2）hash类型，上代码
using (RedisClient client = new RedisClient("127.0.0.1", 6379, "12345", 10))
{
//删除当前数据库中的所有Key 默认删除的是db0
client.FlushDb();
//删除所有数据库中的key
//client.FlushAll();
//大key
string hashid = "pengbo";
#region 向hashid集合中添加key/value
client.SetEntryInHash(hashid, "id", "001");
Console.WriteLine(client.GetValuesFromHash(hashid, "id").FirstOrDefault());
client.SetEntryInHash(hashid, "name", "world");
Console.WriteLine(client.GetValuesFromHash(hashid, "name").FirstOrDefault());
client.SetEntryInHash(hashid, "socre", "100");
Console.WriteLine(client.GetValuesFromHash(hashid, "socre").FirstOrDefault());
#endregion
#region 批量新增key的值
client.FlushDb();
Dictionary<string, string> pairs = new Dictionary<string, string>();
pairs.Add("id", "001");
pairs.Add("name", "world");
client.SetRangeInHash(hashid, pairs);
//获取当前key的值
Console.WriteLine(client.GetValueFromHash(hashid, "id"));
Console.WriteLine(client.GetValueFromHash(hashid, "name"));
//一次性的获取所有想要获取的小key（属性的）值 如果key不存在，则返回空，不抛出异常
var list = client.GetValuesFromHash(hashid, "id", "name", "abc");
Console.WriteLine("*********");
foreach (var item in list)
{
Console.WriteLine(item);
}
#endregion
#region 如果hashid集合中存在key/value则不添加返回false，如果不存在在添加key/value,返回true
client.FlushDb();
Console.WriteLine(client.SetEntryInHashIfNotExists(hashid, "name", "你好美"));
Console.WriteLine(client.SetEntryInHashIfNotExists(hashid, "name", "你好美 哈哈哈"));
Console.WriteLine(client.GetValuesFromHash(hashid, "name").FirstOrDefault());
#endregion
#region 存储对象T t到hash集合中
client.FlushDb();
//urn: 类名: id的值
client.StoreAsHash<UserInfo>(new UserInfo() { Id = 2, Name = "world", number = 0 });
//如果id存在的话，则覆盖之前相同的id 他帮助我们序列化或者反射了一些事儿
client.StoreAsHash<UserInfo>(new UserInfo() { Id = 2, Name = "world2" });
//获取对象T中ID为id的数据。 必须要有属性id，不区分大小写
Console.WriteLine(client.GetFromHash<UserInfo>(2).Name);
var olduserinfo = client.GetFromHash<UserInfo>(2);
olduserinfo.number = 4;
client.StoreAsHash<UserInfo>(olduserinfo);
Console.WriteLine("最后的结果" + client.GetFromHash<UserInfo>(2).number);
client.StoreAsHash<UserInfoTwo>(new UserInfoTwo() { Id = "001", Name = "world2" });
Console.WriteLine(client.GetFromHash<UserInfoTwo>("001").Name);
client.StoreAsHash<UserInfoTwo>(new UserInfoTwo() { Id = "002", Name = "world" });
Console.WriteLine(client.GetFromHash<UserInfoTwo>("002").Name);
UserInfo lisi = new UserInfo() { Id = 1, Name = "李四", number = 0 };
client.StoreAsHash<UserInfo>(lisi);
Console.WriteLine(client.GetFromHash<UserInfo>(1).number);
//做个自增
var oldzhang = client.GetFromHash<UserInfo>(1);
oldzhang.number++;
client.StoreAsHash<UserInfo>(oldzhang);
#endregion
#region 获取所有hashid数据集的key/value数据集合
client.FlushDb();
Dictionary<string, string> pairs2 = new Dictionary<string, string>();
pairs2.Add("id", "001");
pairs2.Add("name", "world");
client.SetRangeInHash(hashid, pairs2);
var dics = client.GetAllEntriesFromHash(hashid);
foreach (var item in dics)
{
Console.WriteLine(item.Key + ":" + item.Value);
}
#endregion
#region 获取hashid数据集中的数据总数
client.FlushDb();
Dictionary<string, string> pairs3 = new Dictionary<string, string>();
pairs3.Add("id", "001");
pairs3.Add("name", "world");
client.SetRangeInHash(hashid, pairs3);
//自己做到心中有数
Console.WriteLine(client.GetHashCount(hashid));
#endregion
#region 获取hashid数据集中所有key的集合
client.FlushDb();
Dictionary<string, string> pairs4 = new Dictionary<string, string>();
pairs4.Add("id", "001");
pairs4.Add("name", "world");
client.SetRangeInHash(hashid, pairs4);
var keys = client.GetHashKeys(hashid);
foreach (var item in keys)
{
Console.WriteLine(item);
}
#endregion
#region 获取hashid数据集中的所有value集合
client.FlushDb();
Dictionary<string, string> pairs5 = new Dictionary<string, string>();
pairs5.Add("id", "001");
pairs5.Add("name", "world");
client.SetRangeInHash(hashid, pairs5);
var values = client.GetHashValues(hashid);
foreach (var item in values)
{
Console.WriteLine(item);
}
#endregion
#region 删除hashid数据集中的key数据
client.FlushDb();
Dictionary<string, string> pairs6 = new Dictionary<string, string>();
pairs6.Add("id", "001");
pairs6.Add("name", "world");
client.SetRangeInHash(hashid, pairs6);
client.RemoveEntryFromHash(hashid, "id");
var values6 = client.GetHashValues(hashid);
foreach (var item in values6)
{
Console.WriteLine(item);
}
#endregion
#region 判断hashid数据集中是否存在key的数据
client.FlushDb();
Dictionary<string, string> pairs7 = new Dictionary<string, string>();
pairs7.Add("id", "001");
pairs7.Add("name", "world");
client.SetRangeInHash(hashid, pairs7);
Console.WriteLine(client.HashContainsEntry(hashid, "id")); //T F
Console.WriteLine(client.HashContainsEntry(hashid, "number"));// T F
#endregion
#region 给hashid数据集key的value加countby，返回相加后的数据
client.FlushDb();
Dictionary<string, string> pairs8 = new Dictionary<string, string>();
pairs8.Add("id", "001");
pairs8.Add("name", "world");
pairs8.Add("number", "2");
client.SetRangeInHash(hashid, pairs8);
Console.WriteLine(client.IncrementValueInHash(hashid, "number", 2));
//注意，存的值必须是数字类型，否则抛出异常
#endregion
#region 自定义
HashTool.StoreAsHash<UserInfoTwo>(new UserInfoTwo() { Id = "10001", Name = "world" });
var user = HashTool.GetFromHash<UserInfoTwo>("10001");
Console.WriteLine("华丽丽的结束");
#endregion
}
hash在redis里面的存储格式有两种，ZipList和Hashtable。ZipList就是压缩版的list，field及其值会依次存储，这个时候在存储时都要先找到最后一个存储的位置。如果存储的key的长度越来越多了或者说这个类的属性越来越多，这个时候找到最后一个位置时就需要使用for循环。Redis经过测试发现，当这个key也就是类的属性个数不超过512或者任意一个key或value的长度小于等于64个字节时，redis的性能影响是忽略不计的，当超过这个限定值时，redis就会使用另外一种数据结构Hashtable进行存储。
HashTable存储时就是使用key-Value的形式进行存储。通过hash算法得出需要存储值对应的hash算法的出来的值作为特征，通过这个hash算法得出的特征值理论上是不会重复的。
哎， 后面全是算法+算法+算法 以后需要恶补一下算法，可能就恍然大悟了吧