Redis Server Optimization
Redis is a high-performance open source key-value storage database that is often used to build cache systems, queue services, counters, etc.
For best performance, we can optimize the Redis server.
This article will introduce some common Redis server optimization tips and strategies.
1. Memory optimization
Redis's biggest performance bottleneck is usually memory, so rational use and management of memory is crucial to the performance of Redis servers.
a. Reasonably set maxmemory parameters
In the Redis configuration file, we can set the maxmemory parameter to limit the amount of memory used by Redis.
By setting maxmemory, we can take corresponding measures when Redis is approaching the memory limit, such as deleting expired keys, memory recovery of newly written keys, etc.
According to actual scenarios and usage requirements, setting maxmemory reasonably can effectively avoid performance degradation of Redis due to memory exceeding limits.
b. Using Redis memory elimination strategy
When Redis's memory reaches the maxmemory limit, we need to adopt appropriate strategies to handle newly written keys and existing keys. Redis provides a variety of memory ed out strategies, such as:
- noeviction: When the memory limit exceeds the limit, the newly written operation will be rejected.
- allkeys-lru: Of all keys, use the least recently used keys for elimination.
- allkeys-random: Among all keys, randomly select the key to be eliminated.
- volatile-lru: Only among the keys that have the expiration time set, use the least used keys in the nearest time to eliminate them.
- volatile-random: Only among the keys that have the expiration time set, randomly select the keys to be eliminated. As needed, you can choose the appropriate memory ed out strategy to ensure that when the memory limit exceeds the limit, the appropriate keys are removed to maintain performance stability.
2. Persistent configuration
Redis provides persistence, which can store data in memory on disk to restore data after the Redis server restarts.
Rational configuration and persistence can improve server reliability and data integrity.
a. RDB persistence
Redis RDB persistence is to save data to disk as a snapshot. You can use the save parameter in the configuration file to set the frequency of saving RDB snapshots.
Setting the save parameter reasonably can quickly restore data after the server restarts.
b. AOF persistence
Redis AOF persistence is to append write operations to log files so that the write operations are re-executed after the server restarts to recover data.
AOF persistence can be enabled using the appendonly parameter in the configuration file.
In addition, according to actual needs, the appropriate fsync option can be selected to configure the data synchronization frequency of AOF to weigh data integrity and performance.
3. Connection configuration
a. Maximum number of connections
In the default configuration of Redis, the maximum number of connections is unlimited.
However, excessive client connections can negatively affect server performance.
We can limit the maximum number of connections to the Redis server by setting the maxclients parameter to avoid performance degradation due to excessive connections.
b. Reasonably set the timeout time
Reasonably setting the connection timeout time can prevent long-term inactive connections from occupying server resources.
You can control the timeout time of the connection by setting the timeout parameter.
Depending on the actual situation, setting an appropriate timeout time can effectively release idle connections and improve server performance.
4. Network optimization
a. Disable the TCP_NODELAY option
In the TCP protocol, the TCP_NODELAY option is enabled by default, which merges smaller TCP packets into larger packets to send.
This is beneficial in most cases, but in certain specific scenarios, disabling the TCP_NODELAY option can reduce the latency of Redis.
The TCP_NODELAY option can be disabled through the tcp-keepalive parameter in the configuration file.
b. Rationally configure TCP connection parameters
In high-load network environments, the parameter configuration of TCP connections is critical to the performance of Redis servers.
The network parameters of the operating system can be adjusted according to actual conditions, such as the maximum number of file descriptors for TCP connections, the delay confirmation time of TCP, etc., to improve the efficiency and performance of network transmission.
When it comes to optimization of Redis servers, here is a sample code that combines practical application scenarios to show how to use an ordered collection of Redis to implement popular article rankings:
pythonCopy code
import redis
# Create a Redis connectionr = (host='localhost', port=6379)
def add_article(article_id, title, views):
# Add articles to an ordered collection and set the initial number of views ("articles", {article_id: views})
# Set the title of the article ("article_info", article_id, title)
print(f"Article {article_id} added with title '{title}' and {views} views")
def increment_views(article_id):
# Increase the number of views of the article ("articles", 1, article_id)
print(f"Views incremented for article {article_id}")
def get_popular_articles(limit):
# Get the top article rankings article_ids = ("articles", 0, limit-1, desc=True, withscores=False)
article_info = ("article_info", article_ids)
popular_articles = [(article_id.decode(), ()) for article_id, info in zip(article_ids, article_info)]
return popular_articles
# Sample call# Add some articlesadd_article("1", "Redis Getting Started Guide", 100)
add_article("2", "Advanced Redis Tips", 50)
add_article("3", "Optimize Redis Performance", 80)
# Increase the number of views of the articleincrement_views("1")
increment_views("1")
increment_views("2")
# Get the top article rankingspopular_articles = get_popular_articles(2)
print("Popular articles:")
for article_id, title in popular_articles:
print(f"Article ID: {article_id}, Title: {title}")In this sample code, we show a specific application scenario: popular article rankings.
First, we created a Redis connection. Then, we define three functions:
- add_articleUsed to add articles to an ordered collection and set the initial number of views and article titles;
- increment_viewsUsed to increase the number of views of an article;
- get_popular_articlesUsed to obtain popular article rankings.
When actually using it, we can call the article when it is accessedincrement_viewsTo increase the number of views of the article. When displaying the rankings of popular articles, you can callget_popular_articlesTo get the information about the ranking article.
The call section in the example shows how to use these functions, first add some articles and set the initial view count, then add some views count, and finally by callingget_popular_articlesFunction to get the top article rankings and print them out.
This sample code shows how to use Redis's ordered collection and hash table to implement popular article rankings.
By storing the number of views of an article in an ordered collection and combining a hash table to store the title information of the article, you can quickly obtain and update the rankings of popular articles without frequent access to the database. This approach can improve application performance and responsiveness.
When it comes to optimization of Redis servers, here is a sample code showing an example of how to use Redis's hash table to store and get user information:
pythonCopy code
import redis
# Create a Redis connectionr = (host='localhost', port=6379)
def save_user_info(user_id, name, email):
# Use Redis hash table to store user information ("users", user_id, f"{name}:{email}")
print(f"User information saved for user ID {user_id}")
def get_user_info(user_id):
# Get user information from Redis hash table user_info = ("users", user_id)
if user_info:
name, email = user_info.decode().split(":")
print(f"User information found for user ID {user_id}: Name: {name}, Email: {email}")
else:
print(f"No user information found for user ID {user_id}")
# Sample callsave_user_info("1", "Alice", "alice@")
get_user_info("1")This sample code shows how to use Redis's hash table to store and get user information.
First, we created a Redis connection. Then, we define asave_user_infoFunction to save user information into the hash table of Redis.
In the function we usehsetThe command stores user information as a field where the key is the user ID and the value is the user name and mailbox separated by a colon.
Next, we define aget_user_infoFunction to obtain user information from Redis's hash table.
In the function we usehgetThe command gets the information of the specified user ID, decodes and extracts the user name and email address.
Finally, we use the example call to test the function that stores and obtains user information, first saving a user information, then obtaining the information of the same user ID and printing it out.
This sample code shows how to efficiently store and retrieve user information using Redis's hash table.
By storing user information in Redis's hash table, user information can be quickly obtained and updated by user ID without accessing the database. This approach can improve application performance and responsiveness.
in conclusion
The above are some common Redis server optimization techniques and strategies, which can be configured and adjusted according to actual application scenarios and server performance requirements. By rationally using and configuring memory, persistence, connection and networking, the performance and reliability of Redis servers can be improved, thereby better meeting business needs.
The above is personal experience. I hope you can give you a reference and I hope you can support me more.