Deequ
💽

Deequ

deequ

awslabs â‹… a month ago

val rdd = spark.sparkContext.parallelize(Seq(
  Item(1, "Thingy A", "awesome thing.", "high", 0),
  Item(2, "Thingy B", "available at http://thingb.com", null, 0),
  Item(3, null, null, "low", 5),
  Item(4, "Thingy D", "checkout https://thingd.ca", "low", 10),
  Item(5, "Thingy E", null, "high", 12)))

val data = spark.createDataFrame(rdd)
import com.amazon.deequ.VerificationSuite
import com.amazon.deequ.checks.{Check, CheckLevel, CheckStatus}


val verificationResult = VerificationSuite()
  .onData(data)
  .addCheck(
    Check(CheckLevel.Error, "unit testing my data")
      .hasSize(_ == 5) // we expect 5 rows
      .isComplete("id") // should never be NULL
      .isUnique("id") // should not contain duplicates
      .isComplete("productName") // should never be NULL
      // should only contain the values "high" and "low"
      .isContainedIn("priority", Array("high", "low"))
      .isNonNegative("numViews") // should not contain negative values
      // at least half of the descriptions should contain a url
      .containsURL("description", _ >= 0.5)
      // half of the items should have less than 10 views
      .hasApproxQuantile("numViews", 0.5, _ <= 10))
    .run()

Example of Anomaly Detection