欢迎关注Hadoop、Spark、Flink、Hive、Hbase、Flume等大数据资料分享微信公共账号:iteblog_hadoop
  1. 文章总数:1078
  2. 浏览总数:14,717,354
  3. 评论:4201
  4. 分类目录:114 个
  5. 注册用户数:7085
  6. 最后更新:2019年12月4日
过往记忆博客公众号iteblog_hadoop
欢迎关注微信公众号:
iteblog_hadoop
大数据技术博客公众号bigdata_ai
开发爱好者社区:
bigdata_ai

HBase 中加盐(Salting)之后的表如何读取:协处理器篇

《HBase Rowkey 设计指南》 文章中,我们介绍了避免数据热点的三种比较常见方法:

  • 加盐 - Salting
  • 哈希 - Hashing
  • 反转 - Reversing

其中在加盐(Salting)的方法里面是这么描述的:给 Rowkey 分配一个随机前缀以使得它和之前排序不同。但是在 Rowkey 前面加了随机前缀,那么我们怎么将这些数据读出来呢?我将分三篇文章来介绍如何读取加盐之后的表,其中每篇文章提供一种方法,主要包括:

  • 使用协处理器读取加盐的表
  • 使用 Spark 读取加盐的表
  • 使用 MapReduce 读取加盐的表

关于协处理器的入门及实战,请参见这里。本文使用的各组件版本:hadoop-2.7.7,hbase-2.0.4,jdk1.8.0_201。

测试数据生成

在介绍如何查询数据之前,我们先创建一张名为 iteblog 的 HBase 表,用于测试。为了数据均匀和介绍的方便,这里使用了预分区,并设置了27个分区,如下:

hbase(main):002:0> create 'iteblog', 'f', SPLITS => ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
0 row(s) in 2.4880 seconds

然后我们使用下面方法生成了1000000条测试数据。RowKey 的形式为 UID + 当前数据生成时间戳;由于 UID 的长度为4,所以1000000条数据会存在大量的 UID 一样的数据,所以我们使用加盐方法将这些数据均匀分散到上述27个 Region 里面(注意,其实第一个 Region 其实没数据)。具体代码如下:

package com.iteblog.data;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HConstants;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.*;
import org.apache.hadoop.hbase.util.Bytes;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import java.util.UUID;

public class HBaseDataGenerator {
    private static byte[] FAMILY = "f".getBytes();
    private static byte[] QUALIFIER_UUID = "uuid".getBytes();
    private static byte[] QUALIFIER_AGE = "age".getBytes();

    private static char generateLetter() {
        return (char) (Math.random() * 26 + 'A');
    }

    private static long generateUid(int n) {
        return (long) (Math.random() * 9 * Math.pow(10, n - 1)) + (long) Math.pow(10, n - 1);
    }

    public static void main(String[] args) throws IOException {
        BufferedMutatorParams bmp = new BufferedMutatorParams(TableName.valueOf("iteblog"));
        bmp.writeBufferSize(1024 * 1024 * 24);

        Configuration conf = HBaseConfiguration.create();
        conf.set(HConstants.ZOOKEEPER_QUORUM, "https://www.iteblog.com:2181");
        Connection connection = ConnectionFactory.createConnection(conf);

        BufferedMutator bufferedMutator = connection.getBufferedMutator(bmp);

        int BATCH_SIZE = 1000;
        int COUNTS = 1000000;
        int count = 0;
        List<Put> putList = new ArrayList<>();

        for (int i = 0; i < COUNTS; i++) {
            String rowKey = generateLetter() + "-"
                    + generateUid(4) + "-"
                    + System.currentTimeMillis();

            Put put = new Put(Bytes.toBytes(rowKey));
            byte[] uuidBytes = UUID.randomUUID().toString().substring(0, 23).getBytes();
            put.addColumn(FAMILY, QUALIFIER_UUID, uuidBytes);
            put.addColumn(FAMILY, QUALIFIER_AGE, Bytes.toBytes("" + new Random().nextInt(100)));
            putList.add(put);
            count++;

            if (count % BATCH_SIZE == 0) {
                bufferedMutator.mutate(putList);
                bufferedMutator.flush();
                putList.clear();
                System.out.println(count);
            }
        }

        if (putList.size() > 0) {
            bufferedMutator.mutate(putList);
            bufferedMutator.flush();
            putList.clear();
        }

    }
}

运行完上面代码之后,会生成1000000条数据(注意,这里其实不严谨,因为 Rowkey 设计问题,可能会导致重复的 Rowkey 生成,所以实际情况下可能没有1000000条数据。)。我们limit 10条数据看下长成什么样:

hbase(main):001:0> scan 'iteblog', {'LIMIT'=>10}
ROW                        COLUMN+CELL
 A-1000-1550572395399      column=f:age, timestamp=1549091990253, value=54
 A-1000-1550572395399      column=f:uuid, timestamp=1549091990253, value=e9b10a9f-1218-43fd-bd01
 A-1000-1550572413799      column=f:age, timestamp=1549092008575, value=4
 A-1000-1550572413799      column=f:uuid, timestamp=1549092008575, value=181aa91e-5f1d-454c-959c
 A-1000-1550572414761      column=f:age, timestamp=1549092009531, value=33
 A-1000-1550572414761      column=f:uuid, timestamp=1549092009531, value=19aad8d3-621a-473c-8f9f
 A-1001-1550572394570      column=f:age, timestamp=1549091989341, value=64
 A-1001-1550572394570      column=f:uuid, timestamp=1549091989341, value=c6712a0d-3793-46d5-865b
 A-1001-1550572405337      column=f:age, timestamp=1549092000108, value=96
 A-1001-1550572405337      column=f:uuid, timestamp=1549092000108, value=4bf05d10-bb4d-43e3-9957
 A-1001-1550572419688      column=f:age, timestamp=1549092014458, value=8
 A-1001-1550572419688      column=f:uuid, timestamp=1549092014458, value=f04ba835-d8ac-49a3-8f96
 A-1002-1550572424041      column=f:age, timestamp=1549092018816, value=84
 A-1002-1550572424041      column=f:uuid, timestamp=1549092018816, value=99d6c989-afb5-4101-9d95
 A-1003-1550572431830      column=f:age, timestamp=1549092026605, value=21
 A-1003-1550572431830      column=f:uuid, timestamp=1549092026605, value=8c1ff1b6-b97c-4059-9b68
 A-1004-1550572395399      column=f:age, timestamp=1549091990253, value=2
 A-1004-1550572395399      column=f:uuid, timestamp=1549091990253, value=e240aa0f-c044-452f-89c0
 A-1004-1550572403783      column=f:age, timestamp=1549091998555, value=6
 A-1004-1550572403783      column=f:uuid, timestamp=1549091998555, value=e8df15c9-02fa-458e-bd0c
10 row(s)
Took 0.1104 seconds

使用协处理器查询加盐之后的表

现在有数据了,我们需要查询所有 UID = 1000 的用户所有历史数据,那么如何查呢?我们知道 UID = 1000 的用户数据是均匀放到上述的27个 Region 里面的,因为经过加盐了,所以这些数据前缀都是类似于 A-,B-,C- 等开头的。其次我们需要知道,每个 Region 其实是有 Start Key 和 End Key 的,这些 Start Key 和 End Key 其实就是我们创建 iteblog 表指定的。如果你看了 《HBase 协处理器入门及实战》 这篇文章,你就知道协处理器的代码其实是在每个 Region 里面执行的;而这些代码在 Region 里面执行的时候是可以拿到当前 Region 的信息,包括了 Start Key 和 End Key,所以其实我们可以将拿到的 Start Key 信息和查询的 UID 进行拼接,这样就可以查询我们要的数据。协处理器处理篇就是基于这样的思想来查询加盐之后的数据的。

定义 proto 文件

为什么需要定义这个请参见 《HBase 协处理器入门及实战》 这篇文章。因为我们查询的时候需要传入查询的参数,比如tableName、 StartKey 、 EndKey 以及是否加盐等标记;同时当查询到结果的时候,我们还需要将数据返回,所以我们定义的 proto 文件如下:

option java_package = "com.iteblog.data.coprocessor.generated";
option java_outer_classname = "DataQueryProtos";
option java_generic_services = true;
option java_generate_equals_and_hash = true;
option optimize_for = SPEED;

message DataQueryRequest {
  optional string tableName = 1;
  optional string startRow = 2;
  optional string endRow = 3;
  optional bool  incluedEnd = 4;
  optional bool  isSalting = 5;
}

message DataQueryResponse {
  message Cell{
    required bytes value = 1;
    required bytes family = 2;
    required bytes qualifier = 3;
    required bytes row = 4;
    required int64 timestamp = 5;
  }

  message Row{
    optional bytes rowKey = 1;
    repeated Cell cellList = 2;
  }

  repeated Row rowList = 1;
}

service QueryDataService{
  rpc queryByStartRowAndEndRow(DataQueryRequest)
    returns (DataQueryResponse);
}

然后我们使用 protobuf-maven-plugin 插件将上面的 proto 生成 java 类,具体如何操作参见 《在 IDEA 中使用 Maven 编译 proto 文件》。我们将生成的 DataQueryProtos.java 类拷贝到 com.iteblog.data.coprocessor.generated 包里面。

编写协处理器代码

有了请求和返回的类,现在我们需要编写协处理器的处理代码了,结合上面的分析,协处理器的代码实现如下:

package com.iteblog.data.coprocessor;

import com.google.protobuf.ByteString;
import com.google.protobuf.RpcCallback;
import com.google.protobuf.RpcController;
import com.google.protobuf.Service;
import com.iteblog.data.coprocessor.generated.DataQueryProtos.QueryDataService;
import com.iteblog.data.coprocessor.generated.DataQueryProtos.DataQueryRequest;
import com.iteblog.data.coprocessor.generated.DataQueryProtos.DataQueryResponse;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CoprocessorEnvironment;
import org.apache.hadoop.hbase.client.Get;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.coprocessor.CoprocessorException;
import org.apache.hadoop.hbase.coprocessor.RegionCoprocessor;
import org.apache.hadoop.hbase.coprocessor.RegionCoprocessorEnvironment;
import org.apache.hadoop.hbase.regionserver.InternalScanner;
import org.apache.hadoop.hbase.shaded.protobuf.ResponseConverter;
import org.apache.hadoop.hbase.util.Bytes;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

public class SlatTableDataSearch extends QueryDataService implements RegionCoprocessor {
    private RegionCoprocessorEnvironment env;

    public Iterable<Service> getServices() {
        return Collections.singleton(this);
    }

    @Override
    public void queryByStartRowAndEndRow(RpcController controller, 
                                         DataQueryRequest request,
                                         RpcCallback<DataQueryResponse> done) {
        DataQueryResponse response = null;

        String startRow = request.getStartRow();
        String endRow = request.getEndRow();
        String regionStartKey = Bytes.toString(this.env.getRegion().getRegionInfo().getStartKey());

        if (request.getIsSalting()) {
            String startSalt = null;
            if (null != regionStartKey && regionStartKey.length() != 0) {
                startSalt = regionStartKey;
            }
            if (null != startSalt && null != startRow) {
                startRow = startSalt + "-" + startRow;
                endRow = startSalt + "-" + endRow;
            }
        }

        Scan scan = new Scan();
        if (null != startRow) {
            scan.withStartRow(Bytes.toBytes(startRow));
        }

        if (null != endRow) {
            scan.withStopRow(Bytes.toBytes(endRow), request.getIncluedEnd());
        }

        try (InternalScanner scanner = this.env.getRegion().getScanner(scan)) {
            List<Cell> results = new ArrayList<>();

            boolean hasMore;
            DataQueryResponse.Builder responseBuilder = DataQueryResponse.newBuilder();
            do {
                hasMore = scanner.next(results);
                DataQueryResponse.Row.Builder rowBuilder = DataQueryResponse.Row.newBuilder();
                if (results.size() > 0) {
                    Cell cell = results.get(0);
                    rowBuilder.setRowKey(ByteString.copyFrom(cell.getRowArray(), cell.getRowOffset(), cell.getRowLength()));
                    for (Cell kv : results) {
                        buildCell(rowBuilder, kv);
                    }
                }

                responseBuilder.addRowList(rowBuilder);
                results.clear();
            } while (hasMore);

            response = responseBuilder.build();

        } catch (IOException e) {
            ResponseConverter.setControllerException(controller, e);
        }
        done.run(response);
    }

    private void buildCell(DataQueryResponse.Row.Builder rowBuilder, Cell kv) {
        DataQueryResponse.Cell.Builder cellBuilder = DataQueryResponse.Cell.newBuilder();
        cellBuilder.setFamily(ByteString.copyFrom(kv.getFamilyArray(), kv.getFamilyOffset(), kv.getFamilyLength()));
        cellBuilder.setQualifier(ByteString.copyFrom(kv.getQualifierArray(), kv.getQualifierOffset(), kv.getQualifierLength()));
        cellBuilder.setRow(ByteString.copyFrom(kv.getRowArray(), kv.getRowOffset(), kv.getRowLength()));
        cellBuilder.setValue(ByteString.copyFrom(kv.getValueArray(), kv.getValueOffset(), kv.getValueLength()));
        cellBuilder.setTimestamp(kv.getTimestamp());
        rowBuilder.addCellList(cellBuilder);
    }

    /**
     * Stores a reference to the coprocessor environment provided by the
     * {@link org.apache.hadoop.hbase.regionserver.RegionCoprocessorHost} from the region where this
     * coprocessor is loaded.  Since this is a coprocessor endpoint, it always expects to be loaded
     * on a table region, so always expects this to be an instance of
     * {@link RegionCoprocessorEnvironment}.
     *
     * @param env the environment provided by the coprocessor host
     * @throws IOException if the provided environment is not an instance of
     *                     {@code RegionCoprocessorEnvironment}
     */
    @Override
    public void start(CoprocessorEnvironment env) throws IOException {
        if (env instanceof RegionCoprocessorEnvironment) {
            this.env = (RegionCoprocessorEnvironment) env;
        } else {
            throw new CoprocessorException("Must be loaded on a table region!");
        }
    }

    @Override
    public void stop(CoprocessorEnvironment env) {
        // nothing to do
    }
}

大家可以看到,这里面的代码框架和 《HBase 协处理器入门及实战》 里面介绍的 HBase 提供的 RowCountEndpoint 示例代码很类似。主要逻辑在 queryByStartRowAndEndRow 函数实现里面。我们通过 DataQueryRequest 拿到客户端查询的表,StartKey 和 EndKey 等数据。通过 this.env.getRegion().getRegionInfo().getStartKey() 可以拿到当前 Region 的 StartKey,然后再和客户端传进来的 StartKey 和 EndKey 进行拼接就可以拿到完整的 Rowkey 前缀。剩下的查询就是正常的 HBase Scan 代码了。

现在我们将 SlatTableDataSearch 类进行编译打包,并部署到 HBase 表里面去,具体如何部署参见 《HBase 协处理器入门及实战》

协处理器客户端代码编写

到这里,我们的协处理器服务器端的代码和部署已经完成了,现在我们需要编写协处理器客户端代码。其实也很简单,如下:

package com.iteblog.data;

import com.iteblog.data.coprocessor.generated.DataQueryProtos.QueryDataService;
import com.iteblog.data.coprocessor.generated.DataQueryProtos.DataQueryRequest;
import com.iteblog.data.coprocessor.generated.DataQueryProtos.DataQueryResponse;
import com.iteblog.data.coprocessor.generated.DataQueryProtos.DataQueryResponse.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.ipc.CoprocessorRpcUtils.BlockingRpcCallback;
import org.apache.hadoop.hbase.ipc.ServerRpcController;

import java.util.LinkedList;
import java.util.List;
import java.util.Map;

public class DataQuery {
    private static Configuration conf = null;

    static {
        conf = HBaseConfiguration.create();
        conf.set("hbase.zookeeper.quorum", "https://www.iteblog.com:2181");
    }

    static List<Row> queryByStartRowAndStopRow(String tableName,
                                               String startRow, String stopRow,
                                               boolean isIncludeEnd, boolean isSalting) {

        final DataQueryRequest.Builder requestBuilder = DataQueryRequest.newBuilder();
        requestBuilder.setTableName(tableName);
        requestBuilder.setStartRow(startRow);
        requestBuilder.setEndRow(stopRow);
        requestBuilder.setIncluedEnd(isIncludeEnd);
        requestBuilder.setIsSalting(isSalting);

        try {
            Connection connection = ConnectionFactory.createConnection(conf);
            HTable table = (HTable) connection.getTable(TableName.valueOf(tableName));
            Map<byte[], List<Row>> result = table.coprocessorService(QueryDataService.class,
                    null, null, counter -> {
                        ServerRpcController controller = new ServerRpcController();
                        BlockingRpcCallback<DataQueryResponse> call = new BlockingRpcCallback<>();
                        counter.queryByStartRowAndEndRow(controller, requestBuilder.build(), call);
                        DataQueryResponse response = call.get();

                        if (controller.failedOnException()) {
                            throw controller.getFailedOn();
                        }

                        return response.getRowListList();
                    });

            List<Row> list = new LinkedList<>();
            for (Map.Entry<byte[], List<Row>> entry : result.entrySet()) {
                if (null != entry.getKey()) {
                    list.addAll(entry.getValue());
                }
            }
            return list;
        } catch (Throwable e) {
            e.printStackTrace();
        }
        return null;

    }

    public static void main(String[] args) {
        List<Row> rows = queryByStartRowAndStopRow("iteblog", "1000", "1001", false, true);
        if (null != rows) {
            System.out.println(rows.size());
            for (DataQueryResponse.Row row : rows) {
                List<DataQueryResponse.Cell> cellListList = row.getCellListList();
                for (DataQueryResponse.Cell cell : cellListList) {
                    System.out.println(row.getRowKey().toStringUtf8() + " \t " +
                            "column=" + cell.getFamily().toStringUtf8() + 
                            ":" + cell.getQualifier().toStringUtf8() + ", " +
                            "timestamp=" + cell.getTimestamp() + ", " +
                            "value=" + cell.getValue().toStringUtf8());
                }
            }
        }
    }
}

我们运行上面的代码,可以得到如下的输出:

A-1000-1550572395399 	 column=f:age, timestamp=1549091990253, value=54
A-1000-1550572395399 	 column=f:uuid, timestamp=1549091990253, value=e9b10a9f-1218-43fd-bd01
A-1000-1550572413799 	 column=f:age, timestamp=1549092008575, value=4
A-1000-1550572413799 	 column=f:uuid, timestamp=1549092008575, value=181aa91e-5f1d-454c-959c
A-1000-1550572414761 	 column=f:age, timestamp=1549092009531, value=33
A-1000-1550572414761 	 column=f:uuid, timestamp=1549092009531, value=19aad8d3-621a-473c-8f9f
B-1000-1550572388491 	 column=f:age, timestamp=1549091983276, value=1
B-1000-1550572388491 	 column=f:uuid, timestamp=1549091983276, value=cf720efe-2ad2-48d6-81b8
B-1000-1550572392922 	 column=f:age, timestamp=1549091987701, value=7
B-1000-1550572392922 	 column=f:uuid, timestamp=1549091987701, value=8a047118-e130-48cb-adfe

hbase(main):020:0> scan 'iteblog', {STARTROW => 'A-1000', ENDROW => 'A-1001'}
ROW                         COLUMN+CELL
 A-1000-1550572395399       column=f:age, timestamp=1549091990253, value=54
 A-1000-1550572395399       column=f:uuid, timestamp=1549091990253, value=e9b10a9f-1218-43fd-bd01
 A-1000-1550572413799       column=f:age, timestamp=1549092008575, value=4
 A-1000-1550572413799       column=f:uuid, timestamp=1549092008575, value=181aa91e-5f1d-454c-959c
 A-1000-1550572414761       column=f:age, timestamp=1549092009531, value=33
 A-1000-1550572414761       column=f:uuid, timestamp=1549092009531, value=19aad8d3-621a-473c-8f9f
3 row(s)
Took 0.0569 seconds

可以看到,和我们使用 HBase Shell 输出的一致,而且我们还把所有的 UID = 1000 的数据拿到了。好了,到这里,使用协处理器查询 HBase 加盐之后的表已经算完成了,明天我将介绍使用 Spark 如何查询加盐之后的表。

本博客文章除特别声明,全部都是原创!
转载本文请加上:转载自过往记忆(https://www.iteblog.com/)
本文链接: 【HBase 中加盐(Salting)之后的表如何读取:协处理器篇】(https://www.iteblog.com/archives/2507.html)
喜欢 (6)
分享 (0)
发表我的评论
取消评论

表情
本博客评论系统带有自动识别垃圾评论功能,请写一些有意义的评论,谢谢!