jedis是一个著名的key-value存储系统，而作为其官方推荐的java版客户端jedis也非常强大和稳定，支持事务、管道及有jedis自身实现的分布式。
在这里对jedis关于事务、管道和分布式的调用方式做一个简单的介绍和对比：
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(”
[email protected] 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(”
[email protected] 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(”
[email protected] 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(”
[email protected] 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。
十、测试
运行上面的代码，进行测试，其结果如下：
Simple SET: 5.227 seconds
Transaction SET: 0.5 seconds
Pipelined SET: 0.353 seconds
Pipelined transaction: 0.509 seconds
[email protected] SET: 5.289 seconds
[email protected] SET: 0.348 seconds
[email protected] SET: 5.039 seconds
[email protected] SET: 0.401 seconds
另外，经测试分布式中用到的机器越多，调用会越慢。上面是2片，下面是5片：
[email protected] SET: 5.494 seconds
[email protected] SET: 0.51 seconds
[email protected] SET: 5.223 seconds
[email protected] SET: 0.518 seconds
下面是10片：
[email protected] SET: 5.9 seconds
[email protected] SET: 0.794 seconds
[email protected] SET: 5.624 seconds
[email protected] SET: 0.762 seconds
下面是100片：
[email protected] SET: 14.055 seconds
[email protected] SET: 8.185 seconds
[email protected] SET: 13.29 seconds
[email protected] SET: 7.767 seconds
分布式中，连接池方式调用不但线程安全外，根据上面的测试数据，也可以看出连接池比直连的效率更好。
十一、完整的测试代码
import java.util.Arrays;
import java.util.List;
import org.junit.AfterClass;
import org.junit.BeforeClass;
import org.junit.Test;
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPoolConfig;
import redis.clients.jedis.JedisShardInfo;
import redis.clients.jedis.Pipeline;
import redis.clients.jedis.ShardedJedis;
import redis.clients.jedis.ShardedJedisPipeline;
import redis.clients.jedis.ShardedJedisPool;
import redis.clients.jedis.Transaction;
import org.junit.FixMethodOrder;
import org.junit.runners.MethodSorters;
@FixMethodOrder(MethodSorters.NAME_ASCENDING)
public class TestJedis {
private static Jedis jedis;
private static ShardedJedis sharding;
private static ShardedJedisPool pool;
@BeforeClass
public static void setUpBeforeClass() throws Exception {
List<JedisShardInfo> shards = Arrays.asList(
new JedisShardInfo(“localhost”,6379),
new JedisShardInfo(“localhost”,6379)); //使用相同的ip:port,仅作测试
jedis = new Jedis(“localhost”);
sharding = new ShardedJedis(shards);
pool = new ShardedJedisPool(new JedisPoolConfig(), shards);
}
@AfterClass
public static void tearDownAfterClass() throws Exception {
jedis.disconnect();
sharding.disconnect();
pool.destroy();
}
@Test
public void test1Normal() {
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”);
}
@Test
public void test2Trans() {
long start = System.currentTimeMillis();
Transaction tx = jedis.multi();
for (int i = 0; i < 100000; i++) {
tx.set(“t” + i, “t” + i);
}
//System.out.println(tx.get(“t1000”).get());
List<Object> results = tx.exec();
long end = System.currentTimeMillis();
System.out.println(“Transaction SET: ” + ((end – start)/1000.0) + ” seconds”);
}
@Test
public void test3Pipelined() {
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());
List<Object> results = pipeline.syncAndReturnAll();
long end = System.currentTimeMillis();
System.out.println(“Pipelined SET: ” + ((end – start)/1000.0) + ” seconds”);
}
@Test
public void test4combPipelineTrans() {
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”);
}
@Test
public void test5shardNormal() {
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(”
[email protected] SET: ” + ((end – start)/1000.0) + ” seconds”);
}
@Test
public void test6shardpipelined() {
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(”
[email protected] SET: ” + ((end – start)/1000.0) + ” seconds”);
}
@Test
public void test7shardSimplePool() {
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(”
[email protected] SET: ” + ((end – start)/1000.0) + ” seconds”);
}
@Test
public void test8shardPipelinedPool() {
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(”
[email protected] SET: ” + ((end – start)/1000.0) + ” seconds”);
}
}
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原创文章，作者：奋斗，如若转载，请注明出处：https://blog.ytso.com/tech/bigdata/9692.html