Initial push of Plunk Next
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Dries Augustyns
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import {prisma} from '../database/prisma.js';
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import {redis} from '../database/redis.js';
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/**
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* Time series data point for analytics
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*/
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export interface TimeSeriesDataPoint {
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date: string;
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emails: number;
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opens: number;
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clicks: number;
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bounces: number;
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delivered: number;
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}
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/**
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* Analytics Service
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*
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* PERFORMANCE CONSIDERATIONS:
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* - Aggregates data by day to reduce row count
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* - Uses Redis caching with 15-minute TTL
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* - Limits date ranges to prevent expensive queries
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* - Uses indexed fields (createdAt, projectId) for efficient filtering
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* - For 1M+ emails, consider background jobs for pre-aggregation
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*/
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export class AnalyticsService {
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private static readonly DEFAULT_DAYS_BACK = 30;
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private static readonly MAX_DAYS_BACK = 90;
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private static readonly TIMESERIES_CACHE_TTL = 900; // 15 minutes
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/**
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* Get time series data for email analytics
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*
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* Performance: O(n) where n = number of emails in date range
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* - Groups emails by day using SQL aggregation
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* - Cached in Redis for 15 minutes
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* - Limited to 90 days max to prevent performance issues
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*
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* For higher scale (1M+ emails/day), consider:
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* - Pre-aggregated daily stats table updated by background job
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* - Materialized view with daily refresh
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* - Time-series database (TimescaleDB, InfluxDB)
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*/
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public static async getTimeSeriesData(
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projectId: string,
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startDate?: Date,
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endDate?: Date,
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): Promise<TimeSeriesDataPoint[]> {
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// Calculate date range with limits
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const now = new Date();
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const effectiveEndDate = endDate || now;
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const defaultStartDate = new Date(now.getTime() - this.DEFAULT_DAYS_BACK * 24 * 60 * 60 * 1000);
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const effectiveStartDate = startDate || defaultStartDate;
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// Enforce max date range
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const maxStartDate = new Date(now.getTime() - this.MAX_DAYS_BACK * 24 * 60 * 60 * 1000);
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const limitedStartDate = effectiveStartDate < maxStartDate ? maxStartDate : effectiveStartDate;
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// Check cache first
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const cacheKey = `analytics:timeseries:${projectId}:${limitedStartDate.toISOString()}:${effectiveEndDate.toISOString()}`;
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const cached = await redis.get(cacheKey);
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if (cached) {
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return JSON.parse(cached);
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}
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// Raw SQL query for efficient daily aggregation
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// Using raw SQL because Prisma's groupBy is less efficient for date truncation
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const result = await prisma.$queryRaw<
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{
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date: Date;
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total_emails: bigint;
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total_opens: bigint;
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total_clicks: bigint;
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total_bounces: bigint;
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total_delivered: bigint;
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}[]
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>`
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SELECT
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DATE_TRUNC('day', "createdAt") as date,
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COUNT(*) as total_emails,
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COUNT(CASE WHEN "openedAt" IS NOT NULL THEN 1 END) as total_opens,
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COUNT(CASE WHEN "clickedAt" IS NOT NULL THEN 1 END) as total_clicks,
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COUNT(CASE WHEN "bouncedAt" IS NOT NULL THEN 1 END) as total_bounces,
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COUNT(CASE WHEN "deliveredAt" IS NOT NULL THEN 1 END) as total_delivered
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FROM "emails"
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WHERE "projectId" = ${projectId}
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AND "createdAt" >= ${limitedStartDate}
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AND "createdAt" <= ${effectiveEndDate}
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GROUP BY DATE_TRUNC('day', "createdAt")
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ORDER BY date ASC
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`;
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// Convert to TimeSeriesDataPoint format
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const timeSeries: TimeSeriesDataPoint[] = result.map(row => ({
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date: row.date.toISOString(),
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emails: Number(row.total_emails),
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opens: Number(row.total_opens),
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clicks: Number(row.total_clicks),
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bounces: Number(row.total_bounces),
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delivered: Number(row.total_delivered),
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}));
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// Fill in missing dates with zero values
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const filledTimeSeries = this.fillMissingDates(timeSeries, limitedStartDate, effectiveEndDate);
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// Cache for 15 minutes
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await redis.setex(cacheKey, this.TIMESERIES_CACHE_TTL, JSON.stringify(filledTimeSeries));
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return filledTimeSeries;
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}
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/**
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* Get campaign performance metrics
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* Returns top performing campaigns by open rate
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*/
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public static async getTopCampaigns(
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projectId: string,
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limit = 10,
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startDate?: Date,
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endDate?: Date,
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): Promise<
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{
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id: string;
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subject: string;
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sentCount: number;
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openedCount: number;
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clickedCount: number;
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openRate: number;
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clickRate: number;
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}[]
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> {
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const now = new Date();
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const defaultStartDate = new Date(now.getTime() - this.DEFAULT_DAYS_BACK * 24 * 60 * 60 * 1000);
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const campaigns = await prisma.campaign.findMany({
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where: {
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projectId,
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sentAt: {
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gte: startDate || defaultStartDate,
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...(endDate ? {lte: endDate} : {}),
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},
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status: 'SENT',
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},
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select: {
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id: true,
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subject: true,
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sentCount: true,
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openedCount: true,
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clickedCount: true,
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},
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orderBy: {
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openedCount: 'desc',
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},
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take: limit,
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});
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return campaigns.map(campaign => ({
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id: campaign.id,
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subject: campaign.subject || 'No subject',
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sentCount: campaign.sentCount || 0,
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openedCount: campaign.openedCount || 0,
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clickedCount: campaign.clickedCount || 0,
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openRate: campaign.sentCount ? ((campaign.openedCount || 0) / campaign.sentCount) * 100 : 0,
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clickRate: campaign.sentCount ? ((campaign.clickedCount || 0) / campaign.sentCount) * 100 : 0,
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}));
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}
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/**
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* Fill in missing dates in time series with zero values
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* Ensures consistent daily data points even when no emails were sent
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*/
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private static fillMissingDates(data: TimeSeriesDataPoint[], startDate: Date, endDate: Date): TimeSeriesDataPoint[] {
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const result: TimeSeriesDataPoint[] = [];
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const dataMap = new Map(data.map(point => [new Date(point.date).toDateString(), point]));
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// Iterate through each day in range
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const currentDate = new Date(startDate);
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currentDate.setHours(0, 0, 0, 0);
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const end = new Date(endDate);
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end.setHours(23, 59, 59, 999);
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while (currentDate <= end) {
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const dateKey = currentDate.toDateString();
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const existingData = dataMap.get(dateKey);
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if (existingData) {
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result.push(existingData);
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} else {
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// Fill with zeros for days with no data
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result.push({
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date: new Date(currentDate).toISOString(),
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emails: 0,
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opens: 0,
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clicks: 0,
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bounces: 0,
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delivered: 0,
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});
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}
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// Move to next day
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currentDate.setDate(currentDate.getDate() + 1);
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}
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return result;
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}
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}
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