[docs] Add Real-Time User Profile quickstart tutorial

website/docs/quickstart/user_profile.md

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+---
+title: Real-Time User Profile
+sidebar_position: 4
+---
+
+# Real-Time User Profile
+
+This tutorial demonstrates a real-time user profiling workflow using two core Apache Fluss features: the **Auto-Increment Column** and the **Aggregation Merge Engine**.
+
+## How the System Works
+
+### Core Concepts
+
+- **Identity Mapping**: Incoming email strings are automatically mapped to compact `INT` UIDs using Fluss's auto-increment column, no manual ID management required.
+- **Storage-Level Aggregation**: Click counts are accumulated directly in the Fluss TabletServers via The Aggregation Merge Engine sums the clicks at the storage layer, with no windowing or state in Flink required.
+
+### Data Flow
+
+1. **Ingestion**: Raw click events arrive with an email address, a click count, and a profile group.
+2. **Mapping**: A Flink lookup join against `user_dict` resolves the email to a UID. If the email is new, the `insert-if-not-exists` hint instructs Fluss to generate a new UID automatically.
+3. **Aggregation**: Each event's click count is written to `user_profiles`. The Aggregation Merge Engine sums the clicks at the storage layer, with no windowing or state in Flink required.
+
+## Environment Setup
+
+### Prerequisites
+
+Before proceeding, ensure that [Docker](https://docs.docker.com/engine/install/) and the [Docker Compose plugin](https://docs.docker.com/compose/install/linux/) are installed.
+
+### Starting the Playground
+
+1. Create a working directory.
+   ```shell
+   mkdir fluss-user-profile
+   cd fluss-user-profile
+   ```
+
+2. Set the version environment variables.
+   ```shell
+   export FLUSS_DOCKER_VERSION=0.9.0-incubating
+   export FLINK_VERSION="1.20"
+   ```
+   :::note
+   If you open a new terminal window, remember to re-run these export commands.
+   :::
+
+3. Create a `lib` directory and download the required JARs.
+   ```shell
+   mkdir lib
+
+   # Download Flink Faker for data generation
+   curl -fL -o lib/flink-faker-0.5.3.jar \
+     https://github.com/knaufk/flink-faker/releases/download/v0.5.3/flink-faker-0.5.3.jar
+
+   # Download Fluss Connector
+   curl -fL -o "lib/fluss-flink-${FLINK_VERSION}-${FLUSS_DOCKER_VERSION}.jar" \
+     "https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-${FLINK_VERSION}/${FLUSS_DOCKER_VERSION}/fluss-flink-${FLINK_VERSION}-${FLUSS_DOCKER_VERSION}.jar"
+   ```
+
+4. Verify both JARs downloaded correctly.
+   ```shell
+   ls -lh lib/
+   ```
+   You should see: `flink-faker-0.5.3.jar` and `fluss-flink-1.20-0.9.0-incubating.jar`.
+
+5. Create the `docker-compose.yml` file using the heredoc command below to avoid indentation issues.
+   ```shell
+   cat > docker-compose.yml << 'EOF'
+   services:
+     coordinator-server:
+       image: apache/fluss:${FLUSS_DOCKER_VERSION}
+       command: coordinatorServer
+       depends_on:
+         - zookeeper
+       environment:
+         - |
+           FLUSS_PROPERTIES=
+           zookeeper.address: zookeeper:2181
+           bind.listeners: FLUSS://coordinator-server:9123
+           remote.data.dir: /remote-data
+       volumes:
+         - fluss-remote-data:/remote-data
+     tablet-server:
+       image: apache/fluss:${FLUSS_DOCKER_VERSION}
+       command: tabletServer
+       depends_on:
+         - coordinator-server
+       environment:
+         - |
+           FLUSS_PROPERTIES=
+           zookeeper.address: zookeeper:2181
+           bind.listeners: FLUSS://tablet-server:9123
+           data.dir: /tmp/fluss/data
+           remote.data.dir: /remote-data
+       volumes:
+         - fluss-remote-data:/remote-data
+     zookeeper:
+       restart: always
+       image: zookeeper:3.9.2
+     jobmanager:
+       image: flink:${FLINK_VERSION}
+       ports:
+         - "8081:8081"
+       environment:
+         - |
+           FLINK_PROPERTIES=
+           jobmanager.rpc.address: jobmanager
+       entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/flink/lib && exec /docker-entrypoint.sh jobmanager"]
+       volumes:
+         - ./lib:/tmp/lib
+         - fluss-remote-data:/remote-data
+     taskmanager:
+       image: flink:${FLINK_VERSION}
+       depends_on:
+         - jobmanager
+       environment:
+         - |
+           FLINK_PROPERTIES=
+           jobmanager.rpc.address: jobmanager
+           taskmanager.numberOfTaskSlots: 2
+       entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/flink/lib && exec /docker-entrypoint.sh taskmanager"]
+       volumes:
+         - ./lib:/tmp/lib
+         - fluss-remote-data:/remote-data
+     sql-client:
+       image: flink:${FLINK_VERSION}
+       depends_on:
+         - jobmanager
+       environment:
+         - |
+           FLINK_PROPERTIES=
+           jobmanager.rpc.address: jobmanager
+           rest.address: jobmanager
+       entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/flink/lib && exec /docker-entrypoint.sh bin/sql-client.sh"]
+       volumes:
+         - ./lib:/tmp/lib
+         - fluss-remote-data:/remote-data
+
+   volumes:
+     fluss-remote-data:
+   EOF
+   ```
+
+6. Start the environment.
+   ```shell
+   docker compose up -d
+   ```
+
+7. Confirm all containers are running.
+   ```shell
+   docker compose ps
+   ```
+   You should see `coordinator-server`, `tablet-server`, `zookeeper`, `jobmanager`, and `taskmanager` all in the `running` state.
+
+8. Launch the Flink SQL Client.
+   ```shell
+   docker compose run sql-client
+   ```
+
+## Step 1: Create the Fluss Catalog
+
+In the SQL Client, run these statements one by one.
+
+:::tip
+Run SQL statements one by one to avoid errors.
+:::
+
+```sql
+CREATE CATALOG fluss_catalog WITH (
+    'type' = 'fluss',
+    'bootstrap.servers' = 'coordinator-server:9123'
+);
+```
+
+```sql
+USE CATALOG fluss_catalog;
+```
+
+## Step 2: Create the User Dictionary Table
+
+Create the `user_dict` table to map emails to UIDs. The `auto-increment.fields` property instructs Fluss to automatically generate a unique `INT` UID for every new email it receives.
+
+```sql
+CREATE TABLE user_dict (
+    email STRING,
+    uid   INT,
+    PRIMARY KEY (email) NOT ENFORCED
+) WITH (
+    'auto-increment.fields' = 'uid'
+);
+```
+
+## Step 3: Create the Aggregated Profile Table
+
+Create the `user_profiles` table using the **Aggregation Merge Engine**. The `sum` aggregator on `total_clicks` means every incoming click count is accumulated directly at the storage layer, so the Flink job does not need to maintain any state.
+
+```sql
+CREATE TABLE user_profiles (
+    profile_id   INT,
+    total_clicks BIGINT,
+    PRIMARY KEY (profile_id) NOT ENFORCED
+) WITH (
+    'table.merge-engine'      = 'aggregation',
+    'fields.total_clicks.agg' = 'sum'
+);
+```
+
+## Step 4: Ingest and Process Data
+
+Create a temporary source table to simulate raw click events using the Faker connector.
+
+```sql
+CREATE TEMPORARY TABLE raw_events (
+    email            STRING,
+    click_count      INT,
+    profile_group_id INT,
+    proctime         AS PROCTIME()
+) WITH (
+    'connector'                          = 'faker',
+    'rows-per-second'                    = '1',
+    'fields.email.expression'            = '#{internet.emailAddress}',
+    'fields.click_count.expression'      = '#{number.numberBetween ''1'',''10''}',
+    'fields.profile_group_id.expression' = '#{number.numberBetween ''1'',''5''}'
+);
+```
+
+Now run the pipeline. The `lookup.insert-if-not-exists` hint ensures that if an email is not found in `user_dict`, Fluss automatically generates a new UID for it on the fly.
+
+Although this minimal quickstart does not use the generated `uid` in the final aggregation, the lookup join is still important because it demonstrates how Fluss automatically assigns and persists stable integer IDs for new email identifiers on first encounter.
+
+```sql
+INSERT INTO user_profiles
+SELECT
+    e.profile_group_id,
+    CAST(e.click_count AS BIGINT)
+FROM raw_events AS e
+JOIN user_dict /*+ OPTIONS('lookup.insert-if-not-exists' = 'true') */
+FOR SYSTEM_TIME AS OF e.proctime AS d
+ON e.email = d.email;
+```
+
+## Step 5: Verify Results
+
+Open a **second terminal**, change into the same working directory, re-run the export commands, and launch another SQL Client session to query the results while the pipeline runs.
+
+```shell
+cd fluss-user-profile
+export FLUSS_DOCKER_VERSION=0.9.0-incubating
+export FLINK_VERSION="1.20"
+docker compose run sql-client
+```
+
+Set up the catalog in the new session:
+
+```sql
+CREATE CATALOG fluss_catalog WITH (
+    'type' = 'fluss',
+    'bootstrap.servers' = 'coordinator-server:9123'
+);
+```
+```sql
+USE CATALOG fluss_catalog;
+```
+
+Query the aggregated profile table:
+
+```sql
+SET 'sql-client.execution.result-mode' = 'tableau';
+```
+
+```sql
+SELECT profile_id, total_clicks FROM user_profiles;
+```
+
+You should see 5 rows (one per profile group) with `total_clicks` increasing in real time as new events arrive.
+
+To verify the dictionary mapping is working:
+
+```sql
+SELECT * FROM user_dict LIMIT 10;
+```
+
+Each email should have a compact `INT` uid automatically assigned by Fluss.
+
+## Clean Up
+
+Exit the SQL Client by typing `exit;`, then stop the Docker containers.
+
+```shell
+docker compose down -v
+```
+
+## What's Next?
+
+This quickstart demonstrates the core mechanics. For a deeper dive into real-time user profiling with bitmap-based unique visitor counting using the `rbm64` aggregator, see the [Real-Time Profiles blog post](/blog/realtime-profiles-fluss).