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
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