大数据海量数据更新策略

女爷i 2022-12-18 00:57 128阅读 0赞

## 问题总结： ##

#### 面临场景 ####

> Spark面对海量数据的增量更新也是乏力的，例如一批数据初始化为4000万，大约10G，  
> 日增量100MB（增量数据分为两部分：新增与老数据变更），那么要如何才能将这100MB数据  
> 更新至数据库中？

#### 之前方案 ####

1.  加载日增量100MB
2.  加载库中10G
3.  使用left join求出增量部分
4.  增量部分写入库中

> 小结：面对上述问题，尽管我们是做大数据的也束手无策，只能牺牲性能来换取业务增量。

## 解决方案 ##

*  重点1：可以准确获取日增量数据
 *  重点2：MongoDB特性，\_id
 *  重点3：Spark DataFrame.write append 特性

#### 重点摘抄分析 ####

[MongoSpark源码片段][MongoSpark]

override def createRelation(sqlContext: SQLContext, mode: SaveMode, parameters: Map[String, String], data: DataFrame): BaseRelation = { 
      val writeConfig = WriteConfig(sqlContext.sparkContext.getConf, parameters)
      val mongoConnector = MongoConnector(writeConfig.asOptions)
      lazy val collectionExists: Boolean = mongoConnector.withDatabaseDo(
        writeConfig, {  db => db.listCollectionNames().asScala.toList.contains(writeConfig.collectionName) }
      )
      mode match { 
        case Append => MongoSpark.save(data, writeConfig)
        case Overwrite =>
          // 如果使用 Overwrite 会发生集合删除操作，慎用
          mongoConnector.withCollectionDo(writeConfig, {  collection: MongoCollection[Document] => collection.drop() })
          MongoSpark.save(data, writeConfig)
        case ErrorIfExists =>
          if (collectionExists) { 
            throw new UnsupportedOperationException("MongoCollection already exists")
          } else { 
            MongoSpark.save(data, writeConfig)
          }
        case Ignore =>
          if (!collectionExists) { 
            MongoSpark.save(data, writeConfig)
          }
      }
      constructRelation(sqlContext, parameters ++ writeConfig.asOptions, Some(data.schema))
    }

[MongoSpark源码片段二][MongoSpark 1]

/** * Save data to MongoDB * * 注意这段注释，如果dataFrame包含_id字段，则数据将被替换集合中任何现有文档 * '''Note:''' If the dataFrame contains an `_id` field the data will upserted and replace any existing documents in the collection. * * @param dataset the dataset to save to MongoDB * @param writeConfig the writeConfig * @tparam D * @since 1.1.0 */
      def save[D](dataset: Dataset[D], writeConfig: WriteConfig): Unit = { 
        val mongoConnector = MongoConnector(writeConfig.asOptions)
        val dataSet = dataset.toDF()
        val mapper = rowToDocumentMapper(dataSet.schema, writeConfig.extendedBsonTypes)
        val documentRdd: RDD[BsonDocument] = dataSet.rdd.map(row => mapper(row))
        val fieldNames = dataset.schema.fieldNames.toList
        val queryKeyList = BsonDocument.parse(writeConfig.shardKey.getOrElse("{_id: 1}")).keySet().asScala.toList
    
        if (writeConfig.forceInsert || !queryKeyList.forall(fieldNames.contains(_))) { 
          MongoSpark.save(documentRdd, writeConfig)
        } else { 
          documentRdd.foreachPartition(iter => if (iter.nonEmpty) { 
            mongoConnector.withCollectionDo(writeConfig, {  collection: MongoCollection[BsonDocument] =>
              iter.grouped(writeConfig.maxBatchSize).foreach(batch => { 
                val requests = batch.map(doc =>
                  if (queryKeyList.forall(doc.containsKey(_))) { 
                    val queryDocument = new BsonDocument()
                    queryKeyList.foreach(key => queryDocument.append(key, doc.get(key)))
                    if (writeConfig.replaceDocument) { 
                      new ReplaceOneModel[BsonDocument](queryDocument, doc, new ReplaceOptions().upsert(true))
                    } else { 
                      queryDocument.keySet().asScala.foreach(doc.remove(_))
                      new UpdateOneModel[BsonDocument](queryDocument, new BsonDocument("$set", doc), new UpdateOptions().upsert(true))
                    }
                  } else { 
                    new InsertOneModel[BsonDocument](doc)
                  })
                collection.bulkWrite(requests.toList.asJava, new BulkWriteOptions().ordered(writeConfig.ordered))
              })
            })
          })
        }
      }

> **有了上述特性，程序设计的时候找到唯一表示列作为\_id即可。**

以 上 来 源 于 项 目 组 老 大 分 享 文 档 \\color\{\#FF3030\}\{以上来源于项目组老大分享文档\} 以上来源于项目组老大分享文档

[MongoSpark]: https://github.com/mongodb/mongo-spark/blob/master/src/main/scala/com/mongodb/spark/sql/DefaultSource.scala#L88-L90
[MongoSpark 1]: https://github.com/mongodb/mongo-spark/blob/master/src/main/scala/com/mongodb/spark/MongoSpark.scala