Here’s how these technologies can work together:

Data Pipeline Architecture:

• MySQL: Primary source of structured data.
• TiDB: Distributed SQL database compatible with MySQL, used for scalability and high availability.
• Kafka: Messaging system for real-time data streaming.
• Logstash: Data processing pipeline tool that ingests data from various sources and sends it to various destinations.
• Redis: Caching layer for fast access to frequently accessed data.
• Elasticsearch: Search and analytics engine for querying large volumes of data.
• CloudCanal: Data integration tool used to synchronize data from various sources like MySQL to TiDB, Kafka, Redis, and Elasticsearch.

Workflow Details:

1. Data Ingestion:

• Applications save data in MySQL.
• CloudCanal is used to sync data from MySQL to TiDB and Kafka.

2. Data Streaming and Processing:

Kafka:

• Kafka ingests data from MySQL via CloudCanal and broadcasts it to various topics.
• Topics contain streams of data events that can be processed by various consumers.

Logstash:

• Logstash acts as a Kafka consumer, processes data from Kafka, and sends it to various outputs such as Elasticsearch and Redis.

3. Data Storage and Retrieval:

TiDB:

• TiDB serves as a scalable and highly available database solution that can handle large volumes of data.
• TiDB is MySQL-compatible, making integration and migration from MySQL straightforward.

Redis:

• Redis is used as a caching layer for frequently accessed data from MySQL or processed events from Kafka.
• Applications can query Redis first before querying MySQL to speed up data retrieval.

Elasticsearch:

• Logstash can ingest data from Kafka and send it to Elasticsearch.
• Elasticsearch indexes the data for fast search and analytics.
• Applications can query Elasticsearch for advanced search capabilities and real-time analytics.

Example Data Flow:

Data Entry in MySQL:

• A user inserts a new record into the MySQL database.
• CloudCanal monitors changes in MySQL and sends events to TiDB and Kafka topics.

Real-Time Processing:

• Kafka broadcasts the event to a topic.
• Logstash acts as a Kafka consumer, processes the event, and sends the parsed data to Elasticsearch for indexing.
• Simultaneously, Redis is updated to cache the new data.

Data Access:

• The application checks the Redis cache for the data.
• If the data is not in the cache, it queries MySQL or TiDB.
• For complex queries and analytics, the application queries Elasticsearch.

This is just for my notes. CTTO