Redis 实践笔记，redis实践笔记

    using (var redisUsers = redisClient.GetTypedClient<User>())
            {
                var ayende = new User { Id = redisUsers.GetNextSequence(), Name = "Oren Eini" };
                var mythz = new User { Id = redisUsers.GetNextSequence(), Name = "Demis Bellot" };
                redisUsers.Store(ayende);
                redisUsers.Store(mythz);
              }

redis 127.0.0.1:6379[1]> keys *
1) "seq:User"
2) "ids:User"
3) "urn:user:1"
4) "urn:user:2"

public long GetNextSequence(int incrBy)
  {
             return IncrementValueBy(SequenceKey, incrBy);
  }
 public long IncrementValue(string key)
    {
              return client.Incr(key);
     }

 public T Store(T entity)
        {
            var urnKey = entity.CreateUrn();
            this.SetEntry(urnKey, entity);

            return entity;
        }
        //entity.CreateUrn();的结果是"urn:user:1"
        public void SetEntry(string key, T value)
        {
            if (key == null)
                throw new ArgumentNullException("key");

            client.Set(key, SerializeValue(value));
            client.RegisterTypeId(value);
        }

        internal void RegisterTypeId<T>(T value)
        {
            var typeIdsSetKey = GetTypeIdsSetKey<T>();
            var id = value.GetId().ToString();

            if (this.Pipeline != null)
            {
                var registeredTypeIdsWithinPipeline = GetRegisteredTypeIdsWithinPipeline(typeIdsSetKey);
                registeredTypeIdsWithinPipeline.Add(id);
            }
            else
            {
                this.AddItemToSet(typeIdsSetKey, id);
            }
        }

%%第一种写法 
           logs.ForEach(n =>
                {
                    pipeline.QueueCommand(r =>
                    {
                        ((RedisClient)r).Store<OpLog>(n, n.GetObjectID(), n.GetUrnKey());
                        ((RedisClient)r).Expire(n.GetUrnKey(), dataLifeTime);
                    });
                });

%%第二种写法
 logs.ForEach(n =>
                {
  
                    pipeline.QueueCommand(r => ((RedisClient)r).Store<Log>(n, n.ID, n.GetUrnKey()));
                    pipeline.QueueCommand(r => ((RedisClient)r).Expire(n.GetUrnKey(), dataLifeTime));

                });

  protected virtual void AddCurrentQueuedOperation()
        {
            this.QueuedCommands.Add(CurrentQueuedOperation);
            CurrentQueuedOperation = null;
        }

这两年Redis火得可以，Redis也常常被当作Memcached的挑战者被提到桌面上来。关于Redis与Memcached的比较更是比比皆是。然而，Redis真的在功能、性能以及内存使用效率上都超越了Memcached吗？
下面内容来自Redis作者在stackoverflow上的一个回答，对应的问题是《Is memcached a dinosaur in comparison to Redis?》（相比Redis，Memcached真的过时了吗？）

You should not care too much about performances. Redis is faster per core with small values, but memcached is able to use multiple cores with a single executable and TCP port without help from the client. Also memcached is faster with big values in the order of 100k. Redis recently improved a lot about big values (unstable branch) but still memcached is faster in this use case. The point here is: nor one or the other will likely going to be your bottleneck for the query-per-second they can deliver.
没有必要过多的关心性能，因为二者的性能都已经足够高了。由于Redis只使用单核，而Memcached可以使用多核，所以在比较上，平均每一个核上Redis在存储小数据时比Memcached性能更高。而在100k以上的数据中，Memcached性能要高于Redis，虽然Redis最近也在存储大数据的性能上进行优化，但是比起Memcached，还是稍有逊色。说了这么多，结论是，无论你使用哪一个，每秒处理请求的次数都不会成为瓶颈。（比如瓶颈可能会在网卡）
You should care about memory usage. For simple key-value pairs memcached is more memory efficient. If you use Redis hashes, Redis is more memory efficient. Depends on the use case.
如果要说内存使用效率，使用简单的key-value存储的话，Memcached的内存利用率更高，而如果Redis采用hash结构来做key-value存储，由于其组合式的压缩，其内存利用率会高于Memcached。当然，这和你的应用场景和数据特性有关。
You should care about persistence and replication, two features only available in Redis. Even if your goal is to build a cache it helps that after an upgrade or a reboot your data are still there.
如果你对数据持久化和数据同步有所要求，那么推荐你选择Redis，因为这两个特性Memcached都不具备。即使你只是希望在升级或者重启系统后缓存数据不会丢失，选择Redis也是明智的。
You should care about the kind of operations you need. In Redis there are a lot of complex operations, even just considering the caching use case, you often can do a lot more in a single operation, without requiring data to be processed client side (a lot of I/O is sometimes needed). This operations are often as fast as plain GET and SET. So if you don’t need just GEt/SET but more complex things Redis can help a lot (think at timeline caching).
当然，最后还得说到你的具体应用需求。Redis相比Memcached来说，拥有更多的数据结构和并支持更丰富的数据操作，通常在Memcached里，你需要将数据拿到客户端来进行类似的修改再set回去。这大大增加了网络IO的次数和数据体积。在Redis中，这些复杂的操作通常和一般的GET/SET一样高效。所以，如果你需要缓存能够支持更复杂的结构和操作，那么Redis会是不错的选择。

注意:实践版本 Redis 2.4.9

Rdbtools is a parser for Redis' dump.rdb files. The parser generates events similar to an xml sax parser, and is very efficient memory wise.

In addition, rdbtools provides utilities to :