Iceberg: COW模式下的MERGE INTO的执行流程

MergeInto命令

MERGE INTO target_table t
USING source_table s
ON s.id = t.id                //这里是JOIN的关联条件
WHEN MATCHED AND s.opType = 'delete' THEN DELETE // WHEN条件是对当前行进行打标的匹配条件
WHEN MATCHED AND s.opType = 'update' THEN UPDATE SET id = s.id, name = s.name
WHEN NOT MATCHED AND s.opType = 'insert' THEN INSERT (key, value) VALUES (key, value)

如上是一条MERGE INTO语句，经过Spark Analyzer解析时，会发现它是MERGE INTO命令，因此将解析target_table对应生成的SparkTable实例封装成RowLevelOperationTable的实例，它会绑定一个SparkCopyOnWriteOperation的实例，并且实现了创建ScanBuilder和WriteBuilder的方法。

ScanBuilder和WriteBuilder是Spark中定义的接口，分别用于构建读数据器（Scan）和写数据器（BatchWrite）。

Iceberg基于Spark 3.x提供的外部Catalog及相关的读写接口，实现了对于Iceberg表（存储格式）的数据读写。

下面以SparkCopyOnWriteOperation跟踪分析如何利用Spark写出数据为Iceberg表格式。

Iceberg行级更新的操作，目前支持UPDATE / DELETE / MERGE INTO三个语法。

预备知识

SparkTable定义

public class SparkTable
    implements org.apache.spark.sql.connector.catalog.Table, // 继承自Spark的接口
        SupportsRead,
        SupportsWrite,
        SupportsDelete, // 支持删除
        SupportsRowLevelOperations, // 支持行级的数据更新
        SupportsMetadataColumns {
  private final Table icebergTable;
  private final Long snapshotId;
  private final boolean refreshEagerly;
  private final Set<TableCapability> capabilities;
  private String branch;
  private StructType lazyTableSchema = null;
  private SparkSession lazySpark = null;

  public SparkTable(Table icebergTable, Long snapshotId, boolean refreshEagerly) {
    this.icebergTable = icebergTable;
    this.snapshotId = snapshotId;
    this.refreshEagerly = refreshEagerly;

    boolean acceptAnySchema =
        PropertyUtil.propertyAsBoolean(
            icebergTable.properties(),
            TableProperties.SPARK_WRITE_ACCEPT_ANY_SCHEMA,
            TableProperties.SPARK_WRITE_ACCEPT_ANY_SCHEMA_DEFAULT);
    this.capabilities = acceptAnySchema ? CAPABILITIES_WITH_ACCEPT_ANY_SCHEMA : CAPABILITIES;
  }
  
  /**
   * 该表支持读取，因此实现了此方法返回一个ScanBuilder实例
   */
  @Override
  public ScanBuilder newScanBuilder(CaseInsensitiveStringMap options) {
    if (options.containsKey(SparkReadOptions.FILE_SCAN_TASK_SET_ID)) {
      // skip planning the job and fetch already staged file scan tasks
      // 如果设置了此参数，则会在读取数据后，将此次生成的Iceberg ScanTasks缓存在本地进程中的ScanTaskSetManager实例里，
      // 后面再对同相同的FileSet集合（或scan file的任务集合）构建时，可以避免重复构建任务集，
      // 起到缓存的作用
      return new SparkFilesScanBuilder(sparkSession(), icebergTable, options);
    }

    if (options.containsKey(SparkReadOptions.SCAN_TASK_SET_ID)) {
      // 作用同上
      return new SparkStagedScanBuilder(sparkSession(), icebergTable, options);
    }

    if (refreshEagerly) {
      icebergTable.refresh();
    }
    // 可以支持基于branch或是基于SnapshotId创建SparkTable
    // 如果基于SnapshotID，则需要显示地解析SnapshotId归属的branch
    CaseInsensitiveStringMap scanOptions =
        branch != null ? options : addSnapshotId(options, snapshotId);
    return new SparkScanBuilder(
        sparkSession(), icebergTable, branch, snapshotSchema(), scanOptions);
  }
  
  /**
   * 该表支持写，因此实现了此方法返回一个WriteBuilder实例
   */
  @Override
  public WriteBuilder newWriteBuilder(LogicalWriteInfo info) {
    Preconditions.checkArgument(
        snapshotId == null, "Cannot write to table at a specific snapshot: %s", snapshotId);

    return new SparkWriteBuilder(sparkSession(), icebergTable, branch, info);
  }

Spark Analyzer Resolving Table

假设我们有如下配置，定义了一个新的用户自定义的catalog，其name为iceberg。并通过spark.sql.catalog.iceberg指定了这个catalog的实现类org.apache.iceberg.spark.SparkCatalog，其类型为hive（意味着会在Iceberg的侧使用HiveCatalog解析库、表），meta存储地址为thrift://metastore-host:port。

spark.sql.catalog.iceberg = org.apache.iceberg.spark.SparkCatalog

spark.sql.catalog.iceberg.type = hive

spark.sql.catalog.iceberg.uri = thrift://metastore-host:port

当我们执行SELECT * FROM iceberg.test_tbl时，在SQL解析过程中，会通过如下的过程来解析CatalogName和TableName，并创建对应的Catalog和Table实例，即对应Iceberg中的实现类SparkCatalog和SparkTable。

  /**
   * 如果当前Plan是还未解析的表视图或是表，或是INSERT INTO语句，则应用此Rule，
   * 查看绑定的目标表名是否是SQL层级的临时视图或是Session级别的全局视图表名，最终返回一个新的SubqueryAlias的实例
   */
  object ResolveTempViews extends Rule[LogicalPlan] {
    def apply(plan: LogicalPlan): LogicalPlan = plan.resolveOperatorsUp {
      case u @ UnresolvedRelation(ident) =>
        lookupTempView(ident).getOrElse(u)
      case i @ InsertIntoStatement(UnresolvedRelation(ident), _, _, _, _) =>
        lookupTempView(ident)
          .map(view => i.copy(table = view))
          .getOrElse(i)
      case u @ UnresolvedTable(ident) =>
        lookupTempView(ident).foreach { _ =>
          u.failAnalysis(s"${ident.quoted} is a temp view not table.")
        }
        u
      case u @ UnresolvedTableOrView(ident) =>
        lookupTempView(ident).map(_ => ResolvedView(ident.asIdentifier)).getOrElse(u)
    }

    def lookupTempView(identifier: Seq[String]): Option[LogicalPlan] = {
      // Permanent View can't refer to temp views, no need to lookup at all.
      if (isResolvingView) return None

      identifier match {
        case Seq(part1) => v1SessionCatalog.lookupTempView(part1)
        case Seq(part1, part2) => v1SessionCatalog.lookupGlobalTempView(part1, part2)
        case _ => None
      }
    }
  }

  /**
   * Resolve table relations with concrete relations from v2 catalog.
   *
   * [[ResolveRelations]] still resolves v1 tables.
   */
  object ResolveTables extends Rule[LogicalPlan] {
    def apply(plan: LogicalPlan): LogicalPlan = ResolveTempViews(plan).resolveOperatorsUp {
      case u: UnresolvedRelation =>
        lookupV2Relation(u.multipartIdentifier)
          .map { rel =>
            val ident = rel.identifier.get
            SubqueryAlias(rel.catalog.get.name +: ident.namespace :+ ident.name, rel)
          }.getOrElse(u)

      case u @ UnresolvedTable(NonSessionCatalogAndIdentifier(catalog, ident)) =>
        // NonSessionCatalogAndIdentifier的unapply方法，会尝试解析catalog，并通过Spark.CatalogManager::catalog(name)解析并构建Catalog实例
        // 这里就是一个Iceberg中定义的SparkCatalog实现类，然后通过工具类的方法加载表，并创建SparkTable实例。
        CatalogV2Util.loadTable(catalog, ident)
          .map(ResolvedTable(catalog.asTableCatalog, ident, _))
          .getOrElse(u)

      case u @ UnresolvedTableOrView(NonSessionCatalogAndIdentifier(catalog, ident)) =>
        CatalogV2Util.loadTable(catalog, ident)
          .map(ResolvedTable(catalog.asTableCatalog, ident, _))
          .getOrElse(u)

      case i @ InsertIntoStatement(u: UnresolvedRelation, _, _, _, _) if i.query.resolved =>
        lookupV2Relation(u.multipartIdentifier)
          .map(v2Relation => i.copy(table = v2Relation))
          .getOrElse(i)

      case alter @ AlterTable(_, _, u: UnresolvedV2Relation, _) =>
        CatalogV2Util.loadRelation(u.catalog, u.tableName)
          .map(rel => alter.copy(table = rel))
          .getOrElse(alter)

      case u: UnresolvedV2Relation =>
        CatalogV2Util.loadRelation(u.catalog, u.tableName).getOrElse(u)
    }
    
    /**
     * Performs the lookup of DataSourceV2 Tables from v2 catalog.
     */
    private def lookupV2Relation(identifier: Seq[String]): Option[DataSourceV2Relation] =
      expandRelationName(identifier) match {
        case NonSessionCatalogAndIdentifier(catalog, ident) =>
          CatalogV2Util.loadTable(catalog, ident) match {
            case Some(table) =>
              Some(DataSourceV2Relation.create(table, Some(catalog), Some(ident)))
            case None => None
          }
        case _ => None
      }
  }

SparkCatalog路由加载表的过程到HiveCatalog

SparkCatalog由于继承自TableCatalog，因此拥有 Table loadTable(Identifier ident) throws NoSuchTableException方法，在Spark内部进行SQL解析时，可以调用此方法，生成用户自定义的Table实例。

SparkCatalog定位于Spark与Iceberg之前的桥梁，最终的实现效果是将某个Catalog的解析并创建表的任务，路由给Iceberg中的Catalog实现，例如HiveCatalog。

/**
 * 实现了Spark中的如下接口：
 * public interface TableCatalog extends CatalogPlugin
 * 可以在SQL解析过程时，应用`ResolveTables`规则时，通过Catalog + Identifier创建
 */
public class SparkCatalog extends BaseCatalog {
  /**
   * 由于继承自CatalogPlugin接口类，因此需要重写initialize(...)方法，以初始化SparkCatalog实例
   */
  @Override
  public final void initialize(String name, CaseInsensitiveStringMap options) {
    this.cacheEnabled =
        PropertyUtil.propertyAsBoolean(
            options, CatalogProperties.CACHE_ENABLED, CatalogProperties.CACHE_ENABLED_DEFAULT);

    long cacheExpirationIntervalMs =
        PropertyUtil.propertyAsLong(
            options,
            CatalogProperties.CACHE_EXPIRATION_INTERVAL_MS,
            CatalogProperties.CACHE_EXPIRATION_INTERVAL_MS_DEFAULT);

    // An expiration interval of 0ms effectively disables caching.
    // Do not wrap with CachingCatalog.
    if (cacheExpirationIntervalMs == 0) {
      this.cacheEnabled = false;
    }
    // 创建Iceberg支持的catalog实例，一共支持如下几个
    //   public static final String ICEBERG_CATALOG_TYPE_HADOOP = "hadoop";
    // public static final String ICEBERG_CATALOG_TYPE_HIVE = "hive";
    // public static final String ICEBERG_CATALOG_TYPE_REST = "rest";

    // public static final String ICEBERG_CATALOG_HADOOP = "org.apache.iceberg.hadoop.HadoopCatalog";
    // public static final String ICEBERG_CATALOG_HIVE = "org.apache.iceberg.hive.HiveCatalog";
    // public static final String ICEBERG_CATALOG_REST = "org.apache.iceberg.rest.RESTCatalog";
    // 默认情况下，我们创建的是HiveCatalog，后续调用loadTable(...)方法创建SparkTable时，则通过HiveCatalog::loadTable(name)方法生成
    Catalog catalog = buildIcebergCatalog(name, options);

    this.catalogName = name;
    SparkSession sparkSession = SparkSession.active();
    this.useTimestampsWithoutZone =
        SparkUtil.useTimestampWithoutZoneInNewTables(sparkSession.conf());
    this.tables =
        new HadoopTables(SparkUtil.hadoopConfCatalogOverrides(SparkSession.active(), name));
    this.icebergCatalog =
        cacheEnabled ? CachingCatalog.wrap(catalog, cacheExpirationIntervalMs) : catalog;
    // 支持通过参数的方式，指定默认的namespace，默认值为default
    if (catalog instanceof SupportsNamespaces) {
      this.asNamespaceCatalog = (SupportsNamespaces) catalog;
      if (options.containsKey("default-namespace")) {
        this.defaultNamespace =
            Splitter.on('.').splitToList(options.get("default-namespace")).toArray(new String[0]);
      }
    }

    EnvironmentContext.put(EnvironmentContext.ENGINE_NAME, "spark");
    EnvironmentContext.put(
        EnvironmentContext.ENGINE_VERSION, sparkSession.sparkContext().version());
    EnvironmentContext.put(CatalogProperties.APP_ID, sparkSession.sparkContext().applicationId());
  }

  @Override
  public Table loadTable(Identifier ident) throws NoSuchTableException {
    // 基于标识符创建SparkTable实例
    try {
      return load(ident);
    } catch (org.apache.iceberg.exceptions.NoSuchTableException e) {
      throw new NoSuchTableException(ident);
    }
  }

  @Override
  public Table loadTable(Identifier ident, String version) throws NoSuchTableException {
    Table table = loadTable(ident);
    // ...
  }

  @Override
  public Table loadTable(Identifier ident, long timestamp) throws NoSuchTableException {
    Table table = loadTable(ident);
    // ...
  }
}

SparkCatalog::buildIcebergCatalog

  public static Catalog buildIcebergCatalog(String name, Map<String, String> options, Object conf) {
    String catalogImpl = options.get(CatalogProperties.CATALOG_IMPL);
    if (catalogImpl == null) {
      String catalogType =
          PropertyUtil.propertyAsString(options, ICEBERG_CATALOG_TYPE, ICEBERG_CATALOG_TYPE_HIVE);
      switch (catalogType.toLowerCase(Locale.ENGLISH)) {
        case ICEBERG_CATALOG_TYPE_HIVE:
          catalogImpl = ICEBERG_CATALOG_HIVE;
          break;
        case ICEBERG_CATALOG_TYPE_HADOOP:
          catalogImpl = ICEBERG_CATALOG_HADOOP;
          break;
        case ICEBERG_CATALOG_TYPE_REST:
          catalogImpl = ICEBERG_CATALOG_REST;
          break;
        default:
          throw new UnsupportedOperationException("Unknown catalog type: " + catalogType);
      }
    } else {
      String catalogType = options.get(ICEBERG_CATALOG_TYPE);
      Preconditions.checkArgument(
          catalogType == null,
          "Cannot create catalog %s, both type and catalog-impl are set: type=%s, catalog-impl=%s",
          name,
          catalogType,
          catalogImpl);
    }

    return CatalogUtil.loadCatalog(catalogImpl, name, options, conf);
  }

CatalogUtil::loadCatalog

这里以Hive为例，解析如何加载Custom Catalog

  public static Catalog loadCatalog(
      String impl, String catalogName, Map<String, String> properties, Object hadoopConf) {
    // impl = ICEBERG_CATALOG_HIVE
    // catalogName = hive
    // properties = spark.sql.catalog.[catalogName].x
    // 其中properties指的是catalogName对应的配置选项，是从Spark.SQLConf解析得到的
    Preconditions.checkNotNull(impl, "Cannot initialize custom Catalog, impl class name is null");
    DynConstructors.Ctor<Catalog> ctor;
    try {
      // 通过默认的impl名字，通过Refect机制，调用无参的构造函数，生成对应的类的实例
      ctor = DynConstructors.builder(Catalog.class).impl(impl).buildChecked();
    } catch (NoSuchMethodException e) {
      throw new IllegalArgumentException(
          String.format("Cannot initialize Catalog implementation %s: %s", impl, e.getMessage()),
          e);
    }

    Catalog catalog;
    try {
      catalog = ctor.newInstance();

    } catch (ClassCastException e) {
      throw new IllegalArgumentException(
          String.format("Cannot initialize Catalog, %s does not implement Catalog.", impl), e);
    }

    configureHadoopConf(catalog, hadoopConf);
    // 通过properties，来助力catalog对象的初始化过程
    catalog.initialize(catalogName, properties);
    return catalog;
  }

HiveCatalog::initialize

  @Override
  public void initialize(String inputName, Map<String, String> properties) {
    this.catalogProperties = ImmutableMap.copyOf(properties);
    this.name = inputName;
    if (conf == null) {
      LOG.warn("No Hadoop Configuration was set, using the default environment Configuration");
      this.conf = new Configuration();
    }
    // 解析指定的Hive metastore地址
    if (properties.containsKey(CatalogProperties.URI)) {
      this.conf.set(HiveConf.ConfVars.METASTOREURIS.varname, properties.get(CatalogProperties.URI));
    }
    // 解析指定的metastore的工作目录
    if (properties.containsKey(CatalogProperties.WAREHOUSE_LOCATION)) {
      this.conf.set(
          HiveConf.ConfVars.METASTOREWAREHOUSE.varname,
          LocationUtil.stripTrailingSlash(properties.get(CatalogProperties.WAREHOUSE_LOCATION)));
    }

    this.listAllTables =
        Boolean.parseBoolean(properties.getOrDefault(LIST_ALL_TABLES, LIST_ALL_TABLES_DEFAULT));
    // 解析指定的读写文件的 接口实现类，如果不指定则默认为HadoopFileIO
    // 否则加载用户自定义的实现类
    String fileIOImpl = properties.get(CatalogProperties.FILE_IO_IMPL);
    this.fileIO =
        fileIOImpl == null
            ? new HadoopFileIO(conf)
            : CatalogUtil.loadFileIO(fileIOImpl, properties, conf);
    // 初始化元数据交互的客户端，使用默认使用HiveClientPool
    this.clients = new CachedClientPool(conf, properties);
  }

HiveCatalog加载SparkTable

在生成SparkCatalog时，会根据spark.sql.catalog.iceberg.type这个配置，知道我们要创建的表在Iceberg中的类型是hive，因此需要通过HiveCatalog加载表。

HiveCatalog::loadTable

  @Override
  public Table loadTable(TableIdentifier identifier) {
    Table result;
    if (isValidIdentifier(identifier)) {
      // 对于HiveCatalog来说，所有的identifier都是合法的，因此会通过下面的方法得到对应类型的TableOperations实例
      // 在Iceberg世界中，实际上是不存在表的，只是利用表的概念，将TableMetadata进行了抽象，
      // 因此Iceberg中的Table都必须绑定一个TableOperations实例，来读取TableMetadata数据
      // 例如在这里会对应生成HiveTableOperations
      TableOperations ops = newTableOps(identifier);
      if (ops.current() == null) {
        // the identifier may be valid for both tables and metadata tables
        if (isValidMetadataIdentifier(identifier)) {
          result = loadMetadataTable(identifier);

        } else {
          throw new NoSuchTableException("Table does not exist: %s", identifier);
        }

      } else {
        // 
        result = new BaseTable(ops, fullTableName(name(), identifier), metricsReporter());
      }

    } else if (isValidMetadataIdentifier(identifier)) {
      result = loadMetadataTable(identifier);

    } else {
      throw new NoSuchTableException("Invalid table identifier: %s", identifier);
    }

    LOG.info("Table loaded by catalog: {}", result);
    return result;
  }

Scan的定义、构建及执行流程

MERGE INTO重写时，会为目标表生成一个DataSourceV2Relation的逻辑计划实例，以读取目标表中的相关数据，因此在过滤表达式下推优化时，会同时构建COW模式下的Scan实例。

Scan是Spark中定义的读取数据的接口。

object V2ScanRelationPushDown extends Rule[LogicalPlan] {
  import DataSourceV2Implicits._

  override def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
    case ScanOperation(project, filters, relation: DataSourceV2Relation) =>
      // 调用这里的Table是一个RowLevelOperationTable的实例，同时它绑定了一个SparkCopyOnWriteOperation实例
      // 因此底层实际上调用的是SparkCopyOnWriteOperation::newScanBuilder方法
      val scanBuilder = relation.table.asReadable.newScanBuilder(relation.options)

      val normalizedFilters = DataSourceStrategy.normalizeExprs(filters, relation.output)
      val (normalizedFiltersWithSubquery, normalizedFiltersWithoutSubquery) =
        normalizedFilters.partition(SubqueryExpression.hasSubquery)

      // `pushedFilters` will be pushed down and evaluated in the underlying data sources.
      // `postScanFilters` need to be evaluated after the scan.
      // `postScanFilters` and `pushedFilters` can overlap, e.g. the parquet row group filter.
      val (pushedFilters, postScanFiltersWithoutSubquery) = PushDownUtils.pushFilters(
        scanBuilder, normalizedFiltersWithoutSubquery)
      val postScanFilters = postScanFiltersWithoutSubquery ++ normalizedFiltersWithSubquery

      val normalizedProjects = DataSourceStrategy
        .normalizeExprs(project, relation.output)
        .asInstanceOf[Seq[NamedExpression]]
      // 列裁剪，同时调用scanBuilder.build()方法，生成一个读取具体数据源的Scan实例
      val (scan, output) = PushDownUtils.pruneColumns(
        scanBuilder, relation, normalizedProjects, postScanFilters)
      logInfo(
        s"""
           |Pushing operators to ${relation.name}
           |Pushed Filters: ${pushedFilters.mkString(", ")}
           |Post-Scan Filters: ${postScanFilters.mkString(",")}
           |Output: ${output.mkString(", ")}
         """.stripMargin)

      val wrappedScan = scan match {
        case v1: V1Scan =>
          val translated = filters.flatMap(DataSourceStrategy.translateFilter(_, true))
          V1ScanWrapper(v1, translated, pushedFilters)
        case _ => scan
      }
      // 这里生成一个读取数据的逻辑计划
      val scanRelation = DataSourceV2ScanRelation(relation.table, wrappedScan, output)

      val projectionOverSchema = ProjectionOverSchema(output.toStructType)
      val projectionFunc = (expr: Expression) => expr transformDown {
        case projectionOverSchema(newExpr) => newExpr
      }

      val filterCondition = postScanFilters.reduceLeftOption(And)
      val newFilterCondition = filterCondition.map(projectionFunc)
      val withFilter = newFilterCondition.map(Filter(_, scanRelation)).getOrElse(scanRelation)

      val withProjection = if (withFilter.output != project) {
        val newProjects = normalizedProjects
          .map(projectionFunc)
          .asInstanceOf[Seq[NamedExpression]]
        Project(newProjects, withFilter)
      } else {
        withFilter
      }
      // 返回最终的逻辑计划
      withProjection
  }
}

Iceberg中实现的Scan

从前面我们知道，在Spark进行Filter Pushdown优化时，会调用Table::newScanBuilder方法构建一个具体的数据描述器（Scan），实际上是会最终调用Iceberg中如下的方法：

class SparkCopyOnWriteOperation implements RowLevelOperation {

  @Override
  public ScanBuilder newScanBuilder(CaseInsensitiveStringMap options) {
    if (lazyScanBuilder == null) {
      lazyScanBuilder =
          new SparkScanBuilder(spark, table, branch, options) {
            @Override
            public Scan build() {
              // 构建COW模式的Scan实例
              Scan scan = super.buildCopyOnWriteScan();
              SparkCopyOnWriteOperation.this.configuredScan = scan;
              return scan;
            }
          };
    }

    return lazyScanBuilder;
  }
}

如下是对SparkCopyOnWriteOperation::buildCopyOnWriteScan方法的完整定义：

  public Scan buildCopyOnWriteScan() {
    // table变量，是一个BaseTable实例，因为从Spark的代码流转到Iceberg侧时，使用的都是Iceberg中定义的类
    // 这里是从当前表找到最新的Snapshot
    Snapshot snapshot = SnapshotUtil.latestSnapshot(table, readConf.branch());

    if (snapshot == null) {
      return new SparkCopyOnWriteScan(
          spark, table, readConf, schemaWithMetadataColumns(), filterExpressions);
    }

    Schema expectedSchema = schemaWithMetadataColumns();
    // Snapshot存在，说明有数据，因此需要生成Scan实例
    //   default BatchScan newBatchScan() {
    //     return new BatchScanAdapter(newScan());
    //   }
    // 由于这里的table类型为BaseTable，因此会调用newScan()方法生成DataTableScan的实例，而BatchScan则是一个代理类
    BatchScan scan =
        table
            .newBatchScan()
            .useSnapshot(snapshot.snapshotId())
            .ignoreResiduals()
            .caseSensitive(caseSensitive)
            .filter(filterExpression())
            .project(expectedSchema);

    scan = configureSplitPlanning(scan);
    // 返回一个实现了Spark中的Scan接口的实例
    return new SparkCopyOnWriteScan(
        spark, table, scan, snapshot, readConf, expectedSchema, filterExpressions);
  }

SparkCopyOnWriteScan负责生成Spark.Batch

我们知道SparkCopyOnWriteScan实现的Spark中的Scan接口，而Scan是一个逻辑上的数据读取器，就像逻辑计划那样，因此还需要通过它的Scan::toBatch方法，创建一个直接可执行的实体类对象

class SparkCopyOnWriteScan extends SparkPartitioningAwareScan<FileScanTask>

  @Override
  public Batch toBatch() {
    // 返回一个Spark可操作的Batch实例，负责对待读取的数据划分Batches
    // 注意这里在创建SparkBatch实例时，taskGroups()的调用，这个方法实际上是调用Iceberg的接口，搜索此次Scan任务需要读取的所有数据。
    return new SparkBatch(
        sparkContext, table, readConf, groupingKeyType(), taskGroups(), expectedSchema, hashCode());
  }
}

SparkCopyOnWriteScan::taskGroups基于SnapshotScan::planFiles方法实现

public abstract class SnapshotScan<ThisT, T extends ScanTask, G extends ScanTaskGroup<T>>

  @Override
  public CloseableIterable<T> planFiles() {
    // 获取要读取的Snapshot
    Snapshot snapshot = snapshot();

    if (snapshot == null) {
      LOG.info("Scanning empty table {}", table());
      return CloseableIterable.empty();
    }

    LOG.info(
        "Scanning table {} snapshot {} created at {} with filter {}",
        table(),
        snapshot.snapshotId(),
        DateTimeUtil.formatTimestampMillis(snapshot.timestampMillis()),
        ExpressionUtil.toSanitizedString(filter()));

    Listeners.notifyAll(new ScanEvent(table().name(), snapshot.snapshotId(), filter(), schema()));
    List<Integer> projectedFieldIds = Lists.newArrayList(TypeUtil.getProjectedIds(schema()));
    List<String> projectedFieldNames =
        projectedFieldIds.stream().map(schema()::findColumnName).collect(Collectors.toList());

    Timer.Timed planningDuration = scanMetrics().totalPlanningDuration().start();

    return CloseableIterable.whenComplete(
        doPlanFiles(), // doPlanFiles()方法会通过Iceberg的接口，搜索所有要读取的data文件和delete文件
        () -> {
          planningDuration.stop();
          Map<String, String> metadata = Maps.newHashMap(context().options());
          metadata.putAll(EnvironmentContext.get());
          ScanReport scanReport =
              ImmutableScanReport.builder()
                  .schemaId(schema().schemaId())
                  .projectedFieldIds(projectedFieldIds)
                  .projectedFieldNames(projectedFieldNames)
                  .tableName(table().name())
                  .snapshotId(snapshot.snapshotId())
                  .filter(ExpressionUtil.sanitize(filter()))
                  .scanMetrics(ScanMetricsResult.fromScanMetrics(scanMetrics()))
                  .metadata(metadata)
                  .build();
          context().metricsReporter().report(scanReport);
        });
  }
}

SparkBatch负责生成Partitions及Partition Reader

SparkBatch继承自Spark中的Batch接口

class SparkBatch implements Batch {
  private final JavaSparkContext sparkContext;
  private final Table table;
  private final String branch;
  private final SparkReadConf readConf;
  private final Types.StructType groupingKeyType;
  // 保存了由SparkCopyOnWriteScan::taskGroups()方法生成的所有要读取的Iceberg管理的data文件和delete文件，
  // 这些文件按对应的分区数据进行分组，并且一个分区的数据文件可能被划分到多个groups
  private final List<? extends ScanTaskGroup<?>> taskGroups;
  private final Schema expectedSchema;
  private final boolean caseSensitive;
  private final boolean localityEnabled;
  private final int scanHashCode;

  @Override
  public InputPartition[] planInputPartitions() {
    // 负责对要读取的数据进行分区
    // broadcast the table metadata as input partitions will be sent to executors
    Broadcast<Table> tableBroadcast =
        sparkContext.broadcast(SerializableTableWithSize.copyOf(table));
    String expectedSchemaString = SchemaParser.toJson(expectedSchema);
    // 一个Group就对应Spark中的一个Partition
    InputPartition[] partitions = new InputPartition[taskGroups.size()];

    Tasks.range(partitions.length)
        .stopOnFailure()
        .executeWith(localityEnabled ? ThreadPools.getWorkerPool() : null)
        .run(
            index ->
                partitions[index] =
                    new SparkInputPartition(
                        groupingKeyType, // 一个taskGroup包含的文件拥有相同的Grouping key
                        taskGroups.get(index),
                        tableBroadcast,
                        branch,
                        expectedSchemaString,
                        caseSensitive,
                        localityEnabled));

    return partitions;
  }

  @Override
  public PartitionReaderFactory createReaderFactory() {
    // 负责创建读取数据的Reader，支持列式读取和行式读取
    if (useParquetBatchReads()) {
      int batchSize = readConf.parquetBatchSize();
      return new SparkColumnarReaderFactory(batchSize);

    } else if (useOrcBatchReads()) {
      int batchSize = readConf.orcBatchSize();
      return new SparkColumnarReaderFactory(batchSize);

    } else {
      return new SparkRowReaderFactory();
    }
  }
}

从SparkBatch构建数据读取的物理执行计划

前文提到的Spark中有关数据的读写接口，都是由DataSourceV2中定义的，因此对于数据读取的逻辑计划（DataSourceV2ScanRelation），会先转换成物理执行计划BatchScanExec。

case class BatchScanExec(
    output: Seq[AttributeReference],
    @transient scan: Scan) extends DataSourceV2ScanExecBase {
  // scan，对应于Iceberg中的SparkCopyOnWriteScan
  // 因此batch变量是一个SparkBatch实例
  @transient lazy val batch = scan.toBatch

  // TODO: unify the equal/hashCode implementation for all data source v2 query plans.
  override def equals(other: Any): Boolean = other match {
    case other: BatchScanExec => this.batch == other.batch
    case _ => false
  }

  override def hashCode(): Int = batch.hashCode()
  // 调用SparkBatch::planInputPartitions生成partitions信息
  @transient override lazy val partitions: Seq[InputPartition] = batch.planInputPartitions()
  // 调用SparkBatch::createReaderFactory生成Reader工厂对象
  override lazy val readerFactory: PartitionReaderFactory = batch.createReaderFactory()

  override lazy val inputRDD: RDD[InternalRow] = {
    // 执行时，净当前的物理执行计划，转换为一个RDD，并传递给所有的RDD以及Reader工厂
    new DataSourceRDD(sparkContext, partitions, readerFactory, supportsColumnar)
  }

  override def doCanonicalize(): BatchScanExec = {
    this.copy(output = output.map(QueryPlan.normalizeExpressions(_, output)))
  }
}

DataSourceRDD计算时实例化Reader并完成读数据

这里需要重点关注的是compute方法，在Spark中，每一个Partition都对应一个Task，这个Task负责最终调用compute方法，触发当前分区上的计算逻辑。

// columnar scan.
class DataSourceRDD(
    sc: SparkContext,
    @transient private val inputPartitions: Seq[InputPartition],
    partitionReaderFactory: PartitionReaderFactory,
    columnarReads: Boolean)
  extends RDD[InternalRow](sc, Nil) {

  override protected def getPartitions: Array[Partition] = {
    inputPartitions.zipWithIndex.map {
      case (inputPartition, index) => new DataSourceRDDPartition(index, inputPartition)
    }.toArray
  }

  private def castPartition(split: Partition): DataSourceRDDPartition = split match {
    case p: DataSourceRDDPartition => p
    case _ => throw new SparkException(s"[BUG] Not a DataSourceRDDPartition: $split")
  }

  override def compute(split: Partition, context: TaskContext): Iterator[InternalRow] = {
    // partition对应于Iceberg中的一个TaskGroup，而一个TaskGroup的数据文件拥有相同的Partition data
    val inputPartition = castPartition(split).inputPartition
    val (iter, reader) = if (columnarReads) {
      // 列读
      // batchReader实际上是一个BatchDataReader的实例
      val batchReader = partitionReaderFactory.createColumnarReader(inputPartition)
      val iter = new MetricsBatchIterator(new PartitionIterator[ColumnarBatch](batchReader))
      (iter, batchReader)
    } else {
      // 行读
      val rowReader = partitionReaderFactory.createReader(inputPartition)
      val iter = new MetricsRowIterator(new PartitionIterator[InternalRow](rowReader))
      (iter, rowReader)
    }
    context.addTaskCompletionListener[Unit](_ => reader.close())
    // TODO: SPARK-25083 remove the type erasure hack in data source scan
    new InterruptibleIterator(context, iter.asInstanceOf[Iterator[InternalRow]])
  }

  override def getPreferredLocations(split: Partition): Seq[String] = {
    castPartition(split).inputPartition.preferredLocations()
  }
}

BatchDataReader读取数据

BatchDataReader继承自Spark中的PartitionReader<ColumnarBatch>接口

class BatchDataReader extends BaseBatchReader<FileScanTask>
    implements PartitionReader<ColumnarBatch> {
  
  // 返回一个迭代器，可以在FileScanTask包含的所有data文件和delete文件，
  @Override
  protected CloseableIterator<ColumnarBatch> open(FileScanTask task) {
    String filePath = task.file().path().toString();
    LOG.debug("Opening data file {}", filePath);

    // update the current file for Spark's filename() function
    InputFileBlockHolder.set(filePath, task.start(), task.length());

    Map<Integer, ?> idToConstant = constantsMap(task, expectedSchema());

    InputFile inputFile = getInputFile(filePath);
    Preconditions.checkNotNull(inputFile, "Could not find InputFile associated with FileScanTask");
    // 创建一个SparkDeleteFilter实例，它负责收集等值删除文件 和 位置删除文件，并建立删除数据记录的索引，
    // 如此在每遍历一个data file时，就可以根据索引信息，确定当前的record是不是存活的。
    SparkDeleteFilter deleteFilter =
        task.deletes().isEmpty()
            ? null
            : new SparkDeleteFilter(filePath, task.deletes(), counter());
    // newBatchIterable()方法会根据inputFile的类型，创建相应的文件读取器，例如为Parquet创建VectorizedParquetReader
    return newBatchIterable(
            inputFile,
            task.file().format(),
            task.start(),
            task.length(),
            task.residual(),
            idToConstant,
            deleteFilter)
        .iterator();
  }
}

读取数据转换成Spark中的ColumnarBatch

不论是Parquet/Orc文件，最底层都是通过ColumnarBatchReader负责真正的数据读取与过滤

public class ColumnarBatchReader extends BaseBatchReader<ColumnarBatch> {
  private final boolean hasIsDeletedColumn;
  private DeleteFilter<InternalRow> deletes = null;
  private long rowStartPosInBatch = 0;

  public ColumnarBatchReader(List<VectorizedReader<?>> readers) {
    super(readers);
    this.hasIsDeletedColumn =
        readers.stream().anyMatch(reader -> reader instanceof DeletedVectorReader);
  }

  @Override
  public void setRowGroupInfo(
      PageReadStore pageStore, Map<ColumnPath, ColumnChunkMetaData> metaData, long rowPosition) {
    super.setRowGroupInfo(pageStore, metaData, rowPosition);
    this.rowStartPosInBatch = rowPosition;
  }

  public void setDeleteFilter(DeleteFilter<InternalRow> deleteFilter) {
    this.deletes = deleteFilter;
  }

  @Override
  public final ColumnarBatch read(ColumnarBatch reuse, int numRowsToRead) {
    if (reuse == null) {
      closeVectors();
    }
    // 通过内部类ColumnBatchLoader代理完成数据的读取与结果转换
    ColumnarBatch columnarBatch = new ColumnBatchLoader(numRowsToRead).loadDataToColumnBatch();
    rowStartPosInBatch += numRowsToRead;
    return columnarBatch;
  }

  private class ColumnBatchLoader {
    private final int numRowsToRead;
    // the rowId mapping to skip deleted rows for all column vectors inside a batch, it is null when
    // there is no deletes
    private int[] rowIdMapping;
    // the array to indicate if a row is deleted or not, it is null when there is no "_deleted"
    // metadata column
    private boolean[] isDeleted;

    /**
     * Build a row id mapping inside a batch, which skips deleted rows. Here is an example of how we
     * delete 2 rows in a batch with 8 rows in total. [0,1,2,3,4,5,6,7] -- Original status of the
     * row id mapping array [F,F,F,F,F,F,F,F] -- Original status of the isDeleted array Position
     * delete 2, 6 [0,1,3,4,5,7,-,-] -- After applying position deletes [Set Num records to 6]
     * [F,F,T,F,F,F,T,F] -- After applying position deletes
     *
     * @param deletedRowPositions a set of deleted row positions
     * @return the mapping array and the new num of rows in a batch, null if no row is deleted
     */
    Pair<int[], Integer> buildPosDelRowIdMapping(PositionDeleteIndex deletedRowPositions) {
      if (deletedRowPositions == null) {
        return null;
      }

      int[] posDelRowIdMapping = new int[numRowsToRead];
      int originalRowId = 0;
      int currentRowId = 0;
      while (originalRowId < numRowsToRead) {
        if (!deletedRowPositions.isDeleted(originalRowId + rowStartPosInBatch)) {
          posDelRowIdMapping[currentRowId] = originalRowId;
          currentRowId++;
        } else {
          if (hasIsDeletedColumn) {
            isDeleted[originalRowId] = true;
          }

          deletes.incrementDeleteCount();
        }
        originalRowId++;
      }

      if (currentRowId == numRowsToRead) {
        // there is no delete in this batch
        return null;
      } else {
        return Pair.of(posDelRowIdMapping, currentRowId);
      }
    }

    int[] initEqDeleteRowIdMapping() {
      int[] eqDeleteRowIdMapping = null;
      if (hasEqDeletes()) {
        eqDeleteRowIdMapping = new int[numRowsToRead];
        for (int i = 0; i < numRowsToRead; i++) {
          eqDeleteRowIdMapping[i] = i;
        }
      }

      return eqDeleteRowIdMapping;
    }

    /**
     * Filter out the equality deleted rows. Here is an example, [0,1,2,3,4,5,6,7] -- Original
     * status of the row id mapping array [F,F,F,F,F,F,F,F] -- Original status of the isDeleted
     * array Position delete 2, 6 [0,1,3,4,5,7,-,-] -- After applying position deletes [Set Num
     * records to 6] [F,F,T,F,F,F,T,F] -- After applying position deletes Equality delete 1 <= x <=
     * 3 [0,4,5,7,-,-,-,-] -- After applying equality deletes [Set Num records to 4]
     * [F,T,T,T,F,F,T,F] -- After applying equality deletes
     *
     * @param columnarBatch the {@link ColumnarBatch} to apply the equality delete
     */
    void applyEqDelete(ColumnarBatch columnarBatch) {
      Iterator<InternalRow> it = columnarBatch.rowIterator();
      int rowId = 0;
      int currentRowId = 0;
      while (it.hasNext()) {
        InternalRow row = it.next();
        if (deletes.eqDeletedRowFilter().test(row)) {
          // the row is NOT deleted
          // skip deleted rows by pointing to the next undeleted row Id
          rowIdMapping[currentRowId] = rowIdMapping[rowId];
          currentRowId++;
        } else {
          if (hasIsDeletedColumn) {
            isDeleted[rowIdMapping[rowId]] = true;
          }

          deletes.incrementDeleteCount();
        }

        rowId++;
      }

      columnarBatch.setNumRows(currentRowId);
    }
  }
}

Write的执行过程

从前面的章节可以看到，在构建Scan的过程中，会同时搜集data files和delete files，因此在调用Reader实例读取每一个TaskGroup中的数据文件时，同时会应用DeleteFilter，来过滤掉那些被删除的记录。

这个过程实际上就是一个\Merge On Read的过程。

而MERGE INTO的Write过程，在我之前的文章有解析，大体的思路就是将从target_table Scan得到的、经过删除过滤后的数据集，与source_table中的数据JOIN；从而产生带有变更标记的结果数据集（每个被标记为INSERT/UPDATE/DELETE）；在写出数据到文件时，就可以根据每一行的标记确定写出行为，最终只会产生Data Files，数据文件更加干净。