DataFrame equality in Apache Spark

Scala (see below for PySpark)

The spark-fast-tests library has two methods for making DataFrame comparisons (I’m the creator of the library):

The assertSmallDataFrameEquality method collects DataFrames on the driver node and makes the comparison

def assertSmallDataFrameEquality(actualDF: DataFrame, expectedDF: DataFrame): Unit = {
  if (!actualDF.schema.equals(expectedDF.schema)) {
    throw new DataFrameSchemaMismatch(schemaMismatchMessage(actualDF, expectedDF))
  }
  if (!actualDF.collect().sameElements(expectedDF.collect())) {
    throw new DataFrameContentMismatch(contentMismatchMessage(actualDF, expectedDF))
  }
}

The assertLargeDataFrameEquality method compares DataFrames spread on multiple machines (the code is basically copied from spark-testing-base)

def assertLargeDataFrameEquality(actualDF: DataFrame, expectedDF: DataFrame): Unit = {
  if (!actualDF.schema.equals(expectedDF.schema)) {
    throw new DataFrameSchemaMismatch(schemaMismatchMessage(actualDF, expectedDF))
  }
  try {
    actualDF.rdd.cache
    expectedDF.rdd.cache

    val actualCount = actualDF.rdd.count
    val expectedCount = expectedDF.rdd.count
    if (actualCount != expectedCount) {
      throw new DataFrameContentMismatch(countMismatchMessage(actualCount, expectedCount))
    }

    val expectedIndexValue = zipWithIndex(actualDF.rdd)
    val resultIndexValue = zipWithIndex(expectedDF.rdd)

    val unequalRDD = expectedIndexValue
      .join(resultIndexValue)
      .filter {
        case (idx, (r1, r2)) =>
          !(r1.equals(r2) || RowComparer.areRowsEqual(r1, r2, 0.0))
      }

    val maxUnequalRowsToShow = 10
    assertEmpty(unequalRDD.take(maxUnequalRowsToShow))

  } finally {
    actualDF.rdd.unpersist()
    expectedDF.rdd.unpersist()
  }
}

assertSmallDataFrameEquality is faster for small DataFrame comparisons and I’ve found it sufficient for my test suites.

PySpark

Here’s a simple function that returns true if the DataFrames are equal:

def are_dfs_equal(df1, df2):
    if df1.schema != df2.schema:
        return False
    if df1.collect() != df2.collect():
        return False
    return True

or simplified

def are_dfs_equal(df1, df2): 
    return (df1.schema == df2.schema) and (df1.collect() == df2.collect())

You’ll typically perform DataFrame equality comparisons in a test suite and will want a descriptive error message when the comparisons fail (a True / False return value doesn’t help much when debugging).

Use the chispa library to access the assert_df_equality method that returns descriptive error messages for test suite workflows.