Java classic interview questions: How Redis and Mysql ensure data consistency

introduction

Data consistency is a very important concept in computer science and distributed systems. It refers to maintaining the correctness and consistency of data among multiple copies of data, ensuring that data has the same value across copies, and that these copies do not produce conflicts or inconsistent states at any point in time. Data consistency is critical to the correct functioning of applications and user experience, especially in distributed systems where data is stored across multiple geographic locations or servers.

importance

• Correctness of data: In any application, ensuring the correctness of data is crucial. If data is inconsistent across multiple replicas, applications may provide incorrect information to users, leading to unpredictable behavior and results.

• User Experience: Consistent data enables users to switch seamlessly between different parts of the application, providing a unified user experience. For example, a shopping cart on a shopping site should display the same content on different devices.

• Reliability and Robustness: Data consistency can enhance the reliability and robustness of the system. When a node or server fails, the system can quickly recover from other nodes and maintain data consistency.

challenge

• Latency: In distributed systems, network delays between different nodes are common. When data is updated on one node, other nodes may not receive the latest data due to delay, resulting in data inconsistency.

• Concurrent Writes: Concurrency conflicts can occur when multiple clients try to write data at the same time. In a distributed system, multiple replicas may receive update requests at the same time, and these updates may conflict with each other, resulting in data inconsistency.

• Failure handling: Node failures may result in data loss or lost updates, which may lead to data inconsistency. Appropriate failure handling mechanisms must be in place to ensure data consistency is restored after a node failure.

• Synchronization mechanism: In a distributed system, in order to maintain data consistency, an effective synchronization mechanism is required. However, the synchronization mechanism may cause performance degradation because data synchronization may take a long time.

Overview of Redis and MySQL

Redis and MySQL databases, their different uses in applications.

Redis（Remote Dictionary Server）

Redis is an open-source memory-based database system, which is known as a key-value store because it stores data as key-value pairs. Following are some features of Redis:

• In-memory data storage: Redis stores data in memory, so the read speed is very fast, suitable for high-performance applications.

• Key-value pair storage: Data is stored in the form of key-value pairs, and various data structures such as strings, hash tables, lists, sets, ordered sets, etc. can be used.

• High performance: Since data is stored in memory, Redis performs well in read and write operations, and is especially suitable as a cache layer.

• Data persistence: Redis supports persisting data to disk to prevent data loss.

• Publish/subscribe model: Redis supports publish and subscribe functionality, making it a good choice for handling real-time messages and events.

Applicable scenarios: Redis is suitable for scenarios that require high performance, low latency, and real-time data processing, such as caching, session storage, real-time statistics and counting, leaderboards, and real-time messaging.

MySQL

MySQL is a relational database management system (RDBMS) that uses Structured Query Language (SQL) for data manipulation. Following are some features of MySQL:

• Relational database: MySQL stores data in the form of tables and supports complex relational data models.

• ACID transaction support: MySQL supports ACID (atomicity, consistency, isolation, and durability) transactions to ensure data consistency and integrity.

• Data persistence: MySQL stores data persistently on disk to ensure that data will not be lost in case of power failure or failure.

• Complex query: MySQL supports a powerful SQL query language, which is suitable for complex data query and report generation.

• Support multiple storage engines: MySQL supports multiple storage engines, such as InnoDB, MyISAM, etc., and you can choose different engines according to your needs.

Applicable scenarios: MySQL is suitable for applications that require strict data consistency and complex queries, such as e-commerce websites, management systems, data analysis and reports, etc.

Redis is suitable for scenarios that require high performance and real-time data processing, such as caching, real-time counting, and messaging, while MySQL is suitable for applications that require strict data consistency and complex queries, such as e-commerce and data management systems. In practical applications, you can choose an appropriate database system or use them in combination according to specific needs and scenarios to achieve the best performance and data consistency.

Data Consistency Overview

• Verify data accuracy: In a distributed system, multiple nodes can read and write data at the same time. If the data is not consistent across nodes, it can lead to data conflicts between nodes, which can lead to incorrect results and data corruption.

• Avoid dirty data: If a node modifies data, but due to network latency or other issues, these modifications have not been replicated to other nodes, then other nodes may read old, inconsistent data, resulting in dirty data.

• High availability and fault tolerance: When one node fails or fails, other nodes need to take over the service and continue to operate. If the data is inconsistent, the new node may not be able to properly handle requests from other nodes.

• Guaranteed correct execution of transactions: In a distributed system, it may be necessary to execute transactions consisting of multiple operations. Data consistency ensures that these operations are either all executed successfully or all fail, avoiding data logic errors caused by partial operations being successful.

• Data backup: In a distributed system, data backup is a very common requirement. If the data is inconsistent on the backup node, the integrity and validity of the backup will be compromised.

Redis data consistency mechanism

• Transactions: Redis supports transactions, allowing multiple commands to be packaged and executed in one atomic operation. In a transaction, all commands either execute successfully or fail, and Redis will not insert commands from other clients during transaction execution. The execution process of the transaction is atomic, that is, it will not be interrupted during the execution process, thus ensuring data consistency.
  The execution of the transaction is divided into three stages: starting the transaction, enqueuing the command, and executing the transaction. If an error is encountered during the execution of a transaction, Redis will roll back the entire transaction to ensure data consistency. In Redis, use the MULTI command to start a transaction, then use the EXEC command to execute the transaction, or use the DISCARD command to cancel the transaction.

• Persistence: In order to ensure that data will not be lost during restart or failure, Redis provides two persistence methods: RDB (Redis Database Dump) and AOF (Append-only File).
  RDB Persistence: Periodically save in-memory data snapshots to RDB files on disk. The persistence process can be triggered by configuring periodic saves or manually executing the SAVE or BGSAVE command. RDB persistence can ensure data consistency during data backup and recovery.

• AOF Persistence: Append write commands to the AOF file to record data modification operations. When Redis restarts, restore the data by re-executing the commands in the AOF file. AOF persistence can ensure data consistency during failure recovery.

• Replication: Redis supports the master-slave replication mechanism, which realizes data backup and high availability by replicating the data of one Redis instance (master node) to other Redis instances (slave nodes).
  During the replication process, the master node sends write commands to all connected slave nodes, and the slave nodes execute the same write command to maintain data consistency. If the master node fails, the slave node can elect a new master node to continue to provide services. Through replication, even if the master node fails, the data is still available, ensuring data consistency and high availability.

Redis ensures data consistency through mechanisms such as transactions, persistence, and replication. Transactions ensure the atomic execution of multiple commands, persistence ensures that data will not be lost in the event of restart or failure, and replication ensures data backup and high availability. These mechanisms work together to make Redis a data storage solution with high performance, high availability and strong data consistency.

MySQL data consistency mechanism

• Transaction support: MySQL supports transactions, which means that a set of operations can be viewed as a single unit of work. Transactions can ensure that these operations are either successfully executed or rolled back, thereby maintaining data consistency.

• ACID properties: MySQL follows the ACID properties, namely atomicity (Atomicity), consistency (Consistency), isolation (Isolation) and persistence (Durability). These properties ensure that the database maintains data consistency and reliability under various circumstances.

• Replication: MySQL provides a replication feature that allows data to be copied from one database server to other servers. This replication can be used to implement data backup and failure recovery. Replication can be established between the master database and one or more slave databases to ensure data consistency on different nodes.

MySQL maintains data consistency by supporting transactions, following ACID properties, and providing replication functions to ensure data correctness and reliability in a distributed environment.

Data consistency challenges between Redis and MySQL

When using Redis and MySQL, you may encounter data consistency issues because the two have different characteristics and purposes. Redis is an in-memory database for caching and fast read and write operations, while MySQL is a traditional relational database for persistent data and supporting complex queries. Here are some consistency issues you might encounter and how to resolve them:

Race Conditions: In a concurrent environment, multiple clients simultaneously read and write data, which may lead to race conditions. For example, one client reads data while another client modifies the same data, which may cause data inconsistency.

Solution:

1. Using transactions: In MySQL, using transactions can treat a series of operations as atomic units, either all successfully submitted, or all rolled back. This ensures that data operations within the transaction are serialized, avoiding race conditions.
   Use Redis transactions: Redis supports transaction operations, you can put multiple commands in one transaction, and then execute them together, which can ensure the atomicity of these commands.
   Data expiration and inconsistency: In Redis, you can set the expiration time of the key, but there is no built-in expiration concept in MySQL. If a key expires in Redis and the corresponding data in MySQL is not updated, it may cause data inconsistency.

2. Reasonably set the expiration time: Set a reasonable expiration time in Redis to ensure that the data is updated within a certain period of time.
   Use regular synchronization mechanism: You can periodically synchronize the data in Redis to MySQL to ensure data consistency.
   Data Loss: Since Redis is an in-memory database, if the server fails or restarts, data in memory may be lost. However, MySQL as a disk database usually has persistence.

3. Use persistence: Redis provides two persistence methods: RDB snapshot and AOF log. You can choose to persist data to disk periodically to prevent data loss.
   Use MySQL as the main database: If data persistence is important to you, you can store the data in MySQL as the main database and Redis as the cache layer. In this way, even if the data in Redis is lost, it can be recovered from MySQL.
   Data synchronization delay: When Redis and MySQL are used for read-write separation or master-slave replication, due to network delay or other reasons, data synchronization may be delayed, resulting in data inconsistency.

4. Change asynchronous replication to synchronous replication: If strong data consistency is very important, you can consider changing MySQL's master-slave replication to synchronous replication to ensure the immediacy of data synchronization.

5. Double-write mode: When performing write operations, write to Redis and MySQL at the same time to ensure data consistency. However, this may affect write performance.

To ensure data consistency between Redis and MySQL, it is necessary to comprehensively consider business needs, performance requirements, and the importance of data. The use of transactions, periodic synchronization, reasonable setting of expiration time and selection of appropriate persistence methods are all ways to solve consistency problems.

Best Practices for Ensuring Data Consistency

For example, reliable message communication based on RocketMQ, RabbitMQ or other message queues to achieve final consistency.

You can also directly monitor the binlog logs in Mysql through the Canal component, and synchronize the updated data to Redis.

Practical application case

Scenario 1: User account balance update

Suppose we have an e-commerce website where users can increase their account balance by topping up. We can use Redis and MySQL to maintain consistent user account balances.

1. When the user performs a recharge operation, the recharge request is first sent to Redis, and the user account balance is increased by the corresponding amount through the INCRBY command of Redis.

2. Then write the recharge request into the recharge record table of MySQL, including information such as user ID and recharge amount.

3. In the background task, by monitoring the recharge request in Redis, it is synchronized to the user account table in MySQL. You can use the SUBSCRIBE command of Redis to monitor the recharge request, and when receiving the request, take out the corresponding user ID and recharge amount from Redis, and then update the user account table in MySQL.

4. In this way, whether the user queries the account balance or performs other operations, the latest balance data can be obtained from MySQL, ensuring data consistency.

Scenario 2: Real-time statistics of article views

1. Suppose we have a news release website and need to count the number of views of each article in real time. We can use Redis and MySQL to maintain the consistency of article views.

2/ When a user visits an article, first store the article ID in Redis, and increase the view volume of the corresponding article by 1 through the INCR command of Redis.

3. Then periodically (for example, every 1 minute) write the pageview data in Redis to the article table in MySQL. You can use Redis's ZREVRANGE command to get the top article IDs and views, and then write them into MySQL.

4. In this way, when the number of user visits is large, the number of visits can be quickly increased through Redis, and it is periodically synchronized to MySQL to ensure data consistency. At the same time, users can also get the latest data from MySQL when querying the page views of articles.

Summarize

How Redis ensures data consistency

• Synchronous on write: Redis writes data to memory by default and to disk asynchronously. Data can be written to disk synchronously by configuring persistence options to ensure data persistence after power failure or restart. This method is suitable for scenarios with low real-time data requirements.
• Master-slave replication: Redis supports a master-slave replication mechanism, where the master node synchronizes data to the backup slave node. Through the replication strategy and monitoring mechanism, the consistency of data between the master and slave nodes is guaranteed. This method is suitable for scenarios where reads and writes are separated and can improve read performance.
• Sentinel mode: The sentinel mode of Redis is used to monitor the health status of the master-slave node and perform automatic master-slave switching when the master node fails. This approach ensures data consistency during failover.

How MySQL guarantees data consistency

• ACID transactions: MySQL supports ACID transactions, namely atomicity, consistency, isolation, and durability. Through the start, commit, and rollback operations of transactions, MySQL guarantees the atomicity of data operations and provides different isolation levels to control the visibility and consistency between concurrent operations.
• Lock mechanism: MySQL uses locks to manage concurrent access and data modification operations. Through read-write locks and transaction locks, the isolation and data consistency between multiple transactions are realized. When reading and modifying data, use appropriate lock types, such as row locks, table locks, and page locks, to ensure data consistency.
• Master-slave replication: MySQL supports master-slave replication, which synchronizes data from the master node to the backup slave node. By configuring appropriate replication strategies and monitoring mechanisms, data consistency between master and slave nodes is ensured.

It is very important to choose the right solution according to the application requirements. If the application has high requirements for real-time performance, you can choose Redis, and configure the synchronization strategy and sentinel mode to ensure the timeliness and availability of data; if the application has higher requirements for data consistency and integrity, you can choose MySQL and use ACID Transactions and appropriate locking mechanisms to ensure data consistency. At the same time, performance can be improved by combining master-slave replication and read-write separation according to the read-write access ratio and load conditions in specific scenarios. In conclusion, choosing the right solution according to application requirements is the key to ensure data consistency.