1. High concurrency cache/shared session:
UserInfo getUserInfo (long id) {}
Get:
userRedisKey = "user:info:" id;
value = redis.get(userRedisKey);
if (value != null) {
userInfo = deserialize(value);
return userInfo;
redis.setex(userRedisKey, 3600, serialize(userInfo)); Using string to store serialized data is not three-dimensional and intuitive enough. It can be converted to hmset and stored as a hash structure, making access more intuitive.2. Simple distributed lock
setnx can only be set successfully if it does not exist, and the rest can only wait. Single thread3. Counter incr, because it is single-threaded, consumes less CPU than cas, etc., and has higher performance
long incrVideoCounter ( long id) { key = "video:playCount:" id;4. Implement stack/queue
Stack: lpush lpop
Queue: lpush rpop
5. Flow control/rate limit
phoneNum = "12345678999"; key = "shortMsg:limit:" phoneNum;
isExists = redis.set( key, 1, "EX 60", "NX");
if (isExists != null || redis.incr(key)
// Pass
##} else {// Do not pass
} 6.Use lpush brpop to implement a blocking queue. The producer inserts elements from the left end of the list through lpush, and multiple consumers block and obtain the tail elements of the queue from the right end of the brpop
7. Every A user has his own articles. Now he wants to display the article list in pages.
hmset article:1 title xx context XXXX
lpush user:1:articles srticle:1 articles:3
articles = lrange user:1:articles 0 9
for article in {articles} hgetall {article}
8. Follow and like
Like: zincrby user:ranking:2016_03_15 mike 1
Cancel: zrem user:ranking:2016_03_15 mike
Get like The top 10 users: zrevrangebyrank user:ranking:2016_03_15 0 9
Display user information and scores: hgetall user:info:tom / zscore user:ranking:2016_03_15 mike / zrank user:ranking:2016_03_15 mike
9. Bitmaps calculate the relationships among big data sets
10. Ranking
mike uploaded a video and received 3 likes zadd user:ranking:2016_03_15 mike 3
Another person liked it zincrby user:ranking:2016_03_15 mike 1
11. Follow together
Add a follow tag to the user sadd user:1:tags tag1 tag2
Add a user to the tag sadd tag1:uses user:1
Common attention sinter user:1:tags user:2:tags sinter/sunion/sdiff
12. Publish and subscribe
Subscribe video:changes: publish video:changeds "video1,video2"
for video in video1,video2
update (video)
Each data type corresponds to a variety of underlying data structure implementations (object encoding), which can be switched through data size, length, scenarios, etc. to achieve higher efficiency
Persistent RDB (child process creation, binary file, fast recovery, not real-time enough)/AOF (appendonly. Text files, real-time writing operations first aop_buffer, and then write to the disk by configuring the write disk interval, and merge when reaching a certain size)
Batch hmget and other operations must be converted to hscan and other progressive traversal methods, otherwise it is easy to blockBuffering: client buffering (input/output), copy backlog buffer, aof buffer
Copy: full/incremental copy offset/copy backlog buffer (write command is sent to the slave server at the same time It also maintains a first-in-first-out queue, which means that the main service also saves the most recently propagated commands)/ID
sentinal: To achieve high availability, it is a special redis node. You can configure the cluster yourself and monitor the redis data cluster through heartbeat and other mechanisms. When a node fails and becomes unavailable, it can be discovered in time and automatically migrated
cluster: Distributed cluster, fault-tolerant leader selection, etc. Mapping physical nodes to 16383 slots to achieve dynamics
For more Redis-related technical articles, please visit the Redis Tutorial column to learn!
The above is the detailed content of Where is redis used?. For more information, please follow other related articles on the PHP Chinese website!

The core function of Redis is a high-performance in-memory data storage and processing system. 1) High-speed data access: Redis stores data in memory and provides microsecond-level read and write speed. 2) Rich data structure: supports strings, lists, collections, etc., and adapts to a variety of application scenarios. 3) Persistence: Persist data to disk through RDB and AOF. 4) Publish subscription: Can be used in message queues or real-time communication systems.

Redis supports a variety of data structures, including: 1. String, suitable for storing single-value data; 2. List, suitable for queues and stacks; 3. Set, used for storing non-duplicate data; 4. Ordered Set, suitable for ranking lists and priority queues; 5. Hash table, suitable for storing object or structured data.

Redis counter is a mechanism that uses Redis key-value pair storage to implement counting operations, including the following steps: creating counter keys, increasing counts, decreasing counts, resetting counts, and obtaining counts. The advantages of Redis counters include fast speed, high concurrency, durability and simplicity and ease of use. It can be used in scenarios such as user access counting, real-time metric tracking, game scores and rankings, and order processing counting.

Use the Redis command line tool (redis-cli) to manage and operate Redis through the following steps: Connect to the server, specify the address and port. Send commands to the server using the command name and parameters. Use the HELP command to view help information for a specific command. Use the QUIT command to exit the command line tool.

Redis cluster mode deploys Redis instances to multiple servers through sharding, improving scalability and availability. The construction steps are as follows: Create odd Redis instances with different ports; Create 3 sentinel instances, monitor Redis instances and failover; configure sentinel configuration files, add monitoring Redis instance information and failover settings; configure Redis instance configuration files, enable cluster mode and specify the cluster information file path; create nodes.conf file, containing information of each Redis instance; start the cluster, execute the create command to create a cluster and specify the number of replicas; log in to the cluster to execute the CLUSTER INFO command to verify the cluster status; make

To read a queue from Redis, you need to get the queue name, read the elements using the LPOP command, and process the empty queue. The specific steps are as follows: Get the queue name: name it with the prefix of "queue:" such as "queue:my-queue". Use the LPOP command: Eject the element from the head of the queue and return its value, such as LPOP queue:my-queue. Processing empty queues: If the queue is empty, LPOP returns nil, and you can check whether the queue exists before reading the element.

Use of zset in Redis cluster: zset is an ordered collection that associates elements with scores. Sharding strategy: a. Hash sharding: Distribute the hash value according to the zset key. b. Range sharding: divide into ranges according to element scores, and assign each range to different nodes. Read and write operations: a. Read operations: If the zset key belongs to the shard of the current node, it will be processed locally; otherwise, it will be routed to the corresponding shard. b. Write operation: Always routed to shards holding the zset key.

How to clear Redis data: Use the FLUSHALL command to clear all key values. Use the FLUSHDB command to clear the key value of the currently selected database. Use SELECT to switch databases, and then use FLUSHDB to clear multiple databases. Use the DEL command to delete a specific key. Use the redis-cli tool to clear the data.


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