【spark系列5】spark 3.0.1集成delta 0.7.0原理解析--delta如何進行DDL DML操作以及Catalog plugin API

前提

本文基於 spark 3.0.1
delta 0.7.0
我們都知道delta.io是一個給數據湖提供可靠性的開源存儲層的軟件，關於他的用處，可以參考Delta Lake，讓你從複雜的Lambda架構中解放出來,上篇文章我們分析了delta是如何自定義自己的sql,這篇文章我們分析一下delta數據是如何基於Catalog plugin API進行DDL DML sql操作的(spark 3.x以前是不支持的)

分析

delta在0.7.0以前是不能夠進行save表操作的，只能存儲到文件中，也就是說他的元數據是和spark的其他元數據是分開的，delta是獨立存在的，也是不能和其他表進行關聯操作的，只有到了delta 0.7.0版本以後，才真正意義上和spark進行了集成，這也得益於spark 3.x的Catalog plugin API 特性。
還是先從delta的configurate sparksession入手,如下:

import org.apache.spark.sql.SparkSession

val spark = SparkSession
  .builder()
  .appName("...")
  .master("...")
  .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")
  .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")
  .getOrCreate()

對於第二個配置 config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")
從spark configuration,我們可以看到對該spark.sql.catalog.spark_catalog的解釋是

A catalog implementation that will be used as the v2 interface to Spark's built-in v1 catalog: spark_catalog. This catalog shares its identifier namespace with the spark_catalog and must be consistent with it; for example, if a table can be loaded by the spark_catalog, this catalog must also return the table metadata. To delegate operations to the spark_catalog, implementations can extend 'CatalogExtension'.

也就是說，通過該配置可以實現元數據的統一性,其實這也是spark社區和delta社區進行交互的一種結果

spark 3.x的Catalog plugin API

爲了能搞懂delta爲什麼能夠進行DDL和DML操作，就得先知道spark 3.x的Catalog plugin機制SPARK-31121.

首先是interface CatalogPlugin,該接口是catalog plugin的頂級接口，正如註釋所說:

 * A marker interface to provide a catalog implementation for Spark.
 * <p>
 * Implementations can provide catalog functions by implementing additional interfaces for tables,
 * views, and functions.
 * <p>
 * Catalog implementations must implement this marker interface to be loaded by
 * {@link Catalogs#load(String, SQLConf)}. The loader will instantiate catalog classes using the
 * required public no-arg constructor. After creating an instance, it will be configured by calling
 * {@link #initialize(String, CaseInsensitiveStringMap)}.
 * <p>
 * Catalog implementations are registered to a name by adding a configuration option to Spark:
 * {@code spark.sql.catalog.catalog-name=com.example.YourCatalogClass}. All configuration properties
 * in the Spark configuration that share the catalog name prefix,
 * {@code spark.sql.catalog.catalog-name.(key)=(value)} will be passed in the case insensitive
 * string map of options in initialization with the prefix removed.
 * {@code name}, is also passed and is the catalog's name; in this case, "catalog-name".

可以通過spark.sql.catalog.catalog-name=com.example.YourCatalogClass集成到spark中
該類的實現還可以集成其他額外的tables views functions的接口，這裏就得提到接口TableCatalog,該類提供了與tables相關的方法:

/**
   * List the tables in a namespace from the catalog.
   * <p>
   * If the catalog supports views, this must return identifiers for only tables and not views.
   *
   * @param namespace a multi-part namespace
   * @return an array of Identifiers for tables
   * @throws NoSuchNamespaceException If the namespace does not exist (optional).
   */
  Identifier[] listTables(String[] namespace) throws NoSuchNamespaceException;

  /**
   * Load table metadata by {@link Identifier identifier} from the catalog.
   * <p>
   * If the catalog supports views and contains a view for the identifier and not a table, this
   * must throw {@link NoSuchTableException}.
   *
   * @param ident a table identifier
   * @return the table's metadata
   * @throws NoSuchTableException If the table doesn't exist or is a view
   */
  Table loadTable(Identifier ident) throws NoSuchTableException;

這樣就可以基於TableCatalog開發自己的catalog，從而實現multi-catalog support

還得有個接口DelegatingCatalogExtension,這是個實現了CatalogExtension接口的抽象類，而CatalogExtension繼承了TableCatalog, SupportsNamespaces。DeltaCatalog實現了DelegatingCatalogExtension ，這部分後續進行分析。
最後還有一個class CatalogManager,這個類是用來管理CatalogPlugins的，且是線程安全的:

/**
 * A thread-safe manager for [[CatalogPlugin]]s. It tracks all the registered catalogs, and allow
 * the caller to look up a catalog by name.
 *
 * There are still many commands (e.g. ANALYZE TABLE) that do not support v2 catalog API. They
 * ignore the current catalog and blindly go to the v1 `SessionCatalog`. To avoid tracking current
 * namespace in both `SessionCatalog` and `CatalogManger`, we let `CatalogManager` to set/get
 * current database of `SessionCatalog` when the current catalog is the session catalog.
 */
// TODO: all commands should look up table from the current catalog. The `SessionCatalog` doesn't
//       need to track current database at all.
private[sql]
class CatalogManager(
    conf: SQLConf,
    defaultSessionCatalog: CatalogPlugin,
    val v1SessionCatalog: SessionCatalog) extends Logging {
 

我們看到CatalogManager管理了v2版本的 CatalogPlugin和v1版本的sessionCatalog,這個是因爲歷史的原因導致必須得兼容v1版本

那CatalogManager在哪裏被調用呢。
我們看一下BaseSessionStateBuilder ,可以看到該類中才是正宗使用CatalogManager的地方：

/**
   * Catalog for managing table and database states. If there is a pre-existing catalog, the state
   * of that catalog (temp tables & current database) will be copied into the new catalog.
   *
   * Note: this depends on the `conf`, `functionRegistry` and `sqlParser` fields.
   */
  protected lazy val catalog: SessionCatalog = {
    val catalog = new SessionCatalog(
      () => session.sharedState.externalCatalog,
      () => session.sharedState.globalTempViewManager,
      functionRegistry,
      conf,
      SessionState.newHadoopConf(session.sparkContext.hadoopConfiguration, conf),
      sqlParser,
      resourceLoader)
    parentState.foreach(_.catalog.copyStateTo(catalog))
    catalog
  }

  protected lazy val v2SessionCatalog = new V2SessionCatalog(catalog, conf)

  protected lazy val catalogManager = new CatalogManager(conf, v2SessionCatalog, catalog)

SessionCatalog 是v1版本的，主要是跟底層的元數據存儲通信，以及管理臨時視圖，udf的，這一部分暫時不分析，重點放到v2版本的sessionCatalog，
我們看一下V2SessionCatalog:

/**
 * A [[TableCatalog]] that translates calls to the v1 SessionCatalog.
 */
class V2SessionCatalog(catalog: SessionCatalog, conf: SQLConf)
  extends TableCatalog with SupportsNamespaces {
  import org.apache.spark.sql.connector.catalog.CatalogV2Implicits.NamespaceHelper
  import V2SessionCatalog._

  override val defaultNamespace: Array[String] = Array("default")

  override def name: String = CatalogManager.SESSION_CATALOG_NAME

  // This class is instantiated by Spark, so `initialize` method will not be called.
  override def initialize(name: String, options: CaseInsensitiveStringMap): Unit = {}

  override def listTables(namespace: Array[String]): Array[Identifier] = {
    namespace match {
      case Array(db) =>
        catalog
          .listTables(db)
          .map(ident => Identifier.of(Array(ident.database.getOrElse("")), ident.table))
          .toArray
      case _ =>
        throw new NoSuchNamespaceException(namespace)
    }
  }

我們分析一下listTables方法可知，v2的sessionCatalog操作 都是委託給了v1版本的sessionCatalog去操作的，其他的方法也是一樣，
而且name默認爲CatalogManager.SESSION_CATALOG_NAME,也就是spark_catalog，這裏後面也會提到，注意一下。
而且，catalogmanager在邏輯計劃中的分析器和優化器中也會用到，因爲會用到其中的元數據：

protected def analyzer: Analyzer = new Analyzer(catalogManager, conf) {
...
protected def optimizer: Optimizer = {
    new SparkOptimizer(catalogManager, catalog, experimentalMethods) {
      override def earlyScanPushDownRules: Seq[Rule[LogicalPlan]] =
        super.earlyScanPushDownRules ++ customEarlyScanPushDownRules

      override def extendedOperatorOptimizationRules: Seq[Rule[LogicalPlan]] =
        super.extendedOperatorOptimizationRules ++ customOperatorOptimizationRules
    }
  }

而analyzer和optimizer正是spark sql進行解析的核心中的核心，當然還有物理計劃的生成。
那這些analyzer和optimizer是在哪裏被調用呢？
我們舉一個例子，DataSet中的filter方法就調用了：

 */
  def filter(conditionExpr: String): Dataset[T] = {
    filter(Column(sparkSession.sessionState.sqlParser.parseExpression(conditionExpr)))
  }

sessionState.sqlParser就是剛纔所說的sqlParser:

protected lazy val sqlParser: ParserInterface = {
    extensions.buildParser(session, new SparkSqlParser(conf))
  }

只有整個邏輯 從sql解析到使用元數據的數據鏈路，我們就能大致知道怎麼一回事了。

delta的DeltaCatalog

我們回過頭來看看，delta的DeltaCatalog是怎麼和spark 3.x進行結合的 ，上源碼DeltaCatalog：

class DeltaCatalog(val spark: SparkSession) extends DelegatingCatalogExtension
  with StagingTableCatalog
  with SupportsPathIdentifier {

  def this() = {
    this(SparkSession.active)
  }
  ...

就如之前所說的DeltaCatalog繼承了DelegatingCatalogExtension,從名字可以看出這是一個委託類，那到底是怎麼委託的呢以及委託給誰呢？

public abstract class DelegatingCatalogExtension implements CatalogExtension {

  private CatalogPlugin delegate;

  public final void setDelegateCatalog(CatalogPlugin delegate) {
    this.delegate = delegate;
  }

該類中有個setDelegateCatalog方法，該方法在CatalogManager中的loadV2SessionCatalog方法中被調用:

private def loadV2SessionCatalog(): CatalogPlugin = {
    Catalogs.load(SESSION_CATALOG_NAME, conf) match {
      case extension: CatalogExtension =>
        extension.setDelegateCatalog(defaultSessionCatalog)
        extension
      case other => other
    }
  }

而該方法被v2SessionCatalog調用:

private[sql] def v2SessionCatalog: CatalogPlugin = {
    conf.getConf(SQLConf.V2_SESSION_CATALOG_IMPLEMENTATION).map { customV2SessionCatalog =>
      try {
        catalogs.getOrElseUpdate(SESSION_CATALOG_NAME, loadV2SessionCatalog())
      } catch {
        case NonFatal(_) =>
          logError(
            "Fail to instantiate the custom v2 session catalog: " + customV2SessionCatalog)
          defaultSessionCatalog
      }
    }.getOrElse(defaultSessionCatalog)
  }

這個就是返回默認的v2版本的SessionCatalog實例,分析一下這個方法：

   首先得到配置項SQLConf.V2_SESSION_CATALOG_IMPLEMENTATION，也就是spark.sql.catalog.spark_catalog配置，
   如果spark配置了的話，就調用loadV2SessionCatalog加載該類，，否則就加載默認的v2SessionCatalog，也就是V2SessionCatalog實例

這裏我們就發現了:
delta配置的spark.sql.catalog.spark_catalog爲"org.apache.spark.sql.delta.catalog.DeltaCatalog",也就是說，spark中的V2SessionCatalog是DeltaCatalog的實例，而DeltaCatalog的委託給了BaseSessionStateBuilder中的V2SessionCatalog實例。

具體看看DeltaCatalog 的createTable方法，其他的方法類似：

override def createTable(
      ident: Identifier,
      schema: StructType,
      partitions: Array[Transform],
      properties: util.Map[String, String]): Table = {
    if (DeltaSourceUtils.isDeltaDataSourceName(getProvider(properties))) {
      createDeltaTable(
        ident, schema, partitions, properties, sourceQuery = None, TableCreationModes.Create)
    } else {
      super.createTable(ident, schema, partitions, properties)
    }
  }
...
private def createDeltaTable(
      ident: Identifier,
      schema: StructType,
      partitions: Array[Transform],
      properties: util.Map[String, String],
      sourceQuery: Option[LogicalPlan],
      operation: TableCreationModes.CreationMode): Table = {
     ...
    val tableDesc = new CatalogTable(
      identifier = TableIdentifier(ident.name(), ident.namespace().lastOption),
      tableType = tableType,
      storage = storage,
      schema = schema,
      provider = Some("delta"),
      partitionColumnNames = partitionColumns,
      bucketSpec = maybeBucketSpec,
      properties = tableProperties.toMap,
      comment = Option(properties.get("comment")))
    // END: copy-paste from the super method finished.

    val withDb = verifyTableAndSolidify(tableDesc, None)
    ParquetSchemaConverter.checkFieldNames(tableDesc.schema.fieldNames)
    CreateDeltaTableCommand(
      withDb,
      getExistingTableIfExists(tableDesc),
      operation.mode,
      sourceQuery,
      operation,
      tableByPath = isByPath).run(spark)

    loadTable(ident)
      }
 override def loadTable(ident: Identifier): Table = {
    try {
      super.loadTable(ident) match {
        case v1: V1Table if DeltaTableUtils.isDeltaTable(v1.catalogTable) =>
          DeltaTableV2(
            spark,
            new Path(v1.catalogTable.location),
            catalogTable = Some(v1.catalogTable),
            tableIdentifier = Some(ident.toString))
        case o => o
      }
    
  }

• 判斷是否是delta數據源，如果是的話，跳到createDeltaTable方法，否則直接調用super.createTable方法，
• createDeltaTable先會進行delta特有的CreateDeltaTableCommand.run()命令寫入delta數據，之後載loadTable
• loadTable則會調用super的loadTable，而方法會調用V2SessionCatalog的loadTable,而V2SessionCatalog最終會調用v1版本sessionCatalog的getTableMetadata方法，從而組成V1Table(catalogTable)返回，這樣就把delta的元數據信息持久化到了v1 SessionCatalog管理的元數據庫中
• 如果不是delta數據源，則調用super.createTable方法，該方法調用V2SessionCatalog的createTable,而最終還是調用v1版本sessionCatalog的createTable方法

我們這裏重點分析了delta數據源到元數據的存儲，非delta數據源的代碼就沒有粘貼過來，有興趣的自己可以編譯源碼跟蹤一下

我們還得提一下spark.sql.defaultCatalog的默認配置爲spark_catalog，也就是sql的默認catalog爲spark_catalog,對應到delta的話就是DeltaCatalog。

至此，我們就把delta爲什麼能夠進行DDL和DML的原理結合spark的Catalog plugin API分析了一遍.其實搞懂了這些以後，自己也可以按照DeltaCatalog的方式擴展catalog，只不過是catalog的名字不要爲spark_catalog，否則會出現異常信息。如果非要爲spark_catalog的話，就得繼承DelegatingCatalogExtension類，把所有的元數據信息委託給V2SessionCatalog