我们介绍了避免数据斑点的三种比较常见方法：
- 加盐-盐腌
- 哈希-散列
- 反转-反转
其中在加盐（Salting）的方法里面是这么描述的：给Rowkey分配一个随机指针以使其和之前排序不同。但是在Rowkey前面加了随机重叠，那么我们怎么将这些数据替换来呢？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，参见请这里。本文使用的各组件版本：Hadoop的2.7.7，HBase的-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，进行测试。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 |
然后我们使用以下方法生成了1000000条测试数据。RowKey的形式为UID +当前数据生成版本；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(); 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(); } }} |
运行完上面的代码之后，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-bd0c10 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个地区里面的，因为经过加盐了，所以这些数据垂直都是垂直A-，B-，C-等开头的。其次我们需要知道，每个区域其实是有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区域里面执行的；而这些代码在区域里面执行的时候是可以拿到当前Region的信息，包括了键和结束键，所以实际上我们可以将拿到的开始键信息和查询的UID进行拆分，这样就可以查询我们要的数据。协处理器处理文章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引用查询的参数，表名，StartKey，EndKey以及是否加盐等标记；同时当查询到结果的当时，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);} |
我们然后使用protobuf-maven-plugin插件将上面的原生成的Java类，具体如何操作参见“在IDEA中使用Maven的编译原文件”。将我们的生成DataQueryProtos.java类拷贝产品到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()可以拿到当前区域的StartKey，然后再和和客户端传进来的StartKey和EndKey进行拼接就可以拿到完整的Rowkey插入。剩下的查询就是正常的HBase扫描代码了。
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(); } 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=54A-1000-1550572395399 column=f:uuid, timestamp=1549091990253, value=e9b10a9f-1218-43fd-bd01A-1000-1550572413799 column=f:age, timestamp=1549092008575, value=4A-1000-1550572413799 column=f:uuid, timestamp=1549092008575, value=181aa91e-5f1d-454c-959cA-1000-1550572414761 column=f:age, timestamp=1549092009531, value=33A-1000-1550572414761 column=f:uuid, timestamp=1549092009531, value=19aad8d3-621a-473c-8f9fB-1000-1550572388491 column=f:age, timestamp=1549091983276, value=1B-1000-1550572388491 column=f:uuid, timestamp=1549091983276, value=cf720efe-2ad2-48d6-81b8B-1000-1550572392922 column=f:age, timestamp=1549091987701, value=7B-1000-1550572392922 column=f:uuid, timestamp=1549091987701, value=8a047118-e130-48cb-adfehbase(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-8f9f3 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。
本文来自博客园，作者：大码王，转载请注明原文链接：https://www.cnblogs.com/huanghanyu/
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