Cassandra的数据分布情况测试

1、规划种子节点ip和Token值的对应

 

4个种子节点ip

	192.168.0.231
	192.168.0.232
	192.168.0.233
	192.168.0.234

	进入python，计算Token
	#192.168.0.231对应的Token为
	>>> print 2 ** 127 / 4 * 1 
	42535295865117307932921825928971026432

	#192.168.0.232对应的Token为
	>>> print 2 ** 127 / 4 * 2
	85070591730234615865843651857942052864

	#192.168.0.233对应的Token为
	>>> print 2 ** 127 / 4 * 3
	127605887595351923798765477786913079296

	#192.168.0.234对应的Token为
	>>> print 2 ** 127 / 4 * 4
	170141183460469231731687303715884105728

 

 

或者

import java.math.BigInteger;

public class InitToken {
	public static void main(String[] args) {
		int nodes = 4;//节点的总数量
		for (int i = 1; i <= nodes; i++) {
			BigInteger hs = new BigInteger("2");
			BigInteger res = hs.pow(127);
			BigInteger div = res.divide(new BigInteger(nodes + ""));
			BigInteger fin = div.multiply(new BigInteger(i + ""));
			System.out.println(fin);
		}

	}

}

 

 

 

2、配置cassandra的每个节点

      a、cassandra/bin/cassandra.in.sh配置本机的jdk安装路径

JAVA_HOME=/usr/local/jdk6

 

      b、cassandra/conf/cassandra.yaml

                cluster_name: 'ots'
		commitlog_directory: cassandra/data/commitlog
		saved_caches_directory: cassandra/data/saved_caches
		data_file_directories:
    			- cassandra/data/data
		#配置种子节点ip列表
		- seeds: "192.168.0.231,192.168.0.232,192.168.0.233,192.168.0.234"
		#上面的配置基本保持一致

		#当前节点的ip（这个ip主要是用来节点和节点之间通讯的ip）
		listen_address: 192.168.0.231
		#当前节点的ip（这个ip主要是用来相应客户端操作的ip）
		rpc_address: 192.168.0.231

 

3、启动每个节点的cassandra

nohup cassandra/bin/cassandra -f &

 

4、设置每个节点的Token值

    进入每个节点，把启动时默认生成的Token值改变为我们规划的Token值
    或者直接在配置文件cassandra.yaml中指定Token值来规划，就不用下面的动态规划了。
    如下:

          ./bin/nodetool -h 192.168.0.231 -p 7199 move 42535295865117307932921825928971026432
	 ./bin/nodetool -h 192.168.0.232 -p 7199 move 42535295865117307932921825928971026432
	。。。。。。。。。。。。。。。。。。。

 5、初始化数据存储结构

        客户端连接到集群中的某一个节点，初始化数据结构。运行初始化脚本,如下

 

./bin/cassandra-cli -h 192.168.0.231 -p 9160

 参考json数据结构模型

{
            "key":{
                     "name":"140 bytes",
                     "cardno":"140 bytes",
                     "ticketno":"140 bytes",
                     "traindate":"140 bytes",
                    "startstation":"140 bytes",
                     "endtstation":"140 bytes",
                     "seatinfo":"140 bytes",
                   }
        }

 

        具体脚本：

                create keyspace user_train;
		use user_train;
		create column family users with comparator=UTF8Type 
		and column_metadata=[{column_name:name,validation_class:UTF8Type,index_type:KEYS},
		{column_name:cardno,validation_class:UTF8Type,index_type:KEYS},
		{column_name:ticketno,validation_class:UTF8Type,index_type:KEYS},
		{column_name:traindate,validation_class:UTF8Type},
		{column_name:startstation,validation_class:UTF8Type},
		{column_name:endtstation,validation_class:UTF8Type},
		{column_name:seatinfo,validation_class:UTF8Type}];

		#分发策略，主要是将存放到一个节点的一份数据，分发到另一个节点一份，节点的选取由Cassandra和配置文件决定。
		update keyspace user_train with strategy_options = {datacenter1:2};

 

5、使用hector客户端api操作cassandra数据库，观察数据分布

 

插入数据的时候观察每个节点的数据分布是否均衡。
    首先需要登录每个节点，不停的使用如下命令，刷新数据的分布情况。

./bin/nodetool -h 192.168.0.232 -p 7199 ring 

 

    写入数据代码

Cluster cluster = HFactory
                .getOrCreateCluster("ots",
                        "192.168.0.231:9160," +
                        "192.168.0.232:9160," +
                        "192.168.0.233:9160," +
                        "192.168.0.234:9160");

        Keyspace keyspace = HFactory.createKeyspace("user_train", cluster);

        String ktest = "";
        for (int i = 0; i < 120; i++) {
            ktest += "x";
        }
        try {
            Mutator<String> mutator = HFactory.createMutator(keyspace,
                    stringSerializer);
            long startTime = System.currentTimeMillis();
            for (int i = 0; i < 10000*10; i++) {
                mutator.addInsertion("username" + i, "users",
                        HFactory.createStringColumn("name", ktest+"username" + i))
                        .addInsertion(
                                "username" + i,
                                "users",
                                HFactory.createStringColumn("cardno", ktest+"cardno"
                                        + i))
                        .addInsertion(
                                "username" + i,
                                "users",
                                HFactory.createStringColumn("ticketno",
                                        ktest+"ticketno" + i))
                        .addInsertion(
                                "username" + i,
                                "users",
                                HFactory.createStringColumn("traindate",
                                        ktest+"traindate" + i))
                        .addInsertion(
                                "username" + i,
                                "users",
                                HFactory.createStringColumn("startstation",
                                        ktest+"startstation" + i))
                        .addInsertion(
                                "username" + i,
                                "users",
                                HFactory.createStringColumn("endtstation",
                                        ktest+"endtstation" + i))
                        .addInsertion(
                                "username" + i,
                                "users",
                                HFactory.createStringColumn("seatinfo",
                                        ktest+"seatinfo" + i));
                if (i % 500 == 0) {
                    mutator.execute();
                    System.out.println(i);
                }
            }
            mutator.execute();
            System.out.println("insert time: " + (System.currentTimeMillis() - startTime));
        } catch (HectorException he) {
            he.printStackTrace();
        }
        cluster.getConnectionManager().shutdown();