Redis: Understanding Its Architecture and Purpose

Redis is a memory data structure storage system, mainly used as a database, cache and message broker. Its core features include single-threaded model, I/O multiplexing, persistence mechanism, replication and clustering functions. Redis is commonly used in practical applications for caching, session storage, and message queues. It can significantly improve its performance by selecting the right data structure, using pipelines and transactions, and monitoring and tuning.

introduction

Redis, this in-memory data structure storage system, may be familiar with it, but do you really understand its architecture and purpose? This article will take you to explore Redis's design philosophy and practical application scenarios in depth, helping you not only master the basic usage of Redis, but also understand the essence of high-performance data processing.

After reading this article, you will be able to understand the core architecture of Redis, master the application methods in actual projects, and be able to select the most suitable Redis data structure according to specific needs.

Redis Basics

Redis, full name Remote Dictionary Server, is an open source memory data structure storage system. It can be used as a database, cache, and message broker. Redis supports a variety of data structures, such as strings, hash tables, lists, collections and ordered collections. These data structures allow Redis to show its skills in various application scenarios.

The core feature of Redis is its fast speed, which is because it stores data in memory. Its single-threaded model and I/O multiplexing technology enable Redis to perform well when handling high concurrent requests.

Redis architecture analysis

Single-threaded model and I/O multiplexing

Redis's single-threaded model is one of the core of its architecture. Single threading means that all commands of Redis are processed by one thread, which can avoid competition among multiple threads and the complexity of locks. However, single threading does not mean that Redis has poor performance. On the contrary, Redis uses I/O multiplexing technology to achieve efficient network communication.

I/O multiplexing allows Redis to handle multiple client connections in one thread. Redis uses the I/O multiplexing mechanism provided by operating systems such as epoll and kqueue to listen for I/O events of multiple file descriptors. When an event occurs, Redis will perform the corresponding operation according to the event type. This method allows Redis to handle a large number of concurrent connections under a single thread, achieving efficient data processing.

 // Redis I/O multiplexing example int aeCreateFileEvent(aeEventLoop *eventLoop, int fd, int mask,
        aeFileProc *proc, void *clientData)
{
    if (fd >= eventLoop->setsize) {
        errno = ERANGE;
        return AE_ERR;
    }
    aeFileEvent *fe = &eventLoop->events[fd];

    if (aeApiAddEvent(eventLoop, fd, mask) == -1)
        return AE_ERR;
    fe->mask |= mask;
    if (mask & AE_READABLE) fe->rfileProc = proc;
    if (mask & AE_WRITABLE) fe->wfileProc = proc;
    fe->clientData = clientData;
    if (fd > eventLoop->maxfd)
        eventLoop->maxfd = fd;
    return AE_OK;
}

Persistence mechanism

Redis provides two persistence mechanisms: RDB and AOF. RDB saves data to disk through snapshots, while AOF records all write operations to achieve persistence of data.

The advantage of RDB snapshots is that they are fast recovery and are suitable for cold backups, but their disadvantage is that they may lose recent data. AOF records all write operations, the data is more complete, but the recovery speed is relatively slow. Redis also supports AOF rewrite function, which can compress the size of AOF files without affecting the service.

 // RDB snapshot example int rdbSave(char *filename) {
    dictIterator *di = NULL;
    dictEntry *de;
    int j;
    FILE *fp;
    char tmpfile[256];
    long long now = mstime();

    snprintf(tmpfile,256,"temp-%d.rdb", (int) getpid());
    fp = fopen(tmpfile,"w");
    if (!fp) {
        redisLog(REDIS_WARNING, "Failed opening .rdb for saving: %s",
            strerror(errno));
        return REDIS_ERR;
    }

    if (rdbSaveRio(fp,0,RDB_SAVE_NONE) == REDIS_ERR) {
        fclose(fp);
        unlink(tmpfile);
        return REDIS_ERR;
    }

    fclose(fp);
    if (rename(tmpfile,filename) == -1) {
        redisLog(REDIS_WARNING,"Error moving temp DB file on the final destination: %s", strrror(errno));
        unlink(tmpfile);
        return REDIS_ERR;
    }

    redisLog(REDIS_NOTICE,"DB saved on disk");
    server.dirty = 0;
    server.lastsave = now;
    return REDIS_OK;
}

Replication and clustering

Redis's replication feature allows one Redis instance (slave library) to copy data from another Redis instance (main library). This mechanism can not only implement redundant backup of data, but also improve the performance of read operations, because the slave library can share the read requests of the main library.

The Redis cluster further expands the scalability of Redis. By storing data shards in multiple Redis instances, Redis clusters can handle larger data sets and higher concurrent requests. The design of Redis cluster allows each node to handle requests independently, improving system availability and performance.

 // Redis copy example void replicationFeedSlaves(list *slaves, int dictid, robj **argv, int argc) {
    listNode *ln;
    listIter li;
    redisClient *slave;
    int j, start, end;

    listRewind(slaves,&li);
    while((ln = listNext(&li))) {
        slave = ln->value;
        if (slave->replstate == REDIS_REPL_WAIT_BGSAVE_START) continue;

        /* Send the MULTI command signaling the start of the transaction. */
        if (slave->flags & REDIS_PRE_PSYNC) {
            addReplyMultiBulkLen(slave,argc);
            for (j = 0; j < argc; j ) {
                addReplyBulk(slave,argv[j]);
            }
        } else {
            start = (slave->flags & REDIS_PRE_PSYNC) ? 0 : 1;
            end = (slave->flags & REDIS_PRE_PSYNC) ? argc : argc-1;
            addReplyMultiBulkLen(slave, end-start);
            for (j = start; j < end; j ) {
                addReplyBulk(slave,argv[j]);
            }
        }
    }
}

The practical application of Redis

Redis has a wide range of application scenarios in actual projects. Here are some common uses:

cache

One of the most common uses of Redis is as a cache layer. By storing hotspot data in Redis, the response speed of your application can be greatly improved. Redis's LRU elimination strategy and expiration mechanism make it very suitable for caching.

 # Example of using Redis as cache import redis

# Connect to Redis server r = redis.Redis(host='localhost', port=6379, db=0)

# Set cache r.set('user:1', 'John Doe')

# Get cache user = r.get('user:1')
print(user.decode('utf-8')) # Output: John Doe

Session storage

Redis can be used to store user session data, especially in distributed systems. By storing session data in Redis, cross-server sharing of sessions can be realized, improving system scalability.

 # Example of using Redis to store session data import redis
import json

# Connect to Redis server r = redis.Redis(host='localhost', port=6379, db=0)

# Set session data session_data = {'user_id': 1, 'username': 'John Doe'}
r.set('session:12345', json.dumps(session_data))

# Get session data session = r.get('session:12345')
if session:
    session_data = json.loads(session.decode('utf-8'))
    print(session_data) # Output: {'user_id': 1, 'username': 'John Doe'}

Message Queue

Redis's list data structure can be used to implement simple message queues. The producer and consumer model can be implemented through LPUSH and RPOP commands.

 # Example of using Redis to implement message queue import redis

# Connect to Redis server r = redis.Redis(host='localhost', port=6379, db=0)

# Producer r.lpush('queue', 'message1')
r.lpush('queue', 'message2')

# Consumer message = r.rpop('queue')
print(message.decode('utf-8')) # Output: message2

Performance optimization and best practices

There are some performance optimizations and best practices worth noting when using Redis:

Select the right data structure

Redis provides a variety of data structures, each of which has its applicable scenarios. Choosing the right data structure can greatly improve the performance of Redis. For example, use ordered sets to implement rankings, use hash tables to store objects, etc.

 # Example of rankings using ordered collections import redis

# Connect to Redis server r = redis.Redis(host='localhost', port=6379, db=0)

# Add user score r.zadd('leaderboard', {'user1': 100, 'user2': 200, 'user3': 150})

# Get the top three in the ranking list top_three = r.zrevrange('leaderboard', 0, 2, withscores=True)
for user, score in top_three:
    print(f'{user.decode("utf-8")}: {score}')

Pipelines and transactions

Redis's pipeline and transaction capabilities can improve the performance of batch operations. Pipeline allows clients to package multiple commands to Redis, reducing network overhead. Transactions ensure the atomicity of a set of commands.

 # Example of using Redis pipeline import redis

# Connect to Redis server r = redis.Redis(host='localhost', port=6379, db=0)

# Use pipe = r.pipeline()
pipe.set('key1', 'value1')
pipe.set('key2', 'value2')
pipe.execute()

Monitoring and tuning

Use Redis's monitoring tools, such as Redis Insight or the MONITOR command of Redis CLI, you can monitor the running status of Redis in real time. By analyzing slow query logs and memory usage, performance bottlenecks can be found and tuned.

 # Monitor Redis using the MONITOR command of Redis CLI
redis-cli MONITOR

Summarize

Redis's architectural design and diverse application scenarios make it indispensable in modern application development. By deeply understanding Redis's core concepts such as single-threaded model, I/O multiplexing, persistence mechanism, replication and clustering, you can better utilize Redis to improve the performance and scalability of your application.

In practical applications, choosing the right data structure, using pipelines and transactions, and monitoring and tuning are the keys to improving Redis performance. I hope this article can help you better understand and apply Redis, and I wish you a smooth journey on Redis!

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