Apache Spark Catalyst API

You are an expert in the Apache Spark Catalyst query optimisation framework. This skill provides comprehensive guidance on using the Catalyst API for query processing, optimisation, and code generation.

Overview of Catalyst

Apache Spark Catalyst is a query optimisation framework that powers Spark SQL and DataFrames. It provides:

• Extensible query optimiser based on functional programming constructs
• Tree-based representation of query plans and expressions
• Rule-based transformations for query optimisation
• Code generation for high-performance query execution
• Cost-based optimisation for join reordering and other decisions

Core Architecture

Module Structure

The Catalyst module is located at sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ and contains:

catalyst/
├── analysis/           # Query analysis and resolution
├── catalog/           # Catalog management and metadata
├── expressions/       # Expression definitions and evaluation
├── optimizer/         # Query optimisation rules
├── parser/            # SQL parsing
├── planning/          # Query planning strategies
├── plans/             # Query plan representations
│   ├── logical/       # Logical plan operators
│   └── physical/      # Physical plan operators
├── rules/             # Rule execution framework
├── trees/             # Tree node infrastructure
├── types/             # Data type utilities
└── util/              # Utility classes

Fundamental Concepts

1. TreeNode

TreeNode is the base class for all tree structures in Catalyst, including expressions and query plans.

Key Concepts

abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
  // Children of this node.
  def children: Seq[BaseType]

  // Transform this tree by applying a function to all nodes.
  def transform(rule: PartialFunction[BaseType, BaseType]): BaseType

  // Transform all nodes bottom-up.
  def transformDown(rule: PartialFunction[BaseType, BaseType]): BaseType

  // Transform all nodes top-down.
  def transformUp(rule: PartialFunction[BaseType, BaseType]): BaseType

  // Fast equality check.
  def fastEquals(other: TreeNode[_]): Boolean

  // Tree pattern matching for efficient traversal.
  def treePatternBits: BitSet
}

Tree Transformation Patterns

Bottom-up transformation:

plan.transformUp {
  case Filter(condition, child) if isAlwaysTrue(condition) =>
    child
}

Top-down transformation:

plan.transformDown {
  case Project(projectList, child) =>
    // Transform project first, then children
    optimiseProject(projectList, child)
}

Collect nodes matching a pattern:

val filters = plan.collect {
  case f @ Filter(_, _) => f
}

2. Expression

Expression is the base class for all expression trees in Catalyst.

Expression Hierarchy

• LeafExpression: No children (e.g., Literal, AttributeReference)
• UnaryExpression: One child (e.g., Cast, Not, IsNull)
• BinaryExpression: Two children (e.g., Add, EqualTo, And)
• TernaryExpression: Three children (e.g., If, Substring)
• QuaternaryExpression: Four children

Key Properties

abstract class Expression extends TreeNode[Expression] {
  // Can this expression be evaluated at query planning time?
  def foldable: Boolean

  // Does this expression always return the same result for fixed inputs?
  def deterministic: Boolean

  // Can this expression evaluate to null?
  def nullable: Boolean

  // Data type of the expression result.
  def dataType: DataType

  // Evaluate this expression given an input row.
  def eval(input: InternalRow): Any

  // Generate code for this expression.
  def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode

  // Attributes referenced by this expression.
  def references: AttributeSet
}

Common Expression Types

Literal values:

Literal(42)                    // Integer literal
Literal("hello")               // String literal
Literal(null, StringType)      // Null literal

Attribute references:

AttributeReference("name", StringType, nullable = true)()
AttributeReference("age", IntegerType, nullable = false)()

Predicates:

EqualTo(left, right)           // left = right
GreaterThan(left, right)       // left > right
LessThanOrEqual(left, right)   // left <= right
And(left, right)               // left AND right
Or(left, right)                // left OR right
Not(child)                     // NOT child

Arithmetic:

Add(left, right)               // left + right
Subtract(left, right)          // left - right
Multiply(left, right)          // left * right
Divide(left, right)            // left / right

String operations:

Substring(str, pos, len)       // substring(str, pos, len)
Upper(child)                   // upper(child)
Lower(child)                   // lower(child)
Concat(children)               // concat(child1, child2, ...)

Type conversion:

Cast(child, targetType)        // cast(child as targetType)

3. QueryPlan

QueryPlan is the base class for both logical and physical query plans.

Key Properties

abstract class QueryPlan[PlanType <: QueryPlan[PlanType]]
  extends TreeNode[PlanType] {

  // Output schema of this plan node.
  def output: Seq[Attribute]

  // Set of output attributes.
  def outputSet: AttributeSet

  // Set of attributes from all children.
  def inputSet: AttributeSet

  // Attributes produced by this node.
  def producedAttributes: AttributeSet

  // Attributes referenced by expressions.
  def references: AttributeSet

  // Attributes referenced but not provided by children.
  def missingInput: AttributeSet

  // All expressions in this plan node.
  def expressions: Seq[Expression]

  // Transform expressions in this plan.
  def transformExpressions(rule: PartialFunction[Expression, Expression]): PlanType
}

4. LogicalPlan

Logical plans represent query semantics without execution strategy.

Common Logical Operators

Data sources:

// Read from a relation.
LogicalRelation(relation, output, catalogTable)

// Local in-memory data.
LocalRelation(output, data)

// Empty relation.
EmptyRelation(output)

Projections and filters:

// Project specific columns.
Project(projectList: Seq[NamedExpression], child: LogicalPlan)

// Filter rows.
Filter(condition: Expression, child: LogicalPlan)

// Select distinct rows.
Distinct(child: LogicalPlan)

Aggregations:

// Group by and aggregate.
Aggregate(
  groupingExpressions: Seq[Expression],
  aggregateExpressions: Seq[NamedExpression],
  child: LogicalPlan
)

Joins:

// Join two relations.
Join(
  left: LogicalPlan,
  right: LogicalPlan,
  joinType: JoinType,
  condition: Option[Expression],
  hint: JoinHint
)

Sorting:

// Sort rows.
Sort(
  order: Seq[SortOrder],
  global: Boolean,
  child: LogicalPlan
)

Limits:

// Limit number of rows.
Limit(limitExpr: Expression, child: LogicalPlan)

Set operations:

// Union of two relations.
Union(children: Seq[LogicalPlan])

// Intersection.
Intersect(left: LogicalPlan, right: LogicalPlan, isAll: Boolean)

// Difference.
Except(left: LogicalPlan, right: LogicalPlan, isAll: Boolean)

5. InternalRow

InternalRow is the internal representation of a row in Catalyst.

Key Methods

abstract class InternalRow extends SpecializedGetters {
  // Number of fields in this row.
  def numFields: Int

  // Check if field at ordinal is null.
  def isNullAt(ordinal: Int): Boolean

  // Get value at ordinal.
  def get(ordinal: Int, dataType: DataType): Any

  // Specialised getters for primitive types.
  def getBoolean(ordinal: Int): Boolean
  def getByte(ordinal: Int): Byte
  def getShort(ordinal: Int): Short
  def getInt(ordinal: Int): Int
  def getLong(ordinal: Int): Long
  def getFloat(ordinal: Int): Float
  def getDouble(ordinal: Int): Double
  def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal
  def getUTF8String(ordinal: Int): UTF8String

  // Update value at ordinal.
  def update(ordinal: Int, value: Any): Unit

  // Set field to null.
  def setNullAt(ordinal: Int): Unit

  // Create a copy of this row.
  def copy(): InternalRow

  // Convert to Scala sequence.
  def toSeq(schema: StructType): Seq[Any]
}

Rule-Based Transformation Framework

6. Rule and RuleExecutor

Rules define tree transformations, and RuleExecutor applies them in batches.

Defining Rules

// Basic rule.
object MyOptimisationRule extends Rule[LogicalPlan] {
  def apply(plan: LogicalPlan): LogicalPlan = plan.transformUp {
    case Filter(condition, child) if isAlwaysTrue(condition) =>
      child
  }
}

// Configurable rule.
case class MyParameterisedRule(conf: SQLConf) extends Rule[LogicalPlan] {
  def apply(plan: LogicalPlan): LogicalPlan = {
    if (conf.myFeatureEnabled) {
      optimisePlan(plan)
    } else {
      plan
    }
  }
}

Rule Execution Strategies

abstract class RuleExecutor[TreeType <: TreeNode[_]] {
  // Define batches of rules to execute.
  protected def batches: Seq[Batch]

  // Execute all batches on the plan.
  def execute(plan: TreeType): TreeType
}

// Batch execution strategies.
abstract class Strategy
case class Once extends Strategy
case class FixedPoint(maxIterations: Int) extends Strategy

Example RuleExecutor

object MyOptimiser extends RuleExecutor[LogicalPlan] {
  val batches = Seq(
    Batch("Normalisation", Once,
      EliminateSubqueryAliases,
      RemoveRedundantAliases
    ),
    Batch("Operator Optimisation", FixedPoint(100),
      PushDownPredicate,
      ConstantFolding,
      ColumnPruning
    ),
    Batch("Join Reordering", Once,
      CostBasedJoinReorder
    )
  )
}

7. Analyzer

The Analyzer resolves unresolved logical plans by binding attributes, functions, and tables.

Key Analysis Rules

• ResolveReferences: Bind attribute references to their sources
• ResolveRelations: Bind table references to catalog entries
• ResolveFunctions: Bind function calls to function implementations
• TypeCoercion: Insert implicit type casts
• ResolveSubquery: Analyse subquery expressions
• CheckAnalysis: Verify the plan is valid

Example Usage

val analyzer = new Analyzer(catalogManager)
val analysedPlan = analyzer.execute(unresolvedPlan)

8. Optimizer

The Optimizer transforms logical plans to improve query performance.

Key Optimisation Rules

Predicate pushdown:

// Push filters below projections.
PushDownPredicate

// Push filters into join conditions.
PushPredicateThroughJoin

// Push filters to data sources.
PushDownPredicates

Projection pushdown:

// Eliminate unnecessary columns.
ColumnPruning

// Combine adjacent projections.
CollapseProject

Constant folding:

// Evaluate constant expressions.
ConstantFolding

// Simplify expressions.
SimplifyConditionals
SimplifyCasts

Join optimisation:

// Reorder joins for better performance.
CostBasedJoinReorder

// Eliminate redundant joins.
EliminateOuterJoin

Subquery optimisation:

// Decorrelate correlated subqueries.
DecorrelateInnerQuery

// Merge scalar subqueries.
MergeScalarSubqueries

Code Generation

9. CodeGen Framework

Catalyst generates optimised Java bytecode for query execution.

Key Components

CodegenContext:

Manages code generation state including variable declarations, functions, and class structure.

ExprCode:

Represents generated code for an expression evaluation.

case class ExprCode(
  code: Block,              // Generated code block
  isNull: ExprValue,        // Variable for null check
  value: ExprValue          // Variable for result value
)

Expression Code Generation

trait CodegenFallback extends Expression {
  // Fallback to interpreted evaluation.
  protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
    ctx.references += this
    val objectTerm = ctx.addReferenceObj("expression", this)
    ExprCode(
      code = code"""
        boolean ${ev.isNull} = true;
        ${CodeGenerator.javaType(dataType)} ${ev.value} =
          ${CodeGenerator.defaultValue(dataType)};
        Object result = $objectTerm.eval(${ctx.INPUT_ROW});
        if (result != null) {
          ${ev.isNull} = false;
          ${ev.value} = (${CodeGenerator.boxedType(dataType)}) result;
        }
      """,
      isNull = ev.isNull,
      value = ev.value
    )
  }
}

Projection Generation

// Generate unsafe projection from expressions.
val projection = GenerateUnsafeProjection.generate(expressions)
val result = projection(inputRow)

// Generate mutable projection.
val mutableProjection = GenerateMutableProjection.generate(expressions)
val outputRow = mutableProjection(inputRow)

// Generate safe projection.
val safeProjection = GenerateSafeProjection.generate(expressions)

Predicate Generation

// Generate predicate for filter condition.
val predicate = GeneratePredicate.generate(condition)
val passes = predicate.eval(row)

Ordering Generation

// Generate row comparator.
val ordering = GenerateOrdering.generate(sortOrders)
val compareResult = ordering.compare(row1, row2)

Working with Data Types

10. Catalyst Type System

Primitive Types

BooleanType
ByteType
ShortType
IntegerType
LongType
FloatType
DoubleType
StringType
BinaryType
DateType
TimestampType
TimestampNTZType

Complex Types

// Array type.
ArrayType(elementType: DataType, containsNull: Boolean)

// Map type.
MapType(keyType: DataType, valueType: DataType, valueContainsNull: Boolean)

// Struct type.
StructType(fields: Seq[StructField])

// Struct field.
StructField(name: String, dataType: DataType, nullable: Boolean, metadata: Metadata)

Decimal Types

DecimalType(precision: Int, scale: Int)
DecimalType.SYSTEM_DEFAULT  // Decimal(38, 18)

Common Patterns and Best Practices

Pattern 1: Creating Custom Expressions

case class MyCustomFunction(child: Expression) extends UnaryExpression {
  // Define output type.
  override def dataType: DataType = StringType

  // Can the result be null?
  override def nullable: Boolean = child.nullable

  // Evaluate the expression.
  override def eval(input: InternalRow): Any = {
    val value = child.eval(input)
    if (value == null) {
      null
    } else {
      // Custom logic here.
      UTF8String.fromString(value.toString.toUpperCase)
    }
  }

  // Generate code for this expression.
  override protected def doGenCode(
      ctx: CodegenContext,
      ev: ExprCode): ExprCode = {
    val childGen = child.genCode(ctx)
    ev.copy(code = code"""
      ${childGen.code}
      boolean ${ev.isNull} = ${childGen.isNull};
      ${CodeGenerator.javaType(dataType)} ${ev.value} =
        ${CodeGenerator.defaultValue(dataType)};
      if (!${ev.isNull}) {
        ${ev.value} = UTF8String.fromString(
          ${childGen.value}.toString().toUpperCase());
      }
    """)
  }

  // Override for pretty printing.
  override def prettyName: String = "my_custom_function"
}

Pattern 2: Creating Custom Optimisation Rules

object EliminateRedundantCasts extends Rule[LogicalPlan] {
  def apply(plan: LogicalPlan): LogicalPlan = plan.transformAllExpressions {
    case Cast(child, dataType, _, _) if child.dataType == dataType =>
      // Remove cast if types match.
      child
  }
}

Pattern 3: Pattern Matching on Plans

plan match {
  case Project(projectList, child) =>
    // Handle projection.

  case Filter(condition, Project(projectList, child)) =>
    // Handle filter over projection.

  case Join(left, right, joinType, Some(condition), _) =>
    // Handle join with condition.

  case Aggregate(grouping, aggregates, child) =>
    // Handle aggregation.

  case _ =>
    // Default case.
}

Pattern 4: Traversing Expression Trees

// Find all attribute references.
val attributes = expr.collect {
  case a: AttributeReference => a
}

// Find all subqueries.
val subqueries = expr.collect {
  case s: SubqueryExpression => s
}

// Transform specific expression types.
val transformed = expr.transformUp {
  case Add(Literal(0, _), right) => right
  case Add(left, Literal(0, _)) => left
}

Pattern 5: Attribute Resolution

// Resolve attribute by name.
def resolve(attrName: String, input: LogicalPlan): Option[Attribute] = {
  input.output.find(_.name == attrName)
}

// Resolve with qualifier.
def resolveQualified(
    qualifier: Seq[String],
    attrName: String,
    input: LogicalPlan): Option[Attribute] = {
  input.output.find { attr =>
    attr.qualifier.startsWith(qualifier) && attr.name == attrName
  }
}

Pattern 6: Working with Schema

// Create schema from attributes.
val schema = StructType(attributes.map { attr =>
  StructField(attr.name, attr.dataType, attr.nullable, attr.metadata)
})

// Convert schema to attributes.
val attributes = schema.toAttributes

// Add column to schema.
val newSchema = schema.add("newColumn", StringType, nullable = true)

// Drop column from schema.
val reducedSchema = StructType(schema.filterNot(_.name == "dropColumn"))

Testing Catalyst Components

Unit Testing Expressions

test("my custom function") {
  val input = Literal("hello")
  val expr = MyCustomFunction(input)

  // Test evaluation.
  assert(expr.eval(null) == UTF8String.fromString("HELLO"))

  // Test properties.
  assert(expr.dataType == StringType)
  assert(expr.foldable)
  assert(expr.deterministic)
}

Testing Optimisation Rules

test("eliminate redundant casts") {
  val plan = Project(
    Seq(Alias(Cast(AttributeReference("x", IntegerType)(), IntegerType), "y")()),
    testRelation
  )

  val optimised = EliminateRedundantCasts(plan)

  val expected = Project(
    Seq(Alias(AttributeReference("x", IntegerType)(), "y")()),
    testRelation
  )

  comparePlans(optimised, expected)
}

Performance Considerations

Efficient Tree Traversal

• Use tree patterns: Tree patterns enable efficient pattern matching without full traversal.
• Cache results: Use lazy vals for expensive computations like outputSet and references.
• Minimise transformations: Only transform nodes that need changes.
• Use fastEquals: For quick equality checks without deep comparison.

Code Generation Best Practices

• Minimise virtual calls: Generated code should avoid virtual method calls in tight loops.
• Use primitive types: Avoid boxing/unboxing in generated code.
• Inline small functions: Inline simple operations for better performance.
• Batch operations: Process multiple rows or columns together when possible.

Rule Application Strategies

• Order rules carefully: Place more common optimisations first.
• Use Once for expensive rules: Cost-based optimisation should run once.
• Set appropriate iteration limits: Balance optimisation quality with compile time.
• Track ineffective rules: Use rule tracking to skip rules that won't apply.

Common Pitfalls

1. Forgetting nullability: Always handle null values in expressions.
2. Incorrect type coercion: Ensure type casts are valid and safe.
3. Infinite rule loops: Rules must converge to a fixed point.
4. Breaking plan invariants: Maintain output schema and semantics.
5. Leaking state: Expressions should be stateless unless explicitly marked.
6. Inefficient traversal: Use tree patterns and targeted transformations.
7. Missing code generation: Implement doGenCode for custom expressions.
8. Incorrect attribute resolution: Use proper resolution with qualifiers.

Additional Resources

• Source code: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/
• Tests: sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/
• Spark SQL documentation: Official Spark SQL programming guide
• Research paper: "Spark SQL: Relational Data Processing in Spark" (SIGMOD 2015)

Summary

The Catalyst framework provides:

• Tree-based representation for queries and expressions
• Rule-based transformation framework for optimisation
• Extensibility for custom expressions and optimisations
• Code generation for high-performance execution
• Type-safe API for query manipulation

Key extension points:

• Custom expressions (extend Expression)
• Custom optimisation rules (extend Rule[LogicalPlan])
• Custom analysis rules (extend Rule[LogicalPlan])
• Custom data sources (implement TableProvider)
• Custom aggregate functions (extend AggregateFunction)

When working with Catalyst, focus on immutability, type safety, and efficient tree traversal patterns.

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