记一次Hbase的行键过滤器事故问题

数据总数：746条数据

因为后面需要进行算法合成，而且spark目前对这种算法支持并不好，因此采用代码编写，所以在查询hbase的过程中采用的是java直接查询，

但是为了加快查询速度，我尽可能的使用了过滤器

1：初期Hbase的rowkey组合：时间+"_"+订单id

查询思路：

1：能快速检索，减少GC，采用过滤器

2：支持时间段查询

根据上面两点，我采用时间过滤，比如：startTime=201904010000  endTime=201904180000|；【注意这个符号：“|” 】然后根据行键过滤器

CompareFilter.CompareOp.GREATER_OR_EQUAL和

CompareFilter.CompareOp.LESS_OR_EQUAL进行大小对比

使用代码在查询的时候，添加了行键过滤器

 FilterList filterList=new FilterList();
            //time+id
            if(startTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL,
                        new BinaryComparator(Bytes.toBytes(startTime)));
                filterList.addFilter(rf);
            }
            if(endTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.LESS_OR_EQUAL,
                        new BinaryComparator(Bytes.toBytes(endTime)));
                filterList.addFilter(rf);
            }
            scan.setFilter(filterList);

完整代码：

 /**
     * 行键过滤器
     * */
    public static List<Map<String , String>> rowFilter(String tableName , String startTime , String endTime){
        Connection connection = null;
        Scan scan = new Scan();
        scan.setCacheBlocks(false);
        ResultScanner rs = null;
        Table table = null;
        List<Map<String , String>> list = new ArrayList<Map<String , String>>();
        try{
            connection = ConnectionFactory.createConnection(config);
            table = connection.getTable(TableName.valueOf(tableName));
            FilterList filterList=new FilterList();
            //time+id
            if(startTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL,
                        new BinaryComparator(Bytes.toBytes(startTime)));
                filterList.addFilter(rf);
            }
            if(endTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.LESS_OR_EQUAL,
                        new BinaryComparator(Bytes.toBytes(endTime)));
                filterList.addFilter(rf);
            }
            scan.setFilter(filterList);
            rs = table.getScanner(scan);
            for (Result r : rs) {
                Map<String , String> map = new HashMap<String , String>();
                for (Cell cell : r.listCells()) {
                    map.put(Bytes.toString(cell.getQualifierArray(), cell.getQualifierOffset(), cell.getQualifierLength())
                            , Bytes.toString(cell.getValueArray(), cell.getValueOffset(), cell.getValueLength()));
                }
                list.add(map);
            }
        }catch (Exception e){
            e.printStackTrace();
        }finally {
            if (null != rs) {
                rs.close();
            }
            try {
                if (null != table) {
                    table.close();
                }
                if (null != connection && !connection.isClosed()) {
                    System.out.println("scan Result is closed");
                    connection.close();
                }
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
        return list;



    }

那么这种方案查询后返回的结果是：361条数据！ 实际Hbase测试表中有746条数据，那么可以肯定，是行键过滤器出错了（后面再研究为啥出错）

改善：

更改rowkey结构，采用：订单id+"_"+time来实现

然后过滤器代码改善：

 FilterList filterList=new FilterList();
            //id+time
            if(startTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL,
                        new RegexStringComparator(".*_"+startTime));
                filterList.addFilter(rf);
            }
            if(endTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.LESS_OR_EQUAL,
                        new RegexStringComparator(".*_"+endTime));
                filterList.addFilter(rf);
            }
            scan.setFilter(filterList);

上面其实就是采用正则表达式进行后缀识别，这样我就可以根据后缀进行时间过滤

完整代码：

    /**
     * 行键过滤器
     * */
    public static List<Map<String , String>> rowEndFilter(String tableName , String startTime , String endTime){
        Connection connection = null;
        Scan scan = new Scan();
        scan.setCacheBlocks(false);
        ResultScanner rs = null;
        Table table = null;
        List<Map<String , String>> list = new ArrayList<Map<String , String>>();
        try{
            connection = ConnectionFactory.createConnection(config);
            table = connection.getTable(TableName.valueOf(tableName));
            FilterList filterList=new FilterList();
            //id+time
            if(startTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL,
                        new RegexStringComparator(".*_"+startTime));
                filterList.addFilter(rf);
            }
            if(endTime != null){
                RowFilter rf = new RowFilter(CompareFilter.CompareOp.LESS_OR_EQUAL,
                        new RegexStringComparator(".*_"+endTime));
                filterList.addFilter(rf);
            }
            scan.setFilter(filterList);
            rs = table.getScanner(scan);
            for (Result r : rs) {
                Map<String , String> map = new HashMap<String , String>();
                for (Cell cell : r.listCells()) {
                    map.put(Bytes.toString(cell.getQualifierArray(), cell.getQualifierOffset(), cell.getQualifierLength())
                            , Bytes.toString(cell.getValueArray(), cell.getValueOffset(), cell.getValueLength()));
                }
                list.add(map);
            }
        }catch (Exception e){
            e.printStackTrace();
        }finally {
            if (null != rs) {
                rs.close();
            }
            try {
                if (null != table) {
                    table.close();
                }
                if (null != connection && !connection.isClosed()) {
                    System.out.println("scan Result is closed");
                    connection.close();
                }
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
        return list;



    }

上面就会查询出完整数据。