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Using ClickHouse for Game Analytics: Real‑Time Event Processing for Indie Studios

ccodeacademy

2026-01-31

10 min read

Step‑by‑step guide: instrument a hobby game, stream events to ClickHouse, and build real‑time dashboards to tune engagement and detect abuse.

Ship better, faster: use real‑time analytics to tune engagement and stop cheaters

Indie devs face the same analytics problems as big studios: fragmented event data, slow ETL, and blind spots where players churn or exploit systems. You don’t need a billion‑dollar budget to get powerful, near‑real‑time insights. In 2026, serverless ingestion and edge collectors are practical choices for hobby studios — fast, low‑cost, and designed for high‑throughput event tracking. This guide gives a step‑by‑step playbook: instrument events in a hobby game, stream them to ClickHouse, and build dashboards that help you tune engagement and detect abuse.

Why ClickHouse for game analytics in 2026?

ClickHouse has become a mainstream choice for real‑time analytics. After a major funding round in late 2025 (ClickHouse raised $400M, accelerating product and cloud features), the platform’s ecosystem matured: better cloud integrations, improved Kafka ingestion, and richer OLAP features that matter for games — fast aggregations, low storage costs, and production‑grade scalability.

Real‑time ingestion: Kafka and HTTP insert patterns let you stream events with low latency.
Fast queries: MergeTree families, materialized views, and projections give sub‑second aggregates for dashboards.
Cost control: Efficient compression, TTLs, and tiered storage help keep costs predictable for indie budgets.
Integrations: First‑class Grafana support and ClickHouse Cloud options make dashboarding and ops easier.

Overview of the pipeline you'll build

We’ll implement a simple, realistic pipeline that fits a hobby project and scales as your player base grows.

Instrument events in the game client (Unity/Godot/HTML5): batch events locally and send to a collector.
Collector (Node.js / Python): validate, enrich, and publish to Kafka or directly insert into ClickHouse.
ClickHouse: an events table using MergeTree; a Kafka engine + materialized view for robust streaming ETL.
Dashboards: Grafana + ClickHouse datasource for DAU, sessions, funnels, and abuse detection queries.

1. Instrument events in your hobby game

Start with a small, consistent event schema. Avoid free‑form strings for critical fields — use enums or normalized strings. Collect these core fields on every event:

event_name (string) — e.g., session_start, level_complete
player_id (string hashed) — don't send raw PII
device_id (string)
event_time (ISO 8601 / epoch ms)
session_id (string)
properties (JSON object) — arbitrary details like level, score

Client‑side rules:

Batch events locally (e.g., every 5–10s or when the buffer hits 50 events).
Retry with exponential backoff; persist to disk if offline.
Never embed DB credentials in the client — always send events to a trusted collector.

Minimal client example (JavaScript/web)

// buffer events and POST to collector
const buffer = [];
function track(eventName, props = {}) {
  buffer.push({
    event_name: eventName,
    player_id: hashPlayerId(getPlayerId()),
    device_id: getDeviceId(),
    event_time: new Date().toISOString(),
    session_id: getSessionId(),
    properties: props
  });
  if (buffer.length >= 50) flush();
}
async function flush() {
  if (buffer.length === 0) return;
  const payload = buffer.splice(0);
  await fetch('https://collector.example.com/track', {
    method: 'POST', headers: {'Content-Type': 'application/json'}, body: JSON.stringify(payload)
  });
}

2. Build a simple collector (Node.js)

Collector responsibilities: validate, enrich (geolocation, user agent), and forward. For reliability you can publish to Kafka; for small players, direct inserts to ClickHouse are fine.

Node.js Express collector (receives batches and inserts to ClickHouse via HTTP)

const express = require('express');
const fetch = require('node-fetch');
const app = express();
app.use(express.json({limit: '2mb'}));

app.post('/track', async (req, res) => {
  const events = (req.body || []).map(e => ({
    event_name: e.event_name,
    player_id: e.player_id,
    device_id: e.device_id,
    event_time: e.event_time,
    session_id: e.session_id,
    properties: JSON.stringify(e.properties || {})
  }));

  // Convert to ClickHouse JSONEachRow
  const payload = events.map(e => JSON.stringify(e)).join('\n');

  // ClickHouse HTTP insert
  await fetch('http://clickhouse:8123/?query=INSERT%20INTO%20events%20FORMAT%20JSONEachRow',
    {method: 'POST', body: payload});

  res.status(204).send();
});

app.listen(8080);

For higher reliability use Kafka: publish events to a Kafka topic and let ClickHouse consume via the Kafka table engine and a materialized view (next section). For teams concerned about supply chain attacks on data pipelines, pairing these patterns with a red‑teaming playbook for supervised pipelines is a sensible precaution.

3. ClickHouse schema and ingestion patterns

Design tables for write throughput and query patterns. Partition by date, order by player_id and event_time to make common queries fast.

DDL: events table (MergeTree)

CREATE TABLE IF NOT EXISTS events (
  event_time DateTime64(3),
  event_date Date,
  event_name String,
  player_id String,
  device_id String,
  session_id String,
  properties String -- JSON
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (player_id, event_time)
TTL event_time + toIntervalDay(90) -- drop raw events after 90 days
SETTINGS index_granularity = 8192;

Notes: use TTL to prune old raw events and projections or materialized views to keep weekly/monthly aggregates long‑term.

Robust streaming: Kafka engine + Materialized View

When you scale to many players, send events to Kafka from your collector. ClickHouse can consume Kafka topics directly and materialize them into the events table.

-- Kafka source table (raw bytes)
CREATE TABLE kafka_events (
  message String
) ENGINE = Kafka SETTINGS
  kafka_broker_list = 'kafka:9092',
  kafka_topic_list = 'game_events',
  kafka_group_name = 'clickhouse-consumer',
  kafka_format = 'JSONEachRow';

-- Materialized view to parse and insert into events
CREATE MATERIALIZED VIEW kafka_to_events TO events AS
SELECT
  parseDateTimeBestEffort(JSONExtractString(message, 'event_time')) AS event_time,
  toDate(event_time) AS event_date,
  JSONExtractString(message, 'event_name') AS event_name,
  JSONExtractString(message, 'player_id') AS player_id,
  JSONExtractString(message, 'device_id') AS device_id,
  JSONExtractString(message, 'session_id') AS session_id,
  JSONExtractString(message, 'properties') AS properties
FROM kafka_events;

This pattern gives you at‑least‑once semantics and a highly durable buffer when collectors fail. If your infra sits behind proxies or requires observability, consider proxy and observability patterns from proxy management tools for small teams to automate ingestion and compliance.

4. Pre‑aggregate for real‑time dashboards

Raw event tables are great for flexibility; pre‑aggregations make dashboards fast. Use materialized views to maintain rolling aggregates like hourly DAU, session counts, and funnel steps.

CREATE TABLE IF NOT EXISTS hourly_dau (
  date Date,
  hour UInt8,
  dau UInt32
) ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (date, hour);

CREATE MATERIALIZED VIEW mv_hourly_dau TO hourly_dau AS
SELECT
  toDate(event_time) AS date,
  toHour(event_time) AS hour,
  uniqExact(player_id) AS dau
FROM events
GROUP BY date, hour;

For large user counts use approximate uniq functions (uniqCombined64) to control memory.

5. Dashboard examples (Grafana)

Grafana supports ClickHouse via a datasource plugin. Below are example queries and dashboard widgets you’ll want.

Key metrics to show

DAU / MAU — daily and monthly active users
Session metrics — sessions per user, median session length
Funnel completion — tutorial start → complete → level 1 complete
Engagement heatmap — events per minute/hour to detect spikes
Abuse signals — top IPs, events per minute > threshold

Sample SQL: rolling 1‑hour active users

SELECT
  toStartOfMinute(event_time) AS minute,
  uniqExact(player_id) OVER (PARTITION BY toStartOfMinute(event_time)) AS active_users
FROM events
WHERE event_time >= now() - INTERVAL 24 HOUR
GROUP BY minute
ORDER BY minute;

Sample SQL: retention (day 0 → day 7)

WITH
  first_seen AS (
    SELECT player_id, min(toDate(event_time)) AS install_date
    FROM events WHERE event_name = 'session_start' GROUP BY player_id
  )
SELECT
  install_date,
  countIf(first_seen.player_id, toDate(events.event_time) = install_date + 7) / count() AS day_7_retention
FROM first_seen
LEFT JOIN events USING (player_id)
GROUP BY install_date
ORDER BY install_date DESC
LIMIT 30;

6. Detecting abuse and exploits

Abuse patterns are different per game, but there are common detectors you can implement quickly in SQL. Build these as Grafana panels and alerts.

Fast anomaly rules

Event rate per player: events/min > threshold (e.g., > 500 events/min)
Duplicate transaction IDs: same tx_id used multiple times
Top IPs/devices: many players from same IP or device fingerprint
Impossible progression: level increases faster than humanly possible

Example SQL: high‑rate players (1 minute window)

SELECT player_id, count() AS events_in_minute
FROM events
WHERE event_time >= now() - INTERVAL 1 MINUTE
GROUP BY player_id
HAVING events_in_minute > 500
ORDER BY events_in_minute DESC LIMIT 50;

Example SQL: duplicate transaction IDs

SELECT properties, count() AS hits
FROM events
WHERE JSONExtractString(properties, 'tx_id') != ''
GROUP BY JSONExtractString(properties, 'tx_id')
HAVING hits > 1
ORDER BY hits DESC
LIMIT 100;

When you detect suspicious players, enrich data with device_id, account age, and velocity. Use ClickHouse joins to fetch history and drive a quick decision (ban, throttle, or flag for review). If you're building automated responses, hardening your pipeline and dev tools with advice from hardening desktop AI agents and supply‑chain red‑teaming resources like red‑teaming supervised pipelines can reduce risk.

7. Practical performance tips

Order key matters: choose ORDER BY to match your most frequent queries (player_id, event_time).
Use TTLs and projections: drop raw events you don’t need and keep long‑term summaries via projections or aggregated tables.
Approximate cardinalities: use uniqCombined64 for large DAU to avoid heavy memory usage.
Batch inserts: send JSONEachRow in batches to reduce overhead.
Compression: ClickHouse compression is strong; store JSON in String but consider extracting indexed properties you query often.

8. Security, privacy, and data governance

Indie teams often forget privacy. Follow these rules:

Hash player identifiers on the client or collector; store only hashed IDs in ClickHouse.
Don’t store raw PII like emails or full IP addresses — either pseudonymize or use truncated IPs.
Keep a data retention policy and enforce it with ClickHouse TTLs.
Limit who can run heavy queries; use separate read replicas for dashboards.

For operational playbooks on managing tool fleets and seasonal workloads, see the operations playbook to help coordinate teams and on-call rotations. If your architecture uses edge collectors and you care about low network latency, the broader trends on low-latency networking are important context when choosing regions for ingestion.

9. Example: tune tutorial completion in 6 steps

This mini case shows how to use the pipeline to improve an onboarding funnel.

Instrument events: tutorial_start, tutorial_step, tutorial_complete with properties.step_id and elapsed_ms.
Collect baseline: run for 7 days and compute funnel conversion rates and per‑step drop‑off.
Identify bottleneck: step 2 dropout is 60% — median elapsed_ms is 45s (too long).
Make a change: reduce friction by simplifying controls or adding a hint.
A/B test: route 50% of new players to variant B and compare funnel conversions over 3 days.
Ship the better variant and monitor retention and revenue lift via ClickHouse dashboards.

SQL to compute funnel step completion:

WITH
  starts AS (SELECT player_id FROM events WHERE event_name = 'tutorial_start'),
  step2 AS (SELECT player_id FROM events WHERE event_name = 'tutorial_step' AND JSONExtractInt(properties, 'step_id') = 2),
  complete AS (SELECT player_id FROM events WHERE event_name = 'tutorial_complete')
SELECT
  (SELECT count() FROM starts) AS started,
  (SELECT count() FROM step2) AS reached_step2,
  (SELECT count() FROM complete) AS completed;

10. Monitoring, alerts, and ops

Set up Grafana alerts for key signals: DAU drops, retention decay, and abuse thresholds. For ClickHouse health, use a metrics exporter (e.g., clickhouse_exporter) and monitor query latency, merge queue, and disk pressure. If using ClickHouse Cloud, connect Grafana via the managed datasource for easier ops. For broader observability patterns and incident response playbooks, review guidance on site search observability & incident response which shares useful alerting and runbook practices.

Advanced strategies and future directions (2026)

Looking forward, here are strategies to keep your analytics modern:

Serverless ingestion: lightweight collectors in edge regions reduce latency for global players.
Feature stores: materialize player features (LTV predictors, churn risk) and feed them back into live matchmakers.
Real‑time ML: integrate streaming aggregations with online models to detect bot behavior faster — consider orchestration patterns covered in micro‑app and feature orchestration guides like micro‑app tutorials.
Projections and column improvements: use ClickHouse projections to speed up specific queries without additional ETL maintenance.

ClickHouse’s 2025–26 momentum means more managed options and tighter integrations with dashboards and streaming tools — good for small teams who want enterprise capabilities without the heavy ops overhead.

Quick checklist: from zero to real‑time insights

Define a small, normalized event schema (event_name, player_id_hash, event_time, session_id, properties).
Implement client batching + collector; avoid DB creds in the client.
Start with HTTP insert for speed; move to Kafka for scale.
Create MergeTree events table with TTLs and sensible ORDER BY.
Materialize hourly DAU and funnels with materialized views.
Connect Grafana and create DAU, retention, funnel, and abuse panels.
Set alerts and monitor ClickHouse metrics and dashboard usage.

Practical tip: for early development, ClickHouse Cloud gives a quick, managed path. When you hit scale, the same SQL and materialized view patterns translate to self‑hosted clusters.

Wrapping up — actionable takeaways

ClickHouse gives indie studios a pragmatic path to real‑time analytics in 2026: low cost, high performance, and production features that matter for games. Start small — instrument a few important events, add an aggregation view, and build one dashboard that answers a pressing question (Why are players dropping from the tutorial?). Then expand: add Kafka for scale, pre‑aggregation for speed, and abuse detection rules to protect your economy.

Want a starter kit? I’ve put together a compact repo with client examples, a Node.js collector, ClickHouse DDLs, and Grafana dashboard JSON you can import. It’s tuned for hobby projects so you can run end‑to‑end in a few hours.

Call to action

Grab the starter repo, spin up a ClickHouse Cloud trial or a single‑node instance, and instrument your game today. Start with one metric — tutorial completion or DAU — and ship a data‑driven change within a week. If you want, I can walk you through a live setup and help craft abuse detectors tailored to your game.

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