Shulou(Shulou.com)06/02 Report--

Today, I will talk to you about how to do Redis caching, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following for you. I hope you can get something from this article.

Advantages

Cache (the relationship with the database can only guarantee the final consistency, not strong consistency)

High performance and high concurrency

Can realize distributed lock

Single-thread working model

Avoid frequent context switching

Adopt the non-blocking Ihammer O multiplexing mechanism (multiple Imax O flows into the queue and reuse the same thread to run)

Memory model

Key points to five memory models

String: often used for caching, etc.

Hash: commonly used for native structural caching, etc.

List: simple queues can be implemented, paging can be achieved

Set: can be used to merge and intersect with computational differences (such as calculations that both parties like), as well as deduplication features

SortedSet (or ZSet): can achieve ranking (incremental operations are time-consuming, read operations achieve high performance through jump tables), delayed tasks, range queries, etc.

Persistence strategy

RDB Snapshot Policy

Generates a point-in-time snapshot of a dataset (point-in-time snapshot) within a specified time interval

Some points: small size, less resource consumption during snapshots, and fast restore speed.

Disadvantages: incomplete data and easy to lose data

AOF Log Policy

Record all write commands executed by the server and restore the dataset by re-executing these commands when the server starts

The background can rewrite the AOF file to reduce its size

Advantages: not easy to lose data

Disadvantages: more resource consumption, too large volume and slow restore speed when logging.

RDB&AOF mixed strategy

When Redis restarts, it will first use AOF files to restore the dataset, followed by RDB snapshots.

Shutdown policy

No persistence and recovery support

Key elimination mechanism

Expired key value deletion policy (the expired key value is recorded in the expiration table, and the expired key value is not guaranteed to be released immediately. Keys with no expiration time are approximately persistent)

Delete periodically (periodically and incrementally look for expired keys)

Lazy deletion (check for expiration when reading key values)

Release strategy in case of insufficient memory

Noeviction: prevent new writes from reporting errors

Allkeys-lru: removes the least recently used key from the total key value

Allkeys-random: randomly removes a key from the total key value

Volatile-lru: remove the least used key with ttl

Volatile-random: randomly remove key with ttl

Volatile-ttl: remove key with ttl that expires earlier

Phase-out policy configuration maxmemory-policy

Common Operations info # Service Overview and Statistics dbsize # current number of key in Library monitor # Monitoring Operation # select dbname # Select Library # keys key_pattern # search for regular key#### value Operation # type key# check key value type del key# delete key value flushdb # clear the value of key under the current library flushall # Common faults in clearing the values of key under all libraries

Cache avalanche

A large number of caches fail at the same time or receive large concurrent requests during cache reconstruction, resulting in a sharp decline in system performance

Possible reason: a large number of caches use the same expiration time, machine restart, etc.

Solution: add random values to the failure time, use queues or locks to update the cache with a single thread

After reading the above, do you have any further understanding of how to do Redis caching? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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