src/test/scala/com/gerritforge/analytics/GerritAnalyticsTransformationsSpec.scala - apps/analytics-etl - Git at Google

 // Copyright (C) 2017 GerritForge Ltd
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
 // You may obtain a copy of the License at
 //
 // http://www.apache.org/licenses/LICENSE-2.0
 //
 // Unless required by applicable law or agreed to in writing, software
 // distributed under the License is distributed on an "AS IS" BASIS,
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.

 package com.gerritforge.analytics

 import java.io.{File, FileWriter}

 import com.gerritforge.analytics.engine.GerritAnalyticsTransformations._
 import com.gerritforge.analytics.model.{GerritEndpointConfig, GerritProjects, ProjectContributionSource}
 import org.apache.spark.sql.Row
 import org.json4s.JsonDSL._
 import org.json4s._
 import org.json4s.jackson.JsonMethods.{compact, render}
 import org.scalatest.{FlatSpec, Inside, Matchers}

 import scala.io.Source

 class GerritAnalyticsTransformationsSpec extends FlatSpec with Matchers
   with SparkTestSupport with Inside {

   "GerritProjects" should "parse project names" in {

     val projectNames = GerritProjects(Source.fromString(
       """)]}'
         |{
         | "All-Projects": {
         |   "id": "All-Projects",
         | },
         | "Test": {
         |   "id": "Test",
         | }
         |}
         |""".stripMargin))

     projectNames should have size 2
     projectNames should contain allOf("All-Projects", "Test")
   }


   "enrichWithSource" should "enrich project RDD object with its source" in {

     val projectRdd = sc.parallelize(Seq("project"))
     implicit val config = GerritEndpointConfig("http://somewhere.com")

     val projectWithSource = projectRdd
       .enrichWithSource
       .collect

     projectWithSource should have size 1
     inside(projectWithSource.head) {
       case ProjectContributionSource(project, url) => {
         project should be("project")
         url should startWith("http://somewhere.com")
       }
     }
   }

   "fetchRawContributors" should "fetch file content from the initial list of project names and file names" in {

     val line1 = "foo" -> "bar"
     val line2 = "foo1" -> "bar1"
     val line3 = "foo2" -> "bar2\u0100" // checks UTF-8 as well
     val line3b = "foo3" -> "bar3\u0101"

     val projectSource1 = ProjectContributionSource("p1", newSource(line1, line2, line3))
     val projectSource2 = ProjectContributionSource("p2", newSource(line3b))

     val rawContributors = sc.parallelize(Seq(projectSource1, projectSource2))
       .fetchRawContributors
       .collect

     rawContributors should have size (4)
     rawContributors should contain allOf(
       ("p1","""{"foo":"bar"}"""),
       ("p1","""{"foo1":"bar1"}"""),
       ("p1", "{\"foo2\":\"bar2\u0100\"}"),
       ("p2", "{\"foo3\":\"bar3\u0101\"}")
     )
   }

   "transformCommitterInfo" should "transform a DataFrame with project and json to a workable DF with separated columns" in {
     import sql.implicits._

     val rdd = sc.parallelize(Seq(
       ("p1","""{"name":"a","email":"a@mail.com","year":2017,"month":9, "day":11, "hour":23, "num_commits":1, "num_files": 2, "added_lines":1, "deleted_lines":1, "last_commit_date":0, "commits":[{ "sha1": "e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":0,"merge":false}]}"""),
       ("p2","""{"name":"b","email":"b@mail.com","year":2017,"month":9, "day":11, "hour":23, "num_commits":428, "num_files": 2, "added_lines":1, "deleted_lines":1, "last_commit_date":1500000000000,"commits":[{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":0,"merge":true },{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":1500000000000,"merge":true}]}"""),
       // last commit is missing hour,day,month,year to check optionality
       ("p3","""{"name":"c","email":"c@mail.com","num_commits":12,"num_files": 2, "added_lines":1, "deleted_lines":1, "last_commit_date":1600000000000,"commits":[{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":0,"merge":true },{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":1600000000000,"merge":true}]}""")
     ))

     val df = rdd.toDF("project", "json")
       .transformCommitterInfo

     df.count should be(3)
     val collected = df.collect

     df.schema.fields.map(_.name) should contain allOf(
       "project", "author", "email",
       "year", "month", "day", "hour",
       "num_files", "added_lines", "deleted_lines",
       "num_commits", "last_commit_date")

     collected should contain allOf(
       Row("p1", "a", "a@mail.com", 2017, 9, 11, 23, 2, 1, 1, 1, 0),
       Row("p2", "b", "b@mail.com", 2017, 9, 11, 23, 2, 1, 1, 428, 1500000000000L),
       Row("p3", "c", "c@mail.com", null, null, null, null, 2, 1, 1, 12, 1600000000000L)
     )
   }

   "handleAliases" should "enrich the data with author from the alias DF" in {
     import spark.implicits._

     val aliasDF = sc.parallelize(Seq(
       ("aliased_author", "aliased_email@aliased_author.com", "")
     )).toDF("author", "email", "organization")

     val inputSampleDF = sc.parallelize(Seq(
       ("author_from_name_a", "non_aliased_email@a_mail.com", "a_mail.com"),
       ("author_from_name_b", "aliased_email@aliased_author.com", "aliased_author.com")
     )).toDF("author", "email", "organization")

     val expectedDF = sc.parallelize(Seq(
       ("author_from_name_a", "non_aliased_email@a_mail.com", "a_mail.com"),
       ("aliased_author", "aliased_email@aliased_author.com", "aliased_author.com")
     )).toDF("author", "email", "organization")

     val df = inputSampleDF.handleAliases(Some(aliasDF))

     df.schema.fields.map(_.name) should contain allOf(
       "author", "email", "organization")

     df.collect should contain theSameElementsAs expectedDF.collect
   }

   it should "enrich the data with organization from the alias DF when available" in {
     import spark.implicits._

     val aliasDF = sc.parallelize(Seq(
       ("aliased_author_with_organization", "aliased_email@aliased_organization.com", "aliased_organization"),
       ("aliased_author_empty_organization", "aliased_email@emtpy_organization.com", ""),
       ("aliased_author_null_organization", "aliased_email@null_organization.com", null)

     )).toDF("author", "email", "organization")

     val inputSampleDF = sc.parallelize(Seq(
       ("author_from_name_a", "aliased_email@aliased_organization.com", "aliased_organization.com"),
       ("author_from_name_b", "aliased_email@emtpy_organization.com", "emtpy_organization.com"),
       ("author_from_name_c", "aliased_email@null_organization.com", "null_organization.com")
     )).toDF("author", "email", "organization")

     val expectedDF = sc.parallelize(Seq(
       ("aliased_author_with_organization", "aliased_email@aliased_organization.com", "aliased_organization"),
       ("aliased_author_empty_organization", "aliased_email@emtpy_organization.com", "emtpy_organization.com"),
       ("aliased_author_null_organization", "aliased_email@null_organization.com", "null_organization.com")
     )).toDF("author", "email", "organization")

     val df = inputSampleDF.handleAliases(Some(aliasDF))

     df.schema.fields.map(_.name) should contain allOf(
       "author", "email", "organization")

     df.collect should contain theSameElementsAs expectedDF.collect
   }

   it should "return correct columns when alias DF is defined" in {
     import spark.implicits._
     val inputSampleDF = sc.parallelize(Seq(
       ("author_name", "email@mail.com", "an_organization")
     )).toDF("author", "email", "organization")

     val aliasDF = sc.parallelize(Seq(
       ("a_random_author", "a_random_email@mail.com", "a_random_organization")
     )).toDF("author", "email", "organization")

     val df = inputSampleDF.handleAliases(Some(aliasDF))

     df.schema.fields.map(_.name) should contain allOf("author", "email", "organization")
   }

   it should "return correct columns when alias DF is not defined" in {
     import spark.implicits._
     val inputSampleDF = sc.parallelize(Seq(
       ("author_name", "email@mail.com", "an_organization")
     )).toDF("author", "email", "organization")

     val df = inputSampleDF.handleAliases(None)

     df.schema.fields.map(_.name) should contain allOf("author", "email", "organization")
   }

   "addOrganization" should "compute organization column from the email" in {
     import sql.implicits._

     val df = sc.parallelize(Seq(
       "",
       "@", // corner case
       "not an email",
       "email@domain"
     )).toDF("email")

     val transformed = df.addOrganization()

     transformed.schema.fields.map(_.name) should contain allOf("email", "organization")

     transformed.collect should contain allOf(
       Row("", ""),
       Row("@", ""),
       Row("not an email", ""),
       Row("email@domain", "domain")
     )
   }

   "convertDates" should "process specific column from Long to ISO date" in {
     // some notable dates converted in UnixMillisecs and ISO format
     val DATES = Map(
       0L -> "1970-01-01T00:00:00Z",
       1500000000000L -> "2017-07-14T02:40:00Z",
       1600000000000L -> "2020-09-13T12:26:40Z")
     import sql.implicits._
     val df = sc.parallelize(Seq(
       ("a", 0L, 1),
       ("b", 1500000000000L, 2),
       ("c", 1600000000000L, 3))).toDF("name", "date", "num")

     val converted = df
       .convertDates("date")

     converted.collect should contain allOf(
       Row("a", DATES(0), 1),
       Row("b", DATES(1500000000000L), 2),
       Row("c", DATES(1600000000000L), 3)
     )
   }

   private def newSource(contributorsJson: JObject*): String = {
     val tmpFile = File.createTempFile(System.getProperty("java.io.tmpdir"),
       s"${getClass.getName}-${System.nanoTime()}")

     val out = new FileWriter(tmpFile)
     contributorsJson.foreach(json => out.write(compact(render(json)) + '\n'))
     out.close
     tmpFile.toURI.toString
   }
 }
	// Copyright (C) 2017 GerritForge Ltd
	//
	// Licensed under the Apache License, Version 2.0 (the "License");
	// you may not use this file except in compliance with the License.
	// You may obtain a copy of the License at
	//
	// http://www.apache.org/licenses/LICENSE-2.0
	//
	// Unless required by applicable law or agreed to in writing, software
	// distributed under the License is distributed on an "AS IS" BASIS,
	// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	// See the License for the specific language governing permissions and
	// limitations under the License.

	package com.gerritforge.analytics

	import java.io.{File, FileWriter}

	import com.gerritforge.analytics.engine.GerritAnalyticsTransformations._
	import com.gerritforge.analytics.model.{GerritEndpointConfig, GerritProjects, ProjectContributionSource}
	import org.apache.spark.sql.Row
	import org.json4s.JsonDSL._
	import org.json4s._
	import org.json4s.jackson.JsonMethods.{compact, render}
	import org.scalatest.{FlatSpec, Inside, Matchers}

	import scala.io.Source

	class GerritAnalyticsTransformationsSpec extends FlatSpec with Matchers
	with SparkTestSupport with Inside {

	"GerritProjects" should "parse project names" in {

	val projectNames = GerritProjects(Source.fromString(
	""")]}'
	\|{
	\| "All-Projects": {
	\| "id": "All-Projects",
	\| },
	\| "Test": {
	\| "id": "Test",
	\| }
	\|}
	\|""".stripMargin))

	projectNames should have size 2
	projectNames should contain allOf("All-Projects", "Test")
	}


	"enrichWithSource" should "enrich project RDD object with its source" in {

	val projectRdd = sc.parallelize(Seq("project"))
	implicit val config = GerritEndpointConfig("http://somewhere.com")

	val projectWithSource = projectRdd
	.enrichWithSource
	.collect

	projectWithSource should have size 1
	inside(projectWithSource.head) {
	case ProjectContributionSource(project, url) => {
	project should be("project")
	url should startWith("http://somewhere.com")
	}
	}
	}

	"fetchRawContributors" should "fetch file content from the initial list of project names and file names" in {

	val line1 = "foo" -> "bar"
	val line2 = "foo1" -> "bar1"
	val line3 = "foo2" -> "bar2\u0100" // checks UTF-8 as well
	val line3b = "foo3" -> "bar3\u0101"

	val projectSource1 = ProjectContributionSource("p1", newSource(line1, line2, line3))
	val projectSource2 = ProjectContributionSource("p2", newSource(line3b))

	val rawContributors = sc.parallelize(Seq(projectSource1, projectSource2))
	.fetchRawContributors
	.collect

	rawContributors should have size (4)
	rawContributors should contain allOf(
	("p1","""{"foo":"bar"}"""),
	("p1","""{"foo1":"bar1"}"""),
	("p1", "{\"foo2\":\"bar2\u0100\"}"),
	("p2", "{\"foo3\":\"bar3\u0101\"}")
	)
	}

	"transformCommitterInfo" should "transform a DataFrame with project and json to a workable DF with separated columns" in {
	import sql.implicits._

	val rdd = sc.parallelize(Seq(
	("p1","""{"name":"a","email":"a@mail.com","year":2017,"month":9, "day":11, "hour":23, "num_commits":1, "num_files": 2, "added_lines":1, "deleted_lines":1, "last_commit_date":0, "commits":[{ "sha1": "e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":0,"merge":false}]}"""),
	("p2","""{"name":"b","email":"b@mail.com","year":2017,"month":9, "day":11, "hour":23, "num_commits":428, "num_files": 2, "added_lines":1, "deleted_lines":1, "last_commit_date":1500000000000,"commits":[{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":0,"merge":true },{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":1500000000000,"merge":true}]}"""),
	// last commit is missing hour,day,month,year to check optionality
	("p3","""{"name":"c","email":"c@mail.com","num_commits":12,"num_files": 2, "added_lines":1, "deleted_lines":1, "last_commit_date":1600000000000,"commits":[{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":0,"merge":true },{"sha1":"e063a806c33bd524e89a87732bd3f1ad9a77a41e", "date":1600000000000,"merge":true}]}""")
	))

	val df = rdd.toDF("project", "json")
	.transformCommitterInfo

	df.count should be(3)
	val collected = df.collect

	df.schema.fields.map(_.name) should contain allOf(
	"project", "author", "email",
	"year", "month", "day", "hour",
	"num_files", "added_lines", "deleted_lines",
	"num_commits", "last_commit_date")

	collected should contain allOf(
	Row("p1", "a", "a@mail.com", 2017, 9, 11, 23, 2, 1, 1, 1, 0),
	Row("p2", "b", "b@mail.com", 2017, 9, 11, 23, 2, 1, 1, 428, 1500000000000L),
	Row("p3", "c", "c@mail.com", null, null, null, null, 2, 1, 1, 12, 1600000000000L)
	)
	}

	"handleAliases" should "enrich the data with author from the alias DF" in {
	import spark.implicits._

	val aliasDF = sc.parallelize(Seq(
	("aliased_author", "aliased_email@aliased_author.com", "")
	)).toDF("author", "email", "organization")

	val inputSampleDF = sc.parallelize(Seq(
	("author_from_name_a", "non_aliased_email@a_mail.com", "a_mail.com"),
	("author_from_name_b", "aliased_email@aliased_author.com", "aliased_author.com")
	)).toDF("author", "email", "organization")

	val expectedDF = sc.parallelize(Seq(
	("author_from_name_a", "non_aliased_email@a_mail.com", "a_mail.com"),
	("aliased_author", "aliased_email@aliased_author.com", "aliased_author.com")
	)).toDF("author", "email", "organization")

	val df = inputSampleDF.handleAliases(Some(aliasDF))

	df.schema.fields.map(_.name) should contain allOf(
	"author", "email", "organization")

	df.collect should contain theSameElementsAs expectedDF.collect
	}

	it should "enrich the data with organization from the alias DF when available" in {
	import spark.implicits._

	val aliasDF = sc.parallelize(Seq(
	("aliased_author_with_organization", "aliased_email@aliased_organization.com", "aliased_organization"),
	("aliased_author_empty_organization", "aliased_email@emtpy_organization.com", ""),
	("aliased_author_null_organization", "aliased_email@null_organization.com", null)

	)).toDF("author", "email", "organization")

	val inputSampleDF = sc.parallelize(Seq(
	("author_from_name_a", "aliased_email@aliased_organization.com", "aliased_organization.com"),
	("author_from_name_b", "aliased_email@emtpy_organization.com", "emtpy_organization.com"),
	("author_from_name_c", "aliased_email@null_organization.com", "null_organization.com")
	)).toDF("author", "email", "organization")

	val expectedDF = sc.parallelize(Seq(
	("aliased_author_with_organization", "aliased_email@aliased_organization.com", "aliased_organization"),
	("aliased_author_empty_organization", "aliased_email@emtpy_organization.com", "emtpy_organization.com"),
	("aliased_author_null_organization", "aliased_email@null_organization.com", "null_organization.com")
	)).toDF("author", "email", "organization")

	val df = inputSampleDF.handleAliases(Some(aliasDF))

	df.schema.fields.map(_.name) should contain allOf(
	"author", "email", "organization")

	df.collect should contain theSameElementsAs expectedDF.collect
	}

	it should "return correct columns when alias DF is defined" in {
	import spark.implicits._
	val inputSampleDF = sc.parallelize(Seq(
	("author_name", "email@mail.com", "an_organization")
	)).toDF("author", "email", "organization")

	val aliasDF = sc.parallelize(Seq(
	("a_random_author", "a_random_email@mail.com", "a_random_organization")
	)).toDF("author", "email", "organization")

	val df = inputSampleDF.handleAliases(Some(aliasDF))

	df.schema.fields.map(_.name) should contain allOf("author", "email", "organization")
	}

	it should "return correct columns when alias DF is not defined" in {
	import spark.implicits._
	val inputSampleDF = sc.parallelize(Seq(
	("author_name", "email@mail.com", "an_organization")
	)).toDF("author", "email", "organization")

	val df = inputSampleDF.handleAliases(None)

	df.schema.fields.map(_.name) should contain allOf("author", "email", "organization")
	}

	"addOrganization" should "compute organization column from the email" in {
	import sql.implicits._

	val df = sc.parallelize(Seq(
	"",
	"@", // corner case
	"not an email",
	"email@domain"
	)).toDF("email")

	val transformed = df.addOrganization()

	transformed.schema.fields.map(_.name) should contain allOf("email", "organization")

	transformed.collect should contain allOf(
	Row("", ""),
	Row("@", ""),
	Row("not an email", ""),
	Row("email@domain", "domain")
	)
	}

	"convertDates" should "process specific column from Long to ISO date" in {
	// some notable dates converted in UnixMillisecs and ISO format
	val DATES = Map(
	0L -> "1970-01-01T00:00:00Z",
	1500000000000L -> "2017-07-14T02:40:00Z",
	1600000000000L -> "2020-09-13T12:26:40Z")
	import sql.implicits._
	val df = sc.parallelize(Seq(
	("a", 0L, 1),
	("b", 1500000000000L, 2),
	("c", 1600000000000L, 3))).toDF("name", "date", "num")

	val converted = df
	.convertDates("date")

	converted.collect should contain allOf(
	Row("a", DATES(0), 1),
	Row("b", DATES(1500000000000L), 2),
	Row("c", DATES(1600000000000L), 3)
	)
	}

	private def newSource(contributorsJson: JObject*): String = {
	val tmpFile = File.createTempFile(System.getProperty("java.io.tmpdir"),
	s"${getClass.getName}-${System.nanoTime()}")

	val out = new FileWriter(tmpFile)
	contributorsJson.foreach(json => out.write(compact(render(json)) + '\n'))
	out.close
	tmpFile.toURI.toString
	}
	}