Real-Time Analytics

Overview

This skill enables AI agents to build self-hosted, real-time analytics systems. It covers the full pipeline from event ingestion through storage to query and visualization, using ClickHouse as the analytical database for sub-second query performance at scale.

Instructions

Event Schema Design

1. Every event must have these base fields:

   • event_name — LowCardinality(String) for efficient storage
   • timestamp — DateTime64(3) for millisecond precision
   • session_id — String, client-generated UUID
   • user_id — Nullable(String) for anonymous tracking
   • device_type — LowCardinality(String): desktop, mobile, tablet
   • country_code — LowCardinality(FixedString(2))
   • properties — String containing JSON for event-specific data

2. ClickHouse table optimization rules:

   • Use MergeTree() engine, partition by toYYYYMM(date)
   • ORDER BY should start with the most filtered column (usually event_name)
   • Add TTL for automatic data expiration (default 90 days)
   • Use LowCardinality() for any string column with fewer than 10,000 distinct values

Ingestion Service

1. Build as a stateless HTTP service accepting POST /events with JSON array body.
2. Validate incoming events: reject if event_name or timestamp is missing.
3. Buffer events in memory. Flush when either condition is met:
   • Buffer reaches 1,000 events
   • 2 seconds have elapsed since last flush
4. Use ClickHouse's INSERT ... FORMAT JSONEachRow for batch inserts.
5. On flush failure, retry 3 times with exponential backoff, then write to a dead-letter file.
6. Expose GET /health returning: { "buffer_size": N, "last_flush": "ISO timestamp", "status": "ok" }.

Aggregation Queries

Write queries as named .sql files. Common dashboard panels:

Active users (last N minutes):

SELECT count(DISTINCT session_id) AS active_users
FROM events
WHERE timestamp > now() - INTERVAL 5 MINUTE;

Funnel analysis (two-step):

SELECT
  toStartOfHour(timestamp) AS hour,
  countIf(event_name = 'signup_started') AS started,
  countIf(event_name = 'signup_completed') AS completed,
  round(completed / started * 100, 1) AS conversion_pct
FROM events
WHERE date = today()
GROUP BY hour
ORDER BY hour;

Top features by unique users:

SELECT
  JSONExtractString(properties, 'feature') AS feature,
  count(DISTINCT user_id) AS unique_users
FROM events
WHERE event_name = 'feature_used' AND date = today()
GROUP BY feature
ORDER BY unique_users DESC
LIMIT 10;

Error rate per minute:

SELECT
  toStartOfMinute(timestamp) AS minute,
  count() AS error_count
FROM events
WHERE event_name = 'error_occurred'
  AND timestamp > now() - INTERVAL 1 HOUR
GROUP BY minute
ORDER BY minute;

Dashboard API Layer

1. Create one REST endpoint per dashboard panel (e.g., GET /api/dashboard/active-users).
2. Each endpoint runs its SQL query and returns JSON.
3. Add a time_range query param: 1h, 6h, 24h, 7d. Adjust the WHERE clause accordingly.
4. Cache results for 3-5 seconds to avoid hammering ClickHouse on concurrent dashboard loads.

Docker Compose Setup

Provide a docker-compose.yml with:

• ClickHouse service (pinned version, volume for data)
• Ingestion service (builds from Dockerfile)
• Dashboard frontend (builds from Dockerfile or served via nginx)
• An init container or entrypoint script that creates the events table

Examples

Example 1 — Schema for an e-commerce app

Input: "Design analytics events for an e-commerce checkout flow."

Output:

-- Core events: product_viewed, add_to_cart, checkout_started,
-- payment_submitted, order_completed, order_failed

CREATE TABLE events (
    event_name   LowCardinality(String),
    timestamp    DateTime64(3),
    session_id   String,
    user_id      Nullable(String),
    device_type  LowCardinality(String),
    country_code LowCardinality(FixedString(2)),
    properties   String,
    date         Date DEFAULT toDate(timestamp)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (event_name, timestamp)
TTL date + INTERVAL 180 DAY;

Properties JSON per event:

• product_viewed: {"product_id": "p-42", "category": "electronics", "price_cents": 4999}
• add_to_cart: {"product_id": "p-42", "quantity": 1}
• payment_submitted: {"method": "card", "amount_cents": 9998}

Example 2 — Ingestion buffer logic

Input: "Show the buffer flush logic in Node.js."

Output:

const FLUSH_INTERVAL_MS = 2000;
const FLUSH_SIZE = 1000;
let buffer = [];

async function addEvents(events) {
  buffer.push(...events);
  if (buffer.length >= FLUSH_SIZE) await flush();
}

setInterval(async () => {
  if (buffer.length > 0) await flush();
}, FLUSH_INTERVAL_MS);

async function flush() {
  const batch = buffer.splice(0, buffer.length);
  const rows = batch.map(e => JSON.stringify(e)).join('\n');
  await clickhouse.insert({
    table: 'events',
    values: batch,
    format: 'JSONEachRow',
  });
}

Guidelines

• Partition wisely. Monthly partitions work for most apps. Daily only if you ingest > 100M events/day.
• Do not use Kafka unless asked. For under 50K events/second, direct HTTP ingestion with in-memory buffering is simpler and sufficient.
• Always add TTL. Unbounded analytical tables grow fast. Default to 90 days; let the user override.
• Test with realistic volume. Generate synthetic events to validate the pipeline handles expected throughput before going live.
• *Avoid SELECT .** Always specify columns in aggregation queries to minimize I/O.