很多數據開發者使用bitmap技術對用戶數據進行編碼和壓縮，然后利用bitmap的與/或/非的極速處理速度，實現類似用戶畫像標簽的人群篩選、運營分析的7日活躍等分析。
本文給出了一個使用MaxCompute MapReduce開發一個對不同日期活躍用戶ID進行bitmap編碼和計算的樣例。供感興趣的用戶進一步了解、分析，并應用在自己的場景下。

import com.aliyun.odps.OdpsException; import com.aliyun.odps.data.Record; import com.aliyun.odps.data.TableInfo; import com.aliyun.odps.mapred.JobClient; import com.aliyun.odps.mapred.MapperBase; import com.aliyun.odps.mapred.ReducerBase; import com.aliyun.odps.mapred.conf.JobConf; import com.aliyun.odps.mapred.utils.InputUtils; import com.aliyun.odps.mapred.utils.OutputUtils; import com.aliyun.odps.mapred.utils.SchemaUtils; import org.roaringbitmap.RoaringBitmap; import org.roaringbitmap.buffer.ImmutableRoaringBitmap;import java.io.DataOutputStream; import java.io.IOException; import java.io.OutputStream; import java.nio.ByteBuffer; import java.util.Base64; import java.util.Iterator;public class bitmapDemo2 {public static class BitMapper extends MapperBase {Record key;Record value;@Overridepublic void setup(TaskContext context) throws IOException {key = context.createMapOutputKeyRecord();value = context.createMapOutputValueRecord();}@Overridepublic void map(long recordNum, Record record, TaskContext context)throws IOException{RoaringBitmap mrb=new RoaringBitmap();long AID=0;{{{{AID=record.getBigint("id");mrb.add((int) AID);//獲取keykey.set(new Object[] {record.getString("active_date")});}}}}ByteBuffer outbb = ByteBuffer.allocate(mrb.serializedSizeInBytes());mrb.serialize(new DataOutputStream(new OutputStream(){ByteBuffer mBB;OutputStream init(ByteBuffer mbb) {mBB=mbb; return this;}public void close() {}public void flush() {}public void write(int b) {mBB.put((byte) b);}public void write(byte[] b) {mBB.put(b);}public void write(byte[] b, int off, int l) {mBB.put(b,off,l);}}.init(outbb)));String serializedstring = Base64.getEncoder().encodeToString(outbb.array());value.set(new Object[] {serializedstring});context.write(key, value);}}public static class BitReducer extends ReducerBase {private Record result = null;public void setup(TaskContext context) throws IOException {result = context.createOutputRecord();}public void reduce(Record key, Iterator<Record> values, TaskContext context) throws IOException {long fcount = 0;RoaringBitmap rbm=new RoaringBitmap();while (values.hasNext()){Record val = values.next();ByteBuffer newbb = ByteBuffer.wrap(Base64.getDecoder().decode((String)val.get(0)));ImmutableRoaringBitmap irb = new ImmutableRoaringBitmap(newbb);RoaringBitmap p= new RoaringBitmap(irb);rbm.or(p);}ByteBuffer outbb = ByteBuffer.allocate(rbm.serializedSizeInBytes());rbm.serialize(new DataOutputStream(new OutputStream(){ByteBuffer mBB;OutputStream init(ByteBuffer mbb) {mBB=mbb; return this;}public void close() {}public void flush() {}public void write(int b) {mBB.put((byte) b);}public void write(byte[] b) {mBB.put(b);}public void write(byte[] b, int off, int l) {mBB.put(b,off,l);}}.init(outbb)));String serializedstring = Base64.getEncoder().encodeToString(outbb.array());result.set(0, key.get(0));result.set(1, serializedstring);context.write(result);}}public static void main( String[] args ) throws OdpsException{System.out.println("begin.........");JobConf job = new JobConf();job.setMapperClass(BitMapper.class);job.setReducerClass(BitReducer.class);job.setMapOutputKeySchema(SchemaUtils.fromString("active_date:string"));job.setMapOutputValueSchema(SchemaUtils.fromString("id:string"));InputUtils.addTable(TableInfo.builder().tableName("bitmap_source").cols(new String[] {"id","active_date"}).build(), job); // +------------+-------------+ // | id | active_date | // +------------+-------------+ // | 1 | 20190729 | // | 2 | 20190729 | // | 3 | 20190730 | // | 4 | 20190801 | // | 5 | 20190801 | // +------------+-------------+OutputUtils.addTable(TableInfo.builder().tableName("bitmap_target").build(), job); // +-------------+------------+ // | active_date | bit_map | // +-------------+------------+ // 20190729,OjAAAAEAAAAAAAEAEAAAAAEAAgA=3D // 20190730,OjAAAAEAAAAAAAAAEAAAAAMA // 20190801,OjAAAAEAAAAAAAEAEAAAAAQABQA=3DJobClient.runJob(job);} }

對Java應用打包后，上傳到MaxCompute項目中，即可在MaxCompute中調用該MR作業，對輸入表的數據按日期作為key進行用戶id的編碼，同時按照相同日期對bitmap后的用戶id取OR操作（根據需要可以取AND，例如存留場景），并將處理后的數據寫入目標結構表當中供后續處理使用。

原文鏈接
本文為云棲社區原創內容，未經允許不得轉載。

總結

以上是生活随笔為你收集整理的如何在MaxCompute中利用bitmap进行数据处理？的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。