Hbase Scala代码

Hbase 老版本代码

公共方法

  def getConn() = {
    val conf = HBaseConfiguration.create
    conf.set("hbase.zookeeper.quorum","hadoop1,hadoop2,hadoop3")
    val conn = ConnectionFactory.createConnection(conf)//重量级
    val admin = conn.getAdmin;//轻量级
    (admin,conn)
  }

  val admin = getConn()._1
  val conn = getConn()._2

依赖

        <dependency>
            <groupId>io.netty</groupId>
            <artifactId>netty-all</artifactId>
            <version>4.1.17.Final</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.4.13</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase</artifactId>
            <version>1.4.13</version>
            <type>pom</type>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-core</artifactId>
            <version>0.20.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.4.13</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-common</artifactId>
            <version>1.4.13</version>
        </dependency>
        <dependency>
            <groupId>org.antlr</groupId>
            <artifactId>antlr4-runtime</artifactId>
            <version>4.7</version>
        </dependency>
        <dependency>
            <groupId>commons-cli</groupId>
            <artifactId>commons-cli</artifactId>
            <version>1.4</version>
        </dependency>
        <dependency>
            <groupId>commons-lang</groupId>
            <artifactId>commons-lang</artifactId>
            <version>2.6</version>
        </dependency>

DDL

    import org.apache.hadoop.hbase.{CellUtil, HBaseConfiguration, HColumnDescriptor}
    import org.apache.hadoop.hbase.{HTableDescriptor, NamespaceDescriptor, TableName}
    import org.apache.hadoop.hbase.client.{ ConnectionFactory, Delete, Get, HBaseAdmin}
    import org.apache.hadoop.hbase.client.{Put, Result, ResultScanner, Scan}
    import org.apache.hadoop.hbase.filter.{ FilterList, SingleColumnValueFilter}
    import org.apache.hadoop.hbase.util.Bytes
    import org.apache.spark.sql.SparkSession

    admin.tableExists(TableName.valueOf("bigdata","student"))//表是否存在

    val hTableDescriptor = new HTableDescriptor(TableName.valueOf("bigdata","student"))//创建表
    hTableDescriptor.addFamily(new HColumnDescriptor("info"));
    admin.createTable(hTableDescriptor)

    admin.disableTables("student")//删除表
    admin.deleteTables("student")

    val namesp = NamespaceDescriptor.create("bigdata").build()//创建命名空间
    admin.createNamespace(namesp)

DML

    val addtable = conn.getTable(TableName.valueOf("bigdata","student"))//添加数据
    val addput = new Put(Bytes.toBytes("row_key"))
    addput.addColumn(Bytes.toBytes("info"),Bytes.toBytes("column_name"),Bytes.toBytes("column_value"))
    addtable.put(addput)//可以方法多个put对象
    addtable.close()

    val gettable = conn.getTable(TableName.valueOf("bigdata","student"))//获取数据
    val get = new Get(Bytes.toBytes("row_key"))
    get.setMaxVersions()//设置最大版本
    get.addFamily(Bytes.toBytes("info"))//指定列簇
    get.addColumn(Bytes.toBytes("info"),Bytes.toBytes("name"))//指定列簇+列
    val result = gettable.get(get).rawCells()//可以方法多个put对象
    for(cell <- result){
      Bytes.toString(CellUtil.cloneRow(cell))//row_key
      Bytes.toString(CellUtil.cloneFamily(cell))//列簇
      Bytes.toString(CellUtil.cloneQualifier(cell))//列名
      Bytes.toString(CellUtil.cloneValue(cell)) //value
    }
    gettable.close()

    val scantable = conn.getTable(TableName.valueOf("bigdata","student"))//scan数据
    val resultScanner = scantable.getScanner(new Scan(Bytes.toBytes("1001"),Bytes.toBytes("1003")))
    import scala.collection.JavaConversions._
    for (result <- resultScanner) {//循环结果
      for(cell <- result.rawCells()){//循环每一个cell
        Bytes.toString(CellUtil.cloneRow(cell))//row_key
      }
      println()
    }

    val deletetable = conn.getTable(TableName.valueOf("bigdata","student"))//删除数据
    val delete = new Delete(Bytes.toBytes("row_key"))
    //生产不用addColumn 因为历史版本会出现
    delete.addColumn(Bytes.toBytes("info"),Bytes.toBytes("name"))//找到最大时间戳，给这个版本添加删除标记
    delete.addColumn(Bytes.toBytes("info"),Bytes.toBytes("name"),1563502276059L)//删除这个时间戳的数据，小于的不删除
    delete.addFamily(Bytes.toBytes("info"))//删除列簇
    delete.addColumns(Bytes.toBytes("info"),Bytes.toBytes("name"))//删除所有版本数据 小于等于这个时间戳的
    deletetable.delete(delete)//可以接受Delete数组

Hbase 新版本代码

依赖

        <dependency>
            <groupId>commons-cli</groupId>
            <artifactId>commons-cli</artifactId>
            <version>1.4</version>
        </dependency>
        <dependency>
            <groupId>commons-lang</groupId>
            <artifactId>commons-lang</artifactId>
            <version>2.6</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-core</artifactId>
            <version>0.20.2</version>
        </dependency>
        <dependency>
            <groupId>org.glassfish</groupId>
            <artifactId>javax.el</artifactId>
            <version>3.0.1-b06</version>
        </dependency>
        <dependency>
            <groupId>io.netty</groupId>
            <artifactId>netty-all</artifactId>
            <version>4.1.17.Final</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>2.4.11</version>
            <exclusions>
                <exclusion>
                    <groupId>org.glassfish</groupId>
                    <artifactId>javax.el</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>2.4.11</version>
            <exclusions>
                <exclusion>
                    <groupId>org.glassfish</groupId>
                    <artifactId>javax.el</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-common</artifactId>
            <version>2.4.11</version>
            <exclusions>
                <exclusion>
                    <groupId>org.glassfish</groupId>
                    <artifactId>javax.el</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.antlr</groupId>
            <artifactId>antlr4-runtime</artifactId>
            <version>4.7</version>
        </dependency>

公共方法

  def getConn() = {
    val conf = HBaseConfiguration.create
    conf.set("hbase.zookeeper.quorum","hadoop1,hadoop2,hadoop3")
    val conn = ConnectionFactory.createConnection(conf)//重量级
    val admin = conn.getAdmin;//轻量级
    (admin,conn)
  }
    val admin = getConn()._1
    val conn = getConn()._2

DDL

    admin.tableExists(TableName.valueOf("bigdata","student"))//表是否存在

    //创建表 表的build对象
    val tableCreateBuilder = TableDescriptorBuilder.newBuilder(TableName.valueOf("bigdata","student"))
    val columFanilyCreateBuild = ColumnFamilyDescriptorBuilder.newBuilder(Bytes.toBytes("info"))//列簇build对象
    columFanilyCreateBuild.setMaxVersions(5)
    tableCreateBuilder.setColumnFamily(columFanilyCreateBuild.build())
    admin.createTable(tableCreateBuilder.build())

    //修改表   不修改其他表属性
    val desc = admin.getDescriptor(TableName.valueOf("bigdata","student"))
    val tableModBuilder = TableDescriptorBuilder.newBuilder(desc)
    val famdesc = desc.getColumnFamily(Bytes.toBytes("info"))
    val columFanilyModBuilder = ColumnFamilyDescriptorBuilder.newBuilder(famdesc)//列簇build对象 不修改其他列簇属性
    columFanilyModBuilder.setMaxVersions(5)
    tableModBuilder.setColumnFamily(columFanilyModBuilder.build())
    admin.modifyTable(tableModBuilder.build())

    admin.disableTable(TableName.valueOf("bigdata","student"))//删除表
    admin.deleteTable(TableName.valueOf("bigdata","student"))

    val namesp = NamespaceDescriptor.create("bigdata").build()//创建命名空间
    admin.createNamespace(namesp)

DML

    val addtable = conn.getTable(TableName.valueOf("bigdata","student"))//添加数据
    val addput = new Put(Bytes.toBytes("row_key"))
    addput.addColumn(Bytes.toBytes("info"),Bytes.toBytes("column_name"),Bytes.toBytes("column_value"))
    addtable.put(addput)//可以方法多个put对象
    addtable.close()

    val gettable = conn.getTable(TableName.valueOf("bigdata","student"))//获取数据
    val get = new Get(Bytes.toBytes("row_key"))
    get.readVersions(10)
    get.addFamily(Bytes.toBytes("info"))//指定列簇
    get.addColumn(Bytes.toBytes("info"),Bytes.toBytes("name"))//指定列簇+列
    val result = gettable.get(get).rawCells()//可以方法多个put对象
    for(cell <- result){
      Bytes.toString(CellUtil.cloneRow(cell))//row_key
      Bytes.toString(CellUtil.cloneFamily(cell))//列簇
      Bytes.toString(CellUtil.cloneQualifier(cell))//列名
      Bytes.toString(CellUtil.cloneValue(cell)) //value
    }
    gettable.close()

    val scantable = conn.getTable(TableName.valueOf("bigdata","student"))//scan数据
    val scan = new Scan
    val filterList = new FilterList()
    //new RowFilter(CompareFilter.CompareOp.LESS_OR_EQUAL, new BinaryComparator(Bytes.toBytes("0003")));  rowkey过滤器
    //new PrefixFilter("00".getBytes());  rowkey过滤器ColumnValueFilter
    //new FamilyFilter(CompareFilter.CompareOp.LESS, new SubstringComparator("f2"));  列簇过滤器
    //new QualifierFilter(CompareFilter.CompareOp.EQUAL, new SubstringComparator("name"));  列过滤器
    //new ColumnPrefixFilter(prefix);根据列（又名限定符）名称的前导部分进行过滤
    //new MultipleColumnPrefixFilter(prefixes);byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")}; 多个前缀过滤
    //new ColumnRangeFilter(startColumn, true, endColumn, true); 列区间过滤
    //new ValueFilter(CompareFilter.CompareOp.EQUAL, new SubstringComparator("8"));  值过滤器
    //new ColumnValueFilter(CompareFilter.CompareOp.EQUAL, new SubstringComparator("8"));  只匹配获取的单元格
    //new SingleColumnValueFilter("f1".getBytes(), "name".getBytes(), CompareFilter.CompareOp.EQUAL, "刘备".getBytes());  满足条件的整列值的所有字段
    //new SingleColumnValueExcludeFilter("f1".getBytes(), "name".getBytes(), CompareFilter.CompareOp.EQUAL, "刘备".getBytes());  排除满足条件的整列值的所有字段
    //new PageFilter(pageSize);  分页过滤
    //comp=new RegexStringComparator("my.")值比较的正则表达式   new SingleColumnValueFilter("f1".getBytes(), "name".getBytes(), CompareFilter.CompareOp.EQUAL,comp);
    //comp=new SubstringComparator("y val");字串包含   new SingleColumnValueFilter("f1".getBytes(), "name".getBytes(), CompareFilter.CompareOp.EQUAL,comp);



    val nameFilter = new ColumnValueFilter(Bytes.toBytes("info"),Bytes.toBytes("name"),CompareOperator.EQUAL,Bytes.toBytes("zhangsan"))
    filterList.addFilter(nameFilter)
    //保留没有当前列的数据
    val singleFilter = new SingleColumnValueFilter(Bytes.toBytes("info"),Bytes.toBytes("name"),CompareOperator.EQUAL,Bytes.toBytes("zhangsan"))
    filterList.addFilter(singleFilter)
    scan.setFilter(filterList)
    scan.withStartRow(Bytes.toBytes("1001"))
    scan.withStopRow(Bytes.toBytes("1003"))
    val resultScanner = scantable.getScanner(scan)
    import scala.collection.JavaConversions._
    for (result <- resultScanner) {//循环结果
      for(cell <- result.rawCells()){//循环每一个cell
        Bytes.toString(CellUtil.cloneRow(cell))//row_key
      }
      println()
    }

    val deletetable = conn.getTable(TableName.valueOf("bigdata","student"))//删除数据
    val delete = new Delete(Bytes.toBytes("row_key"))
    //生产不用addColumn 因为历史版本会出现
    delete.addColumn(Bytes.toBytes("info"),Bytes.toBytes("name"))//找到最大时间戳，给这个版本添加删除标记
    delete.addColumn(Bytes.toBytes("info"),Bytes.toBytes("name"),1563502276059L)//删除这个时间戳的数据，小于的不删除
    delete.addFamily(Bytes.toBytes("info"))//删除列簇
    delete.addColumns(Bytes.toBytes("info"),Bytes.toBytes("name"))//删除所有版本数据 小于等于这个时间戳的
    deletetable.delete(delete)//可以接受Delete数组

相关阅读:
Spark_3：Spark集群搭建
 Spark_2：Spark 快速入门教程
 Spark快速大数据分析_11：第十一章
 Spark快速大数据分析_10：第十章
 Spark快速大数据分析_9：第九章
 Spark快速大数据分析_8：第八章
 Spark快速大数据分析_7：第七章
 Spark快速大数据分析_6：第六章
 Spark快速大数据分析_5：第五章
 java 内部类详解
原文地址：https://www.cnblogs.com/wuxiaolong4/p/16731678.html

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