` is unique, so an offline flush that replays a batch produces duplicates that are counted and dropped, not stored. A retry must never be able to invent a second add-to-cart. 5. **No raw identifiers are retained.** The visitor id is salted-hashed, the IP address is used to resolve a country and then discarded, and property values that look like an email or a phone number are dropped rather than stored. """ from __future__ import annotations import hashlib import re import uuid from datetime import timedelta from decimal import Decimal, InvalidOperation from typing import Any from django.conf import settings from django.db import IntegrityError, transaction from django.db.models import F from django.http import HttpRequest from django.utils import timezone from apps.catalog.models import Product, ProductVariant from apps.orders.models import Order from apps.tracking.selectors import consent_from_cookie from .contracts import ( PRODUCT_REQUIRED_EVENTS, STORED_EVENTS, classify_browser, classify_channel, classify_device, classify_platform, funnel_stage, referrer_host, ) from .models import AnalyticsEvent, AnalyticsSession #: A generous shopping session emits well under this many events per flush; the #: cap exists so one request cannot be used to bulk-insert. MAX_EVENTS_PER_REQUEST = 40 #: Older than this and the event is dropped rather than back-dated. An offline #: tab reopened next week must not silently rewrite a closed reporting day. MAX_EVENT_AGE = timedelta(hours=48) #: Client clocks run fast. A small skew is clamped to "now"; anything beyond it #: is a broken or hostile clock and the event is dropped. MAX_CLOCK_SKEW = timedelta(minutes=10) MAX_PROPERTY_KEYS = 20 MAX_PROPERTY_VALUE_LENGTH = 200 _PROPERTY_KEY_RE = re.compile(r"^[a-z][a-z0-9_]{0,39}$") _EMAIL_RE = re.compile(r"[^@\s]+@[^@\s]+\.[^@\s]+") _LONG_DIGITS_RE = re.compile(r"\d{7,}") class IngestResult(dict): """Plain dict result; named for readability at the call site.""" def analytics_consent(request: HttpRequest) -> bool: """Whether this request carries evidence of analytics consent.""" analytics, _marketing = consent_from_cookie(request) return analytics def hash_visitor(value: str | None) -> str: """Salted hash of a visitor id — a stable key without a raw identifier.""" if not value: return "" salt = getattr(settings, "SECRET_KEY", "") return hashlib.sha256(f"analytics:{salt}:{value}".encode()).hexdigest() def resolve_country(request: HttpRequest) -> str: """ISO-3166 alpha-2 country from the configured edge header, if trusted. Geo headers are only meaningful when every hop in front of the application sets them, so the header name is configuration rather than a hard-coded guess. With nothing configured the country stays blank — a wrong country in a geography report is worse than an absent one. """ header = getattr(settings, "ANALYTICS_GEO_HEADER", "") or "" if not header: return "" value = (request.META.get(header) or "").strip().upper() return value[:2] if len(value) >= 2 and value.isalpha() else "" def _user_agent(request: HttpRequest) -> str: return (request.META.get("HTTP_USER_AGENT") or "")[:400] def _clamp_occurred_at(value, now): """Trust the client's timestamp only inside a defensible window.""" if value is None: return now if value > now + MAX_CLOCK_SKEW: return None if value > now: return now if value < now - MAX_EVENT_AGE: return None return value def sanitise_properties(raw: Any) -> dict[str, Any]: """Bound and clean free-form event properties. Structure is flattened away (no nested objects or lists) so the JSON column cannot become an unbounded arbitrary document, and anything that pattern- matches an email address or a long digit run is dropped: free-text fields are exactly where personal data ends up by accident. """ if not isinstance(raw, dict): return {} cleaned: dict[str, Any] = {} for key, value in raw.items(): if len(cleaned) >= MAX_PROPERTY_KEYS: break name = str(key).strip().lower() if not _PROPERTY_KEY_RE.match(name): continue if isinstance(value, bool | int | float): cleaned[name] = value continue if not isinstance(value, str): continue text = value.strip()[:MAX_PROPERTY_VALUE_LENGTH] if not text or _EMAIL_RE.search(text) or _LONG_DIGITS_RE.search(text): continue cleaned[name] = text return cleaned def _money(value) -> Decimal | None: if value in (None, ""): return None try: amount = Decimal(str(value)) except (InvalidOperation, ValueError): return None return amount if amount >= 0 else None def _get_or_create_session( *, request: HttpRequest, session_key: str, visitor_id: str, landing_path: str, referrer: str, utm: dict[str, str], now, ) -> AnalyticsSession: agent = _user_agent(request) defaults = { "visitor_hash": hash_visitor(visitor_id), "last_event_at": now, "device_type": classify_device(agent), "browser": classify_browser(agent), "platform": classify_platform(agent), "country": resolve_country(request), "landing_path": (landing_path or "")[:500], "referrer_host": referrer_host(referrer)[:255], "channel": classify_channel( utm_source=utm.get("source", ""), utm_medium=utm.get("medium", ""), referrer=referrer, site_host=request.get_host() if hasattr(request, "get_host") else "", ), "utm_source": utm.get("source", "")[:120], "utm_medium": utm.get("medium", "")[:120], "utm_campaign": utm.get("campaign", "")[:120], "utm_term": utm.get("term", "")[:120], "utm_content": utm.get("content", "")[:120], } user = getattr(request, "user", None) if user is not None and getattr(user, "is_authenticated", False): defaults["user"] = user session, created = AnalyticsSession.objects.get_or_create( session_key=session_key, defaults=defaults ) if not created and defaults.get("user") and session.user_id is None: # A visit that began anonymously and then signed in. This is the one # session column that may change after creation, and it is additive: # it links a visit to an account, it never re-attributes the visit. session.user = defaults["user"] session.save(update_fields=["user"]) return session def _resolve_product(product_id) -> Product | None: if not product_id: return None return Product.objects.filter(id=product_id).first() def _resolve_variant(variant_id) -> ProductVariant | None: if not variant_id: return None return ProductVariant.objects.filter(id=variant_id).first() def _resolve_order(order_number: str, user) -> Order | None: """Link a purchase event to an order only when the caller owns it. Without the ownership check an anonymous caller could bind arbitrary behavioural rows to someone else's order number and pollute that order's attribution. """ if not order_number or user is None or not getattr(user, "is_authenticated", False): return None return Order.objects.filter(order_number=order_number, customer=user).first() @transaction.atomic def record_events( *, request: HttpRequest, session_key: str, visitor_id: str = "", landing_path: str = "", referrer: str = "", utm: dict[str, str] | None = None, events: list[dict[str, Any]], ) -> IngestResult: """Store a batch of behavioural events, if consent allows it.""" if not analytics_consent(request): return IngestResult( {"stored": 0, "duplicates": 0, "rejected": 0, "consent": False, "session_id": None} ) now = timezone.now() session = _get_or_create_session( request=request, session_key=session_key, visitor_id=visitor_id, landing_path=landing_path, referrer=referrer, utm=utm or {}, now=now, ) user = getattr(request, "user", None) stored = 0 duplicates = 0 rejected = 0 allowed = set(STORED_EVENTS) for payload in events[:MAX_EVENTS_PER_REQUEST]: name = str(payload.get("name") or "") if name not in allowed: rejected += 1 continue occurred_at = _clamp_occurred_at(payload.get("occurred_at"), now) if occurred_at is None: rejected += 1 continue product = _resolve_product(payload.get("product_id")) if name in PRODUCT_REQUIRED_EVENTS and product is None: # An add-to-cart with no resolvable product cannot appear in any # product report, so storing it would only inflate row counts. rejected += 1 continue variant = _resolve_variant(payload.get("variant_id")) if variant is not None and product is not None and variant.product_id != product.id: variant = None try: # Savepoint per event: a duplicate must abort only its own insert. # On PostgreSQL an IntegrityError poisons the enclosing transaction, # so without this the first replayed event would discard the whole # batch — including the events that were new. with transaction.atomic(): AnalyticsEvent.objects.create( session=session, event_id=str(payload.get("event_id") or uuid.uuid4())[:64], name=name, funnel_stage=funnel_stage(name), occurred_at=occurred_at, path=str(payload.get("path") or "")[:500], product=product, variant=variant, order=_resolve_order(str(payload.get("order_number") or ""), user), quantity=payload.get("quantity"), value_amount=_money(payload.get("value")), currency=str(payload.get("currency") or "")[:3].upper(), properties=sanitise_properties(payload.get("properties")), ) except IntegrityError: # Duplicate event_id: a replayed batch. Idempotent by construction. duplicates += 1 continue stored += 1 rejected += max(0, len(events) - MAX_EVENTS_PER_REQUEST) if stored: AnalyticsSession.objects.filter(pk=session.pk).update( event_count=F("event_count") + stored, last_event_at=now ) return IngestResult( { "stored": stored, "duplicates": duplicates, "rejected": rejected, "consent": True, "session_id": str(session.id), } 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the project's gates? Let me check whether the rest of the repo is already format-clean — if pre-existing files across the repo also fail, formatting isn't an enforced gate and I shouldn't churn files. Let me check."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-0"}],["p",{},["text",{},"Ruff lint passes. Let me check whether "],["code",{},"ruff format"],["text",{}," is actually an enforced gate in this repo before churning files."]],["AssistantMessageContentPart",{"part":{"createdAt":1785514612098,"finishedAt":1785514613231,"lastPartSentAt":1785514612848,"taskNameActive":"Checking format gate","taskNameComplete":"Checked format gate","toolCallId":"toolu_01DhKy73gAqAticN8Gc1N9aD","agentTurnId":"turn_VO1IAjy0IsKcb6J7","filePath":null,"id":"qkH3Rz5GXJyNIBEr","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/backend && . .venv/bin/activate && echo \"=== repo-wide format state ===\" && ruff format --check . 2>&1 | tail -3 && echo \"=== is format in config/CI? ===\" && grep -rn \"ruff format\\|ruff-format\" ../.github pyproject.toml Makefile ../AI_RULES.md ../AI_CONTEXT.md 2>/dev/null | head -10","timeout":300000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"vE4NkKqL0TmDFo3i"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"=== repo-wide format state ===\nWould reformat: scripts/seed_e2e_storefront.py\nWould reformat: scripts/seed_review_data.py\n94 files would be reformatted, 310 files already formatted\n=== is format in config/CI? ===\n"},"id":"YxqKnh8Jg6jslFWU"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-1","parts":[{"type":"thinking-end","duration":1.611764705882353,"thought":"94 of 404 files repo-wide would be reformatted, and `ruff format` appears nowhere in CI or config. So formatting is not an enforced gate — reformatting would be churn outside my task. `ruff check` (the actual gate) passes. Moving on to the full test suite + migration check."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-1"}],["p",{},["code",{},"ruff format"],["text",{}," isn't an enforced gate here (94 pre-existing files repo-wide would reformat, and it appears in no CI config), so I'll leave formatting alone and run the real gates."]],["AssistantMessageContentPart",{"part":{"createdAt":1785514624475,"finishedAt":1785514624941,"stopped":{"reason":"permissions","suggestedPermissions":[{"type":"ALLOW_DYNAMIC_TOOL_STRICT","toolName":"Bash","input":{"command":"cd /vercel/share/v0-project/backend && . .venv/bin/activate && python manage.py makemigrations --check --dry-run && echo \"MIGRATIONS OK\" && python -m pytest -q 2>&1 | tail -25","timeout":600000},"userMessage":"Run command"}],"permissionPrompt":"Run migration checks and full test suite","isMaybeDangerous":true,"riskExplanation":"This command checks for database migrations which could affect project state","rememberPatterns":["Bash(. .venv/bin/activate)","Bash(python manage.py makemigrations --check --dry-run)","Bash(python -m pytest -q 2 1)"],"icon":"TerminalWindow"},"lastPartSentAt":1785514624879,"taskNameActive":"Running full test suite","taskNameComplete":"Ran full test suite","toolCallId":"toolu_01DAYHpwxUWiFkP92acmyexx","agentTurnId":"turn_DjBqoeEAWM0jMuGW","filePath":null,"id":"zgLHgyvgJhd2nv4g","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/backend && . .venv/bin/activate && python manage.py makemigrations --check --dry-run && echo \"MIGRATIONS OK\" && python -m pytest -q 2>&1 | tail -25","timeout":600000}}]}}]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"pgoFJB5irBHFN4PWXGkoNTJJNCRMwy8I","gitRevision":{"baseSha":"918342eff9e4b2427d661605142e1b68f77056b7","headSha":"ca9dbe57dc9df22c6bea1ee8fb22868c3049179f","readyAt":"2026-07-31T16:17:09.988Z","version":1},"streaming":false,"finishReason":"tool-calls","parentId":"WjuqOGnwWS1XT1xXPR6N15LWt3zAM8rX","type":"message","creditCost":1.128593,"workMetrics":{"diffMap":{},"linesRead":634,"agentActions":20},"createdAt":"$D2026-07-31T16:15:21.262Z","updatedAt":"$D2026-07-31T16:17:09.988Z"},{"content":{"type":"parts","parts":[{"type":"mdx","content":"Continue "}],"version":1},"role":"user","id":"AVb0WkaifN1hgE1yvLWUJCS16oBry5yB","attachments":[],"parentId":"pgoFJB5irBHFN4PWXGkoNTJJNCRMwy8I","type":"message","authorId":"kMQ7pVIgOB4IoG78GCbh3JbI","createdAt":"$D2026-07-31T16:15:50.546Z"},{"v0ModelId":"v0-kimi-k3","content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"createdAt":1785514553849,"finishedAt":1785514555817,"lastPartSentAt":1785514555817,"taskNameActive":null,"id":"nSWO8oEld65s3uHW","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":2,"thought":"Let me re-establish context. I was in the middle of fixing ruff lint findings in the analytics module. I had fixed:\n1. caching.py - replaced try/except/pass with contextlib.suppress\n2. exports.py - wrapped a long line\n\nThere were 10 lint findings total. I need to re-run ruff to see what's left. Let me check the current state."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785514555892,"finishedAt":1785514557451,"lastPartSentAt":1785514557137,"taskNameActive":"Re-running ruff","taskNameComplete":"Re-ran ruff","toolCallId":"Bash_51","agentTurnId":"turn_48IljKTaDSuVSJFw","filePath":null,"id":"lCV43wyOU1KyUr0y","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/backend && . .venv/bin/activate && ruff check apps/analytics 2>&1 | tail -40","timeout":120000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"k3tlyum5yNVcG4SH"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"306 | # usable points, not three.\n307 | assert (\n308 | forecast_series(_series(\"100.00\", \"200.00\", \"10.00\", partial_last=True), buckets=1, grain=\"day\")\n | "},"id":"BrcjhzBYHCY0lb1q"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" ^^^^ E501\n309 | == []\n310 | )\n |\n\napps/analytics/tests/test_rollups.py:323:101: E5\n\n...\n\n order=order)\n |\n\nFound 8 errors.\nNo fixes available (2 hidden fixes can be enabled with the `--unsafe-fixes` option).\n"},"id":"8UoCcUhjVBd1lvGb"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785514558785,"finishedAt":1785514559231,"lastPartSentAt":1785514559231,"taskNameActive":null,"id":"mBxAYTyMJ7rZAw6R","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"8 remaining E501 line-too-long errors. Let me see all of them."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785514560220,"finishedAt":1785514561744,"lastPartSentAt":1785514561547,"taskNameActive":"Listing all lint findings","taskNameComplete":"Listed all lint findings","toolCallId":"Bash_52","agentTurnId":"turn_j6RNO2G7xD7aBsSP","filePath":null,"id":"GJfhaxh0mETJe7Ei","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/backend && . .venv/bin/activate && ruff check apps/analytics --output-format=concise 2>&1 | head -20","timeout":120000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"o82YWBG4ps4yrc5y"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"apps/analytics/exports.py:206:101: E501 Line too long (101 > 100)\napps/analytics/reports.py:281:101: E501 Line too long (106 > 100)\napps/analytics/services.py:143:12: UP038 Use `X | Y` in `isinstance` call instead of `(X, Y)`\napps/analytics/tests/tes"},"id":"BbacLTrPCDHpUUc8"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"t_exports.py:123:11: C416 Unnecessary dict comprehension (rewrite using `dict()`)\napps/analytics/tests/test_reports.py:30\n\n...\n\ntoo long (105 > 100)\nFound 8 errors.\nNo fixes available (2 hidden fixes can be enabled with the `--unsafe-fixes` 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)\n\n"},"id":"xvcuMh0HnhGYHI0I"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785514599726,"finishedAt":1785514618184,"lastPartSentAt":1785514618184,"taskNameActive":null,"id":"KbeeMAxcMmz3iwia","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":18,"thought":"The line numbers from ruff don't match the current file content — the files must have been auto-fixed/reformatted after my edits (the Edit tool's autofix). The ruff run happened before some autofix? No — ruff ran after all edits. But sed shows line 206 of exports.py is ` {` which is short.\n\nWait — maybe ruff's line numbers refer to a different state. Actually, the ruff run I did was AFTER the edits to exports.py (the _is_money fix and Font fix). Hmm, but the Edit tool autofix may have run after... no, edits complete before the bash call.\n\nHold on — maybe there are TWO exports.py issues: ruff said `exports.py:206:101`. sed shows line 206 = ` {`. That's bizarre. Unless the file has \\r or something. Let me just re-run ruff fresh to get current findings."}]}}],["p",{},["text",{},"The line numbers have shifted from autofix. Let me get fresh findings:"]],["AssistantMessageContentPart",{"part":{"createdAt":1785514618292,"finishedAt":1785514621582,"lastPartSentAt":1785514620980,"taskNameActive":"Fresh ruff check","taskNameComplete":"Fresh ruff check","toolCallId":"Bash_57","agentTurnId":"turn_oGaCKww0X94CuTOV","filePath":null,"id":"a5ZC2GlHoRM27VH3","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/backend && . .venv/bin/activate && ruff check apps/analytics --output-format=concise 2>&1 | head -20 && echo \"=== long lines ===\" && awk 'length > 100 {print FILENAME\":\"FNR\": \"length}' apps/analytics/exports.py apps/analytics/reports.py apps/analytics/services.py apps/analytics/tests/test_exports.py apps/analytics/tests/test_reports.py apps/analytics/tests/test_rollups.py","timeout":60000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"BjPBqYd2tpiU5pEo"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"All checks passed!\n=== long lines ===\n"},"id":"9COrbdz0L8ojPA2K"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785514624122,"finishedAt":1785514902563,"lastPartSentAt":1785514902563,"taskNameActive":null,"id":"GJx9Tg2gPHODs4SA","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":278,"thought":"$b9"}]}}],["p",{},["text",{},"Ruff is clean — the earlier findings were against a stale filesystem state before my edits synced. Now the remaining backend gates:"]],["AssistantMessageContentPart",{"part":{"createdAt":1785514904098,"finishedAt":1785514946113,"lastPartSentAt":1785514943850,"taskNameActive":"Running migration check and full suite","taskNameComplete":"Ran migration check and full suite","toolCallId":"Bash_58","agentTurnId":"turn_CwEUUjaX2KbIBF7V","filePath":null,"id":"KVkpHAU6wmRoGb3R","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/backend && . .venv/bin/activate && python manage.py makemigrations --check --dry-run && echo \"MIGRATIONS OK\" && python -m pytest -q 2>&1 | tail -8","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"nWS5BLe76y0UYIrj"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"No changes detected\nMIGRATIONS OK\n"},"id":"CuzvASCex3gxrbSC"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"apps/core/tests/test_checks.py::test_production_settings_accept_explicit_secure_configuration\n /vercel/share/v0-project/backend/.venv/lib/python3.13/site-packages/django/test/utils.py:443: UserWarning: Overriding setting DATABAS\n\n...\n\n===== short test summary info ============================\nSKIPPED [1] apps/inventory/tests/test_services.py:140: SQLite does not provide SELECT FOR UPDATE row-level locking semantics.\n1096 passed, 1 skipped, 2 warnings in 30.53s\n"},"id":"tvZ7JJlPXuHDx9Sv"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785514952380,"finishedAt":1785514989645,"lastPartSentAt":1785514989645,"taskNameActive":null,"id":"e5d94PczYCXSXOrb","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":37,"thought":"All backend gates pass:\n- ruff: clean\n- migrations: no changes detected\n- full suite: 1096 passed, 1 skipped\n\nNow let me check the todo list to see where I am and what's next. Per the session summary, the remaining task was the frontend: \"Build analytics frontend registry, API client, and live tabs\" then \"Verify backend tests, typecheck, build, and browser\"."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785514990232,"finishedAt":1785514993969,"lastPartSentAt":1785514993949,"taskNameActive":"Reading todos","taskNameComplete":"Read 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