c# - 与基准测试相比，StackExchange redis 客户端非常慢

Question

我正在使用 Stackexchange Redis 客户端实现一个 Redis 缓存层，而现在的性能几乎无法使用。

我有一个本地环境，其中 Web 应用程序和 Redis 服务器在同一台机器上运行。我针对我的 Redis 服务器运行了 Redis 基准测试，结果实际上非常好(我只是在我的文章中包括设置和获取操作):

C:\Program Files\Redis>redis-benchmark -n 100000
====== PING_INLINE ======
  100000 requests completed in 0.88 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

====== SET ======
  100000 requests completed in 0.89 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.70% <= 1 milliseconds
99.90% <= 2 milliseconds
100.00% <= 3 milliseconds
111982.08 requests per second

====== GET ======
  100000 requests completed in 0.81 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.87% <= 1 milliseconds
99.98% <= 2 milliseconds
100.00% <= 2 milliseconds
124069.48 requests per second

因此，根据基准，我每秒查看超过 100,000 组和 100,000 次获取。我编写了一个单元测试来执行 300,000 次设置/获取:

private string redisCacheConn = "localhost:6379,allowAdmin=true,abortConnect=false,ssl=false";


[Fact]
public void PerfTestWriteShortString()
{
    CacheManager cm = new CacheManager(redisCacheConn);

    string svalue = "t";
    string skey = "testtesttest";
    for (int i = 0; i < 300000; i++)
    {
        cm.SaveCache(skey + i, svalue);
        string valRead = cm.ObtainItemFromCacheString(skey + i);
     }

}

这使用以下类通过 Stackexchange 客户端执行 Redis 操作:

using StackExchange.Redis;    

namespace Caching
{
    public class CacheManager:ICacheManager, ICacheManagerReports
    {
        private static string cs;
        private static ConfigurationOptions options;
        private int pageSize = 5000;
        public ICacheSerializer serializer { get; set; }

        public CacheManager(string connectionString)
        {
            serializer = new SerializeJSON();
            cs = connectionString;
            options = ConfigurationOptions.Parse(connectionString);
            options.SyncTimeout = 60000;
        }

        private static readonly Lazy<ConnectionMultiplexer> lazyConnection = new Lazy<ConnectionMultiplexer>(() => ConnectionMultiplexer.Connect(options));
        private static ConnectionMultiplexer Connection => lazyConnection.Value;
        private static IDatabase cache => Connection.GetDatabase();

        public string ObtainItemFromCacheString(string cacheId)
        {
            return cache.StringGet(cacheId);
        }

        public void SaveCache<T>(string cacheId, T cacheEntry, TimeSpan? expiry = null)
        {
            if (IsValueType<T>())
            {
                cache.StringSet(cacheId, cacheEntry.ToString(), expiry);
            }
            else
            {
                cache.StringSet(cacheId, serializer.SerializeObject(cacheEntry), expiry);
            }
        }

        public bool IsValueType<T>()
        {
            return typeof(T).IsValueType || typeof(T) == typeof(string);
        }

    }
}

我的 JSON 序列化程序只使用 Newtonsoft.JSON:

using System.Collections.Generic;
using Newtonsoft.Json;

namespace Caching
{
    public class SerializeJSON:ICacheSerializer
    {
        public string SerializeObject<T>(T cacheEntry)
        {
            return JsonConvert.SerializeObject(cacheEntry, Formatting.None,
                new JsonSerializerSettings()
                {
                    ReferenceLoopHandling = ReferenceLoopHandling.Ignore
                });
        }

        public T DeserializeObject<T>(string data)
        {
            return JsonConvert.DeserializeObject<T>(data, new JsonSerializerSettings()
            {
                ReferenceLoopHandling = ReferenceLoopHandling.Ignore
            });

        }


    }
}

我的测试时间约为 21 秒(300,000 组和 300,000 次获取)。这给了我每秒大约 28,500 次操作(至少比我预期使用基准测试慢 3 倍)。我正在转换为使用 Redis 的应用程序非常繁琐，某些繁重的请求可能对 Redis 进行大约 200,000 次总操作。显然，我没有预料到我在使用系统运行时缓存时会遇到同样的情况，但此更改后的延迟很明显。我的实现是否有问题，有人知道为什么我的基准数据比我的 Stackechange 测试数据快得多吗？

谢谢，保罗

Answer 1

我从下面的代码中得到的结果:

Connecting to server...
Connected
PING (sync per op)
    1709ms for 1000000 ops on 50 threads took 1.709594 seconds
    585137 ops/s
SET (sync per op)
    759ms for 500000 ops on 50 threads took 0.7592914 seconds
    658761 ops/s
GET (sync per op)
    780ms for 500000 ops on 50 threads took 0.7806102 seconds
    641025 ops/s
PING (pipelined per thread)
    3751ms for 1000000 ops on 50 threads took 3.7510956 seconds
    266595 ops/s
SET (pipelined per thread)
    1781ms for 500000 ops on 50 threads took 1.7819831 seconds
    280741 ops/s
GET (pipelined per thread)
    1977ms for 500000 ops on 50 threads took 1.9772623 seconds
    252908 ops/s

===

服务器配置:确保禁用持久性等

您应该在基准测试中做的第一件事是:基准测试一件事。目前，您包含了大量的序列化开销，这无助于获得清晰的画面。理想情况下，对于同类基准测试，您应该使用 3 字节的固定负载，因为:

3 bytes payload

接下来，您需要了解并行性:

50 parallel clients

不清楚您的测试是否是并行的，但如果不是，我们绝对期望看到更少的原始吞吐量。方便的是，SE.Redis 被设计成易于并行化:你可以启动多个线程与同一个连接(这实际上也有避免数据包碎片的优点，因为你最终可以每个数据包多条消息，其中单线程同步方法保证每个数据包最多使用一条消息。

最后，我们需要了解列出的基准测试在做什么。它在做什么:

(send, receive) x n

或者它在做什么

send x n, receive separately until all n are received

?两种选择都是可能的。您的同步 API 使用是第一个，但第二个测试同样定义明确，据我所知:这就是它正在测量的内容。有两种方法可以模拟第二个设置:

• 发送前 (n-1) strip 有“即发即弃”标志的消息，因此您实际上只等待最后一条
• 对所有消息使用 *Async API，并且仅对最后一个 Task 使用 Wait() 或 await

这是我在上面使用的基准测试，它显示了“每个操作同步”(通过同步 API)和“每个线程的管道”(使用 *Async API 并等待每个线程的最后一个任务)，都使用 50 个线程:

using StackExchange.Redis;
using System;
using System.Diagnostics;
using System.Threading;
using System.Threading.Tasks;

static class P
{
    static void Main()
    {
        Console.WriteLine("Connecting to server...");
        using (var muxer = ConnectionMultiplexer.Connect("127.0.0.1"))
        {
            Console.WriteLine("Connected");
            var db = muxer.GetDatabase();

            RedisKey key = "some key";
            byte[] payload = new byte[3];
            new Random(12345).NextBytes(payload);
            RedisValue value = payload;
            DoWork("PING (sync per op)", db, 1000000, 50, x => { x.Ping(); return null; });
            DoWork("SET (sync per op)", db, 500000, 50, x => { x.StringSet(key, value); return null; });
            DoWork("GET (sync per op)", db, 500000, 50, x => { x.StringGet(key); return null; });

            DoWork("PING (pipelined per thread)", db, 1000000, 50, x => x.PingAsync());
            DoWork("SET (pipelined per thread)", db, 500000, 50, x => x.StringSetAsync(key, value));
            DoWork("GET (pipelined per thread)", db, 500000, 50, x => x.StringGetAsync(key));
        }
    }
    static void DoWork(string action, IDatabase db, int count, int threads, Func<IDatabase, Task> op)
    {
        object startup = new object(), shutdown = new object();
        int activeThreads = 0, outstandingOps = count;
        Stopwatch sw = default(Stopwatch);
        var threadStart = new ThreadStart(() =>
        {
            lock(startup)
            {
                if(++activeThreads == threads)
                {
                    sw = Stopwatch.StartNew();
                    Monitor.PulseAll(startup);
                }
                else
                {
                    Monitor.Wait(startup);
                }
            }
            Task final = null;
            while (Interlocked.Decrement(ref outstandingOps) >= 0)
            {
                final = op(db);
            }
            if (final != null) final.Wait();
            lock(shutdown)
            {
                if (--activeThreads == 0)
                {
                    sw.Stop();
                    Monitor.PulseAll(shutdown);
                }
            }
        });
        lock (shutdown)
        {
            for (int i = 0; i < threads; i++)
            {
                new Thread(threadStart).Start();
            }
            Monitor.Wait(shutdown);
            Console.WriteLine($@"{action}
    {sw.ElapsedMilliseconds}ms for {count} ops on {threads} threads took {sw.Elapsed.TotalSeconds} seconds
    {(count * 1000) / sw.ElapsedMilliseconds} ops/s");
        }
    }
}