Redis Caching & Messaging (Pub/Sub, Streams) · 课时

使用 Redis Cluster 实现分片

实现 Redis Cluster，将数据分片到多个节点上，从而实现水平扩展和容错

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使用 Redis Cluster 实现分片 是 CoddyKit 上的免费 Redis Caching & Messaging (Pub/Sub, Streams) 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Redis Caching & Messaging (Pub/Sub, Streams) 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Redis Caching & Messaging (Pub/Sub, Streams) 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Scaling Beyond a Single Node

So far, we've learned about Redis replication and Sentinel for high availability. These are great for redundancy and read scaling, but what about write scaling or handling datasets larger than a single server's memory?

This is where Redis Cluster comes in. It's designed to provide horizontal scaling and fault tolerance by distributing your data across multiple Redis instances.

What is Sharding?

At its core, Redis Cluster uses sharding (also known as data partitioning). Imagine you have a massive library of books.

Instead of one giant shelf, you split the books across many smaller shelves.
Each shelf holds a portion of the books, and a librarian manages that shelf.

In Redis Cluster, each Redis instance (or node) acts like a 'librarian' managing a 'shelf' of your data. This allows you to scale both memory and CPU.

Redis Cluster Architecture

A Redis Cluster is a collection of interconnected Redis instances. Each instance can be a master or a replica.

Master Nodes: These nodes hold and manage a portion of the dataset.
Replica Nodes: These are copies of master nodes, providing high availability. If a master fails, one of its replicas can be promoted to take its place.

The cluster ensures data is distributed, and it can continue operating even if some nodes fail.

Hash Slots: How Data is Distributed

How does Redis know which node stores which piece of data? It uses hash slots.

The entire dataset is divided into 16,384 logical slots (from 0 to 16383).
Each master node in the cluster is responsible for a subset of these hash slots.
When you store a key, Redis calculates a hash of the key to determine which slot it belongs to.
This slot then maps to a specific master node.

This system allows for flexible data distribution and easy rebalancing.

Creating a Redis Cluster

To create a cluster, you need at least three master nodes for a fault-tolerant setup (each master can have replicas). Here's a conceptual CLI command using redis-cli:

redis-cli --cluster create 127.0.0.1:7000 127.0.0.1:7001 127.0.0.1:7002 --cluster-replicas 1

This command creates a cluster with 3 masters (on ports 7000, 7001, 7002).
--cluster-replicas 1 means each master will have one replica node.

The redis-cli tool automatically assigns hash slots to the master nodes.

Client Interaction & Redirection

When a client wants to interact with Redis Cluster, it can connect to any node. If the key the client requests doesn't belong to the connected node's hash slots, the node will respond with a MOVED redirection error.

Modern Redis client libraries (like Jedis for Java) are cluster-aware. They automatically handle these redirections by:

Learning the cluster topology (which node owns which hash slots).
Directly connecting to the correct node for a given key.

This makes interacting with a cluster surprisingly seamless for developers.

Connecting with a Java Client

Let's see how a Java client (using the Jedis library) connects to a Redis Cluster. The client needs to know at least one node in the cluster to discover the full topology.

Try running this example:

import redis.clients.jedis.HostAndPort;
import redis.clients.jedis.JedisCluster;
import java.util.HashSet;
import java.util.Set;

public class RedisClusterClient {
    public static void main(String[] args) {
        // Provide at least one node to connect to the cluster
        Set<HostAndPort> jedisClusterNodes = new HashSet<>();
        jedisClusterNodes.add(new HostAndPort("127.0.0.1", 7000)); // Example node

        try (JedisCluster jc = new JedisCluster(jedisClusterNodes)) {
            System.out.println("Connected to Redis Cluster!");

            String key = "user:100:name";
            String value = "Alice";
            jc.set(key, value);
            System.out.println("Set: " + key + " = " + value);

            String retrievedValue = jc.get(key);
            System.out.println("Get: " + key + " = " + retrievedValue);

            // Client automatically handles sharding for different keys
            jc.set("product:5:price", "99.99");
            System.out.println("Set: product:5:price = " + jc.get("product:5:price"));

        } catch (Exception e) {
            System.err.println("Error connecting to Redis Cluster: " + e.getMessage());
            System.err.println("Ensure a Redis Cluster is running on 127.0.0.1:7000");
        }
    }
}

Resharding & Rebalancing

One of the powerful features of Redis Cluster is its ability to reshard. This means you can dynamically add or remove nodes from the cluster while it's running.

When you add new master nodes, you can migrate hash slots from existing masters to the new ones.
When you remove nodes, their hash slots can be moved to other remaining masters.

This allows for flexible scaling up or down of your cluster resources without downtime.

Fault Tolerance in Cluster

Redis Cluster provides robust fault tolerance:

If a master node fails, its assigned replica is automatically promoted to become the new master.
The cluster continues to operate, as the hash slots previously managed by the failed master are now handled by its promoted replica.

However, if a master and all its replicas fail, the portion of the data managed by that master's hash slots becomes unavailable, and the cluster might cease to operate unless configured otherwise (cluster-require-full-coverage no).

Check Your Cluster Knowledge

Which of the following are primary benefits or characteristics of Redis Cluster?

在交互式课程中回答

Recap: Redis Cluster for Scale

In this lesson, we explored Redis Cluster, a powerful solution for scaling Redis horizontally. We learned:

Cluster uses sharding to distribute data across multiple master nodes.
Hash slots determine which node stores which key.
Client libraries are cluster-aware, handling redirections automatically.
It offers fault tolerance by promoting replicas upon master failure.
The ability to reshard allows for dynamic scaling.

Redis Cluster is your go-to when a single Redis instance can no longer meet your application's data size or throughput demands.

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常见问题解答

「使用 Redis Cluster 实现分片」课时是免费的吗？

是的 — 「使用 Redis Cluster 实现分片」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Redis Caching & Messaging (Pub/Sub, Streams) 课程的其余内容，请升级到 CoddyKit PRO。 Redis Caching & Messaging (Pub/Sub, Streams) 课程共包含 4 节课。

「使用 Redis Cluster 实现分片」这节课中我会学到什么？

实现 Redis Cluster，将数据分片到多个节点上，从而实现水平扩展和容错你通过在浏览器中直接运行的动手代码来练习 Redis Caching & Messaging (Pub/Sub, Streams)，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Redis Caching & Messaging (Pub/Sub, Streams) 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Redis Caching & Messaging (Pub/Sub, Streams) 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「使用 Redis Cluster 实现分片」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Redis Caching & Messaging (Pub/Sub, Streams) 课中编写并运行代码吗？

能。每节 Redis Caching & Messaging (Pub/Sub, Streams) 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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