Building Logical Plans

The source code discussed in this chapter can be found in the logical-plan module of the KQuery project.

Building Logical Plans The Hard Way

Now that we have defined classes for a subset of logical plans, we can combine them programmatically.

Here is some verbose code for building a plan for the query SELECT * FROM employee WHERE state = 'CO' against a CSV file containing the columns id, first_name, last_name, state, job_title, salary.

// create a plan to represent the data source
val csv = CsvDataSource("employee.csv")

// create a plan to represent the scan of the data source (FROM)
val scan = Scan("employee", csv, listOf())

// create a plan to represent the selection (WHERE)
val filterExpr = Eq(Column("state"), LiteralString("CO"))
val selection = Selection(scan, filterExpr)

// create a plan to represent the projection (SELECT)
val projectionList = listOf(Column("id"), 
val plan = Projection(selection, projectionList)

// print the plan

This prints the following plan:

Projection: #id, #first_name, #last_name, #state, #salary
    Filter: #state = 'CO'
        Scan: employee; projection=None

The same code can also be written more concisely like this:

val plan = Projection(
    Scan("employee", CsvDataSource("employee.csv"), listOf()),
    Eq(Column(3), LiteralString("CO"))

Although this is more concise, it is also harder to interpret, so it would be nice to have a more elegant way to create logical plans. This is where a DataFrame interface can help.

Building Logical Plans using DataFrames

Implementing a DataFrame style API allows us to build logical query plans in a much more user-friendly way. A DataFrame is just an abstraction around a logical query plan and has methods to perform transformations and actions. It is similar to a fluent-style builder API.

Here is a minimal starting point for a DataFrame interface that allows us to apply projections and selections to an existing DataFrame.

interface DataFrame {

  /** Apply a projection */
  fun project(expr: List<LogicalExpr>): DataFrame
  /** Apply a filter */
  fun filter(expr: LogicalExpr): DataFrame
  /** Aggregate */
  fun aggregate(groupBy: List<LogicalExpr>, 
                aggregateExpr: List<AggregateExpr>): DataFrame
  /** Returns the schema of the data that will be produced by this DataFrame. */
  fun schema(): Schema
  /** Get the logical plan */
  fun logicalPlan() : LogicalPlan


Here is the implementation of this interface.

class DataFrameImpl(private val plan: LogicalPlan) : DataFrame {

  override fun project(expr: List<LogicalExpr>): DataFrame {
    return DataFrameImpl(Projection(plan, expr))

  override fun filter(expr: LogicalExpr): DataFrame {
    return DataFrameImpl(Selection(plan, expr))

  override fun aggregate(groupBy: List<LogicalExpr>,
                         aggregateExpr: List<AggregateExpr>): DataFrame {
    return DataFrameImpl(Aggregate(plan, groupBy, aggregateExpr))

  override fun schema(): Schema {
    return plan.schema()

  override fun logicalPlan(): LogicalPlan {
    return plan


Before we can apply a projection or selection, we need a way to create an initial DataFrame that represents an underlying data source. This is usually obtained through an execution context.

Here is a simple starting point for an execution context that we will enhance later.

class ExecutionContext {

  fun csv(filename: String): DataFrame {
    return DataFrameImpl(Scan(filename, CsvDataSource(filename), listOf()))

  fun parquet(filename: String): DataFrame {
    return DataFrameImpl(Scan(filename, ParquetDataSource(filename), listOf()))

With this groundwork in place, we can now create a logical query plan using the context and the DataFrame API.

val ctx = ExecutionContext()

val plan = ctx.csv("employee.csv")
              .filter(Eq(Column("state"), LiteralString("CO")))

This is much cleaner and more intuitive, but we can go a step further and add some convenience methods to make this a little more comprehensible. This is specific to Kotlin, but other languages have similar concepts.

We can create some convenience methods for creating the supported expression objects.

fun col(name: String) = Column(name)
fun lit(value: String) = LiteralString(value)
fun lit(value: Long) = LiteralLong(value)
fun lit(value: Double) = LiteralDouble(value)

We can also define infix operators on the LogicalExpr interface for building binary expressions.

infix fun LogicalExpr.eq(rhs: LogicalExpr): LogicalExpr { return Eq(this, rhs) }
infix fun LogicalExpr.neq(rhs: LogicalExpr): LogicalExpr { return Neq(this, rhs) }
infix fun LogicalExpr): LogicalExpr { return Gt(this, rhs) }
infix fun LogicalExpr.gteq(rhs: LogicalExpr): LogicalExpr { return GtEq(this, rhs) }
infix fun LogicalExpr): LogicalExpr { return Lt(this, rhs) }
infix fun LogicalExpr.lteq(rhs: LogicalExpr): LogicalExpr { return LtEq(this, rhs) }

With these convenience methods in place, we can now write expressive code to build our logical query plan.

val df = ctx.csv(employeeCsv)
   .filter(col("state") eq lit("CO"))
       (col("salary") mult lit(0.1)) alias "bonus"))
   .filter(col("bonus") gt lit(1000))

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