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Redis缓存层,现在的性能接近于无法使用.

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(我只是在编写中包含set和get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,根据基准测试,我看到每秒超过100,000套和100,000套.我写了一个单元测试来做300,000 set/gets:

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
            });

        }


    }
}

我的测试时间大约是21秒(300,000套和300,000套).这给了我每秒大约28,500次操作(至少比我预期的使用基准测试慢3倍).我转换为使用Redis的应用程序非常繁琐,某些繁重的请求可以对Redis进行大约200,000次操作.显然,我并没有期待在使用系统运行时缓存时获得的任何时间,但是在此更改之后的延迟非常重要.我的实施方式有问题,有没有人知道为什么我的基准数字比我的Stackechange测试数据快得多？

谢谢,保罗

Answer 1

我的结果来自以下代码:

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

你应该在基准测试中做的第一件事是:基准测试.目前,你要包含大量的序列化开销,这无助于获得清晰的图像.理想情况下,对于类似的基准测试,您应该使用3字节的固定有效负载,因为:

3字节有效载荷

接下来,您需要查看并行性:

50个并行客户端

目前尚不清楚您的测试是否是并行的,但如果不是,我们绝对应该期望看到更少的原始吞吐量.方便的是,SE.Redis易于并行化:您可以通过多个线程来启动与同一连接的通信(这实际上还具有避免数据包碎片的优势,因为每个数据包最终可能会有多个消息,其中 -因为单线程同步方法保证每个数据包最多使用一个消息).

最后,我们需要了解列出的基准测试的作用.它在做:

(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使用率是第一个,但第二个测试同样定义明确,而且据我所知:这就是它所测量的内容.有两种方法可以模拟第二种设置:

• 使用"fire and forget"标志发送第一个(n-1)消息,因此您实际上只等待最后一个消息
• 将*AsyncAPI用于所有消息,仅限Wait()或await最后一个消息Task

这是我在上面使用的基准测试,它显示了"每个操作的同步"(通过同步API)和"每个线程的管道"(使用*AsyncAPI并且只是等待每个线程的最后一个任务),两者都使用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");
        }
    }
}