Detailed explanation of hotspot keys and data tilt examples in Redis

Hotspot keys and data tilts in Redis

Hot Key

definition

Hotspot keys refer to specific keys that are frequently accessed in Redis. These keys may cause performance problems with Redis servers due to their high access frequency, especially in high concurrency scenarios.

Features

• High access frequency: The hotspot key is accessed by a large number of requests in a short period of time.
• Resource consumption: Frequent access will cause the Redis server's CPU, memory, and network bandwidth to be consumed in large quantities.
• Performance bottleneck: Access to hotspot keys may become a performance bottleneck for the entire system, affecting access to other normal keys.

Coping strategies

• Cache warm-up: Load hotspot data into Redis when the system is started or the service is online to ensure that the latest hotspot data is available for access in the cache.
• Dynamic cache updates: Timely synchronize data updates in the database to Redis to maintain real-time cached data.
• Set expiration time: Set a reasonable expiration time for hotspot keys to avoid data consuming memory for a long time.
• Using the LRU algorithm: Using Redis's LRU (Least Recently Used) algorithm, the least recently used keys are automatically eliminated when there is insufficient memory.
• Distributed Cache: In high load situations, use the distributed features of Redis to distribute hotspot data across multiple Redis nodes.

Example

Suppose there is a social platform, and the personal information of some popular users is frequently accessed. These hotspot keys can be managed using the following methods:

import redis
# Connect to Redisr = (host='localhost', port=6379, db=0)
def get_user_info(user_id):
    key = f"user_info:{user_id}"
    data = (key)
    if not data:
        # Get data from the database        data = "User Details"
        # Set cache, expiration time is 3600 seconds        (key, data, ex=3600)
    return data
# Sample callprint(get_user_info("hot_user_123"))

Data Skew

definition

Data skew refers to the uneven distribution of data on each node in a distributed system, which causes some nodes to undertake too much data storage and processing tasks, while others are relatively idle.

Features

• Uneven data distribution: Some nodes store much more data than others.
• Performance imbalance: Data skew will cause the load on some nodes to be too high, affecting the performance of the entire system.
• Waste of resources: Some nodes have low resource utilization, while other nodes may experience performance problems due to excessive load.

Common Scenes

• Difference in user activity: Some users generate much more data than others.
• Datasets caused by business rules: For example, some business rules may cause a specific type of data to be concentrated on a node.
• Data access mode: Some data is frequently accessed, while others are rarely accessed.

Solution

• Redesign partitioning strategy: Adjust the data partition keys to make the data more evenly distributed to each node.
• Data sampling and analysis: Regularly sample and analyze data, understand the data distribution, and promptly discover and deal with data skew problems.
• Load balancing: Use load balancing strategies to distribute hotspot data to multiple nodes to avoid single point of overload.
• Data sharding: Split the large data set into multiple small data sets and stored on different nodes.
• Optimize query strategy: Optimize data access mode and reduce centralized access to hot data.

Example

Suppose there is an e-commerce platform, and user order data is stored on different Redis nodes according to user ID partition. If some users generate orders much larger than others, it may cause data skew. The following methods can be used to solve it:

import redis
# Connect to Redis clusterfrom rediscluster import RedisCluster
startup_nodes = [{"host": "127.0.0.1", "port": "7000"}]
rc = RedisCluster(startup_nodes=startup_nodes, decode_responses=True)
def get_order(user_id, order_id):
    key = f"order:{user_id}:{order_id}"
    data = (key)
    if not data:
        # Get data from the database        data = "Order Details"
        # Set cache, expiration time is 3600 seconds        (key, data, ex=3600)
    return data
# Sample callprint(get_order("user_123", "order_456"))

Through the above methods, hotspot keys and data tilt problems in Redis can be effectively managed and optimized, and the performance and stability of the system can be improved.

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