readis windows servrer 搭建与Java客户端的连接

1：首先下载redis:redis-2.0.2.zip (32 bit)，解压。
从下面地址下：http://code.google.com/p/servicestack/wiki/RedisWindowsDownload，看到下面有redis-2.0.2.zip (32 bit)，就是他了，下载完成后，解压到D:\redis-2.0.2.
2：创建redis.conf文件：
这是一个配置文件，指定了redis的监听端口，timeout等。如下面有：port 6379。
把下面内容COPY到一新建文件中，取名redis.conf,再保存到redis-2.0.2目录下：


# Redis configuration file example

# By default Redis does not run as a daemon. Use 'yes' if you need it.
# Note that Redis will write a pid file in /var/run/redis.pid when daemonized.
daemonize no

# When run as a daemon, Redis write a pid file in /var/run/redis.pid by default.
# You can specify a custom pid file location here.
pidfile /var/run/redis.pid

# Accept connections on the specified port, default is 6379
port 6379

# If you want you can bind a single interface, if the bind option is not
# specified all the interfaces will listen for connections.
#
# bind 127.0.0.1

# Close the connection after a client is idle for N seconds (0 to disable)
timeout 300

# Set server verbosity to 'debug'
# it can be one of:
# debug (a lot of information, useful for development/testing)
# notice (moderately verbose, what you want in production probably)
# warning (only very important / critical messages are logged)
loglevel debug

# Specify the log file name. Also 'stdout' can be used to force
# the demon to log on the standard output. Note that if you use standard
# output for logging but daemonize, logs will be sent to /dev/null
logfile stdout

# Set the number of databases. The default database is DB 0, you can select
# a different one on a per-connection basis using SELECT <dbid> where
# dbid is a number between 0 and 'databases'-1
databases 16

################################ SNAPSHOTTING  #################################
#
# Save the DB on disk:
#
#   save <seconds> <changes>
#
#   Will save the DB if both the given number of seconds and the given
#   number of write operations against the DB occurred.
#
#   In the example below the behaviour will be to save:
#   after 900 sec (15 min) if at least 1 key changed
#   after 300 sec (5 min) if at least 10 keys changed
#   after 60 sec if at least 10000 keys changed
save 900 1
save 300 10
save 60 10000

# Compress string objects using LZF when dump .rdb databases?
# For default that's set to 'yes' as it's almost always a win.
# If you want to save some CPU in the saving child set it to 'no' but
# the dataset will likely be bigger if you have compressible values or keys.
rdbcompression yes

# The filename where to dump the DB
dbfilename dump.rdb

# For default save/load DB in/from the working directory
# Note that you must specify a directory not a file name.
dir ./

################################# REPLICATION #################################

# Master-Slave replication. Use slaveof to make a Redis instance a copy of
# another Redis server. Note that the configuration is local to the slave
# so for example it is possible to configure the slave to save the DB with a
# different interval, or to listen to another port, and so on.
#
# slaveof <masterip> <masterport>

# If the master is password protected (using the "requirepass" configuration
# directive below) it is possible to tell the slave to authenticate before
# starting the replication synchronization process, otherwise the master will
# refuse the slave request.
#
# masterauth <master-password>

################################## SECURITY ###################################

# Require clients to issue AUTH <PASSWORD> before processing any other
# commands.  This might be useful in environments in which you do not trust
# others with access to the host running redis-server.
#
# This should stay commented out for backward compatibility and because most
# people do not need auth (e.g. they run their own servers).
#
# requirepass foobared

################################### LIMITS ####################################

# Set the max number of connected clients at the same time. By default there
# is no limit, and it's up to the number of file descriptors the Redis process
# is able to open. The special value '0' means no limts.
# Once the limit is reached Redis will close all the new connections sending
# an error 'max number of clients reached'.
#
# maxclients 128

# Don't use more memory than the specified amount of bytes.
# When the memory limit is reached Redis will try to remove keys with an
# EXPIRE set. It will try to start freeing keys that are going to expire
# in little time and preserve keys with a longer time to live.
# Redis will also try to remove objects from free lists if possible.
#
# If all this fails, Redis will start to reply with errors to commands
# that will use more memory, like SET, LPUSH, and so on, and will continue
# to reply to most read-only commands like GET.
#
# WARNING: maxmemory can be a good idea mainly if you want to use Redis as a
# 'state' server or cache, not as a real DB. When Redis is used as a real
# database the memory usage will grow over the weeks, it will be obvious if
# it is going to use too much memory in the long run, and you'll have the time
# to upgrade. With maxmemory after the limit is reached you'll start to get
# errors for write operations, and this may even lead to DB inconsistency.
#
# maxmemory <bytes>

############################## APPEND ONLY MODE ###############################

# By default Redis asynchronously dumps the dataset on disk. If you can live
# with the idea that the latest records will be lost if something like a crash
# happens this is the preferred way to run Redis. If instead you care a lot
# about your data and don't want to that a single record can get lost you should
# enable the append only mode: when this mode is enabled Redis will append
# every write operation received in the file appendonly.log. This file will
# be read on startup in order to rebuild the full dataset in memory.
#
# Note that you can have both the async dumps and the append only file if you
# like (you have to comment the "save" statements above to disable the dumps).
# Still if append only mode is enabled Redis will load the data from the
# log file at startup ignoring the dump.rdb file.
#
# The name of the append only file is "appendonly.log"
#
# IMPORTANT: Check the BGREWRITEAOF to check how to rewrite the append
# log file in background when it gets too big.

appendonly no

# The fsync() call tells the Operating System to actually write data on disk
# instead to wait for more data in the output buffer. Some OS will really flush
# data on disk, some other OS will just try to do it ASAP.
#
# Redis supports three different modes:
#
# no: don't fsync, just let the OS flush the data when it wants. Faster.
# always: fsync after every write to the append only log . Slow, Safest.
# everysec: fsync only if one second passed since the last fsync. Compromise.
#
# The default is "always" that's the safer of the options. It's up to you to
# understand if you can relax this to "everysec" that will fsync every second
# or to "no" that will let the operating system flush the output buffer when
# it want, for better performances (but if you can live with the idea of
# some data loss consider the default persistence mode that's snapshotting).

appendfsync always
# appendfsync everysec
# appendfsync no

############################### ADVANCED CONFIG ###############################

# Glue small output buffers together in order to send small replies in a
# single TCP packet. Uses a bit more CPU but most of the times it is a win
# in terms of number of queries per second. Use 'yes' if unsure.
glueoutputbuf yes

# Use object sharing. Can save a lot of memory if you have many common
# string in your dataset, but performs lookups against the shared objects
# pool so it uses more CPU and can be a bit slower. Usually it's a good
# idea.
#
# When object sharing is enabled (shareobjects yes) you can use
# shareobjectspoolsize to control the size of the pool used in order to try
# object sharing. A bigger pool size will lead to better sharing capabilities.
# In general you want this value to be at least the double of the number of
# very common strings you have in your dataset.
#
# WARNING: object sharing is experimental, don't enable this feature
# in production before of Redis 1.0-stable. Still please try this feature in
# your development environment so that we can test it better.
# shareobjects no
# shareobjectspoolsize 1024

3：在cmd下面执行以下命令，指定它使用我们的redis.conf，同时也是启动，把redis运行起来，这里指定用redis.conf的配置运行服务器
D:\redis-2.0.2>redis-server.exe redis.conf

4:开一新DOS窗口cmd.执行以下命令，这是Redis的客户端程序：
redis-cli.exe -h 172.18.5.1 -p 6379
172.18.5.1是我本机IP地址，端口6379就是上面配置文件中指定的监听端口
执行完成后，应该能看到redis启动了，这时在第一个cmd窗口可以看到连接信息。

接下来就是Java客户端的连接了：
一、普通同步方式

最简单和基础的调用方式，

@Test
public void test1Normal() {
    Jedis jedis = new Jedis("localhost");
    long start = System.currentTimeMillis();
    for (int i = 0; i < 100000; i++) {
        String result = jedis.set("n" + i, "n" + i);
    }
    long end = System.currentTimeMillis();
    System.out.println("Simple SET: " + ((end - start)/1000.0) + " seconds");
    jedis.disconnect();
}

很简单吧，每次set之后都可以返回结果，标记是否成功。
二、事务方式(Transactions)

redis的事务很简单，他主要目的是保障，一个client发起的事务中的命令可以连续的执行，而中间不会插入其他client的命令。

看下面例子：

@Test
public void test2Trans() {
    Jedis jedis = new Jedis("localhost");
    long start = System.currentTimeMillis();
    Transaction tx = jedis.multi();
    for (int i = 0; i < 100000; i++) {
        tx.set("t" + i, "t" + i);
    }
    List<Object> results = tx.exec();
    long end = System.currentTimeMillis();
    System.out.println("Transaction SET: " + ((end - start)/1000.0) + " seconds");
    jedis.disconnect();
}

我们调用jedis.watch(…)方法来监控key，如果调用后key值发生变化，则整个事务会执行失败。另外，事务中某个操作失败，并不会回滚其他操作。这一点需要注意。还有，我们可以使用discard()方法来取消事务。
三、管道(Pipelining)

有时，我们需要采用异步方式，一次发送多个指令，不同步等待其返回结果。这样可以取得非常好的执行效率。这就是管道，调用方法如下：

@Test
public void test3Pipelined() {
    Jedis jedis = new Jedis("localhost");
    Pipeline pipeline = jedis.pipelined();
    long start = System.currentTimeMillis();
    for (int i = 0; i < 100000; i++) {
        pipeline.set("p" + i, "p" + i);
    }
    List<Object> results = pipeline.syncAndReturnAll();
    long end = System.currentTimeMillis();
    System.out.println("Pipelined SET: " + ((end - start)/1000.0) + " seconds");
    jedis.disconnect();
}

四、管道中调用事务

就Jedis提供的方法而言，是可以做到在管道中使用事务，其代码如下：

@Test
public void test4combPipelineTrans() {
    jedis = new Jedis("localhost");
    long start = System.currentTimeMillis();
    Pipeline pipeline = jedis.pipelined();
    pipeline.multi();
    for (int i = 0; i < 100000; i++) {
        pipeline.set("" + i, "" + i);
    }
    pipeline.exec();
    List<Object> results = pipeline.syncAndReturnAll();
    long end = System.currentTimeMillis();
    System.out.println("Pipelined transaction: " + ((end - start)/1000.0) + " seconds");
    jedis.disconnect();
}

但是经测试（见本文后续部分），发现其效率和单独使用事务差不多，甚至还略微差点。
五、分布式直连同步调用

@Test
public void test5shardNormal() {
    List<JedisShardInfo> shards = Arrays.asList(
            new JedisShardInfo("localhost",6379),
            new JedisShardInfo("localhost",6380));

    ShardedJedis sharding = new ShardedJedis(shards);

    long start = System.currentTimeMillis();
    for (int i = 0; i < 100000; i++) {
        String result = sharding.set("sn" + i, "n" + i);
    }
    long end = System.currentTimeMillis();
    System.out.println("Simple@Sharing SET: " + ((end - start)/1000.0) + " seconds");

    sharding.disconnect();
}

这个是分布式直接连接，并且是同步调用，每步执行都返回执行结果。类似地，还有异步管道调用。
六、分布式直连异步调用

@Test
public void test6shardpipelined() {
    List<JedisShardInfo> shards = Arrays.asList(
            new JedisShardInfo("localhost",6379),
            new JedisShardInfo("localhost",6380));

    ShardedJedis sharding = new ShardedJedis(shards);

    ShardedJedisPipeline pipeline = sharding.pipelined();
    long start = System.currentTimeMillis();
    for (int i = 0; i < 100000; i++) {
        pipeline.set("sp" + i, "p" + i);
    }
    List<Object> results = pipeline.syncAndReturnAll();
    long end = System.currentTimeMillis();
    System.out.println("Pipelined@Sharing SET: " + ((end - start)/1000.0) + " seconds");

    sharding.disconnect();
}

七、分布式连接池同步调用

如果，你的分布式调用代码是运行在线程中，那么上面两个直连调用方式就不合适了，因为直连方式是非线程安全的，这个时候，你就必须选择连接池调用。

@Test
public void test7shardSimplePool() {
    List<JedisShardInfo> shards = Arrays.asList(
            new JedisShardInfo("localhost",6379),
            new JedisShardInfo("localhost",6380));

    ShardedJedisPool pool = new ShardedJedisPool(new JedisPoolConfig(), shards);

    ShardedJedis one = pool.getResource();

    long start = System.currentTimeMillis();
    for (int i = 0; i < 100000; i++) {
        String result = one.set("spn" + i, "n" + i);
    }
    long end = System.currentTimeMillis();
    pool.returnResource(one);
    System.out.println("Simple@Pool SET: " + ((end - start)/1000.0) + " seconds");

    pool.destroy();
}

上面是同步方式，当然还有异步方式。
八、分布式连接池异步调用

@Test
public void test8shardPipelinedPool() {
    List<JedisShardInfo> shards = Arrays.asList(
            new JedisShardInfo("localhost",6379),
            new JedisShardInfo("localhost",6380));

    ShardedJedisPool pool = new ShardedJedisPool(new JedisPoolConfig(), shards);

    ShardedJedis one = pool.getResource();

    ShardedJedisPipeline pipeline = one.pipelined();

    long start = System.currentTimeMillis();
    for (int i = 0; i < 100000; i++) {
        pipeline.set("sppn" + i, "n" + i);
    }
    List<Object> results = pipeline.syncAndReturnAll();
    long end = System.currentTimeMillis();
    pool.returnResource(one);
    System.out.println("Pipelined@Pool SET: " + ((end - start)/1000.0) + " seconds");
    pool.destroy();
}

九、需要注意的地方

    事务和管道都是异步模式。在事务和管道中不能同步查询结果。比如下面两个调用，都是不允许的：

     Transaction tx = jedis.multi();
     for (int i = 0; i < 100000; i++) {
         tx.set("t" + i, "t" + i);
     }
     System.out.println(tx.get("t1000").get());  //不允许

     List<Object> results = tx.exec();

     …
     …

     Pipeline pipeline = jedis.pipelined();
     long start = System.currentTimeMillis();
     for (int i = 0; i < 100000; i++) {
         pipeline.set("p" + i, "p" + i);
     }
     System.out.println(pipeline.get("p1000").get()); //不允许

     List<Object> results = pipeline.syncAndReturnAll();

    事务和管道都是异步的，个人感觉，在管道中再进行事务调用，没有必要，不如直接进行事务模式。

    分布式中，连接池的性能比直连的性能略好(见后续测试部分)。

    分布式调用中不支持事务。

    因为事务是在服务器端实现，而在分布式中，每批次的调用对象都可能访问不同的机器，所以，没法进行事务。