analytics

from django.db.models import F, Window
from django.db.models.functions import RowNumber, Rank, DenseRank
from products.models import Sale


def get_sales_with_ranking():

class ShardedCounter
  SHARDS = 16
  SNAPSHOT_TTL = 10 # seconds

  def initialize(name, redis: REDIS)
    @name = name

-- ROW_NUMBER: Unique sequential number
SELECT
  name,
  department,
  salary,
  ROW_NUMBER() OVER (ORDER BY salary DESC) as overall_rank,

import pandas as pd
import plotly.express as px

df = pd.read_csv('marketing_performance.csv')
fig = px.scatter(
    df,

WITH ordered_events AS (
  SELECT
    customer_id,
    event_time,
    revenue,
    ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY event_time DESC) AS event_rank,

import pandas as pd

df = pd.read_parquet('events.parquet')
df['event_date'] = pd.to_datetime(df['event_date'])
df['month'] = df['event_date'].dt.to_period('M').astype(str)

import geopandas as gpd
from shapely.geometry import Point

stores = gpd.read_file('stores.geojson').to_crs(epsg=3857)
customers = gpd.GeoDataFrame(
    customer_df,

CREATE TABLE daily_events (
    id          BIGSERIAL PRIMARY KEY,
    category    TEXT        NOT NULL,
    item_id     BIGINT      NOT NULL,
    score       NUMERIC(10,2) NOT NULL DEFAULT 0,
    occurred_at TIMESTAMPTZ NOT NULL DEFAULT now()

CREATE TABLE daily_metrics (
    id          BIGGENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    tenant_id   BIGINT      NOT NULL,
    metric      TEXT        NOT NULL,
    day         DATE        NOT NULL,
    count       BIGINT      NOT NULL DEFAULT 0,

from django.db import models
from django.db.models import Avg, Count, Sum, DecimalField
from django.db.models.functions import Coalesce, TruncDate
from django.utils import timezone

-- Basic LATERAL join
SELECT
  u.username,
  recent.order_id,
  recent.total,
  recent.created_at

export interface AnalyticsEvent {
  name: string;
  props?: Record<string, unknown>;
  ts: number;
}