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很多数据开发者使用 bitmap 技术对用户数据进行编码和压缩,然后利用 bitmap 的与 / 或 / 非的极速处理速度,实现类似用户画像标签的人群筛选、运营分析的 7 日活跃等分析。
本文给出了一个使用 MaxCompute MapReduce 开发一个对不同日期活跃用户 ID 进行 bitmap 编码和计算的样例。供感兴趣的用户进一步了解、分析,并应用在自己的场景下。
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;
@Override
public void setup(TaskContext context) throws IOException {key = context.createMapOutputKeyRecord();
value = context.createMapOutputValueRecord();}
@Override
public 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);
// 获取 key
key.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=3D
JobClient.runJob(job);
}
}
对 Java 应用打包后,上传到 MaxCompute 项目中,即可在 MaxCompute 中调用该 MR 作业,对输入表的数据按日期作为 key 进行用户 id 的编码,同时按照相同日期对 bitmap 后的用户 id 取 OR 操作(根据需要可以取 AND,例如存留场景),并将处理后的数据写入目标结构表当中供后续处理使用。
本文作者:圣远
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