小编给大家分享一下spark 3.0.1集成delta 0.7.0之delta如何进行DDL操作，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！
分析
delta在0.7.0以前是不能够进行save表操作的，只能存储到文件中，也就是说他的元数据是和spark的其他元数据是分开的，delta是独立存在的，也是不能和其他表进行关联操作的，只有到了delta 0.7.0版本以后，才真正意义上和spark进行了集成，这也得益于spark 3.x的Catalog plugin API 特性。
还是先从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()
对于第二个配置 config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") 从spark configuration,我们可以看到对该spark.sql.catalog.spark_catalog的解释是
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'.
也就是说，通过该配置可以实现元数据的统一性,其实这也是spark社区和delta社区进行交互的一种结果
spark 3.x的Catalog plugin API
为了能搞懂delta为什么能够进行DDL和DML操作，就得先知道spark 3.x的Catalog plugin机制SPARK-31121.
首先是interface CatalogPlugin,该接口是catalog plugin的顶级接口，正如注释所说:
* 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".
可以通过spark.sql.catalog.catalog-name=com.example.YourCatalogClass集成到spark中
该类的实现还可以集成其他额外的tables views functions的接口，这里就得提到接口TableCatalog,该类提供了与tables相关的方法:
/**
* 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;
这样就可以基于TableCatalog开发自己的catalog，从而实现multi-catalog support
还得有个接口DelegatingCatalogExtension,这是个实现了CatalogExtension接口的抽象类，而CatalogExtension继承了TableCatalog, SupportsNamespaces。DeltaCatalog实现了DelegatingCatalogExtension ，这部分后续进行分析。
最后还有一个class CatalogManager,这个类是用来管理CatalogPlugins的，且是线程安全的:
/**
* 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 {
我们看到CatalogManager管理了v2版本的 CatalogPlugin和v1版本的sessionCatalog,这个是因为历史的原因导致必须得兼容v1版本
那CatalogManager在哪里被调用呢。 我们看一下BaseSessionStateBuilder ,可以看到该类中才是正宗使用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，主要是跟底层的元数据存储通信，以及管理临时视图，udf的，这一部分暂时不分析，重点放到v2版本的sessionCatalog， 我们看一下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)
}
}
我们分析一下listTables方法可知，v2的sessionCatalog操作 都是委托给了v1版本的sessionCatalog去操作的，其他的方法也是一样， 而且name默认为CatalogManager.SESSION_CATALOG_NAME,也就是spark_catalog，这里后面也会提到，注意一下。 而且，catalogmanager在逻辑计划中的分析器和优化器中也会用到，因为会用到其中的元数据：
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进行解析的核心中的核心，当然还有物理计划的生成。 那这些analyzer和optimizer是在哪里被调用呢？
我们举一个例子，DataSet中的filter方法就调用了：
*/
def filter(conditionExpr: String): Dataset[T] = {
filter(Column(sparkSession.sessionState.sqlParser.parseExpression(conditionExpr)))
}
sessionState.sqlParser就是刚才所说的sqlParser:
protected lazy val sqlParser: ParserInterface = {
extensions.buildParser(session, new SparkSqlParser(conf))
}
只有整个逻辑 从sql解析到使用元数据的数据链路，我们就能大致知道怎么一回事了。
delta的DeltaCatalog
我们回过头来看看，delta的DeltaCatalog是怎么和spark 3.x进行结合的 ，上源码DeltaCatalog：
class DeltaCatalog(val spark: SparkSession) extends DelegatingCatalogExtension
with StagingTableCatalog
with SupportsPathIdentifier {
def this() = {
this(SparkSession.active)
}
...
就如之前所说的DeltaCatalog继承了DelegatingCatalogExtension,从名字可以看出这是一个委托类，那到底是怎么委托的呢以及委托给谁呢？
public abstract class DelegatingCatalogExtension implements CatalogExtension {
private CatalogPlugin delegate;
public final void setDelegateCatalog(CatalogPlugin delegate) {
this.delegate = delegate;
}
该类中有个setDelegateCatalog方法，该方法在CatalogManager中的loadV2SessionCatalog方法中被调用:
private def loadV2SessionCatalog(): CatalogPlugin = {
Catalogs.load(SESSION_CATALOG_NAME, conf) match {
case extension: CatalogExtension =>
extension.setDelegateCatalog(defaultSessionCatalog)
extension
case other => other
}
}
而该方法被v2SessionCatalog调用:
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)
}
这个就是返回默认的v2版本的SessionCatalog实例,分析一下这个方法：
首先得到配置项SQLConf.V2_SESSION_CATALOG_IMPLEMENTATION，也就是spark.sql.catalog.spark_catalog配置， 如果spark配置了的话，就调用loadV2SessionCatalog加载该类，，否则就加载默认的v2SessionCatalog，也就是V2SessionCatalog实例
这里我们就发现了:
delta配置的spark.sql.catalog.spark_catalog为"org.apache.spark.sql.delta.catalog.DeltaCatalog",也就是说，spark中的V2SessionCatalog是DeltaCatalog的实例，而DeltaCatalog的委托给了BaseSessionStateBuilder中的V2SessionCatalog实例。
具体看看DeltaCatalog 的createTable方法，其他的方法类似：
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
}
}
-
判断是否是delta数据源，如果是的话，跳到createDeltaTable方法，否则直接调用super.createTable方法，
-
createDeltaTable先会进行delta特有的CreateDeltaTableCommand.run()命令写入delta数据，之后载loadTable
-
loadTable则会调用super的loadTable，而方法会调用V2SessionCatalog的loadTable,而V2SessionCatalog最终会调用v1版本sessionCatalog的getTableMetadata方法，从而组成V1Table(catalogTable)返回，这样就把delta的元数据信息持久化到了v1 SessionCatalog管理的元数据库中
-
如果不是delta数据源，则调用super.createTable方法，该方法调用V2SessionCatalog的createTable,而最终还是调用v1版本sessionCatalog的createTable方法
我们这里重点分析了delta数据源到元数据的存储，非delta数据源的代码就没有粘贴过来，有兴趣的自己可以编译源码跟踪一下
我们还得提一下spark.sql.defaultCatalog的默认配置为spark_catalog，也就是sql的默认catalog为spark_catalog,对应到delta的话就是DeltaCatalog。
以上是“spark 3.0.1集成delta 0.7.0之delta如何进行DDL操作”这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注亿速云行业资讯频道！
原创文章，作者：Maggie-Hunter，如若转载，请注明出处：https://blog.ytso.com/223004.html