下载镜像：dingms/ucas-bdms-hw-u64-2019
创建一个新的容器（Desktop启动会报错，一定使用命令启动！）：
docker run -itd <Image Id>
回显容器编号:<CONTAINER ID>，此时容器已经完成创建。
进入容器中启动bash
docker exec -it <CONTAINER ID> /bin/bash
PS:此模块在容器停止后每次启动都要执行。
重新加载SSH模块：
root@<CONTAINER ID>:/# service ssh stop
* Stopping OpenBSD Secure Shell server sshd
start-stop-daemon: warning: failed to kill 4008: No such process
[ OK ]
root@<CONTAINER ID>:/# service ssh start
* Starting OpenBSD Secure Shell server sshd [ OK ]
启动HDFS模块：
root@<CONTAINER ID>:/# start-dfs.sh
Starting namenodes on [localhost]
localhost: starting namenode, logging to /home/bdms/setup/hadoop-2.9.2/logs/hadoop-root-namenode-2ce55076d336.out
localhost: starting datanode, logging to /home/bdms/setup/hadoop-2.9.2/logs/hadoop-root-datanode-2ce55076d336.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /home/bdms/setup/hadoop-2.9.2/logs/hadoop-root-secondarynamenode-2ce55076d336.out
启动HBase模块：
root@<CONTAINER ID>:~# start-hbase.sh
localhost: starting zookeeper, logging to /home/bdms/setup/hbase-0.98.11-hadoop2/bin/../logs/hbase-root-zookeeper-<CONTAINER ID>.out
starting master, logging to /home/bdms/setup/hbase-0.98.11-hadoop2//logs/hbase--master-<CONTAINER ID>.out
localhost: starting regionserver, logging to /home/bdms/setup/hbase-0.98.11-hadoop2/bin/../logs/hbase-root-regionserver-<CONTAINER ID>.out
确认HDFS和HBase可以使用：
root@<CONTAINER ID>:/# jps
1377 Jps
194 NameNode
949 HQuorumPeer
1158 HRegionServer
1015 HMaster
522 SecondaryNameNode
300 DataNode
root@<CONTAINER ID>:/# hadoop fs -ls /
Found 1 items
drwxr-xr-x - root supergroup 0 2019-03-15 10:03 /hbase
root@<CONTAINER ID>:~# hbase shell
2023-03-22 13:36:18,592 INFO [main] Configuration.deprecation: hadoop.native.lib is deprecated. Instead, use io.native.lib.available
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 0.98.11-hadoop2, r6e6cf74c1161035545d95921816121eb3a516fe0, Tue Mar 3 00:23:49 PST 2015
hbase(main):002:0> exit
实验一数据路径存放在/home/bdms/homework/hw1/input中
root@<CONTAINER ID>:/# cd home/bdms/homework/hw1/input
root@<CONTAINER ID>:/home/bdms/homework/hw1/input# ls
customer.tbl nation.tbl part.tbl region.tbl
lineitem.tbl orders.tbl partsupp.tbl supplier.tbl
在hadoop中创建目录hw1
root@<CONTAINER ID>:/home/bdms/homework/hw1/input# hadoop fs -mkdir /hw1
将实验数据存入HDFS中，并验证是否存入成功
root@<CONTAINER ID>:/home/bdms/homework/hw1/input# hadoop fs -put *.tbl /hw1
root@<CONTAINER ID>:/home/bdms/homework/hw1/input# hadoop fs -ls -R /hw1
-rw-r--r-- 1 root supergroup 240990 2023-03-16 10:17 /hw1/customer.tbl
-rw-r--r-- 1 root supergroup 7264250 2023-03-16 10:17 /hw1/lineitem.tbl
-rw-r--r-- 1 root supergroup 2224 2023-03-16 10:17 /hw1/nation.tbl
-rw-r--r-- 1 root supergroup 1659137 2023-03-16 10:17 /hw1/orders.tbl
-rw-r--r-- 1 root supergroup 238074 2023-03-16 10:17 /hw1/part.tbl
-rw-r--r-- 1 root supergroup 1161705 2023-03-16 10:17 /hw1/partsupp.tbl
-rw-r--r-- 1 root supergroup 389 2023-03-16 10:17 /hw1/region.tbl
-rw-r--r-- 1 root supergroup 13795 2023-03-16 10:17 /hw1/supplier.tbl
使用测试代码HDFSTest.java尝试打开某一个数据
root@<CONTAINER ID>:/home/bdms/homework/hw1/input# cd ../example/
root@<CONTAINER ID>:/home/bdms/homework/hw1/example# javac HDFSTest.java
root@<CONTAINER ID>:/home/bdms/homework/hw1/example# java HDFSTest hdfs://localhost:9000/hw1/customer.tbl
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/bdms/setup/hadoop-2.9.2/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/bdms/setup/hbase-0.98.11-hadoop2/lib/slf4j-log4j12-1.6.4.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
23/03/16 10:21:23 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
1|Customer#000000001|IVhzIApeRb ot,c,E|15|25-989-741-2988|711.56|BUILDING|to the even, regular platelets. regular, ironic epitaphs nag e|
2|Customer#000000002|XSTf4,NCwDVaWNe6tEgvwfmRchLXak|13|23-768-687-3665|121.65|AUTOMOBILE|l accounts. blithely ironic theodolites integrate boldly: caref|
......
1500|Customer#000001500|4zaoUzuWUTNFiNPbmu43|5|15-200-872-4790|6910.79|MACHINERY|s boost blithely above the fluffily ironic dolphins! ironic accounts|
R=<file 1> S=<file 2> join:R2=S3 res:R4,S5
对位置参数args解析目标变量如下：
args[0]:R=/hw1/a.tbl --------> /*目标：File_R_Uri = "/hw1/a.tbl"*/
args[1]:S=/hw1/b.tbl --------> /*目标：File_S_Uri = "/hw1/a.tbl"*/
args[2]:join:R2=S3 --------> /*目标：JoinKeyForR = 2,JoinKeyForS = 3*/
args[3]:res:R4,S5 --------> /*目标：ResList = [R4,S5]*/
进行参数处理如下：
public void ParamProcessing(String[] args){
File_R_Uri = args[0].split("=")[1];
File_S_Uri = args[1].split("=")[1];
JoinKeyForR = Integer.parseInt(args[2].split(":")[1].split("=")[0].substring(1));
JoinKeyForS = Integer.parseInt(args[2].split(":")[1].split("=")[1].substring(1));
resList = Arrays.asList(args[3].split(":")[1].split(","));
}
基本使用baseline代码即可，最终将数据读取为List<String>的形式。
public List<String> readHDFSTable(String file_Uri) throws IOException, URISyntaxException {
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(URI.create(HDFSUri + file_Uri), conf);
Path path = new Path(HDFSUri + file_Uri);
FSDataInputStream in_stream = fs.open(path);
BufferedReader in = new BufferedReader(new InputStreamReader(in_stream));
String line = null;
List<String> strings = new ArrayList<>();
while ((line = in.readLine()) != null) {
strings.add(line);
}
in.close();
fs.close();
return strings;
}
而后将List<string>变为hashtable
public Hashtable<String,List<String[]>> HDFSToHashTable(List<String> lines){
Hashtable<String, List<String[]>> stringListHashtable = new Hashtable<String, List<String[]>>();
for (String line : lines){
String[] split = line.split("\\|");
stringListHashtable.put(split[joinKeyForR], Collections.singletonList(split));
}
return stringListHashtable;
}
使用customer.tbl试运行的中间结果如下；
root@<CONTAINER ID>:~# java Hw1Grp0 R=/hw1/customer.tbl S=/hw1/customer.tbl join:R2=S3 res:R4,S5
{
kbYrf d uR=[[Ljava.lang.String;@2a640157],
naLuK8XKUP72msE0e=[[Ljava.lang.String;@52851b44],
e53JADEeGvM1ikhN7aa=[[Ljava.lang.String;@584f54e6],
5J941XxxkE=[[Ljava.lang.String;@5d8bafa9],
...,
GLZCUQrtiNTrPKdK 0O86ZF=[[Ljava.lang.String;@2755d705]
}
这里可能会出现的问题：如果需要做hashjoin的key存在一个key对应多个value的情况该如何保存？
解决方法，通过对key判定是否存值分类讨论。
public Hashtable<String,List<String[]>> HDFSToHashTable(List<String> lines){
Hashtable<String, List<String[]>> stringListHashtable = new Hashtable<String, List<String[]>>();
for (String line : lines){
String[] split = line.split("\\|");
/*使用分类讨论方式代替直接寻值覆盖*/
if (stringListHashtable.get(split[joinKeyForR])!=null){
List<String[]> strings = stringListHashtable.get(split[joinKeyForR]);
strings.add(split);
stringListHashtable.put(split[joinKeyForR],strings);
}
else {
ArrayList<String[]> strings = new ArrayList<>();
strings.add(split);
stringListHashtable.put(split[joinKeyForR],strings);
}
}
return stringListHashtable;
}
此时结果：
root@<CONTAINER ID>:~# java Hw1Grp0 R=/hw1/customer.tbl S=/hw1/customer.tbl join:R3=S3 res:R4,S5
{
19=[[Ljava.lang.String;@2a640157,..., [Ljava.lang.String;@10ded6a9],
18=[[Ljava.lang.String;@c5dc4a2,..., [Ljava.lang.String;@5911e990],
17=[[Ljava.lang.String;@31000e60,..., [Ljava.lang.String;@49872d67],
...,
20=[[Ljava.lang.String;@770d0ea6,..., [Ljava.lang.String;@2755d705]
}
/*此时，一个key中可对应保存多个value值*/
使用已经对表R生成的HashTable和已经完成表S分词的String[]进行hashJoin连接
List<String> stringsForR = hw1Grp0.readHDFSTable(file_R_Uri);
Hashtable<String, List<String[]>> stringListHashtable = hw1Grp0.HDFSToHashTable(stringsForR);
List<String> stringsForS = hw1Grp0.readHDFSTable(file_S_Uri);
hw1Grp0.hashJoin(stringListHashtable,stringsForS);
如何实现：
public void hashJoin(Hashtable<String, List<String[]>> R,List<String> strings_S){
//创建投影集合，确保在HashJoin后可以准确投影
ArrayList<Integer> projectionForR = new ArrayList<>();
ArrayList<Integer> projectionForS = new ArrayList<>();
for (String res : resList){
if (res.charAt(0) == 'R'){
projectionForR.add(Integer.valueOf(res.substring(1)));
}
if (res.charAt(0) == 'S'){
projectionForS.add(Integer.valueOf(res.substring(1)));
}
}
//对表S中每一条记录进行Hash查询，找到可以进行HashJoin的表R中的所有记录，对每一条记录进行hashJoin
for (String line : strings_S){
String[] split = line.split("\\|");
if (R.get(split[joinKeyForS])!=null){
List<String[]> strings = R.get(split[joinKeyForS]);
for (String[] record : strings){
/*hashJoinForOneRecord方法完成两条记录连接和投影*/
hashJoinForOneRecord(record,split,projectionForR,projectionForS);
}
}
}
}
使用数据集进行结果验证：
root@<CONTAINER ID>:~# java Hw1Grp0 R=/hw1/nation.tbl S=/hw1/customer.tbl join:R2=S3 res:R1,S4,S5
-join key=1,R1=ARGENTINA,S4=11-719-748-3364,S5=7498.12
-join key=1,R1=BRAZIL,S4=11-719-748-3364,S5=7498.12
-join key=1,R1=CANADA,S4=11-719-748-3364,S5=7498.12
-join key=1,R1=PERU,S4=11-719-748-3364,S5=7498.12
-join key=1,R1=UNITED STATES,S4=11-719-748-3364,S5=7498.12
-join key=4,R1=EGYPT,S4=14-128-190-5944,S5=2866.83
...
-join key=3,R1=UNITED KINGDOM,S4=13-802-978-9538,S5=-496.49
-join key=3,R1=FRANCE,S4=13-273-527-9609,S5=9128.69
-join key=3,R1=GERMANY,S4=13-273-527-9609,S5=9128.69
-join key=3,R1=ROMANIA,S4=13-273-527-9609,S5=9128.69
-join key=3,R1=RUSSIA,S4=13-273-527-9609,S5=9128.69
-join key=3,R1=UNITED KINGDOM,S4=13-273-527-9609,S5=9128.69
total 1615
public Configuration HBaseConfiguration() throws MasterNotRunningException, IOException{
HTableDescriptor hTableDescriptor = new HTableDescriptor(TableName.valueOf(tableName));
HColumnDescriptor res = new HColumnDescriptor(column_family);
hTableDescriptor.addFamily(res);
Configuration configuration = HBaseConfiguration.create();
HBaseAdmin hAdmin = new HBaseAdmin(configuration);
//此处保证如果HBase中存入同名表进行替换
if (hAdmin.tableExists(tableName)) {
hAdmin.disableTable(tableName);
hAdmin.deleteTable(tableName);
hAdmin.createTable(hTableDescriptor);
System.out.println("table "+tableName+ " update successfully");
}
else {
hAdmin.createTable(hTableDescriptor);
System.out.println("table "+tableName+ " created successfully");
}
hAdmin.close();
return configuration;
}
/*对hashJoin部分两条记录连接投影由输出改为存表*/
public void hashJoinForOneRecord(String[] recordForR,String[] recordForS,ArrayList<Integer> projectionForR,ArrayList<Integer> projectionForS,HTable table) throws NullPointerException,IOException {
joinKeyCountMap.putIfAbsent(recordForR[joinKeyForR], 0);
Put put = new Put(recordForR[joinKeyForR].getBytes());
for (Integer res_R:projectionForR){
put.add(column_family.getBytes(),
joinKeyCountMap.get(recordForR[joinKeyForR])!=0?("R"+res_R+"."+joinKeyCountMap.get(recordForR[joinKeyForR])).getBytes():("R"+res_R).getBytes(),
recordForR[res_R].getBytes());
}
for (Integer res_S:projectionForS){
put.add(column_family.getBytes(),
joinKeyCountMap.get(recordForR[joinKeyForR])!=0?("S"+res_S+"."+joinKeyCountMap.get(recordForR[joinKeyForR])).getBytes():("S"+res_S).getBytes(),
recordForS[res_S].getBytes());
}
table.put(put);
joinKeyCountMap.put(recordForR[joinKeyForR],joinKeyCountMap.get(recordForR[joinKeyForR]) + 1);
}
在对两条记录根据JoinKey做连接投影的过程中，首先要确定本次连接属于该JoinKey的第几次连接次数，为了避免在HBase中产生覆盖。
采用三元式根据该key是否是第一次Join表示对非0次key做角标标注。
hbase(main):001:0> scan 'Result'
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/bdms/setup/hbase-0.98.11-hadoop2/lib/slf4j-log4j12-1.6.4.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/bdms/setup/hadoop-2.9.2/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
ROW COLUMN+CELL
0 column=res:R1, timestamp=1679544959750, value=ALGERIA
0 column=res:R1.1, timestamp=1679544959753, value=ETHIOPIA
0 column=res:R1.10, timestamp=1679544959892, value=ALGERIA
0 column=res:R1.100, timestamp=1679544961697, value=ALGERIA
0 column=res:R1.101, timestamp=1679544961699, value=ETHIOPIA
0 column=res:R1.102, timestamp=1679544961703, value=KENYA
0 column=res:R1.103, timestamp=1679544961705, value=MOROCCO
0 column=res:R1.104, timestamp=1679544961708, value=MOZAMBIQUE
...
4 column=res:S5.91, timestamp=1679544960976, value=7760.52
4 column=res:S5.92, timestamp=1679544960978, value=7760.52
4 column=res:S5.93, timestamp=1679544960981, value=7760.52
4 column=res:S5.94, timestamp=1679544960983, value=7760.52
4 column=res:S5.95, timestamp=1679544961046, value=3001.94
4 column=res:S5.96, timestamp=1679544961049, value=3001.94
4 column=res:S5.97, timestamp=1679544961053, value=3001.94
4 column=res:S5.98, timestamp=1679544961056, value=3001.94
4 column=res:S5.99, timestamp=1679544961058, value=3001.94
5 row(s) in 1.9070 seconds
total 4845
通过结果集判定，HashJoin共输出1615条记录，在HBase中每条记录存储三个column key，共1615*3=4845条记录，数据吻合。准备提交测试。
根据自动化机制README.md进行环境准备配置。
特别注意：执行 $ ./myprepare 之前需要确保HDFS系统中已经创建好 /hw1 文件夹
如果报错需要进行下面操作：
root@<CONTAINER ID>:~/hw1-check# hadoop fs -mkdir /hw1
执行自动化测试结果如下：
root@<CONTAINER ID>:~/hw1-check# ./run-test.pl ./score ../0_202228013329016_hw1.java
------------------------------------------------------------
[Fri Mar 24 06:07:19 UTC 2023] 1 ../0_202228013329016_hw1.java
------------------------------------------------------------
rm -rf sandbox
mkdir sandbox
cp ../0_202228013329016_hw1.java sandbox/Hw1Grp0.java
------------------------------------------------------------
Compile
------------------------------------------------------------
cd sandbox; javac Hw1Grp0.java 2>&1; cd ..
------------------------------------------------------------
Run R=/hw1/join_0R.tbl S=/hw1/join_0S.tbl join:R0=S0 res:R1,S1
------------------------------------------------------------
hbase shell ./hbase-drop-result >/dev/null 2>&1
cd sandbox; java Hw1Grp0 R=/hw1/join_0R.tbl S=/hw1/join_0S.tbl join:R0=S0 res:R1,S1 >0.out 2>&1; cd ..
cd bld; java HBase2TableAll Result rowkey res:R1 res:S1 > r.tbl; cd ..
...
mv bld/r.tbl sandbox/0.tbl
------------------------------------------------------------
Run R=/hw1/join_1R.tbl S=/hw1/join_1S.tbl join:R0=S0 res:R1,S1
------------------------------------------------------------
hbase shell ./hbase-drop-result >/dev/null 2>&1
cd sandbox; java Hw1Grp0 R=/hw1/join_1R.tbl S=/hw1/join_1S.tbl join:R0=S0 res:R1,S1 >1.out 2>&1; cd ..
cd bld; java HBase2TableAll Result rowkey res:R1 res:S1 > r.tbl; cd ..
...
mv bld/r.tbl sandbox/1.tbl
------------------------------------------------------------
Run R=/hw1/join_2R.tbl S=/hw1/join_2S.tbl join:R0=S0 res:R1,S1
------------------------------------------------------------
hbase shell ./hbase-drop-result >/dev/null 2>&1
cd sandbox; java Hw1Grp0 R=/hw1/join_2R.tbl S=/hw1/join_2S.tbl join:R0=S0 res:R1,S1 >2.out 2>&1; cd ..
cd bld; java HBase2TableAll Result rowkey res:R1 res:S1 > r.tbl; cd ..
...
mv bld/r.tbl sandbox/2.tbl
------------------------------------------------------------
checkResult sandbox/0.tbl result-sorted/join_0res.tbl
------------------------------------------------------------
sort sandbox/0.tbl > sandbox/tmp.tbl
diff sandbox/tmp.tbl result-sorted/join_0res.tbl >sandbox/0.tbl.diff 2>&1
good
------------------------------------------------------------
checkResult sandbox/1.tbl result-sorted/join_1res.tbl
------------------------------------------------------------
sort sandbox/1.tbl > sandbox/tmp.tbl
diff sandbox/tmp.tbl result-sorted/join_1res.tbl >sandbox/1.tbl.diff 2>&1
good
------------------------------------------------------------
checkResult sandbox/2.tbl result-sorted/join_2res.tbl
------------------------------------------------------------
sort sandbox/2.tbl > sandbox/tmp.tbl
diff sandbox/tmp.tbl result-sorted/join_2res.tbl >sandbox/2.tbl.diff 2>&1
good
rm -rf ../0_202228013329016_hw1; mv sandbox ../0_202228013329016_hw1
查看分数：
root@711673ba01b7:~/hw1-check# cat score
../0_202228013329016_hw1.java raw score: 3
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文章浏览阅读894次。Q: SylixOS 版权是什么形式, 是否分为<开发版税>和<运行时版税>.A: SylixOS 是开源并免费的操作系统, 支持 BSD/GPL 协议(GPL 版本暂未确定). 没有任何的运行时版税. 您可以用她来做任何 您喜欢做的项目. 也可以修改 SylixOS 的源代码, 不需要支付任何费用. 当然笔者希望您可以将使用 SylixOS 开发的项目 (不需要开源)或对 SylixOS 源码的修改及时告知笔者.需要指出: SylixOS 本身仅是笔者用来提升自己水平而开发的_select函数 导致堆栈溢出 sylixos