java实现hbase数据导出

1. HBase-client方式实现

1.1 依赖

 <!--HBase依赖坐标--><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-client</artifactId><version>1.2.6</version></dependency><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-server</artifactId><version>1.2.6</version><exclusions><!--排除依赖：不加入这句会报错--><exclusion><groupId>*</groupId><artifactId>*</artifactId></exclusion></exclusions></dependency>

1.2 配置及代码

1.2.1 get方式

public class HBaseService {private static final Logger logger = LoggerFactory.getLogger(HBaseService.class);/*** 配置文件读取的配置信息*/static Configuration configuration = HBaseConfiguration.create();/*** 链接信息*/private static Connection conn = null;static {try {conn = ConnectionFactory.createConnection(configuration);} catch (IOException e) {e.printStackTrace();}}/*** 进行数据的查询以及写入到文件中（通过get方式查询获得数据并写入文件）* @param rowKey rowKey信息* @param tableName 表名* @param dirName 文件目录* @param fileExist 文件是否存在的标志*/public static void addInfoToFile(String rowKey, String tableName, String dirName, boolean fileExist){Table table = null;ResultScanner result = null;try {Connection connection = ConnectionFactory.createConnection(configuration);table = connection.getTable(TableName.valueOf(tableName));List<Get> gets = new ArrayList<>();Get get = new Get(Bytes.toBytes(rowKey));gets.add(get);// result的集合Result[] resultArr = table.get(gets);Map<String, Map<String,String>> dataMap = new HashMap<>();for (Result r : resultArr) {String rowKey1 = Bytes.toString(r.getRow());Map<String, String> columnDataMap;if (dataMap.containsKey(rowKey1)){columnDataMap = dataMap.get(rowKey1);}else {columnDataMap = new HashMap<>();}for (Cell kv : r.rawCells()) {String qualifire = Bytes.toString(CellUtil.cloneQualifier(kv));String value = Base64Encoder.encode(CellUtil.cloneValue(kv));columnDataMap.put(qualifire, value);dataMap.put(rowKey1, columnDataMap);}}if (MapUtil.isNotEmpty(dataMap)){for (String r : dataMap.keySet()) {Map<String, String> columnMap = dataMap.get(r);StrBuilder lineStr = new StrBuilder();lineStr.append(r + "||");for (String s : columnMap.keySet()) {lineStr.append(s + ":" + columnMap.get(s) + "\t");}String fileName = dirName + File.separator + "data.txt";File f = new File(fileName);if (!f.exists()){try {f.createNewFile();}catch (IOException e){logger.error("创建文件失败,异常信息:{}", e.getMessage());}}BufferedWriter writer = new BufferedWriter(new FileWriter(fileName, true));writer.write(lineStr.toString()  + "\n");logger.info("写入rowkey:{}的波形数据到：{}", r, fileName);writer.close();}}}catch (Exception e){logger.error("写入rowkey:{}的波形数据到：{}失败，错误的信息：{}", rowKey, dirName, e.getMessage());}}
}

1.3.1 Scan方式

   /*** 通过scan的方式进行数据获取* @param rowKey rowkey* @param startKey 开始的rowKey* @param stopKey 结束的rowKey* @param regexStr rowKey的正则匹配表达式*/public static void findRowKey(String rowKey, String startKey, String stopKey, String regexStr){Table table = null;ResultScanner result = null;try {TableName[] tbs = conn.getAdmin().listTableNames();FilterList filters = new FilterList();table = conn.getTable(TableName.valueOf("Vibration_WaveData"));Scan scan = new Scan();// 通过正则匹配的方式+rowkey进行数据过滤RegexStringComparator regexComparator = new RegexStringComparator(regexStr);RowFilter rowFilter = new RowFilter(CompareFilter.CompareOp.EQUAL, regexComparator);// 设置start和stop Rowkey 可以提供检索效率scan.setStartRow(startKey.getBytes());scan.setStopRow(stopKey.getBytes());scan.setFilter(rowFilter);// 每次从服务器端获取的行数scan.setCaching(100000);ResultScanner result1 = table.getScanner(scan);for (Result r : result1) {for (KeyValue kv : r.raw()) {System.out.println(String.format("row:%s, family:%s, qualifier:%s, qualifiervalue:%s, timestamp:%s.",Bytes.toString(kv.getRow()),Bytes.toString(kv.getFamily()),Bytes.toString(kv.getQualifier()),Bytes.toString(kv.getValue()),kv.getTimestamp()));}}result1.close();conn.close();}catch (Exception e){System.out.println(e.getMessage());}}

2. mapReduce实现

2.1 依赖

 <!--hadoop依赖坐标--><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>2.7.6</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-jobclient</artifactId><version>2.7.6</version></dependency><dependency><groupId>commons-cli</groupId><artifactId>commons-cli</artifactId><version>1.2</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>2.7.6</version></dependency>

2.2 配置文件

hbase-site.xml:

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration><property><!-- 指定 hbase 是分布式的 --><name>hbase.cluster.distributed</name><value>true</value></property><property><!-- 指定 zk 的地址，多个用“,”分割 --><name>hbase.zookeeper.quorum</name><value>192.168.1.100:2181,192.168.1.102:2181</value></property><!-- 开启 uber 模式，默认关闭 --><property><name>mapreduce.job.ubertask.enable</name><value>true</value></property><!-- uber 模式中最大的 mapTask 数量，可向下修改 --><property><name>mapreduce.job.ubertask.maxmaps</name><value>9</value></property><!-- uber 模式中最大的 reduce 数量，可向下修改 --><property><name>mapreduce.job.ubertask.maxreduces</name><value>1</value></property><!-- uber 模式中最大的输入数据量，默认使用 dfs.blocksize 的值，可向下修改 --><property><name>mapreduce.job.ubertask.maxbytes</name><value></value></property>
</configuration>

2.3 导出的代码

public class ReadHbaseDataByMRToHDFS {
static Configuration configuration = HBaseConfiguration.create();/*** 进行hbase数据导出的操作* @param tableName 表名* @param dirName   文件夹名称* @param startRow  开始的row key* @param stopRow   结束的row key* @param regexStr  进行匹配的字符*/public void exportHbaseData(String tableName, String dirName, String startRow, String stopRow, String regexStr) {logger.info("开始进行HBase数据导出，tableName:{}, dirName:{}, startRow:{}, stopRow:{}, regexStr:{}", tableName, dirName, startRow, stopRow, regexStr);System.setProperty("HADOOP_USER_NAME", "root");// 一次rpc请求的超时时间，如果某次RPC请求超过该值，客户端就会主动管理Socketconfiguration.set("hbase.rpc.timeout", "600000");// ，该参数是表示HBase客户端发起一次scan操作的rpc调用至得到响应之间总的超时时间configuration.set("hbase.client.scanner.timeout.period", "600000");configuration.set("mapreduce.job.ubertask.maxmaps", "10");configuration.set("mapreduce.job.ubertask.maxreduces", "1");configuration.set("mapreduce.task.io.sort.mb", "1024");configuration.set("mapred.map.tasks", "10");try {Job job = Job.getInstance(configuration);job.setJarByClass(ReadHbaseDataByMRToHDFS.class);//设置reduce个数job.setNumReduceTasks(0);//设置mapScan scan = new Scan();
// 设置start和stop rowkey以及regex提高检索效率RegexStringComparator regexComparator = new RegexStringComparator(regexStr);scan.setStartRow(startRow.getBytes()).setStopRow(stopRow.getBytes());RowFilter rowFilter = new RowFilter(CompareFilter.CompareOp.EQUAL, regexComparator);scan.setFilter(rowFilter);// 每次从服务器端获取的行数scan.setCaching(900000);//参数false,关于添加依赖jarTableMapReduceUtil.initTableMapperJob(tableName,scan,ReadHBaseDataByMRToHDFSMapper.class,Text.class,NullWritable.class,job,false);//输出目录FileOutputFormat.setOutputPath(job, new Path(dirName));//提交boolean isDone = job.waitForCompletion(true);if (isDone){Thread.sleep(3000);logger.info("进行HBase数据导出成功，tableName:{}, dirName:{}, startRow:{}, stopRow:{}, regexStr:{},状态：{}", tableName, dirName, startRow, stopRow, regexStr, isDone);}} catch (Exception e) {logger.error("进行HBase数据导出时出现异常，tableName:{}, dirName:{}, startRow:{}, stopRow:{}, regexStr:{},异常信息：{}",tableName, dirName, startRow, stopRow, regexStr, e.getMessage());}}/*** 参数* ImmutableBytesWritable* Result ：HBase中的数据每次取出来是一个Result：就是一个rowkey做一个result* <p>* keyOut:* valueOut:*/static class ReadHBaseDataByMRToHDFSMapper extends TableMapper<Text, NullWritable> {Text outKey = new Text();@Overrideprotected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {List<Cell> cells = value.listCells();Map<String, Map<String, String>> cellMap = new HashMap<>();//一个cell一条数据 包含一个columnfor (Cell cell : cells) {String rowkey = Bytes.toString(CellUtil.cloneRow(cell));Map<String, String> columnMap = new HashMap<>();if (cellMap.containsKey(rowkey)){columnMap = cellMap.get(rowkey);}// String family = Bytes.toString(CellUtil.cloneFamily(cell));String column = Bytes.toString(CellUtil.cloneQualifier(cell));String columnValue = Base64Encoder.encode(CellUtil.cloneValue(cell));columnMap.put(column, columnValue);cellMap.put(rowkey, columnMap);// long timeStamp = cell.getTimestamp();// outKey.set(rowkey + "\t\t" + column + "\t\t" + columnValue + "\n");}if (CollUtil.isNotEmpty(cellMap)){String lineStr = "";for (String s : cellMap.keySet()) {Map<String, String> columnMap = cellMap.get(s);lineStr = s + "||";for (String c : columnMap.keySet()) {lineStr += c + ":" + columnMap.get(c) + "\t";}}outKey.set(lineStr);context.write(outKey, NullWritable.get());outKey.clear();}}}
}