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Posted to commits@spark.apache.org by fe...@apache.org on 2017/06/11 07:00:41 UTC
[5/7] spark git commit: [SPARK-20877][SPARKR] refactor tests to basic
tests only for CRAN
http://git-wip-us.apache.org/repos/asf/spark/blob/dc4c3518/R/pkg/inst/tests/testthat/test_sparkSQL.R
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diff --git a/R/pkg/inst/tests/testthat/test_sparkSQL.R b/R/pkg/inst/tests/testthat/test_sparkSQL.R
deleted file mode 100644
index c790d02..0000000
--- a/R/pkg/inst/tests/testthat/test_sparkSQL.R
+++ /dev/null
@@ -1,3474 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You 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.
-#
-
-library(testthat)
-
-context("SparkSQL functions")
-
-# Utility function for easily checking the values of a StructField
-checkStructField <- function(actual, expectedName, expectedType, expectedNullable) {
- expect_equal(class(actual), "structField")
- expect_equal(actual$name(), expectedName)
- expect_equal(actual$dataType.toString(), expectedType)
- expect_equal(actual$nullable(), expectedNullable)
-}
-
-markUtf8 <- function(s) {
- Encoding(s) <- "UTF-8"
- s
-}
-
-setHiveContext <- function(sc) {
- if (exists(".testHiveSession", envir = .sparkREnv)) {
- hiveSession <- get(".testHiveSession", envir = .sparkREnv)
- } else {
- # initialize once and reuse
- ssc <- callJMethod(sc, "sc")
- hiveCtx <- tryCatch({
- newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc, FALSE)
- },
- error = function(err) {
- skip("Hive is not build with SparkSQL, skipped")
- })
- hiveSession <- callJMethod(hiveCtx, "sparkSession")
- }
- previousSession <- get(".sparkRsession", envir = .sparkREnv)
- assign(".sparkRsession", hiveSession, envir = .sparkREnv)
- assign(".prevSparkRsession", previousSession, envir = .sparkREnv)
- hiveSession
-}
-
-unsetHiveContext <- function() {
- previousSession <- get(".prevSparkRsession", envir = .sparkREnv)
- assign(".sparkRsession", previousSession, envir = .sparkREnv)
- remove(".prevSparkRsession", envir = .sparkREnv)
-}
-
-# Tests for SparkSQL functions in SparkR
-
-filesBefore <- list.files(path = sparkRDir, all.files = TRUE)
-sparkSession <- if (not_cran_or_windows_with_hadoop()) {
- sparkR.session(master = sparkRTestMaster)
- } else {
- sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)
- }
-sc <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "getJavaSparkContext", sparkSession)
-
-mockLines <- c("{\"name\":\"Michael\"}",
- "{\"name\":\"Andy\", \"age\":30}",
- "{\"name\":\"Justin\", \"age\":19}")
-jsonPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
-parquetPath <- tempfile(pattern = "sparkr-test", fileext = ".parquet")
-orcPath <- tempfile(pattern = "sparkr-test", fileext = ".orc")
-writeLines(mockLines, jsonPath)
-
-# For test nafunctions, like dropna(), fillna(),...
-mockLinesNa <- c("{\"name\":\"Bob\",\"age\":16,\"height\":176.5}",
- "{\"name\":\"Alice\",\"age\":null,\"height\":164.3}",
- "{\"name\":\"David\",\"age\":60,\"height\":null}",
- "{\"name\":\"Amy\",\"age\":null,\"height\":null}",
- "{\"name\":null,\"age\":null,\"height\":null}")
-jsonPathNa <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
-writeLines(mockLinesNa, jsonPathNa)
-
-# For test complex types in DataFrame
-mockLinesComplexType <-
- c("{\"c1\":[1, 2, 3], \"c2\":[\"a\", \"b\", \"c\"], \"c3\":[1.0, 2.0, 3.0]}",
- "{\"c1\":[4, 5, 6], \"c2\":[\"d\", \"e\", \"f\"], \"c3\":[4.0, 5.0, 6.0]}",
- "{\"c1\":[7, 8, 9], \"c2\":[\"g\", \"h\", \"i\"], \"c3\":[7.0, 8.0, 9.0]}")
-complexTypeJsonPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
-writeLines(mockLinesComplexType, complexTypeJsonPath)
-
-# For test map type and struct type in DataFrame
-mockLinesMapType <- c("{\"name\":\"Bob\",\"info\":{\"age\":16,\"height\":176.5}}",
- "{\"name\":\"Alice\",\"info\":{\"age\":20,\"height\":164.3}}",
- "{\"name\":\"David\",\"info\":{\"age\":60,\"height\":180}}")
-mapTypeJsonPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
-writeLines(mockLinesMapType, mapTypeJsonPath)
-
-if (.Platform$OS.type == "windows") {
- Sys.setenv(TZ = "GMT")
-}
-
-test_that("calling sparkRSQL.init returns existing SQL context", {
- skip_on_cran()
-
- sqlContext <- suppressWarnings(sparkRSQL.init(sc))
- expect_equal(suppressWarnings(sparkRSQL.init(sc)), sqlContext)
-})
-
-test_that("calling sparkRSQL.init returns existing SparkSession", {
- skip_on_cran()
-
- expect_equal(suppressWarnings(sparkRSQL.init(sc)), sparkSession)
-})
-
-test_that("calling sparkR.session returns existing SparkSession", {
- skip_on_cran()
-
- expect_equal(sparkR.session(), sparkSession)
-})
-
-test_that("infer types and check types", {
- expect_equal(infer_type(1L), "integer")
- expect_equal(infer_type(1.0), "double")
- expect_equal(infer_type("abc"), "string")
- expect_equal(infer_type(TRUE), "boolean")
- expect_equal(infer_type(as.Date("2015-03-11")), "date")
- expect_equal(infer_type(as.POSIXlt("2015-03-11 12:13:04.043")), "timestamp")
- expect_equal(infer_type(c(1L, 2L)), "array<integer>")
- expect_equal(infer_type(list(1L, 2L)), "array<integer>")
- expect_equal(infer_type(listToStruct(list(a = 1L, b = "2"))), "struct<a:integer,b:string>")
- e <- new.env()
- assign("a", 1L, envir = e)
- expect_equal(infer_type(e), "map<string,integer>")
-
- expect_error(checkType("map<integer,integer>"), "Key type in a map must be string or character")
-
- expect_equal(infer_type(as.raw(c(1, 2, 3))), "binary")
-})
-
-test_that("structType and structField", {
- testField <- structField("a", "string")
- expect_is(testField, "structField")
- expect_equal(testField$name(), "a")
- expect_true(testField$nullable())
-
- testSchema <- structType(testField, structField("b", "integer"))
- expect_is(testSchema, "structType")
- expect_is(testSchema$fields()[[2]], "structField")
- expect_equal(testSchema$fields()[[1]]$dataType.toString(), "StringType")
-})
-
-test_that("structField type strings", {
- # positive cases
- primitiveTypes <- list(byte = "ByteType",
- integer = "IntegerType",
- float = "FloatType",
- double = "DoubleType",
- string = "StringType",
- binary = "BinaryType",
- boolean = "BooleanType",
- timestamp = "TimestampType",
- date = "DateType",
- tinyint = "ByteType",
- smallint = "ShortType",
- int = "IntegerType",
- bigint = "LongType",
- decimal = "DecimalType(10,0)")
-
- complexTypes <- list("map<string,integer>" = "MapType(StringType,IntegerType,true)",
- "array<string>" = "ArrayType(StringType,true)",
- "struct<a:string>" = "StructType(StructField(a,StringType,true))")
-
- typeList <- c(primitiveTypes, complexTypes)
- typeStrings <- names(typeList)
-
- for (i in seq_along(typeStrings)){
- typeString <- typeStrings[i]
- expected <- typeList[[i]]
- testField <- structField("_col", typeString)
- expect_is(testField, "structField")
- expect_true(testField$nullable())
- expect_equal(testField$dataType.toString(), expected)
- }
-
- # negative cases
- primitiveErrors <- list(Byte = "Byte",
- INTEGER = "INTEGER",
- numeric = "numeric",
- character = "character",
- raw = "raw",
- logical = "logical",
- short = "short",
- varchar = "varchar",
- long = "long",
- char = "char")
-
- complexErrors <- list("map<string, integer>" = " integer",
- "array<String>" = "String",
- "struct<a:string >" = "string ",
- "map <string,integer>" = "map <string,integer>",
- "array< string>" = " string",
- "struct<a: string>" = " string")
-
- errorList <- c(primitiveErrors, complexErrors)
- typeStrings <- names(errorList)
-
- for (i in seq_along(typeStrings)){
- typeString <- typeStrings[i]
- expected <- paste0("Unsupported type for SparkDataframe: ", errorList[[i]])
- expect_error(structField("_col", typeString), expected)
- }
-})
-
-test_that("create DataFrame from RDD", {
- skip_on_cran()
-
- rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
- df <- createDataFrame(rdd, list("a", "b"))
- dfAsDF <- as.DataFrame(rdd, list("a", "b"))
- expect_is(df, "SparkDataFrame")
- expect_is(dfAsDF, "SparkDataFrame")
- expect_equal(count(df), 10)
- expect_equal(count(dfAsDF), 10)
- expect_equal(nrow(df), 10)
- expect_equal(nrow(dfAsDF), 10)
- expect_equal(ncol(df), 2)
- expect_equal(ncol(dfAsDF), 2)
- expect_equal(dim(df), c(10, 2))
- expect_equal(dim(dfAsDF), c(10, 2))
- expect_equal(columns(df), c("a", "b"))
- expect_equal(columns(dfAsDF), c("a", "b"))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
- expect_equal(dtypes(dfAsDF), list(c("a", "int"), c("b", "string")))
-
- df <- createDataFrame(rdd)
- dfAsDF <- as.DataFrame(rdd)
- expect_is(df, "SparkDataFrame")
- expect_is(dfAsDF, "SparkDataFrame")
- expect_equal(columns(df), c("_1", "_2"))
- expect_equal(columns(dfAsDF), c("_1", "_2"))
-
- schema <- structType(structField(x = "a", type = "integer", nullable = TRUE),
- structField(x = "b", type = "string", nullable = TRUE))
- df <- createDataFrame(rdd, schema)
- expect_is(df, "SparkDataFrame")
- expect_equal(columns(df), c("a", "b"))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
-
- rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) })
- df <- createDataFrame(rdd)
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 10)
- expect_equal(columns(df), c("a", "b"))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
-
- schema <- structType(structField("name", "string"), structField("age", "integer"),
- structField("height", "float"))
- df <- read.df(jsonPathNa, "json", schema)
- df2 <- createDataFrame(toRDD(df), schema)
- df2AsDF <- as.DataFrame(toRDD(df), schema)
- expect_equal(columns(df2), c("name", "age", "height"))
- expect_equal(columns(df2AsDF), c("name", "age", "height"))
- expect_equal(dtypes(df2), list(c("name", "string"), c("age", "int"), c("height", "float")))
- expect_equal(dtypes(df2AsDF), list(c("name", "string"), c("age", "int"), c("height", "float")))
- expect_equal(as.list(collect(where(df2, df2$name == "Bob"))),
- list(name = "Bob", age = 16, height = 176.5))
- expect_equal(as.list(collect(where(df2AsDF, df2AsDF$name == "Bob"))),
- list(name = "Bob", age = 16, height = 176.5))
-
- localDF <- data.frame(name = c("John", "Smith", "Sarah"),
- age = c(19L, 23L, 18L),
- height = c(176.5, 181.4, 173.7))
- df <- createDataFrame(localDF, schema)
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 3)
- expect_equal(columns(df), c("name", "age", "height"))
- expect_equal(dtypes(df), list(c("name", "string"), c("age", "int"), c("height", "float")))
- expect_equal(as.list(collect(where(df, df$name == "John"))),
- list(name = "John", age = 19L, height = 176.5))
- expect_equal(getNumPartitions(df), 1)
-
- df <- as.DataFrame(cars, numPartitions = 2)
- expect_equal(getNumPartitions(df), 2)
- df <- createDataFrame(cars, numPartitions = 3)
- expect_equal(getNumPartitions(df), 3)
- # validate limit by num of rows
- df <- createDataFrame(cars, numPartitions = 60)
- expect_equal(getNumPartitions(df), 50)
- # validate when 1 < (length(coll) / numSlices) << length(coll)
- df <- createDataFrame(cars, numPartitions = 20)
- expect_equal(getNumPartitions(df), 20)
-
- df <- as.DataFrame(data.frame(0))
- expect_is(df, "SparkDataFrame")
- df <- createDataFrame(list(list(1)))
- expect_is(df, "SparkDataFrame")
- df <- as.DataFrame(data.frame(0), numPartitions = 2)
- # no data to partition, goes to 1
- expect_equal(getNumPartitions(df), 1)
-
- setHiveContext(sc)
- sql("CREATE TABLE people (name string, age double, height float)")
- df <- read.df(jsonPathNa, "json", schema)
- insertInto(df, "people")
- expect_equal(collect(sql("SELECT age from people WHERE name = 'Bob'"))$age,
- c(16))
- expect_equal(collect(sql("SELECT height from people WHERE name ='Bob'"))$height,
- c(176.5))
- sql("DROP TABLE people")
- unsetHiveContext()
-})
-
-test_that("createDataFrame uses files for large objects", {
- skip_on_cran()
-
- # To simulate a large file scenario, we set spark.r.maxAllocationLimit to a smaller value
- conf <- callJMethod(sparkSession, "conf")
- callJMethod(conf, "set", "spark.r.maxAllocationLimit", "100")
- df <- suppressWarnings(createDataFrame(iris, numPartitions = 3))
- expect_equal(getNumPartitions(df), 3)
-
- # Resetting the conf back to default value
- callJMethod(conf, "set", "spark.r.maxAllocationLimit", toString(.Machine$integer.max / 10))
- expect_equal(dim(df), dim(iris))
-})
-
-test_that("read/write csv as DataFrame", {
- if (not_cran_or_windows_with_hadoop()) {
- csvPath <- tempfile(pattern = "sparkr-test", fileext = ".csv")
- mockLinesCsv <- c("year,make,model,comment,blank",
- "\"2012\",\"Tesla\",\"S\",\"No comment\",",
- "1997,Ford,E350,\"Go get one now they are going fast\",",
- "2015,Chevy,Volt",
- "NA,Dummy,Placeholder")
- writeLines(mockLinesCsv, csvPath)
-
- # default "header" is false, inferSchema to handle "year" as "int"
- df <- read.df(csvPath, "csv", header = "true", inferSchema = "true")
- expect_equal(count(df), 4)
- expect_equal(columns(df), c("year", "make", "model", "comment", "blank"))
- expect_equal(sort(unlist(collect(where(df, df$year == 2015)))),
- sort(unlist(list(year = 2015, make = "Chevy", model = "Volt"))))
-
- # since "year" is "int", let's skip the NA values
- withoutna <- na.omit(df, how = "any", cols = "year")
- expect_equal(count(withoutna), 3)
-
- unlink(csvPath)
- csvPath <- tempfile(pattern = "sparkr-test", fileext = ".csv")
- mockLinesCsv <- c("year,make,model,comment,blank",
- "\"2012\",\"Tesla\",\"S\",\"No comment\",",
- "1997,Ford,E350,\"Go get one now they are going fast\",",
- "2015,Chevy,Volt",
- "Empty,Dummy,Placeholder")
- writeLines(mockLinesCsv, csvPath)
-
- df2 <- read.df(csvPath, "csv", header = "true", inferSchema = "true", na.strings = "Empty")
- expect_equal(count(df2), 4)
- withoutna2 <- na.omit(df2, how = "any", cols = "year")
- expect_equal(count(withoutna2), 3)
- expect_equal(count(where(withoutna2, withoutna2$make == "Dummy")), 0)
-
- # writing csv file
- csvPath2 <- tempfile(pattern = "csvtest2", fileext = ".csv")
- write.df(df2, path = csvPath2, "csv", header = "true")
- df3 <- read.df(csvPath2, "csv", header = "true")
- expect_equal(nrow(df3), nrow(df2))
- expect_equal(colnames(df3), colnames(df2))
- csv <- read.csv(file = list.files(csvPath2, pattern = "^part", full.names = T)[[1]])
- expect_equal(colnames(df3), colnames(csv))
-
- unlink(csvPath)
- unlink(csvPath2)
- }
-})
-
-test_that("Support other types for options", {
- skip_on_cran()
-
- csvPath <- tempfile(pattern = "sparkr-test", fileext = ".csv")
- mockLinesCsv <- c("year,make,model,comment,blank",
- "\"2012\",\"Tesla\",\"S\",\"No comment\",",
- "1997,Ford,E350,\"Go get one now they are going fast\",",
- "2015,Chevy,Volt",
- "NA,Dummy,Placeholder")
- writeLines(mockLinesCsv, csvPath)
-
- csvDf <- read.df(csvPath, "csv", header = "true", inferSchema = "true")
- expected <- read.df(csvPath, "csv", header = TRUE, inferSchema = TRUE)
- expect_equal(collect(csvDf), collect(expected))
-
- expect_error(read.df(csvPath, "csv", header = TRUE, maxColumns = 3))
- unlink(csvPath)
-})
-
-test_that("convert NAs to null type in DataFrames", {
- rdd <- parallelize(sc, list(list(1L, 2L), list(NA, 4L)))
- df <- createDataFrame(rdd, list("a", "b"))
- expect_true(is.na(collect(df)[2, "a"]))
- expect_equal(collect(df)[2, "b"], 4L)
-
- l <- data.frame(x = 1L, y = c(1L, NA_integer_, 3L))
- df <- createDataFrame(l)
- expect_equal(collect(df)[2, "x"], 1L)
- expect_true(is.na(collect(df)[2, "y"]))
-
- rdd <- parallelize(sc, list(list(1, 2), list(NA, 4)))
- df <- createDataFrame(rdd, list("a", "b"))
- expect_true(is.na(collect(df)[2, "a"]))
- expect_equal(collect(df)[2, "b"], 4)
-
- l <- data.frame(x = 1, y = c(1, NA_real_, 3))
- df <- createDataFrame(l)
- expect_equal(collect(df)[2, "x"], 1)
- expect_true(is.na(collect(df)[2, "y"]))
-
- l <- list("a", "b", NA, "d")
- df <- createDataFrame(l)
- expect_true(is.na(collect(df)[3, "_1"]))
- expect_equal(collect(df)[4, "_1"], "d")
-
- l <- list("a", "b", NA_character_, "d")
- df <- createDataFrame(l)
- expect_true(is.na(collect(df)[3, "_1"]))
- expect_equal(collect(df)[4, "_1"], "d")
-
- l <- list(TRUE, FALSE, NA, TRUE)
- df <- createDataFrame(l)
- expect_true(is.na(collect(df)[3, "_1"]))
- expect_equal(collect(df)[4, "_1"], TRUE)
-})
-
-test_that("toDF", {
- skip_on_cran()
-
- rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
- df <- toDF(rdd, list("a", "b"))
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 10)
- expect_equal(columns(df), c("a", "b"))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
-
- df <- toDF(rdd)
- expect_is(df, "SparkDataFrame")
- expect_equal(columns(df), c("_1", "_2"))
-
- schema <- structType(structField(x = "a", type = "integer", nullable = TRUE),
- structField(x = "b", type = "string", nullable = TRUE))
- df <- toDF(rdd, schema)
- expect_is(df, "SparkDataFrame")
- expect_equal(columns(df), c("a", "b"))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
-
- rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) })
- df <- toDF(rdd)
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 10)
- expect_equal(columns(df), c("a", "b"))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
-})
-
-test_that("create DataFrame from list or data.frame", {
- l <- list(list(1, 2), list(3, 4))
- df <- createDataFrame(l, c("a", "b"))
- expect_equal(columns(df), c("a", "b"))
-
- l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
- df <- createDataFrame(l)
- expect_equal(columns(df), c("a", "b"))
-
- a <- 1:3
- b <- c("a", "b", "c")
- ldf <- data.frame(a, b)
- df <- createDataFrame(ldf)
- expect_equal(columns(df), c("a", "b"))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
- expect_equal(count(df), 3)
- ldf2 <- collect(df)
- expect_equal(ldf$a, ldf2$a)
-
- irisdf <- suppressWarnings(createDataFrame(iris))
- iris_collected <- collect(irisdf)
- expect_equivalent(iris_collected[, -5], iris[, -5])
- expect_equal(iris_collected$Species, as.character(iris$Species))
-
- mtcarsdf <- createDataFrame(mtcars)
- expect_equivalent(collect(mtcarsdf), mtcars)
-
- bytes <- as.raw(c(1, 2, 3))
- df <- createDataFrame(list(list(bytes)))
- expect_equal(collect(df)[[1]][[1]], bytes)
-})
-
-test_that("create DataFrame with different data types", {
- l <- list(a = 1L, b = 2, c = TRUE, d = "ss", e = as.Date("2012-12-13"),
- f = as.POSIXct("2015-03-15 12:13:14.056"))
- df <- createDataFrame(list(l))
- expect_equal(dtypes(df), list(c("a", "int"), c("b", "double"), c("c", "boolean"),
- c("d", "string"), c("e", "date"), c("f", "timestamp")))
- expect_equal(count(df), 1)
- expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE))
-})
-
-test_that("SPARK-17811: can create DataFrame containing NA as date and time", {
- df <- data.frame(
- id = 1:2,
- time = c(as.POSIXlt("2016-01-10"), NA),
- date = c(as.Date("2016-10-01"), NA))
-
- DF <- collect(createDataFrame(df))
- expect_true(is.na(DF$date[2]))
- expect_equal(DF$date[1], as.Date("2016-10-01"))
- expect_true(is.na(DF$time[2]))
- expect_equal(DF$time[1], as.POSIXlt("2016-01-10"))
-})
-
-test_that("create DataFrame with complex types", {
- e <- new.env()
- assign("n", 3L, envir = e)
-
- s <- listToStruct(list(a = "aa", b = 3L))
-
- l <- list(as.list(1:10), list("a", "b"), e, s)
- df <- createDataFrame(list(l), c("a", "b", "c", "d"))
- expect_equal(dtypes(df), list(c("a", "array<int>"),
- c("b", "array<string>"),
- c("c", "map<string,int>"),
- c("d", "struct<a:string,b:int>")))
- expect_equal(count(df), 1)
- ldf <- collect(df)
- expect_equal(names(ldf), c("a", "b", "c", "d"))
- expect_equal(ldf[1, 1][[1]], l[[1]])
- expect_equal(ldf[1, 2][[1]], l[[2]])
-
- e <- ldf$c[[1]]
- expect_equal(class(e), "environment")
- expect_equal(ls(e), "n")
- expect_equal(e$n, 3L)
-
- s <- ldf$d[[1]]
- expect_equal(class(s), "struct")
- expect_equal(s$a, "aa")
- expect_equal(s$b, 3L)
-})
-
-test_that("create DataFrame from a data.frame with complex types", {
- skip_on_cran()
-
- ldf <- data.frame(row.names = 1:2)
- ldf$a_list <- list(list(1, 2), list(3, 4))
- ldf$an_envir <- c(as.environment(list(a = 1, b = 2)), as.environment(list(c = 3)))
-
- sdf <- createDataFrame(ldf)
- collected <- collect(sdf)
-
- expect_identical(ldf[, 1, FALSE], collected[, 1, FALSE])
- expect_equal(ldf$an_envir, collected$an_envir)
-})
-
-test_that("Collect DataFrame with complex types", {
- skip_on_cran()
-
- # ArrayType
- df <- read.json(complexTypeJsonPath)
- ldf <- collect(df)
- expect_equal(nrow(ldf), 3)
- expect_equal(ncol(ldf), 3)
- expect_equal(names(ldf), c("c1", "c2", "c3"))
- expect_equal(ldf$c1, list(list(1, 2, 3), list(4, 5, 6), list (7, 8, 9)))
- expect_equal(ldf$c2, list(list("a", "b", "c"), list("d", "e", "f"), list ("g", "h", "i")))
- expect_equal(ldf$c3, list(list(1.0, 2.0, 3.0), list(4.0, 5.0, 6.0), list (7.0, 8.0, 9.0)))
-
- # MapType
- schema <- structType(structField("name", "string"),
- structField("info", "map<string,double>"))
- df <- read.df(mapTypeJsonPath, "json", schema)
- expect_equal(dtypes(df), list(c("name", "string"),
- c("info", "map<string,double>")))
- ldf <- collect(df)
- expect_equal(nrow(ldf), 3)
- expect_equal(ncol(ldf), 2)
- expect_equal(names(ldf), c("name", "info"))
- expect_equal(ldf$name, c("Bob", "Alice", "David"))
- bob <- ldf$info[[1]]
- expect_equal(class(bob), "environment")
- expect_equal(bob$age, 16)
- expect_equal(bob$height, 176.5)
-
- # StructType
- df <- read.json(mapTypeJsonPath)
- expect_equal(dtypes(df), list(c("info", "struct<age:bigint,height:double>"),
- c("name", "string")))
- ldf <- collect(df)
- expect_equal(nrow(ldf), 3)
- expect_equal(ncol(ldf), 2)
- expect_equal(names(ldf), c("info", "name"))
- expect_equal(ldf$name, c("Bob", "Alice", "David"))
- bob <- ldf$info[[1]]
- expect_equal(class(bob), "struct")
- expect_equal(bob$age, 16)
- expect_equal(bob$height, 176.5)
-})
-
-test_that("read/write json files", {
- if (not_cran_or_windows_with_hadoop()) {
- # Test read.df
- df <- read.df(jsonPath, "json")
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 3)
-
- # Test read.df with a user defined schema
- schema <- structType(structField("name", type = "string"),
- structField("age", type = "double"))
-
- df1 <- read.df(jsonPath, "json", schema)
- expect_is(df1, "SparkDataFrame")
- expect_equal(dtypes(df1), list(c("name", "string"), c("age", "double")))
-
- # Test loadDF
- df2 <- loadDF(jsonPath, "json", schema)
- expect_is(df2, "SparkDataFrame")
- expect_equal(dtypes(df2), list(c("name", "string"), c("age", "double")))
-
- # Test read.json
- df <- read.json(jsonPath)
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 3)
-
- # Test write.df
- jsonPath2 <- tempfile(pattern = "jsonPath2", fileext = ".json")
- write.df(df, jsonPath2, "json", mode = "overwrite")
-
- # Test write.json
- jsonPath3 <- tempfile(pattern = "jsonPath3", fileext = ".json")
- write.json(df, jsonPath3)
-
- # Test read.json()/jsonFile() works with multiple input paths
- jsonDF1 <- read.json(c(jsonPath2, jsonPath3))
- expect_is(jsonDF1, "SparkDataFrame")
- expect_equal(count(jsonDF1), 6)
- # Suppress warnings because jsonFile is deprecated
- jsonDF2 <- suppressWarnings(jsonFile(c(jsonPath2, jsonPath3)))
- expect_is(jsonDF2, "SparkDataFrame")
- expect_equal(count(jsonDF2), 6)
-
- unlink(jsonPath2)
- unlink(jsonPath3)
- }
-})
-
-test_that("read/write json files - compression option", {
- skip_on_cran()
-
- df <- read.df(jsonPath, "json")
-
- jsonPath <- tempfile(pattern = "jsonPath", fileext = ".json")
- write.json(df, jsonPath, compression = "gzip")
- jsonDF <- read.json(jsonPath)
- expect_is(jsonDF, "SparkDataFrame")
- expect_equal(count(jsonDF), count(df))
- expect_true(length(list.files(jsonPath, pattern = ".gz")) > 0)
-
- unlink(jsonPath)
-})
-
-test_that("jsonRDD() on a RDD with json string", {
- skip_on_cran()
-
- sqlContext <- suppressWarnings(sparkRSQL.init(sc))
- rdd <- parallelize(sc, mockLines)
- expect_equal(countRDD(rdd), 3)
- df <- suppressWarnings(jsonRDD(sqlContext, rdd))
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 3)
-
- rdd2 <- flatMap(rdd, function(x) c(x, x))
- df <- suppressWarnings(jsonRDD(sqlContext, rdd2))
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 6)
-})
-
-test_that("test tableNames and tables", {
- count <- count(listTables())
-
- df <- read.json(jsonPath)
- createOrReplaceTempView(df, "table1")
- expect_equal(length(tableNames()), count + 1)
- expect_equal(length(tableNames("default")), count + 1)
-
- tables <- listTables()
- expect_equal(count(tables), count + 1)
- expect_equal(count(tables()), count(tables))
- expect_true("tableName" %in% colnames(tables()))
- expect_true(all(c("tableName", "database", "isTemporary") %in% colnames(tables())))
-
- suppressWarnings(registerTempTable(df, "table2"))
- tables <- listTables()
- expect_equal(count(tables), count + 2)
- suppressWarnings(dropTempTable("table1"))
- expect_true(dropTempView("table2"))
-
- tables <- listTables()
- expect_equal(count(tables), count + 0)
-})
-
-test_that(
- "createOrReplaceTempView() results in a queryable table and sql() results in a new DataFrame", {
- df <- read.json(jsonPath)
- createOrReplaceTempView(df, "table1")
- newdf <- sql("SELECT * FROM table1 where name = 'Michael'")
- expect_is(newdf, "SparkDataFrame")
- expect_equal(count(newdf), 1)
- expect_true(dropTempView("table1"))
-
- createOrReplaceTempView(df, "dfView")
- sqlCast <- collect(sql("select cast('2' as decimal) as x from dfView limit 1"))
- out <- capture.output(sqlCast)
- expect_true(is.data.frame(sqlCast))
- expect_equal(names(sqlCast)[1], "x")
- expect_equal(nrow(sqlCast), 1)
- expect_equal(ncol(sqlCast), 1)
- expect_equal(out[1], " x")
- expect_equal(out[2], "1 2")
- expect_true(dropTempView("dfView"))
-})
-
-test_that("test cache, uncache and clearCache", {
- skip_on_cran()
-
- df <- read.json(jsonPath)
- createOrReplaceTempView(df, "table1")
- cacheTable("table1")
- uncacheTable("table1")
- clearCache()
- expect_true(dropTempView("table1"))
-
- expect_error(uncacheTable("foo"),
- "Error in uncacheTable : no such table - Table or view 'foo' not found in database 'default'")
-})
-
-test_that("insertInto() on a registered table", {
- if (not_cran_or_windows_with_hadoop()) {
- df <- read.df(jsonPath, "json")
- write.df(df, parquetPath, "parquet", "overwrite")
- dfParquet <- read.df(parquetPath, "parquet")
-
- lines <- c("{\"name\":\"Bob\", \"age\":24}",
- "{\"name\":\"James\", \"age\":35}")
- jsonPath2 <- tempfile(pattern = "jsonPath2", fileext = ".tmp")
- parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet")
- writeLines(lines, jsonPath2)
- df2 <- read.df(jsonPath2, "json")
- write.df(df2, parquetPath2, "parquet", "overwrite")
- dfParquet2 <- read.df(parquetPath2, "parquet")
-
- createOrReplaceTempView(dfParquet, "table1")
- insertInto(dfParquet2, "table1")
- expect_equal(count(sql("select * from table1")), 5)
- expect_equal(first(sql("select * from table1 order by age"))$name, "Michael")
- expect_true(dropTempView("table1"))
-
- createOrReplaceTempView(dfParquet, "table1")
- insertInto(dfParquet2, "table1", overwrite = TRUE)
- expect_equal(count(sql("select * from table1")), 2)
- expect_equal(first(sql("select * from table1 order by age"))$name, "Bob")
- expect_true(dropTempView("table1"))
-
- unlink(jsonPath2)
- unlink(parquetPath2)
- }
-})
-
-test_that("tableToDF() returns a new DataFrame", {
- df <- read.json(jsonPath)
- createOrReplaceTempView(df, "table1")
- tabledf <- tableToDF("table1")
- expect_is(tabledf, "SparkDataFrame")
- expect_equal(count(tabledf), 3)
- tabledf2 <- tableToDF("table1")
- expect_equal(count(tabledf2), 3)
- expect_true(dropTempView("table1"))
-})
-
-test_that("toRDD() returns an RRDD", {
- skip_on_cran()
-
- df <- read.json(jsonPath)
- testRDD <- toRDD(df)
- expect_is(testRDD, "RDD")
- expect_equal(countRDD(testRDD), 3)
-})
-
-test_that("union on two RDDs created from DataFrames returns an RRDD", {
- skip_on_cran()
-
- df <- read.json(jsonPath)
- RDD1 <- toRDD(df)
- RDD2 <- toRDD(df)
- unioned <- unionRDD(RDD1, RDD2)
- expect_is(unioned, "RDD")
- expect_equal(getSerializedMode(unioned), "byte")
- expect_equal(collectRDD(unioned)[[2]]$name, "Andy")
-})
-
-test_that("union on mixed serialization types correctly returns a byte RRDD", {
- skip_on_cran()
-
- # Byte RDD
- nums <- 1:10
- rdd <- parallelize(sc, nums, 2L)
-
- # String RDD
- textLines <- c("Michael",
- "Andy, 30",
- "Justin, 19")
- textPath <- tempfile(pattern = "sparkr-textLines", fileext = ".tmp")
- writeLines(textLines, textPath)
- textRDD <- textFile(sc, textPath)
-
- df <- read.json(jsonPath)
- dfRDD <- toRDD(df)
-
- unionByte <- unionRDD(rdd, dfRDD)
- expect_is(unionByte, "RDD")
- expect_equal(getSerializedMode(unionByte), "byte")
- expect_equal(collectRDD(unionByte)[[1]], 1)
- expect_equal(collectRDD(unionByte)[[12]]$name, "Andy")
-
- unionString <- unionRDD(textRDD, dfRDD)
- expect_is(unionString, "RDD")
- expect_equal(getSerializedMode(unionString), "byte")
- expect_equal(collectRDD(unionString)[[1]], "Michael")
- expect_equal(collectRDD(unionString)[[5]]$name, "Andy")
-})
-
-test_that("objectFile() works with row serialization", {
- skip_on_cran()
-
- objectPath <- tempfile(pattern = "spark-test", fileext = ".tmp")
- df <- read.json(jsonPath)
- dfRDD <- toRDD(df)
- saveAsObjectFile(coalesceRDD(dfRDD, 1L), objectPath)
- objectIn <- objectFile(sc, objectPath)
-
- expect_is(objectIn, "RDD")
- expect_equal(getSerializedMode(objectIn), "byte")
- expect_equal(collectRDD(objectIn)[[2]]$age, 30)
-})
-
-test_that("lapply() on a DataFrame returns an RDD with the correct columns", {
- skip_on_cran()
-
- df <- read.json(jsonPath)
- testRDD <- lapply(df, function(row) {
- row$newCol <- row$age + 5
- row
- })
- expect_is(testRDD, "RDD")
- collected <- collectRDD(testRDD)
- expect_equal(collected[[1]]$name, "Michael")
- expect_equal(collected[[2]]$newCol, 35)
-})
-
-test_that("collect() returns a data.frame", {
- df <- read.json(jsonPath)
- rdf <- collect(df)
- expect_true(is.data.frame(rdf))
- expect_equal(names(rdf)[1], "age")
- expect_equal(nrow(rdf), 3)
- expect_equal(ncol(rdf), 2)
-
- # collect() returns data correctly from a DataFrame with 0 row
- df0 <- limit(df, 0)
- rdf <- collect(df0)
- expect_true(is.data.frame(rdf))
- expect_equal(names(rdf)[1], "age")
- expect_equal(nrow(rdf), 0)
- expect_equal(ncol(rdf), 2)
-
- # collect() correctly handles multiple columns with same name
- df <- createDataFrame(list(list(1, 2)), schema = c("name", "name"))
- ldf <- collect(df)
- expect_equal(names(ldf), c("name", "name"))
-})
-
-test_that("limit() returns DataFrame with the correct number of rows", {
- df <- read.json(jsonPath)
- dfLimited <- limit(df, 2)
- expect_is(dfLimited, "SparkDataFrame")
- expect_equal(count(dfLimited), 2)
-})
-
-test_that("collect() and take() on a DataFrame return the same number of rows and columns", {
- df <- read.json(jsonPath)
- expect_equal(nrow(collect(df)), nrow(take(df, 10)))
- expect_equal(ncol(collect(df)), ncol(take(df, 10)))
-})
-
-test_that("collect() support Unicode characters", {
- lines <- c("{\"name\":\"안녕하세요\"}",
- "{\"name\":\"您好\", \"age\":30}",
- "{\"name\":\"こんにちは\", \"age\":19}",
- "{\"name\":\"Xin chào\"}")
-
- jsonPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(lines, jsonPath)
-
- df <- read.df(jsonPath, "json")
- rdf <- collect(df)
- expect_true(is.data.frame(rdf))
- expect_equal(rdf$name[1], markUtf8("안녕하세요"))
- expect_equal(rdf$name[2], markUtf8("您好"))
- expect_equal(rdf$name[3], markUtf8("こんにちは"))
- expect_equal(rdf$name[4], markUtf8("Xin chào"))
-
- df1 <- createDataFrame(rdf)
- expect_equal(collect(where(df1, df1$name == markUtf8("您好")))$name, markUtf8("您好"))
-})
-
-test_that("multiple pipeline transformations result in an RDD with the correct values", {
- skip_on_cran()
-
- df <- read.json(jsonPath)
- first <- lapply(df, function(row) {
- row$age <- row$age + 5
- row
- })
- second <- lapply(first, function(row) {
- row$testCol <- if (row$age == 35 && !is.na(row$age)) TRUE else FALSE
- row
- })
- expect_is(second, "RDD")
- expect_equal(countRDD(second), 3)
- expect_equal(collectRDD(second)[[2]]$age, 35)
- expect_true(collectRDD(second)[[2]]$testCol)
- expect_false(collectRDD(second)[[3]]$testCol)
-})
-
-test_that("cache(), storageLevel(), persist(), and unpersist() on a DataFrame", {
- df <- read.json(jsonPath)
- expect_false(df@env$isCached)
- cache(df)
- expect_true(df@env$isCached)
-
- unpersist(df)
- expect_false(df@env$isCached)
-
- persist(df, "MEMORY_AND_DISK")
- expect_true(df@env$isCached)
-
- expect_equal(storageLevel(df),
- "MEMORY_AND_DISK - StorageLevel(disk, memory, deserialized, 1 replicas)")
-
- unpersist(df)
- expect_false(df@env$isCached)
-
- # make sure the data is collectable
- expect_true(is.data.frame(collect(df)))
-})
-
-test_that("setCheckpointDir(), checkpoint() on a DataFrame", {
- if (not_cran_or_windows_with_hadoop()) {
- checkpointDir <- file.path(tempdir(), "cproot")
- expect_true(length(list.files(path = checkpointDir, all.files = TRUE)) == 0)
-
- setCheckpointDir(checkpointDir)
- df <- read.json(jsonPath)
- df <- checkpoint(df)
- expect_is(df, "SparkDataFrame")
- expect_false(length(list.files(path = checkpointDir, all.files = TRUE)) == 0)
- }
-})
-
-test_that("schema(), dtypes(), columns(), names() return the correct values/format", {
- df <- read.json(jsonPath)
- testSchema <- schema(df)
- expect_equal(length(testSchema$fields()), 2)
- expect_equal(testSchema$fields()[[1]]$dataType.toString(), "LongType")
- expect_equal(testSchema$fields()[[2]]$dataType.simpleString(), "string")
- expect_equal(testSchema$fields()[[1]]$name(), "age")
-
- testTypes <- dtypes(df)
- expect_equal(length(testTypes[[1]]), 2)
- expect_equal(testTypes[[1]][1], "age")
-
- testCols <- columns(df)
- expect_equal(length(testCols), 2)
- expect_equal(testCols[2], "name")
-
- testNames <- names(df)
- expect_equal(length(testNames), 2)
- expect_equal(testNames[2], "name")
-})
-
-test_that("names() colnames() set the column names", {
- df <- read.json(jsonPath)
- names(df) <- c("col1", "col2")
- expect_equal(colnames(df)[2], "col2")
-
- colnames(df) <- c("col3", "col4")
- expect_equal(names(df)[1], "col3")
-
- expect_error(names(df) <- NULL, "Invalid column names.")
- expect_error(names(df) <- c("sepal.length", "sepal_width"),
- "Column names cannot contain the '.' symbol.")
- expect_error(names(df) <- c(1, 2), "Invalid column names.")
- expect_error(names(df) <- c("a"),
- "Column names must have the same length as the number of columns in the dataset.")
- expect_error(names(df) <- c("1", NA), "Column names cannot be NA.")
-
- expect_error(colnames(df) <- c("sepal.length", "sepal_width"),
- "Column names cannot contain the '.' symbol.")
- expect_error(colnames(df) <- c(1, 2), "Invalid column names.")
- expect_error(colnames(df) <- c("a"),
- "Column names must have the same length as the number of columns in the dataset.")
- expect_error(colnames(df) <- c("1", NA), "Column names cannot be NA.")
-
- # Note: if this test is broken, remove check for "." character on colnames<- method
- irisDF <- suppressWarnings(createDataFrame(iris))
- expect_equal(names(irisDF)[1], "Sepal_Length")
-
- # Test base::colnames base::names
- m2 <- cbind(1, 1:4)
- expect_equal(colnames(m2, do.NULL = FALSE), c("col1", "col2"))
- colnames(m2) <- c("x", "Y")
- expect_equal(colnames(m2), c("x", "Y"))
-
- z <- list(a = 1, b = "c", c = 1:3)
- expect_equal(names(z)[3], "c")
- names(z)[3] <- "c2"
- expect_equal(names(z)[3], "c2")
-
- # Test subset assignment
- colnames(df)[1] <- "col5"
- expect_equal(colnames(df)[1], "col5")
- names(df)[2] <- "col6"
- expect_equal(names(df)[2], "col6")
-})
-
-test_that("head() and first() return the correct data", {
- df <- read.json(jsonPath)
- testHead <- head(df)
- expect_equal(nrow(testHead), 3)
- expect_equal(ncol(testHead), 2)
-
- testHead2 <- head(df, 2)
- expect_equal(nrow(testHead2), 2)
- expect_equal(ncol(testHead2), 2)
-
- testFirst <- first(df)
- expect_equal(nrow(testFirst), 1)
-
- # head() and first() return the correct data on
- # a DataFrame with 0 row
- df0 <- limit(df, 0)
-
- testHead <- head(df0)
- expect_equal(nrow(testHead), 0)
- expect_equal(ncol(testHead), 2)
-
- testFirst <- first(df0)
- expect_equal(nrow(testFirst), 0)
- expect_equal(ncol(testFirst), 2)
-})
-
-test_that("distinct(), unique() and dropDuplicates() on DataFrames", {
- lines <- c("{\"name\":\"Michael\"}",
- "{\"name\":\"Andy\", \"age\":30}",
- "{\"name\":\"Justin\", \"age\":19}",
- "{\"name\":\"Justin\", \"age\":19}")
- jsonPathWithDup <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(lines, jsonPathWithDup)
-
- df <- read.json(jsonPathWithDup)
- uniques <- distinct(df)
- expect_is(uniques, "SparkDataFrame")
- expect_equal(count(uniques), 3)
-
- uniques2 <- unique(df)
- expect_is(uniques2, "SparkDataFrame")
- expect_equal(count(uniques2), 3)
-
- # Test dropDuplicates()
- df <- createDataFrame(
- list(
- list(2, 1, 2), list(1, 1, 1),
- list(1, 2, 1), list(2, 1, 2),
- list(2, 2, 2), list(2, 2, 1),
- list(2, 1, 1), list(1, 1, 2),
- list(1, 2, 2), list(1, 2, 1)),
- schema = c("key", "value1", "value2"))
- result <- collect(dropDuplicates(df))
- expected <- rbind.data.frame(
- c(1, 1, 1), c(1, 1, 2), c(1, 2, 1),
- c(1, 2, 2), c(2, 1, 1), c(2, 1, 2),
- c(2, 2, 1), c(2, 2, 2))
- names(expected) <- c("key", "value1", "value2")
- expect_equivalent(
- result[order(result$key, result$value1, result$value2), ],
- expected)
-
- result <- collect(dropDuplicates(df, c("key", "value1")))
- expected <- rbind.data.frame(
- c(1, 1, 1), c(1, 2, 1), c(2, 1, 2), c(2, 2, 2))
- names(expected) <- c("key", "value1", "value2")
- expect_equivalent(
- result[order(result$key, result$value1, result$value2), ],
- expected)
-
- result <- collect(dropDuplicates(df, "key", "value1"))
- expected <- rbind.data.frame(
- c(1, 1, 1), c(1, 2, 1), c(2, 1, 2), c(2, 2, 2))
- names(expected) <- c("key", "value1", "value2")
- expect_equivalent(
- result[order(result$key, result$value1, result$value2), ],
- expected)
-
- result <- collect(dropDuplicates(df, "key"))
- expected <- rbind.data.frame(
- c(1, 1, 1), c(2, 1, 2))
- names(expected) <- c("key", "value1", "value2")
- expect_equivalent(
- result[order(result$key, result$value1, result$value2), ],
- expected)
-})
-
-test_that("sample on a DataFrame", {
- df <- read.json(jsonPath)
- sampled <- sample(df, FALSE, 1.0)
- expect_equal(nrow(collect(sampled)), count(df))
- expect_is(sampled, "SparkDataFrame")
- sampled2 <- sample(df, FALSE, 0.1, 0) # set seed for predictable result
- expect_true(count(sampled2) < 3)
-
- count1 <- count(sample(df, FALSE, 0.1, 0))
- count2 <- count(sample(df, FALSE, 0.1, 0))
- expect_equal(count1, count2)
-
- # Also test sample_frac
- sampled3 <- sample_frac(df, FALSE, 0.1, 0) # set seed for predictable result
- expect_true(count(sampled3) < 3)
-
- # nolint start
- # Test base::sample is working
- #expect_equal(length(sample(1:12)), 12)
- # nolint end
-})
-
-test_that("select operators", {
- df <- select(read.json(jsonPath), "name", "age")
- expect_is(df$name, "Column")
- expect_is(df[[2]], "Column")
- expect_is(df[["age"]], "Column")
-
- expect_warning(df[[1:2]],
- "Subset index has length > 1. Only the first index is used.")
- expect_is(suppressWarnings(df[[1:2]]), "Column")
- expect_warning(df[[c("name", "age")]],
- "Subset index has length > 1. Only the first index is used.")
- expect_is(suppressWarnings(df[[c("name", "age")]]), "Column")
-
- expect_warning(df[[1:2]] <- df[[1]],
- "Subset index has length > 1. Only the first index is used.")
- expect_warning(df[[c("name", "age")]] <- df[[1]],
- "Subset index has length > 1. Only the first index is used.")
-
- expect_is(df[, 1, drop = F], "SparkDataFrame")
- expect_equal(columns(df[, 1, drop = F]), c("name"))
- expect_equal(columns(df[, "age", drop = F]), c("age"))
-
- df2 <- df[, c("age", "name")]
- expect_is(df2, "SparkDataFrame")
- expect_equal(columns(df2), c("age", "name"))
-
- df$age2 <- df$age
- expect_equal(columns(df), c("name", "age", "age2"))
- expect_equal(count(where(df, df$age2 == df$age)), 2)
- df$age2 <- df$age * 2
- expect_equal(columns(df), c("name", "age", "age2"))
- expect_equal(count(where(df, df$age2 == df$age * 2)), 2)
- df$age2 <- df[["age"]] * 3
- expect_equal(columns(df), c("name", "age", "age2"))
- expect_equal(count(where(df, df$age2 == df$age * 3)), 2)
-
- df$age2 <- 21
- expect_equal(columns(df), c("name", "age", "age2"))
- expect_equal(count(where(df, df$age2 == 21)), 3)
-
- df$age2 <- c(22)
- expect_equal(columns(df), c("name", "age", "age2"))
- expect_equal(count(where(df, df$age2 == 22)), 3)
-
- expect_error(df$age3 <- c(22, NA),
- "value must be a Column, literal value as atomic in length of 1, or NULL")
-
- df[["age2"]] <- 23
- expect_equal(columns(df), c("name", "age", "age2"))
- expect_equal(count(where(df, df$age2 == 23)), 3)
-
- df[[3]] <- 24
- expect_equal(columns(df), c("name", "age", "age2"))
- expect_equal(count(where(df, df$age2 == 24)), 3)
-
- df[[3]] <- df$age
- expect_equal(count(where(df, df$age2 == df$age)), 2)
-
- df[["age2"]] <- df[["name"]]
- expect_equal(count(where(df, df$age2 == df$name)), 3)
-
- expect_error(df[["age3"]] <- c(22, 23),
- "value must be a Column, literal value as atomic in length of 1, or NULL")
-
- # Test parameter drop
- expect_equal(class(df[, 1]) == "SparkDataFrame", T)
- expect_equal(class(df[, 1, drop = T]) == "Column", T)
- expect_equal(class(df[, 1, drop = F]) == "SparkDataFrame", T)
- expect_equal(class(df[df$age > 4, 2, drop = T]) == "Column", T)
- expect_equal(class(df[df$age > 4, 2, drop = F]) == "SparkDataFrame", T)
-})
-
-test_that("select with column", {
- df <- read.json(jsonPath)
- df1 <- select(df, "name")
- expect_equal(columns(df1), c("name"))
- expect_equal(count(df1), 3)
-
- df2 <- select(df, df$age)
- expect_equal(columns(df2), c("age"))
- expect_equal(count(df2), 3)
-
- df3 <- select(df, lit("x"))
- expect_equal(columns(df3), c("x"))
- expect_equal(count(df3), 3)
- expect_equal(collect(select(df3, "x"))[[1, 1]], "x")
-
- df4 <- select(df, c("name", "age"))
- expect_equal(columns(df4), c("name", "age"))
- expect_equal(count(df4), 3)
-
- # Test select with alias
- df5 <- alias(df, "table")
-
- expect_equal(columns(select(df5, column("table.name"))), "name")
- expect_equal(columns(select(df5, "table.name")), "name")
-
- # Test that stats::alias is not masked
- expect_is(alias(aov(yield ~ block + N * P * K, npk)), "listof")
-
-
- expect_error(select(df, c("name", "age"), "name"),
- "To select multiple columns, use a character vector or list for col")
-})
-
-test_that("drop column", {
- df <- select(read.json(jsonPath), "name", "age")
- df1 <- drop(df, "name")
- expect_equal(columns(df1), c("age"))
-
- df$age2 <- df$age
- df1 <- drop(df, c("name", "age"))
- expect_equal(columns(df1), c("age2"))
-
- df1 <- drop(df, df$age)
- expect_equal(columns(df1), c("name", "age2"))
-
- df$age2 <- NULL
- expect_equal(columns(df), c("name", "age"))
- df$age3 <- NULL
- expect_equal(columns(df), c("name", "age"))
-
- # Test to make sure base::drop is not masked
- expect_equal(drop(1:3 %*% 2:4), 20)
-})
-
-test_that("subsetting", {
- # read.json returns columns in random order
- df <- select(read.json(jsonPath), "name", "age")
- filtered <- df[df$age > 20, ]
- expect_equal(count(filtered), 1)
- expect_equal(columns(filtered), c("name", "age"))
- expect_equal(collect(filtered)$name, "Andy")
-
- df2 <- df[df$age == 19, 1, drop = F]
- expect_is(df2, "SparkDataFrame")
- expect_equal(count(df2), 1)
- expect_equal(columns(df2), c("name"))
- expect_equal(collect(df2)$name, "Justin")
-
- df3 <- df[df$age > 20, 2, drop = F]
- expect_equal(count(df3), 1)
- expect_equal(columns(df3), c("age"))
-
- df4 <- df[df$age %in% c(19, 30), 1:2]
- expect_equal(count(df4), 2)
- expect_equal(columns(df4), c("name", "age"))
-
- df5 <- df[df$age %in% c(19), c(1, 2)]
- expect_equal(count(df5), 1)
- expect_equal(columns(df5), c("name", "age"))
-
- df6 <- subset(df, df$age %in% c(30), c(1, 2))
- expect_equal(count(df6), 1)
- expect_equal(columns(df6), c("name", "age"))
-
- df7 <- subset(df, select = "name", drop = F)
- expect_equal(count(df7), 3)
- expect_equal(columns(df7), c("name"))
-
- # Test base::subset is working
- expect_equal(nrow(subset(airquality, Temp > 80, select = c(Ozone, Temp))), 68)
-})
-
-test_that("selectExpr() on a DataFrame", {
- df <- read.json(jsonPath)
- selected <- selectExpr(df, "age * 2")
- expect_equal(names(selected), "(age * 2)")
- expect_equal(collect(selected), collect(select(df, df$age * 2L)))
-
- selected2 <- selectExpr(df, "name as newName", "abs(age) as age")
- expect_equal(names(selected2), c("newName", "age"))
- expect_equal(count(selected2), 3)
-})
-
-test_that("expr() on a DataFrame", {
- df <- read.json(jsonPath)
- expect_equal(collect(select(df, expr("abs(-123)")))[1, 1], 123)
-})
-
-test_that("column calculation", {
- df <- read.json(jsonPath)
- d <- collect(select(df, alias(df$age + 1, "age2")))
- expect_equal(names(d), c("age2"))
- df2 <- select(df, lower(df$name), abs(df$age))
- expect_is(df2, "SparkDataFrame")
- expect_equal(count(df2), 3)
-})
-
-test_that("test HiveContext", {
- if (not_cran_or_windows_with_hadoop()) {
- setHiveContext(sc)
-
- schema <- structType(structField("name", "string"), structField("age", "integer"),
- structField("height", "float"))
- createTable("people", source = "json", schema = schema)
- df <- read.df(jsonPathNa, "json", schema)
- insertInto(df, "people")
- expect_equal(collect(sql("SELECT age from people WHERE name = 'Bob'"))$age, c(16))
- sql("DROP TABLE people")
-
- df <- createTable("json", jsonPath, "json")
- expect_is(df, "SparkDataFrame")
- expect_equal(count(df), 3)
- df2 <- sql("select * from json")
- expect_is(df2, "SparkDataFrame")
- expect_equal(count(df2), 3)
-
- jsonPath2 <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- saveAsTable(df, "json2", "json", "append", path = jsonPath2)
- df3 <- sql("select * from json2")
- expect_is(df3, "SparkDataFrame")
- expect_equal(count(df3), 3)
- unlink(jsonPath2)
-
- hivetestDataPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- saveAsTable(df, "hivetestbl", path = hivetestDataPath)
- df4 <- sql("select * from hivetestbl")
- expect_is(df4, "SparkDataFrame")
- expect_equal(count(df4), 3)
- unlink(hivetestDataPath)
-
- parquetDataPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- saveAsTable(df, "parquetest", "parquet", mode = "overwrite", path = parquetDataPath)
- df5 <- sql("select * from parquetest")
- expect_is(df5, "SparkDataFrame")
- expect_equal(count(df5), 3)
- unlink(parquetDataPath)
-
- unsetHiveContext()
- }
-})
-
-test_that("column operators", {
- c <- column("a")
- c2 <- (- c + 1 - 2) * 3 / 4.0
- c3 <- (c + c2 - c2) * c2 %% c2
- c4 <- (c > c2) & (c2 <= c3) | (c == c2) & (c2 != c3)
- c5 <- c2 ^ c3 ^ c4
- c6 <- c2 %<=>% c3
- c7 <- !c6
-})
-
-test_that("column functions", {
- skip_on_cran()
-
- c <- column("a")
- c1 <- abs(c) + acos(c) + approxCountDistinct(c) + ascii(c) + asin(c) + atan(c)
- c2 <- avg(c) + base64(c) + bin(c) + bitwiseNOT(c) + cbrt(c) + ceil(c) + cos(c)
- c3 <- cosh(c) + count(c) + crc32(c) + hash(c) + exp(c)
- c4 <- explode(c) + expm1(c) + factorial(c) + first(c) + floor(c) + hex(c)
- c5 <- hour(c) + initcap(c) + last(c) + last_day(c) + length(c)
- c6 <- log(c) + (c) + log1p(c) + log2(c) + lower(c) + ltrim(c) + max(c) + md5(c)
- c7 <- mean(c) + min(c) + month(c) + negate(c) + posexplode(c) + quarter(c)
- c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + sha1(c) + monotonically_increasing_id()
- c9 <- signum(c) + sin(c) + sinh(c) + size(c) + stddev(c) + soundex(c) + sqrt(c) + sum(c)
- c10 <- sumDistinct(c) + tan(c) + tanh(c) + toDegrees(c) + toRadians(c)
- c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c)
- c12 <- variance(c)
- c13 <- lead("col", 1) + lead(c, 1) + lag("col", 1) + lag(c, 1)
- c14 <- cume_dist() + ntile(1) + corr(c, c1)
- c15 <- dense_rank() + percent_rank() + rank() + row_number()
- c16 <- is.nan(c) + isnan(c) + isNaN(c)
- c17 <- cov(c, c1) + cov("c", "c1") + covar_samp(c, c1) + covar_samp("c", "c1")
- c18 <- covar_pop(c, c1) + covar_pop("c", "c1")
- c19 <- spark_partition_id() + coalesce(c) + coalesce(c1, c2, c3)
- c20 <- to_timestamp(c) + to_timestamp(c, "yyyy") + to_date(c, "yyyy")
- c21 <- posexplode_outer(c) + explode_outer(c)
- c22 <- not(c)
-
- # Test if base::is.nan() is exposed
- expect_equal(is.nan(c("a", "b")), c(FALSE, FALSE))
-
- # Test if base::rank() is exposed
- expect_equal(class(rank())[[1]], "Column")
- expect_equal(rank(1:3), as.numeric(c(1:3)))
-
- df <- read.json(jsonPath)
- df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))
- expect_equal(collect(df2)[[2, 1]], TRUE)
- expect_equal(collect(df2)[[2, 2]], FALSE)
- expect_equal(collect(df2)[[3, 1]], FALSE)
- expect_equal(collect(df2)[[3, 2]], TRUE)
-
- # Test that input_file_name()
- actual_names <- sort(collect(distinct(select(df, input_file_name()))))
- expect_equal(length(actual_names), 1)
- expect_equal(basename(actual_names[1, 1]), basename(jsonPath))
-
- df3 <- select(df, between(df$name, c("Apache", "Spark")))
- expect_equal(collect(df3)[[1, 1]], TRUE)
- expect_equal(collect(df3)[[2, 1]], FALSE)
- expect_equal(collect(df3)[[3, 1]], TRUE)
-
- df4 <- select(df, countDistinct(df$age, df$name))
- expect_equal(collect(df4)[[1, 1]], 2)
-
- expect_equal(collect(select(df, sum(df$age)))[1, 1], 49)
- expect_true(abs(collect(select(df, stddev(df$age)))[1, 1] - 7.778175) < 1e-6)
- expect_equal(collect(select(df, var_pop(df$age)))[1, 1], 30.25)
-
- df5 <- createDataFrame(list(list(a = "010101")))
- expect_equal(collect(select(df5, conv(df5$a, 2, 16)))[1, 1], "15")
-
- # Test array_contains() and sort_array()
- df <- createDataFrame(list(list(list(1L, 2L, 3L)), list(list(6L, 5L, 4L))))
- result <- collect(select(df, array_contains(df[[1]], 1L)))[[1]]
- expect_equal(result, c(TRUE, FALSE))
-
- result <- collect(select(df, sort_array(df[[1]], FALSE)))[[1]]
- expect_equal(result, list(list(3L, 2L, 1L), list(6L, 5L, 4L)))
- result <- collect(select(df, sort_array(df[[1]])))[[1]]
- expect_equal(result, list(list(1L, 2L, 3L), list(4L, 5L, 6L)))
-
- # Test that stats::lag is working
- expect_equal(length(lag(ldeaths, 12)), 72)
-
- # Test struct()
- df <- createDataFrame(list(list(1L, 2L, 3L), list(4L, 5L, 6L)),
- schema = c("a", "b", "c"))
- result <- collect(select(df, alias(struct("a", "c"), "d")))
- expected <- data.frame(row.names = 1:2)
- expected$"d" <- list(listToStruct(list(a = 1L, c = 3L)),
- listToStruct(list(a = 4L, c = 6L)))
- expect_equal(result, expected)
-
- result <- collect(select(df, alias(struct(df$a, df$b), "d")))
- expected <- data.frame(row.names = 1:2)
- expected$"d" <- list(listToStruct(list(a = 1L, b = 2L)),
- listToStruct(list(a = 4L, b = 5L)))
- expect_equal(result, expected)
-
- # Test encode(), decode()
- bytes <- as.raw(c(0xe5, 0xa4, 0xa7, 0xe5, 0x8d, 0x83, 0xe4, 0xb8, 0x96, 0xe7, 0x95, 0x8c))
- df <- createDataFrame(list(list(markUtf8("大千世界"), "utf-8", bytes)),
- schema = c("a", "b", "c"))
- result <- collect(select(df, encode(df$a, "utf-8"), decode(df$c, "utf-8")))
- expect_equal(result[[1]][[1]], bytes)
- expect_equal(result[[2]], markUtf8("大千世界"))
-
- # Test first(), last()
- df <- read.json(jsonPath)
- expect_equal(collect(select(df, first(df$age)))[[1]], NA_real_)
- expect_equal(collect(select(df, first(df$age, TRUE)))[[1]], 30)
- expect_equal(collect(select(df, first("age")))[[1]], NA_real_)
- expect_equal(collect(select(df, first("age", TRUE)))[[1]], 30)
- expect_equal(collect(select(df, last(df$age)))[[1]], 19)
- expect_equal(collect(select(df, last(df$age, TRUE)))[[1]], 19)
- expect_equal(collect(select(df, last("age")))[[1]], 19)
- expect_equal(collect(select(df, last("age", TRUE)))[[1]], 19)
-
- # Test bround()
- df <- createDataFrame(data.frame(x = c(2.5, 3.5)))
- expect_equal(collect(select(df, bround(df$x, 0)))[[1]][1], 2)
- expect_equal(collect(select(df, bround(df$x, 0)))[[1]][2], 4)
-
- # Test to_json(), from_json()
- df <- sql("SELECT array(named_struct('name', 'Bob'), named_struct('name', 'Alice')) as people")
- j <- collect(select(df, alias(to_json(df$people), "json")))
- expect_equal(j[order(j$json), ][1], "[{\"name\":\"Bob\"},{\"name\":\"Alice\"}]")
-
- df <- read.json(mapTypeJsonPath)
- j <- collect(select(df, alias(to_json(df$info), "json")))
- expect_equal(j[order(j$json), ][1], "{\"age\":16,\"height\":176.5}")
- df <- as.DataFrame(j)
- schema <- structType(structField("age", "integer"),
- structField("height", "double"))
- s <- collect(select(df, alias(from_json(df$json, schema), "structcol")))
- expect_equal(ncol(s), 1)
- expect_equal(nrow(s), 3)
- expect_is(s[[1]][[1]], "struct")
- expect_true(any(apply(s, 1, function(x) { x[[1]]$age == 16 } )))
-
- # passing option
- df <- as.DataFrame(list(list("col" = "{\"date\":\"21/10/2014\"}")))
- schema2 <- structType(structField("date", "date"))
- s <- collect(select(df, from_json(df$col, schema2)))
- expect_equal(s[[1]][[1]], NA)
- s <- collect(select(df, from_json(df$col, schema2, dateFormat = "dd/MM/yyyy")))
- expect_is(s[[1]][[1]]$date, "Date")
- expect_equal(as.character(s[[1]][[1]]$date), "2014-10-21")
-
- # check for unparseable
- df <- as.DataFrame(list(list("a" = "")))
- expect_equal(collect(select(df, from_json(df$a, schema)))[[1]][[1]], NA)
-
- # check if array type in string is correctly supported.
- jsonArr <- "[{\"name\":\"Bob\"}, {\"name\":\"Alice\"}]"
- df <- as.DataFrame(list(list("people" = jsonArr)))
- schema <- structType(structField("name", "string"))
- arr <- collect(select(df, alias(from_json(df$people, schema, as.json.array = TRUE), "arrcol")))
- expect_equal(ncol(arr), 1)
- expect_equal(nrow(arr), 1)
- expect_is(arr[[1]][[1]], "list")
- expect_equal(length(arr$arrcol[[1]]), 2)
- expect_equal(arr$arrcol[[1]][[1]]$name, "Bob")
- expect_equal(arr$arrcol[[1]][[2]]$name, "Alice")
-
- # Test create_array() and create_map()
- df <- as.DataFrame(data.frame(
- x = c(1.0, 2.0), y = c(-1.0, 3.0), z = c(-2.0, 5.0)
- ))
-
- arrs <- collect(select(df, create_array(df$x, df$y, df$z)))
- expect_equal(arrs[, 1], list(list(1, -1, -2), list(2, 3, 5)))
-
- maps <- collect(select(
- df, create_map(lit("x"), df$x, lit("y"), df$y, lit("z"), df$z)))
-
- expect_equal(
- maps[, 1],
- lapply(
- list(list(x = 1, y = -1, z = -2), list(x = 2, y = 3, z = 5)),
- as.environment))
-
- df <- as.DataFrame(data.frame(is_true = c(TRUE, FALSE, NA)))
- expect_equal(
- collect(select(df, alias(not(df$is_true), "is_false"))),
- data.frame(is_false = c(FALSE, TRUE, NA))
- )
-})
-
-test_that("column binary mathfunctions", {
- lines <- c("{\"a\":1, \"b\":5}",
- "{\"a\":2, \"b\":6}",
- "{\"a\":3, \"b\":7}",
- "{\"a\":4, \"b\":8}")
- jsonPathWithDup <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(lines, jsonPathWithDup)
- df <- read.json(jsonPathWithDup)
- expect_equal(collect(select(df, atan2(df$a, df$b)))[1, "ATAN2(a, b)"], atan2(1, 5))
- expect_equal(collect(select(df, atan2(df$a, df$b)))[2, "ATAN2(a, b)"], atan2(2, 6))
- expect_equal(collect(select(df, atan2(df$a, df$b)))[3, "ATAN2(a, b)"], atan2(3, 7))
- expect_equal(collect(select(df, atan2(df$a, df$b)))[4, "ATAN2(a, b)"], atan2(4, 8))
- ## nolint start
- expect_equal(collect(select(df, hypot(df$a, df$b)))[1, "HYPOT(a, b)"], sqrt(1^2 + 5^2))
- expect_equal(collect(select(df, hypot(df$a, df$b)))[2, "HYPOT(a, b)"], sqrt(2^2 + 6^2))
- expect_equal(collect(select(df, hypot(df$a, df$b)))[3, "HYPOT(a, b)"], sqrt(3^2 + 7^2))
- expect_equal(collect(select(df, hypot(df$a, df$b)))[4, "HYPOT(a, b)"], sqrt(4^2 + 8^2))
- ## nolint end
- expect_equal(collect(select(df, shiftLeft(df$b, 1)))[4, 1], 16)
- expect_equal(collect(select(df, shiftRight(df$b, 1)))[4, 1], 4)
- expect_equal(collect(select(df, shiftRightUnsigned(df$b, 1)))[4, 1], 4)
- expect_equal(class(collect(select(df, rand()))[2, 1]), "numeric")
- expect_equal(collect(select(df, rand(1)))[1, 1], 0.134, tolerance = 0.01)
- expect_equal(class(collect(select(df, randn()))[2, 1]), "numeric")
- expect_equal(collect(select(df, randn(1)))[1, 1], -1.03, tolerance = 0.01)
-})
-
-test_that("string operators", {
- df <- read.json(jsonPath)
- expect_equal(count(where(df, like(df$name, "A%"))), 1)
- expect_equal(count(where(df, startsWith(df$name, "A"))), 1)
- expect_true(first(select(df, startsWith(df$name, "M")))[[1]])
- expect_false(first(select(df, startsWith(df$name, "m")))[[1]])
- expect_true(first(select(df, endsWith(df$name, "el")))[[1]])
- expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi")
- if (as.numeric(R.version$major) >= 3 && as.numeric(R.version$minor) >= 3) {
- expect_true(startsWith("Hello World", "Hello"))
- expect_false(endsWith("Hello World", "a"))
- }
- expect_equal(collect(select(df, cast(df$age, "string")))[[2, 1]], "30")
- expect_equal(collect(select(df, concat(df$name, lit(":"), df$age)))[[2, 1]], "Andy:30")
- expect_equal(collect(select(df, concat_ws(":", df$name)))[[2, 1]], "Andy")
- expect_equal(collect(select(df, concat_ws(":", df$name, df$age)))[[2, 1]], "Andy:30")
- expect_equal(collect(select(df, instr(df$name, "i")))[, 1], c(2, 0, 5))
- expect_equal(collect(select(df, format_number(df$age, 2)))[2, 1], "30.00")
- expect_equal(collect(select(df, sha1(df$name)))[2, 1],
- "ab5a000e88b5d9d0fa2575f5c6263eb93452405d")
- expect_equal(collect(select(df, sha2(df$name, 256)))[2, 1],
- "80f2aed3c618c423ddf05a2891229fba44942d907173152442cf6591441ed6dc")
- expect_equal(collect(select(df, format_string("Name:%s", df$name)))[2, 1], "Name:Andy")
- expect_equal(collect(select(df, format_string("%s, %d", df$name, df$age)))[2, 1], "Andy, 30")
- expect_equal(collect(select(df, regexp_extract(df$name, "(n.y)", 1)))[2, 1], "ndy")
- expect_equal(collect(select(df, regexp_replace(df$name, "(n.y)", "ydn")))[2, 1], "Aydn")
-
- l2 <- list(list(a = "aaads"))
- df2 <- createDataFrame(l2)
- expect_equal(collect(select(df2, locate("aa", df2$a)))[1, 1], 1)
- expect_equal(collect(select(df2, locate("aa", df2$a, 2)))[1, 1], 2)
- expect_equal(collect(select(df2, lpad(df2$a, 8, "#")))[1, 1], "###aaads") # nolint
- expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###") # nolint
-
- l3 <- list(list(a = "a.b.c.d"))
- df3 <- createDataFrame(l3)
- expect_equal(collect(select(df3, substring_index(df3$a, ".", 2)))[1, 1], "a.b")
- expect_equal(collect(select(df3, substring_index(df3$a, ".", -3)))[1, 1], "b.c.d")
- expect_equal(collect(select(df3, translate(df3$a, "bc", "12")))[1, 1], "a.1.2.d")
-
- l4 <- list(list(a = "a.b@c.d 1\\b"))
- df4 <- createDataFrame(l4)
- expect_equal(
- collect(select(df4, split_string(df4$a, "\\s+")))[1, 1],
- list(list("a.b@c.d", "1\\b"))
- )
- expect_equal(
- collect(select(df4, split_string(df4$a, "\\.")))[1, 1],
- list(list("a", "b@c", "d 1\\b"))
- )
- expect_equal(
- collect(select(df4, split_string(df4$a, "@")))[1, 1],
- list(list("a.b", "c.d 1\\b"))
- )
- expect_equal(
- collect(select(df4, split_string(df4$a, "\\\\")))[1, 1],
- list(list("a.b@c.d 1", "b"))
- )
-
- l5 <- list(list(a = "abc"))
- df5 <- createDataFrame(l5)
- expect_equal(
- collect(select(df5, repeat_string(df5$a, 1L)))[1, 1],
- "abc"
- )
- expect_equal(
- collect(select(df5, repeat_string(df5$a, 3)))[1, 1],
- "abcabcabc"
- )
- expect_equal(
- collect(select(df5, repeat_string(df5$a, -1)))[1, 1],
- ""
- )
-})
-
-test_that("date functions on a DataFrame", {
- .originalTimeZone <- Sys.getenv("TZ")
- Sys.setenv(TZ = "UTC")
- l <- list(list(a = 1L, b = as.Date("2012-12-13")),
- list(a = 2L, b = as.Date("2013-12-14")),
- list(a = 3L, b = as.Date("2014-12-15")))
- df <- createDataFrame(l)
- expect_equal(collect(select(df, dayofmonth(df$b)))[, 1], c(13, 14, 15))
- expect_equal(collect(select(df, dayofyear(df$b)))[, 1], c(348, 348, 349))
- expect_equal(collect(select(df, weekofyear(df$b)))[, 1], c(50, 50, 51))
- expect_equal(collect(select(df, year(df$b)))[, 1], c(2012, 2013, 2014))
- expect_equal(collect(select(df, month(df$b)))[, 1], c(12, 12, 12))
- expect_equal(collect(select(df, last_day(df$b)))[, 1],
- c(as.Date("2012-12-31"), as.Date("2013-12-31"), as.Date("2014-12-31")))
- expect_equal(collect(select(df, next_day(df$b, "MONDAY")))[, 1],
- c(as.Date("2012-12-17"), as.Date("2013-12-16"), as.Date("2014-12-22")))
- expect_equal(collect(select(df, date_format(df$b, "y")))[, 1], c("2012", "2013", "2014"))
- expect_equal(collect(select(df, add_months(df$b, 3)))[, 1],
- c(as.Date("2013-03-13"), as.Date("2014-03-14"), as.Date("2015-03-15")))
- expect_equal(collect(select(df, date_add(df$b, 1)))[, 1],
- c(as.Date("2012-12-14"), as.Date("2013-12-15"), as.Date("2014-12-16")))
- expect_equal(collect(select(df, date_sub(df$b, 1)))[, 1],
- c(as.Date("2012-12-12"), as.Date("2013-12-13"), as.Date("2014-12-14")))
-
- l2 <- list(list(a = 1L, b = as.POSIXlt("2012-12-13 12:34:00", tz = "UTC")),
- list(a = 2L, b = as.POSIXlt("2014-12-15 01:24:34", tz = "UTC")))
- df2 <- createDataFrame(l2)
- expect_equal(collect(select(df2, minute(df2$b)))[, 1], c(34, 24))
- expect_equal(collect(select(df2, second(df2$b)))[, 1], c(0, 34))
- expect_equal(collect(select(df2, from_utc_timestamp(df2$b, "JST")))[, 1],
- c(as.POSIXlt("2012-12-13 21:34:00 UTC"), as.POSIXlt("2014-12-15 10:24:34 UTC")))
- expect_equal(collect(select(df2, to_utc_timestamp(df2$b, "JST")))[, 1],
- c(as.POSIXlt("2012-12-13 03:34:00 UTC"), as.POSIXlt("2014-12-14 16:24:34 UTC")))
- expect_gt(collect(select(df2, unix_timestamp()))[1, 1], 0)
- expect_gt(collect(select(df2, unix_timestamp(df2$b)))[1, 1], 0)
- expect_gt(collect(select(df2, unix_timestamp(lit("2015-01-01"), "yyyy-MM-dd")))[1, 1], 0)
-
- l3 <- list(list(a = 1000), list(a = -1000))
- df3 <- createDataFrame(l3)
- result31 <- collect(select(df3, from_unixtime(df3$a)))
- expect_equal(grep("\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}", result31[, 1], perl = TRUE),
- c(1, 2))
- result32 <- collect(select(df3, from_unixtime(df3$a, "yyyy")))
- expect_equal(grep("\\d{4}", result32[, 1]), c(1, 2))
- Sys.setenv(TZ = .originalTimeZone)
-})
-
-test_that("greatest() and least() on a DataFrame", {
- l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
- df <- createDataFrame(l)
- expect_equal(collect(select(df, greatest(df$a, df$b)))[, 1], c(2, 4))
- expect_equal(collect(select(df, least(df$a, df$b)))[, 1], c(1, 3))
-})
-
-test_that("time windowing (window()) with all inputs", {
- df <- createDataFrame(data.frame(t = c("2016-03-11 09:00:07"), v = c(1)))
- df$window <- window(df$t, "5 seconds", "5 seconds", "0 seconds")
- local <- collect(df)$v
- # Not checking time windows because of possible time zone issues. Just checking that the function
- # works
- expect_equal(local, c(1))
-})
-
-test_that("time windowing (window()) with slide duration", {
- df <- createDataFrame(data.frame(t = c("2016-03-11 09:00:07"), v = c(1)))
- df$window <- window(df$t, "5 seconds", "2 seconds")
- local <- collect(df)$v
- # Not checking time windows because of possible time zone issues. Just checking that the function
- # works
- expect_equal(local, c(1, 1))
-})
-
-test_that("time windowing (window()) with start time", {
- df <- createDataFrame(data.frame(t = c("2016-03-11 09:00:07"), v = c(1)))
- df$window <- window(df$t, "5 seconds", startTime = "2 seconds")
- local <- collect(df)$v
- # Not checking time windows because of possible time zone issues. Just checking that the function
- # works
- expect_equal(local, c(1))
-})
-
-test_that("time windowing (window()) with just window duration", {
- df <- createDataFrame(data.frame(t = c("2016-03-11 09:00:07"), v = c(1)))
- df$window <- window(df$t, "5 seconds")
- local <- collect(df)$v
- # Not checking time windows because of possible time zone issues. Just checking that the function
- # works
- expect_equal(local, c(1))
-})
-
-test_that("when(), otherwise() and ifelse() on a DataFrame", {
- l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
- df <- createDataFrame(l)
- expect_equal(collect(select(df, when(df$a > 1 & df$b > 2, 1)))[, 1], c(NA, 1))
- expect_equal(collect(select(df, otherwise(when(df$a > 1, 1), 0)))[, 1], c(0, 1))
- expect_equal(collect(select(df, ifelse(df$a > 1 & df$b > 2, 0, 1)))[, 1], c(1, 0))
-})
-
-test_that("when(), otherwise() and ifelse() with column on a DataFrame", {
- l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
- df <- createDataFrame(l)
- expect_equal(collect(select(df, when(df$a > 1 & df$b > 2, lit(1))))[, 1], c(NA, 1))
- expect_equal(collect(select(df, otherwise(when(df$a > 1, lit(1)), lit(0))))[, 1], c(0, 1))
- expect_equal(collect(select(df, ifelse(df$a > 1 & df$b > 2, lit(0), lit(1))))[, 1], c(1, 0))
-})
-
-test_that("group by, agg functions", {
- skip_on_cran()
-
- df <- read.json(jsonPath)
- df1 <- agg(df, name = "max", age = "sum")
- expect_equal(1, count(df1))
- df1 <- agg(df, age2 = max(df$age))
- expect_equal(1, count(df1))
- expect_equal(columns(df1), c("age2"))
-
- gd <- groupBy(df, "name")
- expect_is(gd, "GroupedData")
- df2 <- count(gd)
- expect_is(df2, "SparkDataFrame")
- expect_equal(3, count(df2))
-
- # Also test group_by, summarize, mean
- gd1 <- group_by(df, "name")
- expect_is(gd1, "GroupedData")
- df_summarized <- summarize(gd, mean_age = mean(df$age))
- expect_is(df_summarized, "SparkDataFrame")
- expect_equal(3, count(df_summarized))
-
- df3 <- agg(gd, age = "stddev")
- expect_is(df3, "SparkDataFrame")
- df3_local <- collect(df3)
- expect_true(is.nan(df3_local[df3_local$name == "Andy", ][1, 2]))
-
- df4 <- agg(gd, sumAge = sum(df$age))
- expect_is(df4, "SparkDataFrame")
- expect_equal(3, count(df4))
- expect_equal(columns(df4), c("name", "sumAge"))
-
- df5 <- sum(gd, "age")
- expect_is(df5, "SparkDataFrame")
- expect_equal(3, count(df5))
-
- expect_equal(3, count(mean(gd)))
- expect_equal(3, count(max(gd)))
- expect_equal(30, collect(max(gd))[2, 2])
- expect_equal(1, collect(count(gd))[1, 2])
-
- mockLines2 <- c("{\"name\":\"ID1\", \"value\": \"10\"}",
- "{\"name\":\"ID1\", \"value\": \"10\"}",
- "{\"name\":\"ID1\", \"value\": \"22\"}",
- "{\"name\":\"ID2\", \"value\": \"-3\"}")
- jsonPath2 <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(mockLines2, jsonPath2)
- gd2 <- groupBy(read.json(jsonPath2), "name")
- df6 <- agg(gd2, value = "sum")
- df6_local <- collect(df6)
- expect_equal(42, df6_local[df6_local$name == "ID1", ][1, 2])
- expect_equal(-3, df6_local[df6_local$name == "ID2", ][1, 2])
-
- df7 <- agg(gd2, value = "stddev")
- df7_local <- collect(df7)
- expect_true(abs(df7_local[df7_local$name == "ID1", ][1, 2] - 6.928203) < 1e-6)
- expect_true(is.nan(df7_local[df7_local$name == "ID2", ][1, 2]))
-
- mockLines3 <- c("{\"name\":\"Andy\", \"age\":30}",
- "{\"name\":\"Andy\", \"age\":30}",
- "{\"name\":\"Justin\", \"age\":19}",
- "{\"name\":\"Justin\", \"age\":1}")
- jsonPath3 <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(mockLines3, jsonPath3)
- df8 <- read.json(jsonPath3)
- gd3 <- groupBy(df8, "name")
- gd3_local <- collect(sum(gd3))
- expect_equal(60, gd3_local[gd3_local$name == "Andy", ][1, 2])
- expect_equal(20, gd3_local[gd3_local$name == "Justin", ][1, 2])
-
- expect_true(abs(collect(agg(df, sd(df$age)))[1, 1] - 7.778175) < 1e-6)
- gd3_local <- collect(agg(gd3, var(df8$age)))
- expect_equal(162, gd3_local[gd3_local$name == "Justin", ][1, 2])
-
- # Test stats::sd, stats::var are working
- expect_true(abs(sd(1:2) - 0.7071068) < 1e-6)
- expect_true(abs(var(1:5, 1:5) - 2.5) < 1e-6)
-
- # Test collect_list and collect_set
- gd3_collections_local <- collect(
- agg(gd3, collect_set(df8$age), collect_list(df8$age))
- )
-
- expect_equal(
- unlist(gd3_collections_local[gd3_collections_local$name == "Andy", 2]),
- c(30)
- )
-
- expect_equal(
- unlist(gd3_collections_local[gd3_collections_local$name == "Andy", 3]),
- c(30, 30)
- )
-
- expect_equal(
- sort(unlist(
- gd3_collections_local[gd3_collections_local$name == "Justin", 3]
- )),
- c(1, 19)
- )
-
- unlink(jsonPath2)
- unlink(jsonPath3)
-})
-
-test_that("pivot GroupedData column", {
- df <- createDataFrame(data.frame(
- earnings = c(10000, 10000, 11000, 15000, 12000, 20000, 21000, 22000),
- course = c("R", "Python", "R", "Python", "R", "Python", "R", "Python"),
- year = c(2013, 2013, 2014, 2014, 2015, 2015, 2016, 2016)
- ))
- sum1 <- collect(sum(pivot(groupBy(df, "year"), "course"), "earnings"))
- sum2 <- collect(sum(pivot(groupBy(df, "year"), "course", c("Python", "R")), "earnings"))
- sum3 <- collect(sum(pivot(groupBy(df, "year"), "course", list("Python", "R")), "earnings"))
- sum4 <- collect(sum(pivot(groupBy(df, "year"), "course", "R"), "earnings"))
-
- correct_answer <- data.frame(
- year = c(2013, 2014, 2015, 2016),
- Python = c(10000, 15000, 20000, 22000),
- R = c(10000, 11000, 12000, 21000)
- )
- expect_equal(sum1, correct_answer)
- expect_equal(sum2, correct_answer)
- expect_equal(sum3, correct_answer)
- expect_equal(sum4, correct_answer[, c("year", "R")])
-
- expect_error(collect(sum(pivot(groupBy(df, "year"), "course", c("R", "R")), "earnings")))
- expect_error(collect(sum(pivot(groupBy(df, "year"), "course", list("R", "R")), "earnings")))
-})
-
-test_that("test multi-dimensional aggregations with cube and rollup", {
- df <- createDataFrame(data.frame(
- id = 1:6,
- year = c(2016, 2016, 2016, 2017, 2017, 2017),
- salary = c(10000, 15000, 20000, 22000, 32000, 21000),
- department = c("management", "rnd", "sales", "management", "rnd", "sales")
- ))
-
- actual_cube <- collect(
- orderBy(
- agg(
- cube(df, "year", "department"),
- expr("sum(salary) AS total_salary"),
- expr("avg(salary) AS average_salary"),
- alias(grouping_bit(df$year), "grouping_year"),
- alias(grouping_bit(df$department), "grouping_department"),
- alias(grouping_id(df$year, df$department), "grouping_id")
- ),
- "year", "department"
- )
- )
-
- expected_cube <- data.frame(
- year = c(rep(NA, 4), rep(2016, 4), rep(2017, 4)),
- department = rep(c(NA, "management", "rnd", "sales"), times = 3),
- total_salary = c(
- 120000, # Total
- 10000 + 22000, 15000 + 32000, 20000 + 21000, # Department only
- 20000 + 15000 + 10000, # 2016
- 10000, 15000, 20000, # 2016 each department
- 21000 + 32000 + 22000, # 2017
- 22000, 32000, 21000 # 2017 each department
- ),
- average_salary = c(
- # Total
- mean(c(20000, 15000, 10000, 21000, 32000, 22000)),
- # Mean by department
- mean(c(10000, 22000)), mean(c(15000, 32000)), mean(c(20000, 21000)),
- mean(c(10000, 15000, 20000)), # 2016
- 10000, 15000, 20000, # 2016 each department
- mean(c(21000, 32000, 22000)), # 2017
- 22000, 32000, 21000 # 2017 each department
- ),
- grouping_year = c(
- 1, # global
- 1, 1, 1, # by department
- 0, # 2016
- 0, 0, 0, # 2016 by department
- 0, # 2017
- 0, 0, 0 # 2017 by department
- ),
- grouping_department = c(
- 1, # global
- 0, 0, 0, # by department
- 1, # 2016
- 0, 0, 0, # 2016 by department
- 1, # 2017
- 0, 0, 0 # 2017 by department
- ),
- grouping_id = c(
- 3, # 11
- 2, 2, 2, # 10
- 1, # 01
- 0, 0, 0, # 00
- 1, # 01
- 0, 0, 0 # 00
- ),
- stringsAsFactors = FALSE
- )
-
- expect_equal(actual_cube, expected_cube)
-
- # cube should accept column objects
- expect_equal(
- count(sum(cube(df, df$year, df$department), "salary")),
- 12
- )
-
- # cube without columns should result in a single aggregate
- expect_equal(
- collect(agg(cube(df), expr("sum(salary) as total_salary"))),
- data.frame(total_salary = 120000)
- )
-
- actual_rollup <- collect(
- orderBy(
- agg(
- rollup(df, "year", "department"),
- expr("sum(salary) AS total_salary"), expr("avg(salary) AS average_salary"),
- alias(grouping_bit(df$year), "grouping_year"),
- alias(grouping_bit(df$department), "grouping_department"),
- alias(grouping_id(df$year, df$department), "grouping_id")
- ),
- "year", "department"
- )
- )
-
- expected_rollup <- data.frame(
- year = c(NA, rep(2016, 4), rep(2017, 4)),
- department = c(NA, rep(c(NA, "management", "rnd", "sales"), times = 2)),
- total_salary = c(
- 120000, # Total
- 20000 + 15000 + 10000, # 2016
- 10000, 15000, 20000, # 2016 each department
- 21000 + 32000 + 22000, # 2017
- 22000, 32000, 21000 # 2017 each department
- ),
- average_salary = c(
- # Total
- mean(c(20000, 15000, 10000, 21000, 32000, 22000)),
- mean(c(10000, 15000, 20000)), # 2016
- 10000, 15000, 20000, # 2016 each department
- mean(c(21000, 32000, 22000)), # 2017
- 22000, 32000, 21000 # 2017 each department
- ),
- grouping_year = c(
- 1, # global
- 0, # 2016
- 0, 0, 0, # 2016 each department
- 0, # 2017
- 0, 0, 0 # 2017 each department
- ),
- grouping_department = c(
- 1, # global
- 1, # 2016
- 0, 0, 0, # 2016 each department
- 1, # 2017
- 0, 0, 0 # 2017 each department
- ),
- grouping_id = c(
- 3, # 11
- 1, # 01
- 0, 0, 0, # 00
- 1, # 01
- 0, 0, 0 # 00
- ),
- stringsAsFactors = FALSE
- )
-
- expect_equal(actual_rollup, expected_rollup)
-
- # cube should accept column objects
- expect_equal(
- count(sum(rollup(df, df$year, df$department), "salary")),
- 9
- )
-
- # rollup without columns should result in a single aggregate
- expect_equal(
- collect(agg(rollup(df), expr("sum(salary) as total_salary"))),
- data.frame(total_salary = 120000)
- )
-})
-
-test_that("arrange() and orderBy() on a DataFrame", {
- df <- read.json(jsonPath)
- sorted <- arrange(df, df$age)
- expect_equal(collect(sorted)[1, 2], "Michael")
-
- sorted2 <- arrange(df, "name", decreasing = FALSE)
- expect_equal(collect(sorted2)[2, "age"], 19)
-
- sorted3 <- orderBy(df, asc(df$age))
- expect_true(is.na(first(sorted3)$age))
- expect_equal(collect(sorted3)[2, "age"], 19)
-
- sorted4 <- orderBy(df, desc(df$name))
- expect_equal(first(sorted4)$name, "Michael")
- expect_equal(collect(sorted4)[3, "name"], "Andy")
-
- sorted5 <- arrange(df, "age", "name", decreasing = TRUE)
- expect_equal(collect(sorted5)[1, 2], "Andy")
-
- sorted6 <- arrange(df, "age", "name", decreasing = c(T, F))
- expect_equal(collect(sorted6)[1, 2], "Andy")
-
- sorted7 <- arrange(df, "name", decreasing = FALSE)
- expect_equal(collect(sorted7)[2, "age"], 19)
-})
-
-test_that("filter() on a DataFrame", {
- df <- read.json(jsonPath)
- filtered <- filter(df, "age > 20")
- expect_equal(count(filtered), 1)
- expect_equal(collect(filtered)$name, "Andy")
- filtered2 <- where(df, df$name != "Michael")
- expect_equal(count(filtered2), 2)
- expect_equal(collect(filtered2)$age[2], 19)
-
- # test suites for %in%
- filtered3 <- filter(df, "age in (19)")
- expect_equal(count(filtered3), 1)
- filtered4 <- filter(df, "age in (19, 30)")
- expect_equal(count(filtered4), 2)
- filtered5 <- where(df, df$age %in% c(19))
- expect_equal(count(filtered5), 1)
- filtered6 <- where(df, df$age %in% c(19, 30))
- expect_equal(count(filtered6), 2)
-
- # test suites for %<=>%
- dfNa <- read.json(jsonPathNa)
- expect_equal(count(filter(dfNa, dfNa$age %<=>% 60)), 1)
- expect_equal(count(filter(dfNa, !(dfNa$age %<=>% 60))), 5 - 1)
- expect_equal(count(filter(dfNa, dfNa$age %<=>% NULL)), 3)
- expect_equal(count(filter(dfNa, !(dfNa$age %<=>% NULL))), 5 - 3)
- # match NA from two columns
- expect_equal(count(filter(dfNa, dfNa$age %<=>% dfNa$height)), 2)
- expect_equal(count(filter(dfNa, !(dfNa$age %<=>% dfNa$height))), 5 - 2)
-
- # Test stats::filter is working
- #expect_true(is.ts(filter(1:100, rep(1, 3)))) # nolint
-})
-
-test_that("join(), crossJoin() and merge() on a DataFrame", {
- skip_on_cran()
-
- df <- read.json(jsonPath)
-
- mockLines2 <- c("{\"name\":\"Michael\", \"test\": \"yes\"}",
- "{\"name\":\"Andy\", \"test\": \"no\"}",
- "{\"name\":\"Justin\", \"test\": \"yes\"}",
- "{\"name\":\"Bob\", \"test\": \"yes\"}")
- jsonPath2 <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(mockLines2, jsonPath2)
- df2 <- read.json(jsonPath2)
-
- # inner join, not cartesian join
- expect_equal(count(where(join(df, df2), df$name == df2$name)), 3)
- # cartesian join
- expect_error(tryCatch(count(join(df, df2)), error = function(e) { stop(e) }),
- paste0(".*(org.apache.spark.sql.AnalysisException: Detected cartesian product for",
- " INNER join between logical plans).*"))
-
- joined <- crossJoin(df, df2)
- expect_equal(names(joined), c("age", "name", "name", "test"))
- expect_equal(count(joined), 12)
- expect_equal(names(collect(joined)), c("age", "name", "name", "test"))
-
- joined2 <- join(df, df2, df$name == df2$name)
- expect_equal(names(joined2), c("age", "name", "name", "test"))
- expect_equal(count(joined2), 3)
-
- joined3 <- join(df, df2, df$name == df2$name, "rightouter")
- expect_equal(names(joined3), c("age", "name", "name", "test"))
- expect_equal(count(joined3), 4)
- expect_true(is.na(collect(orderBy(joined3, joined3$age))$age[2]))
-
- joined4 <- select(join(df, df2, df$name == df2$name, "outer"),
- alias(df$age + 5, "newAge"), df$name, df2$test)
- expect_equal(names(joined4), c("newAge", "name", "test"))
- expect_equal(count(joined4), 4)
- expect_equal(collect(orderBy(joined4, joined4$name))$newAge[3], 24)
-
- joined5 <- join(df, df2, df$name == df2$name, "leftouter")
- expect_equal(names(joined5), c("age", "name", "name", "test"))
- expect_equal(count(joined5), 3)
- expect_true(is.na(collect(orderBy(joined5, joined5$age))$age[1]))
-
- joined6 <- join(df, df2, df$name == df2$name, "inner")
- expect_equal(names(joined6), c("age", "name", "name", "test"))
- expect_equal(count(joined6), 3)
-
- joined7 <- join(df, df2, df$name == df2$name, "leftsemi")
- expect_equal(names(joined7), c("age", "name"))
- expect_equal(count(joined7), 3)
-
- joined8 <- join(df, df2, df$name == df2$name, "left_outer")
- expect_equal(names(joined8), c("age", "name", "name", "test"))
- expect_equal(count(joined8), 3)
- expect_true(is.na(collect(orderBy(joined8, joined8$age))$age[1]))
-
- joined9 <- join(df, df2, df$name == df2$name, "right_outer")
- expect_equal(names(joined9), c("age", "name", "name", "test"))
- expect_equal(count(joined9), 4)
- expect_true(is.na(collect(orderBy(joined9, joined9$age))$age[2]))
-
- merged <- merge(df, df2, by.x = "name", by.y = "name", all.x = TRUE, all.y = TRUE)
- expect_equal(count(merged), 4)
- expect_equal(names(merged), c("age", "name_x", "name_y", "test"))
- expect_equal(collect(orderBy(merged, merged$name_x))$age[3], 19)
-
- merged <- merge(df, df2, suffixes = c("-X", "-Y"))
- expect_equal(count(merged), 3)
- expect_equal(names(merged), c("age", "name-X", "name-Y", "test"))
- expect_equal(collect(orderBy(merged, merged$"name-X"))$age[1], 30)
-
- merged <- merge(df, df2, by = "name", suffixes = c("-X", "-Y"), sort = FALSE)
- expect_equal(count(merged), 3)
- expect_equal(names(merged), c("age", "name-X", "name-Y", "test"))
- expect_equal(collect(orderBy(merged, merged$"name-Y"))$"name-X"[3], "Michael")
-
- merged <- merge(df, df2, by = "name", all = T, sort = T)
- expect_equal(count(merged), 4)
- expect_equal(names(merged), c("age", "name_x", "name_y", "test"))
- expect_equal(collect(orderBy(merged, merged$"name_y"))$"name_x"[1], "Andy")
-
- merged <- merge(df, df2, by = NULL)
- expect_equal(count(merged), 12)
- expect_equal(names(merged), c("age", "name", "name", "test"))
-
- mockLines3 <- c("{\"name\":\"Michael\", \"name_y\":\"Michael\", \"test\": \"yes\"}",
- "{\"name\":\"Andy\", \"name_y\":\"Andy\", \"test\": \"no\"}",
- "{\"name\":\"Justin\", \"name_y\":\"Justin\", \"test\": \"yes\"}",
- "{\"name\":\"Bob\", \"name_y\":\"Bob\", \"test\": \"yes\"}")
- jsonPath3 <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(mockLines3, jsonPath3)
- df3 <- read.json(jsonPath3)
- expect_error(merge(df, df3),
- paste("The following column name: name_y occurs more than once in the 'DataFrame'.",
- "Please use different suffixes for the intersected columns.", sep = ""))
-
- unlink(jsonPath2)
- unlink(jsonPath3)
-
- # Join with broadcast hint
- df1 <- sql("SELECT * FROM range(10e10)")
- df2 <- sql("SELECT * FROM range(10e10)")
-
- execution_plan <- capture.output(explain(join(df1, df2, df1$id == df2$id)))
- expect_false(any(grepl("BroadcastHashJoin", execution_plan)))
-
- execution_plan_hint <- capture.output(
- explain(join(df1, hint(df2, "broadcast"), df1$id == df2$id))
- )
- expect_true(any(grepl("BroadcastHashJoin", execution_plan_hint)))
-
- execution_plan_broadcast <- capture.output(
- explain(join(df1, broadcast(df2), df1$id == df2$id))
- )
- expect_true(any(grepl("BroadcastHashJoin", execution_plan_broadcast)))
-})
-
-test_that("toJSON() on DataFrame", {
- df <- as.DataFrame(cars)
- df_json <- toJSON(df)
- expect_is(df_json, "SparkDataFrame")
- expect_equal(colnames(df_json), c("value"))
- expect_equal(head(df_json, 1),
- data.frame(value = "{\"speed\":4.0,\"dist\":2.0}", stringsAsFactors = FALSE))
-})
-
-test_that("showDF()", {
- df <- read.json(jsonPath)
- expected <- paste("+----+-------+\n",
- "| age| name|\n",
- "+----+-------+\n",
- "|null|Michael|\n",
- "| 30| Andy|\n",
- "| 19| Justin|\n",
- "+----+-------+\n", sep = "")
- expected2 <- paste("+---+----+\n",
- "|age|name|\n",
- "+---+----+\n",
- "|nul| Mic|\n",
- "| 30| And|\n",
- "| 19| Jus|\n",
- "+---+----+\n", sep = "")
- expect_output(showDF(df), expected)
- expect_output(showDF(df, truncate = 3), expected2)
-})
-
-test_that("isLocal()", {
- df <- read.json(jsonPath)
- expect_false(isLocal(df))
-})
-
-test_that("union(), rbind(), except(), and intersect() on a DataFrame", {
- df <- read.json(jsonPath)
-
- lines <- c("{\"name\":\"Bob\", \"age\":24}",
- "{\"name\":\"Andy\", \"age\":30}",
- "{\"name\":\"James\", \"age\":35}")
- jsonPath2 <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
- writeLines(lines, jsonPath2)
- df2 <- read.df(jsonPath2, "json")
-
- unioned <- arrange(union(df, df2), df$age)
- expect_is(unioned, "SparkDataFrame")
- expect_equal(count(unioned), 6)
- expect_equal(first(unioned)$name, "Michael")
- expect_equal(count(arrange(suppressWarnings(unionAll(df, df2)), df$age)), 6)
-
- unioned2 <- arrange(rbind(unioned, df, df2), df$age)
- expect_is(unioned2, "SparkDataFrame")
- expect_equal(count(unioned2), 12)
- expect_equal(first(unioned2)$name, "Michael")
-
- df3 <- df2
- names(df3)[1] <- "newName"
- expect_error(rbind(df, df3),
- "Names of input data frames are different.")
- expect_error(rbind(df, df2, df3),
- "Names of input data frames are different.")
-
- excepted <- arrange(except(df, df2), desc(df$age))
- expect_is(unioned, "SparkDataFrame")
- expect_equal(count(excepted), 2)
- expect_equal(first(excepted)$name, "Justin")
-
- intersected <- arrange(intersect(df, df2), df$age)
- expect_is(unioned, "SparkDataFrame")
- expect_equal(count(intersected), 1)
- expect_equal(first(intersected)$name, "Andy")
-
- # Test base::union is working
- expect_equal(union(c(1:3), c(3:5)), c(1:5))
-
- # Test base::rbind is working
- expect_equal(length(rbind(1:4, c = 2, a = 10, 10, deparse.level = 0)), 16)
-
- # Test base::intersect is working
- expect_equal(length(intersect(1:20, 3:23)), 18)
-
- unlink(jsonPath2)
-})
-
-test_that("withColumn() and withColumnRenamed()", {
- df <- read.json(jsonPath)
- newDF <- withColumn(df, "newAge", df$age + 2)
- expect_equal(length(columns(newDF)), 3)
- expect_equal(columns(newDF)[3], "newAge")
- expect_equal(first(filter(newDF, df$name != "Michael"))$newAge, 32)
-
- # Replace existing column
- newDF <- withColumn(df, "age", df$age + 2)
- expect_equal(length(columns(newDF)), 2)
- expect_equal(first(filter(newDF, df$name != "Michael"))$age, 32)
-
- newDF <- withColumn(df, "age", 18)
- expect_equal(length(columns(newDF)), 2)
- expect_equal(first(newDF)$age, 18)
-
- expect_error(withColumn(df, "age", list("a")),
- "Literal value must be atomic in length of 1")
-
- newDF2 <- withColumnRenamed(df, "age", "newerAge")
- expect_equal(length(columns(newDF2)), 2)
- expect_equal(columns(newDF2)[1], "newerAge")
-})
-
-test_that("mutate(), transform(), rename() and names()", {
- df <- read.json(jsonPath)
- newDF <- mutate(df, newAge = df$age + 2)
- expect_equal(length(columns(newDF)), 3)
- expect_equal(columns(newDF)[3], "newAge")
- expect_equal(first(filter(newDF, df$name != "Michael"))$newAge, 32)
-
- newDF <- mut
<TRUNCATED>
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