Realtime Analytics System Design

### System Design for Real-Time Analytics Dashboard #### **Architecture Overview** The system consists of three primary layers: 1. **Data Ingestion Layer** - Ingests raw event data using **Apache Kafka**. 2. **Data Processing Layer** - Processes and aggregates metrics in real-time using **Apache Flink**. 3. **Data Storage and Query Layer** - Stores data in **PostgreSQL** optimized for both high write throughput and efficient querying. --- ### **Architecture Components** 1. **Event Ingestion with Kafka** - **Role:** Captures high-velocity event streams, ensuring durability and scalability. - **Mechanism:** Kafka topics are partitioned to handle up to 10,000 events per second with minimal latency. 2. **Real-Time Processing with Apache Flink** - **Role:** Processes event streams, calculates metrics (e.g., total revenue per hour, average cart size), and handles data spikes. - **Mechanism:** - Streams events from Kafka. - Aggregates events using sliding windows (e.g., hourly windows for revenue). 3. **Database Storage with PostgreSQL** - **Role:** Persists aggregated metrics and event data. - **Configuration:** - High-write throughput: Leverages **partitioned tables**. - Efficient queries: Utilizes **GIN indexes** for JSONB data if needed. 4. **API Layer** - **Role:** Exposes RESTful endpoints for querying metrics. - **Framework:** Flask (Python) or Express.js (Node.js). 5. **Monitoring with Prometheus** - **Role:** Tracks system health, metrics, and alerting. 6. **Reliability Layer** - Fault tolerance is ensured by Kafka's replication and Flink’s checkpointing. --- ### **Database Recommendation** - **PostgreSQL** - **Pros:** - High scalability via table partitioning and indexing. - Supports concurrent writes and efficient queries. - **Comparison:** - Compared to NoSQL (e.g., MongoDB), PostgreSQL ensures ACID compliance and richer query capabilities, suitable for structured event data. --- ### **Data Processing Pipeline** 1. **Ingestion:** - Kafka captures raw events from user interactions. - Events are serialized using Avro or JSON and streamed to specific Kafka topics (e.g., `clicks`, `purchases`). 2. **Real-Time Processing:** - Flink consumes Kafka streams, applies transformations, and computes sliding window aggregates. - Flink writes results back to a new Kafka topic for API consumption or directly to PostgreSQL. 3. **Metrics Calculation:** - Total revenue: Aggregates purchase events. - Average cart size: Divides total items in purchases by the count of purchase events. --- ### **Sample Code Snippets** #### 1. API Endpoint for Aggregated Metrics ```python from flask import Flask, jsonify import psycopg2 app = Flask(__name__) @app.route('/metrics', methods=['GET']) def get_metrics(): conn = psycopg2.connect("dbname=analytics user=admin password=secret") cursor = conn.cursor() query = """ SELECT metric_name, metric_value FROM metrics WHERE timestamp >= NOW() - INTERVAL '1 HOUR'; """ cursor.execute(query) results = cursor.fetchall() conn.close() return jsonify(results) if __name__ == '__main__': app.run(debug=True) ``` #### 2. Writing Data to PostgreSQL ```python import psycopg2 def write_event(event): conn = psycopg2.connect("dbname=analytics user=admin password=secret") cursor = conn.cursor() insert_query = """ INSERT INTO events (event_type, event_data, timestamp) VALUES (%s, %s, NOW()); """ cursor.execute(insert_query, (event['type'], event['data'])) conn.commit() conn.close() ``` #### 3. Handling Data Spikes with Flink ```python from pyflink.datastream import StreamExecutionEnvironment from pyflink.table import StreamTableEnvironment, EnvironmentSettings env = StreamExecutionEnvironment.get_execution_environment() settings = EnvironmentSettings.new_instance().in_streaming_mode().build() table_env = StreamTableEnvironment.create(env, environment_settings=settings) table_env.execute_sql(""" CREATE TABLE input_events ( event_type STRING, event_value DOUBLE, ts TIMESTAMP(3), WATERMARK FOR ts AS ts - INTERVAL '1' SECOND ) WITH ( 'connector' = 'kafka', 'topic' = 'event_topic', 'properties.bootstrap.servers' = 'localhost:9092', 'format' = 'json' ) """) table_env.execute_sql(""" CREATE TABLE output_metrics ( metric_name STRING, metric_value DOUBLE, timestamp TIMESTAMP(3) ) WITH ( 'connector' = 'jdbc', 'url' = 'jdbc:postgresql://localhost:5432/analytics', 'table-name' = 'metrics', 'username' = 'admin', 'password' = 'secret' ) """) table_env.execute_sql(""" INSERT INTO output_metrics SELECT 'total_revenue' AS metric_name, SUM(event_value) AS metric_value, TUMBLE_END(ts, INTERVAL '1' HOUR) AS timestamp FROM input_events WHERE event_type = 'purchase' GROUP BY TUMBLE(ts, INTERVAL '1' HOUR) """) ``` --- ### **Fault Tolerance Strategy** 1. **Handling Dropped Events:** - Kafka’s replication factor ensures durability. - Flink retries failed tasks automatically. 2. **Failure Recovery:** - Flink uses state checkpointing. - PostgreSQL allows point-in-time recovery. 3. **System Monitoring:** - Prometheus tracks Kafka lag, Flink processing times, and database performance. --- ### **Architecture Diagram** A diagram representing the architecture will include: - **Kafka**: For ingestion, illustrated as the entry point. - **Flink**: For real-time processing. - **PostgreSQL**: For storage. - **REST API**: For exposing metrics. - **Prometheus**: For monitoring. Let me generate the visualization. Here is the architecture diagram for the real-time analytics dashboard system, showcasing all the components and data flow. Let me know if you need further adjustments or clarifications!