Cassandra的数据分布景况测试

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

192.168.0.231192.168.0.232192.168.0.233192.168.0.234进入python，计算Token#192.168.0.231对应的Token为&gt;&gt;&gt; print 2 ** 127 / 4 * 1 42535295865117307932921825928971026432#192.168.0.232对应的Token为&gt;&gt;&gt; print 2 ** 127 / 4 * 285070591730234615865843651857942052864#192.168.0.233对应的Token为&gt;&gt;&gt; print 2 ** 127 / 4 * 3127605887595351923798765477786913079296#192.168.0.234对应的Token为&gt;&gt;&gt; print 2 ** 127 / 4 * 4170141183460469231731687303715884105728

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 &lt;= 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/commitlogsaved_caches_directory: cassandra/data/saved_cachesdata_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 &lt; 120; i++) {            ktest += "x";        }        try {            Mutator&lt;String&gt; mutator = HFactory.createMutator(keyspace,                    stringSerializer);            long startTime = System.currentTimeMillis();            for (int i = 0; i &lt; 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();

Cassandra的数据分布景况测试

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