Comparison of data sharding capabilities between TiDB and MySQL

Comparison of data sharding capabilities between TiDB and MySQL

Introduction:
As the amount of data increases, database performance has become an important consideration. In order to solve the limitation that a single database cannot carry large-scale data, data sharding technology came into being. In this article, we will focus on comparing the differences in data sharding capabilities of the open source databases TiDB and MySQL, and illustrate them with code examples.

1. TiDB’s sharding architecture
TiDB is a distributed NewSQL database that adopts a distributed architecture similar to Google Spanner and F1. It divides data into logical tables, each logical table contains multiple shards, and each shard stores and processes data on nodes within the cluster.
The following is a code example to create a sharded table:

CREATE TABLE shard_table ( id INT PRIMARY KEY, name VARCHAR(50) ) SHARD_ROW_ID_BITS=4;

In this example, we create a sharded table named shard_table, with the id column as the primary key, and set the SHARD_ROW_ID_BITS parameter to 4 , indicating that the data is divided into 4 bits.

2. MySQL’s sharding architecture
MySQL is a traditional relational database and does not directly support distributed architecture. But data sharding can be done through the application layer. Data sharding is usually implemented using database and table sharding. Database sharding stores data in different databases, and table sharding stores data in different tables.

The following is a code example that uses MySQL Proxy to shard databases and tables:

function read_query(packet) if packet:byte() == proxy.COM_QUERY then local query = packet:sub(2) local shard_id = calculate_shard_id(query) proxy.queries:append(1, string.char(proxy.COM_QUERY) .. query, "backend-" .. shard_id) return proxy.PROXY_SEND_QUERY end end function calculate_shard_id(query) -- 根据查询语句计算分片id end

In this example, we use MySQL Proxy to intercept query statements and calculate the shard id based on the calculate_shard_id function , and then forward the query to the corresponding backend database.

3. Comparison of sharding between TiDB and MySQL

1. Sharding granularity
   TiDB is sharded in units of logical tables, which can dynamically adjust the number of shards more flexibly. MySQL is sharded in units of databases and tables, so you need to plan the sharding strategy in advance.
2. Automatic load balancing
   TiDB's distributed architecture supports automatic load balancing, which can automatically distribute query requests to nodes in the cluster. MySQL requires manual configuration of load balancing strategies or the use of third-party tools to achieve load balancing.
3. Elastic Scalability
   TiDB has good horizontal scalability, and higher throughput and storage capacity can be achieved by adding nodes. MySQL needs to be expanded by sharding databases and tables, and adding nodes requires data migration and re-sharding operations.
4. Consistency Guarantee
   TiDB uses a distributed transaction protocol to ensure transaction consistency. In the case of sharding, MySQL can only guarantee transaction consistency within a single shard. Transaction consistency across shards requires control by the application layer.

Conclusion:
TiDB and MySQL have certain differences in data sharding capabilities. As a distributed database, TiDB can implement dynamic sharding at the logical table level, with automatic load balancing and good scalability. MySQL needs to implement sharding through the application layer, which requires manual configuration of load balancing and data migration. Therefore, TiDB is a more flexible and efficient choice when processing large-scale data.

(Note: The above sample code is only for demonstration, and may need to be modified according to specific needs and environment during actual use.)

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