Query Planning and Optimization

/**
 * Abstract base class for Blink planners providing common functionality
 */
abstract class PlannerBase(
  executor: Executor,
  config: TableConfig, 
  functionCatalog: FunctionCatalog,
  catalogManager: CatalogManager,
  isStreamingMode: Boolean
) extends Planner {

  /**
   * Translates modify operation to relational algebra representation
   * @param modifyOperation The operation to translate (INSERT, UPDATE, DELETE)
   * @return RelNode representing the operation in Calcite's algebra
   */
  def translateToRel(modifyOperation: ModifyOperation): RelNode
  
  /**
   * Optimizes a sequence of relational nodes using optimization rules
   * @param relNodes Sequence of RelNode instances to optimize
   * @return Optimized sequence of RelNode instances
   */
  def optimize(relNodes: Seq[RelNode]): Seq[RelNode]
  
  /**
   * Translates execution graph to Flink transformations
   * @param execGraph Execution node graph to translate
   * @return List of Flink transformations for job execution
   */
  def translateToPlan(execGraph: ExecNodeGraph): util.List[Transformation[_]]
  
  /**
   * Generates execution plan explanation for debugging and analysis
   * @param operations List of operations to explain
   * @param extraDetails Additional detail levels for explanation
   * @return String representation of the execution plan
   */
  def explain(operations: util.List[Operation], extraDetails: ExplainDetail*): String
  
  /**
   * Returns trait definitions for relational algebra optimization
   * @return Array of trait definitions used by this planner
   */
  protected def getTraitDefs: Array[RelTraitDef[_ <: RelTrait]]
  
  /**
   * Returns optimizer instance for this planner
   * @return Optimizer instance implementing optimization strategies
   */  
  protected def getOptimizer: Optimizer
  
  /**
   * Returns execution node graph processors for post-optimization processing
   * @return Sequence of processors for execution graph transformation
   */
  protected def getExecNodeGraphProcessors: Seq[ExecNodeGraphProcessor]
}

/**
 * Planner implementation for streaming execution mode
 */
class StreamPlanner(
  executor: Executor,
  config: TableConfig,
  functionCatalog: FunctionCatalog, 
  catalogManager: CatalogManager
) extends PlannerBase(executor, config, functionCatalog, catalogManager, isStreamingMode = true) {

  /**
   * Returns trait definitions specific to streaming execution
   * Includes distribution, mini-batch interval, modify kind, and update kind traits
   */
  protected def getTraitDefs: Array[RelTraitDef[_ <: RelTrait]] = Array(
    ConventionTraitDef.INSTANCE,
    FlinkRelDistributionTraitDef.INSTANCE,
    MiniBatchIntervalTraitDef.INSTANCE,
    ModifyKindSetTraitDef.INSTANCE,
    UpdateKindTraitDef.INSTANCE
  )
  
  /**
   * Returns stream-specific optimizer with common sub-graph optimization
   * @return StreamCommonSubGraphBasedOptimizer instance
   */
  protected def getOptimizer: Optimizer = new StreamCommonSubGraphBasedOptimizer(this)
  
  /**
   * Returns empty processor sequence for streaming (no additional processing needed)
   * @return Empty sequence of processors
   */
  protected def getExecNodeGraphProcessors: Seq[ExecNodeGraphProcessor] = Seq()
  
  /**
   * Translates execution graph to stream transformations
   * @param execGraph Execution node graph for streaming
   * @return List of streaming transformations
   */
  protected def translateToPlan(execGraph: ExecNodeGraph): util.List[Transformation[_]]
}

import org.apache.flink.table.planner.delegation.StreamPlanner

// StreamPlanner is typically created via BlinkPlannerFactory
// but can be instantiated directly for advanced use cases
val streamPlanner = new StreamPlanner(executor, tableConfig, functionCatalog, catalogManager)

// Translate SQL to execution plan
val operations = parseAndValidateSQL("SELECT * FROM source WHERE value > 100")
val transformations = streamPlanner.translateToRel(operations)

/**
 * Planner implementation for batch execution mode
 */
class BatchPlanner(
  executor: Executor,
  config: TableConfig,
  functionCatalog: FunctionCatalog,
  catalogManager: CatalogManager  
) extends PlannerBase(executor, config, functionCatalog, catalogManager, isStreamingMode = false) {

  /**
   * Returns trait definitions specific to batch execution
   * Focuses on distribution and convention traits without streaming-specific traits
   */
  protected def getTraitDefs: Array[RelTraitDef[_ <: RelTrait]]
  
  /**
   * Returns batch-specific optimizer with batch optimization strategies
   * @return BatchCommonSubGraphBasedOptimizer instance
   */
  protected def getOptimizer: Optimizer
  
  /**
   * Returns batch-specific processors for execution graph optimization
   * @return Sequence of batch-specific processors
   */
  protected def getExecNodeGraphProcessors: Seq[ExecNodeGraphProcessor]
  
  /**
   * Translates execution graph to batch transformations
   * @param execGraph Execution node graph for batch processing
   * @return List of batch transformations
   */
  protected def translateToPlan(execGraph: ExecNodeGraph): util.List[Transformation[_]]
}

/**
 * Base optimizer interface for query optimization
 */
trait Optimizer {
  /**
   * Optimizes relational algebra expressions using optimization rules
   * @param roots Root nodes of the query plan to optimize
   * @return Optimized execution node graph
   */
  def optimize(roots: Seq[RelNode]): ExecNodeGraph
}

/**
 * Stream-specific optimizer with common sub-graph based optimization
 */
class StreamCommonSubGraphBasedOptimizer(planner: PlannerBase) extends Optimizer {
  /**
   * Applies streaming-specific optimization rules and strategies
   * Includes watermark propagation, mini-batch optimization, and state optimization
   */
  def optimize(roots: Seq[RelNode]): ExecNodeGraph
}

/**
 * Batch-specific optimizer with batch optimization strategies
 */
class BatchCommonSubGraphBasedOptimizer(planner: PlannerBase) extends Optimizer {
  /**
   * Applies batch-specific optimization rules and strategies  
   * Includes join reordering, aggregation pushdown, and partition pruning
   */
  def optimize(roots: Seq[RelNode]): ExecNodeGraph
}

/**
 * Graph representation of execution nodes for query execution
 */
public class ExecNodeGraph {
  /**
   * Returns root nodes of the execution graph
   * @return List of root execution nodes
   */
  public List<ExecNode<?>> getRootNodes();
  
  /**
   * Accepts visitor for traversing the execution graph
   * @param visitor Visitor implementation for graph traversal
   */
  public void accept(ExecNodeVisitor visitor);
  
  /**
   * Returns all nodes in the execution graph  
   * @return Set of all execution nodes in the graph
   */
  public Set<ExecNode<?>> getAllNodes();
}

/**
 * Base interface for execution nodes in the graph
 */
public interface ExecNode<T> {
  /**
   * Translates this execution node to Flink transformation
   * @param planner Planner context for translation
   * @return Flink transformation representing this node
   */
  Transformation<T> translateToPlan(Planner planner);
  
  /**
   * Returns input nodes of this execution node
   * @return List of input execution nodes
   */
  List<ExecNode<?>> getInputNodes();
  
  /**
   * Returns output type of this execution node
   * @return Type information for the output
   */
  TypeInformation<T> getOutputType();
}

// Enable cost-based optimization
tableConfig.getConfiguration().setString("table.optimizer.cbo-enabled", "true");

// Set join reordering optimization
tableConfig.getConfiguration().setString("table.optimizer.join-reorder-enabled", "true");

// Configure mini-batch settings for streaming
tableConfig.getConfiguration().setString("table.exec.mini-batch.enabled", "true");
tableConfig.getConfiguration().setString("table.exec.mini-batch.allow-latency", "1s");