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();