MySQL vs MongoDB: Caching and Data Persistence

MySQL vs MongoDB: Comparison in caching and data persistence

Introduction:
In the development process, the database is a very important component. Traditional relational databases such as MySQL and non-relational databases that have emerged in recent years such as MongoDB have different characteristics and advantages in caching and data persistence. This article will introduce the comparison between the two in terms of caching and data persistence, and demonstrate the differences between the two through code examples.

1. Caching
Cache is an important technical means to improve reading performance. MySQL and MongoDB have different mechanisms for caching.

MySQL’s caching mechanism is mainly implemented through query cache (Query Cache). When a query is executed, MySQL will first check whether the result of the query already exists in the query cache. If it exists, it will directly return the cached result, avoiding frequent IO operations. However, MySQL's query cache only works for identical queries and does not work for dynamic queries with parameters. In addition, the query cache also has some restrictions on update operations. Once a record in a table is updated, all related query caches in the table will be cleared.

The caching mechanism of MongoDB stores data in memory and uses the LRU (least recently used) algorithm to determine which data is retained in memory. MongoDB's caching mechanism applies not only to query operations, but also to update and insert operations. Compared with MySQL, MongoDB's caching mechanism is more flexible and efficient.

The following code examples are used to demonstrate the differences in caching between MySQL and MongoDB.

MySQL cache sample code:

import pymysql

# 连接数据库
conn = pymysql.connect(host='localhost', user='root', password='password', database='test')
cursor = conn.cursor()

# 查询语句
sql = "SELECT * FROM users WHERE username='Alice'"

# 开启查询缓存
cursor.execute("SET SESSION query_cache_type=1")

# 执行查询
cursor.execute(sql)

# 第一次查询
res1 = cursor.fetchone()
print(res1)

# 第二次查询，结果仍然从缓存中获取
cursor.execute(sql)
res2 = cursor.fetchone()
print(res2)

# 更新数据
cursor.execute("UPDATE users SET age=30 WHERE username='Alice'")

# 被更新后，缓存将被清空

# 第三次查询，结果来自于数据库
cursor.execute(sql)
res3 = cursor.fetchone()
print(res3)

# 关闭连接
cursor.close()
conn.close()

MongoDB cache sample code:

from pymongo import MongoClient

# 连接数据库
client = MongoClient()
db = client.test

# 查询语句
query = {"username": "Alice"}

# 执行查询
res1 = db.users.find_one(query)
print(res1)

# 执行查询，结果仍然来自于内存缓存
res2 = db.users.find_one(query)
print(res2)

# 更新数据
db.users.update_one(query, {"$set": {"age": 30}})

# 第三次查询，结果仍然来自于内存缓存
res3 = db.users.find_one(query)
print(res3)

# 关闭连接
client.close()

2. Data persistence
Data persistence refers to permanently storing data to disk to ensure data durability. MySQL and MongoDB also differ in data persistence.

MySQL uses a traditional relational database, and data is stored on the hard disk in the form of tables. MySQL implements transaction persistence through log files and writes transaction operation logs to disk to ensure that data will not be lost in the event of system failure or power outage. In addition, MySQL also supports data backup and recovery to further ensure data reliability.

MongoDB stores data in the form of documents, and each document is a collection of key-value pairs. MongoDB improves read performance by storing data in memory and uses a persistent storage engine to ensure data persistence. MongoDB's persistent storage engine uses copy-on-write (WiredTiger) and log files (oplog) to achieve data persistence. MongoDB also supports replica sets and sharding technologies to further improve data reliability and scalability.

Conclusion:
MySQL and MongoDB have different characteristics and advantages in caching and data persistence. MySQL's query caching mechanism works for the exact same query, while MongoDB's caching mechanism is more flexible and efficient. Regarding data persistence, MySQL ensures data reliability through log files and backup and recovery, while MongoDB achieves data persistence and reliability through a persistent storage engine and replica set sharding technology. Developers should consider comprehensive considerations based on specific needs when selecting a database.

The above is the detailed content of MySQL vs MongoDB: Caching and Data Persistence. For more information, please follow other related articles on the PHP Chinese website!