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+---
+title: Real-Time User Profile
+sidebar_position: 4
+---
+
+# Real-Time User Profile
+
+This tutorial demonstrates how to build a production-grade, real-time user profiling system using Apache Fluss. You will learn how to map high-cardinality string identifiers (like emails) to compact integers and aggregate user behavior directly in the storage layer with exactly-once guarantees.
+
+![arch](/img/user-profile.png)
+
+## How the System Works
+
+### Core Concepts
+*   **Identity Mapping**: High-cardinality strings (Emails) ➔ Compact 32-bit `INT` UIDs.
+*   **Offloaded Aggregation**: Computation happens in Fluss TabletServers, keeping Flink state-free.
+*   **Optimized Storage**: Native [RoaringBitmap](https://roaringbitmap.org/) support for sub-second unique visitor (UV) counts.
+
+### Data Flow
+1.  **Ingestion**: Raw event streams (e.g., clicks, page views) enter the system from a source like [Apache Kafka](https://kafka.apache.org/).
+2.  **Mapping**: The [Apache Flink](https://flink.apache.org/) job performs a temporal lookup join against the `user_dict` table. If a user is new, the `insert-if-not-exists` hint triggers Fluss to automatically generate a unique `INT` UID using its Auto-Increment feature.
+3.  **Merge**: The **Aggregation Merge Engine** updates clicks and bitmaps in the storage layer.
+4.  **Recovery**: The **Undo Recovery** mechanism ensures exactly-once accuracy during failovers.
+
+## 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 Fluss version environment variable.
+   ```shell
+   # Set to 0.9.0 or 0.10-SNAPSHOT for latest features
+   export FLUSS_DOCKER_VERSION=0.10-SNAPSHOT
+   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"
+   ```
+   :::note
+   For now : cp ../fluss-flink/fluss-flink-1.20/target/fluss-flink-1.20-0.10-SNAPSHOT.jar lib/
+   :::
+
+4. Create a `docker-compose.yml` file.
+   ```yaml
+   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:${FLINK_VERSION}
+       depends_on:
+         - jobmanager
+       environment:
+         - |
+           FLINK_PROPERTIES=
+           jobmanager.rpc.address: jobmanager
+           rest.address: jobmanager
+       entrypoint: ["sh", "-c", "cp -v /tmp/lib/*.jar /opt/flink/lib && exec /docker-entrypoint.sh bin/sql-client.sh"]
+       volumes:
+         - ./lib:/tmp/lib
+         - fluss-remote-data:/remote-data
+
+   volumes:
+     fluss-remote-data:
+   ```
+
+5. Start the environment.
+   ```shell
+   docker compose up -d
+   ```
+
+6. Launch the Flink SQL Client.
+   ```shell
+   # Explicitly include the Fluss JAR in the classpath for the session
+   docker compose run sql-client bin/sql-client.sh -j /tmp/lib/fluss-flink-${FLINK_VERSION}-${FLUSS_DOCKER_VERSION}.jar
+   ```
+
+## Step 1: Create the Fluss Catalog
+
+In the SQL Client, create and use the Fluss catalog.
+
+:::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. We use `auto-increment.fields` to automatically generate unique IDs.
+
+```sql
+CREATE TABLE user_dict (
+    email STRING,
+    uid INT,
+    PRIMARY KEY (email) NOT ENFORCED
+) WITH (
+    'connector' = 'fluss',
+    'auto-increment.fields' = 'uid',
+    'bucket.num' = '1'
+);
+```
+
+## Step 3: Create the Aggregated Profile Table
+
+Create the `user_profiles` table using the **Aggregation Merge Engine**. This pushes the aggregation logic (summing clicks, unioning bitmaps) to the Fluss TabletServers.
+
+:::tip
+We use `rbm64` ([RoaringBitmap](https://roaringbitmap.org/)) for efficient unique visitor counting. This allows you to store millions of unique IDs in a highly compressed format.
+:::
+
+```sql
+CREATE TABLE user_profiles (
+    profile_id INT,
+    unique_visitors BYTES,
+    total_clicks BIGINT,
+    PRIMARY KEY (profile_id) NOT ENFORCED
+) WITH (
+    'connector' = 'fluss',
+    'table.merge-engine' = 'aggregation',
+    'fields.unique_visitors.agg' = 'rbm64',
+    'fields.total_clicks.agg' = 'sum',
+    'bucket.num' = '1'
+);
+```
+
+## Step 4: Ingest and Process Data
+
+Create a temporary source table to simulate raw user 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 generates a new `uid` on the fly.
+
+```sql
+INSERT INTO user_profiles
+SELECT
+    d.uid,
+    -- Convert INT to BYTES for rbm64. 
+    -- Note: In a real production job, you might use a UDF to ensure correct bitmap initialization.
+    CAST(CAST(d.uid AS STRING) AS BYTES),
+    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
+
+You can query the `user_profiles` table to see the aggregated metrics updating in real-time.
+
+```sql
+-- Set result mode to tableau for better visualization
+SET 'sql-client.execution.result-mode' = 'tableau';
+```
+
+```sql
+--user profile
+SELECT 
+    profile_id, 
+    total_clicks, 
+    unique_visitors 
+FROM user_profiles;
+```
+
+You should see rows with `profile_id`, `total_clicks`, and `unique_visitors` (as raw bytes representing the bitmap). The `total_clicks` will increase as more events arrive for the same user.
+
+To verify the dictionary mapping:
+```sql
+SELECT * FROM user_dict;
+```
+
+## Clean Up
+
+Exit the SQL Client (`exit;`) and stop the Docker containers.
+
+```shell
+docker compose down -v
+```
+
+## Architectural Benefits
+
+*   **Stateless Flink Jobs:** Offloading the dictionary and aggregation state to Fluss makes Flink jobs lightweight, enabling faster checkpoints and nearly instant recovery.
+*   **Compact Storage:** Using `INT` UIDs instead of `STRING` emails reduces the memory footprint for deduplication tasks by up to 90%.
+*   **Exactly-Once Accuracy:** Even during failovers, the **Undo Recovery** mechanism ensures replayed data does not result in double-counting, maintaining perfect accuracy.
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