tests/testthat/test_sparkSQL.R
In akzaidi/SparkR: R frontend for Spark
#
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# 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
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#
# http://www.apache.org/licenses/LICENSE-2.0
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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
}
# Tests for SparkSQL functions in SparkR
sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
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")
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)
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("create DataFrame from RDD", {
rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
df <- createDataFrame(sqlContext, rdd, list("a", "b"))
dfAsDF <- as.DataFrame(sqlContext, rdd, list("a", "b"))
expect_is(df, "DataFrame")
expect_is(dfAsDF, "DataFrame")
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(sqlContext, rdd)
dfAsDF <- as.DataFrame(sqlContext, rdd)
expect_is(df, "DataFrame")
expect_is(dfAsDF, "DataFrame")
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(sqlContext, rdd, schema)
expect_is(df, "DataFrame")
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(sqlContext, rdd)
expect_is(df, "DataFrame")
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(sqlContext, jsonPathNa, "json", schema)
df2 <- createDataFrame(sqlContext, toRDD(df), schema)
df2AsDF <- as.DataFrame(sqlContext, 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(sqlContext, localDF, schema)
expect_is(df, "DataFrame")
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$name == "John"))),
list(name = "John", age = 19L, height = 176.5))
ssc <- callJMethod(sc, "sc")
hiveCtx <- tryCatch({
newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc)
},
error = function(err) {
skip("Hive is not build with SparkSQL, skipped")
})
sql(hiveCtx, "CREATE TABLE people (name string, age double, height float)")
df <- read.df(hiveCtx, jsonPathNa, "json", schema)
invisible(insertInto(df, "people"))
expect_equal(collect(sql(hiveCtx, "SELECT age from people WHERE name = 'Bob'"))$age,
c(16))
expect_equal(collect(sql(hiveCtx, "SELECT height from people WHERE name ='Bob'"))$height,
c(176.5))
})
test_that("convert NAs to null type in DataFrames", {
rdd <- parallelize(sc, list(list(1L, 2L), list(NA, 4L)))
df <- createDataFrame(sqlContext, 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(sqlContext, 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(sqlContext, 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(sqlContext, l)
expect_equal(collect(df)[2, "x"], 1)
expect_true(is.na(collect(df)[2, "y"]))
l <- list("a", "b", NA, "d")
df <- createDataFrame(sqlContext, 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(sqlContext, l)
expect_true(is.na(collect(df)[3, "_1"]))
expect_equal(collect(df)[4, "_1"], "d")
l <- list(TRUE, FALSE, NA, TRUE)
df <- createDataFrame(sqlContext, l)
expect_true(is.na(collect(df)[3, "_1"]))
expect_equal(collect(df)[4, "_1"], TRUE)
})
test_that("toDF", {
rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
df <- toDF(rdd, list("a", "b"))
expect_is(df, "DataFrame")
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, "DataFrame")
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, "DataFrame")
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, "DataFrame")
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(sqlContext, 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(sqlContext, l)
expect_equal(columns(df), c("a", "b"))
a <- 1:3
b <- c("a", "b", "c")
ldf <- data.frame(a, b)
df <- createDataFrame(sqlContext, 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$a)
irisdf <- suppressWarnings(createDataFrame(sqlContext, iris))
iris_collected <- collect(irisdf)
expect_equivalent(iris_collected[,-5], iris[,-5])
expect_equal(iris_collected$Species, as.character(iris$Species))
mtcarsdf <- createDataFrame(sqlContext, mtcars)
expect_equivalent(collect(mtcarsdf), mtcars)
bytes <- as.raw(c(1, 2, 3))
df <- createDataFrame(sqlContext, list(list(bytes)))
expect_equal(collect(df)[[1]][[1]], bytes)
})
test_that("create DataFrame with different data types", {
l <- list(a = 1L, b = 2, c = TRUE, d = "ss", e = as.Date("2012-12-13"),
f = as.POSIXct("2015-03-15 12:13:14.056"))
df <- createDataFrame(sqlContext, list(l))
expect_equal(dtypes(df), list(c("a", "int"), c("b", "double"), c("c", "boolean"),
c("d", "string"), c("e", "date"), c("f", "timestamp")))
expect_equal(count(df), 1)
expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE))
})
test_that("create DataFrame with complex types", {
e <- new.env()
assign("n", 3L, envir = e)
s <- listToStruct(list(a = "aa", b = 3L))
l <- list(as.list(1:10), list("a", "b"), e, s)
df <- createDataFrame(sqlContext, list(l), c("a", "b", "c", "d"))
expect_equal(dtypes(df), list(c("a", "array<int>"),
c("b", "array<string>"),
c("c", "map<string,int>"),
c("d", "struct<a:string,b:int>")))
expect_equal(count(df), 1)
ldf <- collect(df)
expect_equal(names(ldf), c("a", "b", "c", "d"))
expect_equal(ldf[1, 1][[1]], l[[1]])
expect_equal(ldf[1, 2][[1]], l[[2]])
e <- ldf$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", {
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(sqlContext, ldf)
collected <- collect(sdf)
expect_identical(ldf[, 1, FALSE], collected[, 1, FALSE])
expect_equal(ldf$an_envir, collected$an_envir)
})
# 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)
test_that("Collect DataFrame with complex types", {
# ArrayType
df <- read.json(sqlContext, 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$c3, list(list(1.0, 2.0, 3.0), list(4.0, 5.0, 6.0), list (7.0, 8.0, 9.0)))
# MapType
schema <- structType(structField("name", "string"),
structField("info", "map<string,double>"))
df <- read.df(sqlContext, mapTypeJsonPath, "json", schema)
expect_equal(dtypes(df), list(c("name", "string"),
c("info", "map<string,double>")))
ldf <- collect(df)
expect_equal(nrow(ldf), 3)
expect_equal(ncol(ldf), 2)
expect_equal(names(ldf), c("name", "info"))
expect_equal(ldf$name, c("Bob", "Alice", "David"))
bob <- ldf$info[[1]]
expect_equal(class(bob), "environment")
expect_equal(bob$age, 16)
expect_equal(bob$height, 176.5)
# StructType
df <- read.json(sqlContext, mapTypeJsonPath)
expect_equal(dtypes(df), list(c("info", "struct<age:bigint,height:double>"),
c("name", "string")))
ldf <- collect(df)
expect_equal(nrow(ldf), 3)
expect_equal(ncol(ldf), 2)
expect_equal(names(ldf), c("info", "name"))
expect_equal(ldf$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", {
# Test read.df
df <- read.df(sqlContext, jsonPath, "json")
expect_is(df, "DataFrame")
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(sqlContext, jsonPath, "json", schema)
expect_is(df1, "DataFrame")
expect_equal(dtypes(df1), list(c("name", "string"), c("age", "double")))
# Test loadDF
df2 <- loadDF(sqlContext, jsonPath, "json", schema)
expect_is(df2, "DataFrame")
expect_equal(dtypes(df2), list(c("name", "string"), c("age", "double")))
# Test read.json
df <- read.json(sqlContext, jsonPath)
expect_is(df, "DataFrame")
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(sqlContext, c(jsonPath2, jsonPath3))
expect_is(jsonDF1, "DataFrame")
expect_equal(count(jsonDF1), 6)
# Suppress warnings because jsonFile is deprecated
jsonDF2 <- suppressWarnings(jsonFile(sqlContext, c(jsonPath2, jsonPath3)))
expect_is(jsonDF2, "DataFrame")
expect_equal(count(jsonDF2), 6)
unlink(jsonPath2)
unlink(jsonPath3)
})
test_that("jsonRDD() on a RDD with json string", {
rdd <- parallelize(sc, mockLines)
expect_equal(count(rdd), 3)
df <- jsonRDD(sqlContext, rdd)
expect_is(df, "DataFrame")
expect_equal(count(df), 3)
rdd2 <- flatMap(rdd, function(x) c(x, x))
df <- jsonRDD(sqlContext, rdd2)
expect_is(df, "DataFrame")
expect_equal(count(df), 6)
})
test_that("test cache, uncache and clearCache", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
cacheTable(sqlContext, "table1")
uncacheTable(sqlContext, "table1")
clearCache(sqlContext)
dropTempTable(sqlContext, "table1")
})
test_that("test tableNames and tables", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
expect_equal(length(tableNames(sqlContext)), 1)
df <- tables(sqlContext)
expect_equal(count(df), 1)
dropTempTable(sqlContext, "table1")
})
test_that("registerTempTable() results in a queryable table and sql() results in a new DataFrame", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
newdf <- sql(sqlContext, "SELECT * FROM table1 where name = 'Michael'")
expect_is(newdf, "DataFrame")
expect_equal(count(newdf), 1)
dropTempTable(sqlContext, "table1")
})
test_that("insertInto() on a registered table", {
df <- read.df(sqlContext, jsonPath, "json")
write.df(df, parquetPath, "parquet", "overwrite")
dfParquet <- read.df(sqlContext, 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(sqlContext, jsonPath2, "json")
write.df(df2, parquetPath2, "parquet", "overwrite")
dfParquet2 <- read.df(sqlContext, parquetPath2, "parquet")
registerTempTable(dfParquet, "table1")
insertInto(dfParquet2, "table1")
expect_equal(count(sql(sqlContext, "select * from table1")), 5)
expect_equal(first(sql(sqlContext, "select * from table1 order by age"))$name, "Michael")
dropTempTable(sqlContext, "table1")
registerTempTable(dfParquet, "table1")
insertInto(dfParquet2, "table1", overwrite = TRUE)
expect_equal(count(sql(sqlContext, "select * from table1")), 2)
expect_equal(first(sql(sqlContext, "select * from table1 order by age"))$name, "Bob")
dropTempTable(sqlContext, "table1")
unlink(jsonPath2)
unlink(parquetPath2)
})
test_that("table() returns a new DataFrame", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
tabledf <- table(sqlContext, "table1")
expect_is(tabledf, "DataFrame")
expect_equal(count(tabledf), 3)
dropTempTable(sqlContext, "table1")
# Test base::table is working
#a <- letters[1:3]
#expect_equal(class(table(a, sample(a))), "table")
})
test_that("toRDD() returns an RRDD", {
df <- read.json(sqlContext, jsonPath)
testRDD <- toRDD(df)
expect_is(testRDD, "RDD")
expect_equal(count(testRDD), 3)
})
test_that("union on two RDDs created from DataFrames returns an RRDD", {
df <- read.json(sqlContext, jsonPath)
RDD1 <- toRDD(df)
RDD2 <- toRDD(df)
unioned <- unionRDD(RDD1, RDD2)
expect_is(unioned, "RDD")
expect_equal(getSerializedMode(unioned), "byte")
expect_equal(collect(unioned)[[2]]$name, "Andy")
})
test_that("union on mixed serialization types correctly returns a byte RRDD", {
# 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(sqlContext, jsonPath)
dfRDD <- toRDD(df)
unionByte <- unionRDD(rdd, dfRDD)
expect_is(unionByte, "RDD")
expect_equal(getSerializedMode(unionByte), "byte")
expect_equal(collect(unionByte)[[1]], 1)
expect_equal(collect(unionByte)[[12]]$name, "Andy")
unionString <- unionRDD(textRDD, dfRDD)
expect_is(unionString, "RDD")
expect_equal(getSerializedMode(unionString), "byte")
expect_equal(collect(unionString)[[1]], "Michael")
expect_equal(collect(unionString)[[5]]$name, "Andy")
})
test_that("objectFile() works with row serialization", {
objectPath <- tempfile(pattern="spark-test", fileext=".tmp")
df <- read.json(sqlContext, jsonPath)
dfRDD <- toRDD(df)
saveAsObjectFile(coalesce(dfRDD, 1L), objectPath)
objectIn <- objectFile(sc, objectPath)
expect_is(objectIn, "RDD")
expect_equal(getSerializedMode(objectIn), "byte")
expect_equal(collect(objectIn)[[2]]$age, 30)
})
test_that("lapply() on a DataFrame returns an RDD with the correct columns", {
df <- read.json(sqlContext, jsonPath)
testRDD <- lapply(df, function(row) {
row$newCol <- row$age + 5
row
})
expect_is(testRDD, "RDD")
collected <- collect(testRDD)
expect_equal(collected[[1]]$name, "Michael")
expect_equal(collected[[2]]$newCol, 35)
})
test_that("collect() returns a data.frame", {
df <- read.json(sqlContext, jsonPath)
rdf <- collect(df)
expect_true(is.data.frame(rdf))
expect_equal(names(rdf)[1], "age")
expect_equal(nrow(rdf), 3)
expect_equal(ncol(rdf), 2)
# collect() returns data correctly from a DataFrame with 0 row
df0 <- limit(df, 0)
rdf <- collect(df0)
expect_true(is.data.frame(rdf))
expect_equal(names(rdf)[1], "age")
expect_equal(nrow(rdf), 0)
expect_equal(ncol(rdf), 2)
# collect() correctly handles multiple columns with same name
df <- createDataFrame(sqlContext, list(list(1, 2)), schema = c("name", "name"))
ldf <- collect(df)
expect_equal(names(ldf), c("name", "name"))
})
test_that("limit() returns DataFrame with the correct number of rows", {
df <- read.json(sqlContext, jsonPath)
dfLimited <- limit(df, 2)
expect_is(dfLimited, "DataFrame")
expect_equal(count(dfLimited), 2)
})
test_that("collect() and take() on a DataFrame return the same number of rows and columns", {
df <- read.json(sqlContext, jsonPath)
expect_equal(nrow(collect(df)), nrow(take(df, 10)))
expect_equal(ncol(collect(df)), ncol(take(df, 10)))
})
test_that("collect() support Unicode characters", {
lines <- c("{\"name\":\"안녕하세요\"}",
"{\"name\":\"您好\", \"age\":30}",
"{\"name\":\"こんにちは\", \"age\":19}",
"{\"name\":\"Xin chào\"}")
jsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(lines, jsonPath)
df <- read.df(sqlContext, jsonPath, "json")
rdf <- collect(df)
expect_true(is.data.frame(rdf))
expect_equal(rdf$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$name[4], markUtf8("Xin chào"))
df1 <- createDataFrame(sqlContext, rdf)
expect_equal(collect(where(df1, df1$name == markUtf8("您好")))$name, markUtf8("您好"))
})
test_that("multiple pipeline transformations result in an RDD with the correct values", {
df <- read.json(sqlContext, jsonPath)
first <- lapply(df, function(row) {
row$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(count(second), 3)
expect_equal(collect(second)[[2]]$age, 35)
expect_true(collect(second)[[2]]$testCol)
expect_false(collect(second)[[3]]$testCol)
})
test_that("cache(), persist(), and unpersist() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
expect_false(df@env$isCached)
cache(df)
expect_true(df@env$isCached)
unpersist(df)
expect_false(df@env$isCached)
persist(df, "MEMORY_AND_DISK")
expect_true(df@env$isCached)
unpersist(df)
expect_false(df@env$isCached)
# make sure the data is collectable
expect_true(is.data.frame(collect(df)))
})
test_that("schema(), dtypes(), columns(), names() return the correct values/format", {
df <- read.json(sqlContext, 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(sqlContext, jsonPath)
names(df) <- c("col1", "col2")
expect_equal(colnames(df)[2], "col2")
colnames(df) <- c("col3", "col4")
expect_equal(names(df)[1], "col3")
# 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_that("head() and first() return the correct data", {
df <- read.json(sqlContext, 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() and unique 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(sqlContext, jsonPathWithDup)
uniques <- distinct(df)
expect_is(uniques, "DataFrame")
expect_equal(count(uniques), 3)
uniques2 <- unique(df)
expect_is(uniques2, "DataFrame")
expect_equal(count(uniques2), 3)
})
test_that("sample on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
sampled <- sample(df, FALSE, 1.0)
expect_equal(nrow(collect(sampled)), count(df))
expect_is(sampled, "DataFrame")
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)
# Test base::sample is working
#expect_equal(length(sample(1:12)), 12)
})
test_that("select operators", {
df <- select(read.json(sqlContext, jsonPath), "name", "age")
expect_is(df$name, "Column")
expect_is(df[[2]], "Column")
expect_is(df[["age"]], "Column")
expect_is(df[,1], "DataFrame")
expect_equal(columns(df[,1]), c("name"))
expect_equal(columns(df[,"age"]), c("age"))
df2 <- df[,c("age", "name")]
expect_is(df2, "DataFrame")
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$age2 <- NULL
expect_equal(columns(df), c("name", "age"))
df$age3 <- NULL
expect_equal(columns(df), c("name", "age"))
})
test_that("select with column", {
df <- read.json(sqlContext, jsonPath)
df1 <- select(df, "name")
expect_equal(columns(df1), c("name"))
expect_equal(count(df1), 3)
df2 <- select(df, df$age)
expect_equal(columns(df2), c("age"))
expect_equal(count(df2), 3)
df3 <- select(df, lit("x"))
expect_equal(columns(df3), c("x"))
expect_equal(count(df3), 3)
expect_equal(collect(select(df3, "x"))[[1, 1]], "x")
df4 <- select(df, c("name", "age"))
expect_equal(columns(df4), c("name", "age"))
expect_equal(count(df4), 3)
expect_error(select(df, c("name", "age"), "name"),
"To select multiple columns, use a character vector or list for col")
})
test_that("subsetting", {
# read.json returns columns in random order
df <- select(read.json(sqlContext, jsonPath), "name", "age")
filtered <- df[df$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]
expect_is(df2, "DataFrame")
expect_equal(count(df2), 1)
expect_equal(columns(df2), c("name"))
expect_equal(collect(df2)$name, "Justin")
df3 <- df[df$age > 20, 2]
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$age %in% c(30), c(1,2))
expect_equal(count(df6), 1)
expect_equal(columns(df6), c("name", "age"))
df7 <- subset(df, select = "name")
expect_equal(count(df7), 3)
expect_equal(columns(df7), c("name"))
# Test base::subset is working
expect_equal(nrow(subset(airquality, Temp > 80, select = c(Ozone, Temp))), 68)
})
test_that("selectExpr() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
selected <- selectExpr(df, "age * 2")
expect_equal(names(selected), "(age * 2)")
expect_equal(collect(selected), collect(select(df, df$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(sqlContext, jsonPath)
expect_equal(collect(select(df, expr("abs(-123)")))[1, 1], 123)
})
test_that("column calculation", {
df <- read.json(sqlContext, 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, "DataFrame")
expect_equal(count(df2), 3)
})
test_that("test HiveContext", {
ssc <- callJMethod(sc, "sc")
hiveCtx <- tryCatch({
newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc)
},
error = function(err) {
skip("Hive is not build with SparkSQL, skipped")
})
df <- createExternalTable(hiveCtx, "json", jsonPath, "json")
expect_is(df, "DataFrame")
expect_equal(count(df), 3)
df2 <- sql(hiveCtx, "select * from json")
expect_is(df2, "DataFrame")
expect_equal(count(df2), 3)
jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp")
invisible(saveAsTable(df, "json2", "json", "append", path = jsonPath2))
df3 <- sql(hiveCtx, "select * from json2")
expect_is(df3, "DataFrame")
expect_equal(count(df3), 3)
unlink(jsonPath2)
})
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
})
test_that("column functions", {
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) + 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) + quarter(c)
c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + sha1(c)
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)
# 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(sqlContext, jsonPath)
df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))
expect_equal(collect(df2)[[2, 1]], TRUE)
expect_equal(collect(df2)[[2, 2]], FALSE)
expect_equal(collect(df2)[[3, 1]], FALSE)
expect_equal(collect(df2)[[3, 2]], TRUE)
df3 <- select(df, between(df$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$age)))[1, 1], 30.25)
df5 <- createDataFrame(sqlContext, list(list(a = "010101")))
expect_equal(collect(select(df5, conv(df5$a, 2, 16)))[1, 1], "15")
# Test array_contains() and sort_array()
df <- createDataFrame(sqlContext, 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(sqlContext,
list(list(1L, 2L, 3L), list(4L, 5L, 6L)),
schema = c("a", "b", "c"))
result <- collect(select(df, struct("a", "c")))
expected <- data.frame(row.names = 1:2)
expected$"struct(a,c)" <- list(listToStruct(list(a = 1L, c = 3L)),
listToStruct(list(a = 4L, c = 6L)))
expect_equal(result, expected)
result <- collect(select(df, struct(df$a, df$b)))
expected <- data.frame(row.names = 1:2)
expected$"struct(a,b)" <- 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(sqlContext,
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_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(sqlContext, 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$b, 1)))[4, 1], 4)
expect_equal(class(collect(select(df, rand()))[2, 1]), "numeric")
expect_equal(collect(select(df, rand(1)))[1, 1], 0.134, tolerance = 0.01)
expect_equal(class(collect(select(df, randn()))[2, 1]), "numeric")
expect_equal(collect(select(df, randn(1)))[1, 1], -1.03, tolerance = 0.01)
})
test_that("string operators", {
df <- read.json(sqlContext, jsonPath)
expect_equal(count(where(df, like(df$name, "A%"))), 1)
expect_equal(count(where(df, startsWith(df$name, "A"))), 1)
expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi")
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$name, "(n.y)", "ydn")))[2, 1], "Aydn")
l2 <- list(list(a = "aaads"))
df2 <- createDataFrame(sqlContext, l2)
expect_equal(collect(select(df2, locate("aa", df2$a)))[1, 1], 1)
expect_equal(collect(select(df2, locate("aa", df2$a, 1)))[1, 1], 2)
expect_equal(collect(select(df2, lpad(df2$a, 8, "#")))[1, 1], "###aaads")
expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###")
l3 <- list(list(a = "a.b.c.d"))
df3 <- createDataFrame(sqlContext, l3)
expect_equal(collect(select(df3, substring_index(df3$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")
})
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(sqlContext, 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$b, 1)))[, 1],
c(as.Date("2012-12-12"), as.Date("2013-12-13"), as.Date("2014-12-14")))
l2 <- list(list(a = 1L, b = as.POSIXlt("2012-12-13 12:34:00", tz = "UTC")),
list(a = 2L, b = as.POSIXlt("2014-12-15 01:24:34", tz = "UTC")))
df2 <- createDataFrame(sqlContext, l2)
expect_equal(collect(select(df2, minute(df2$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$b, "JST")))[, 1],
c(as.POSIXlt("2012-12-13 03:34:00 UTC"), as.POSIXlt("2014-12-14 16:24:34 UTC")))
expect_more_than(collect(select(df2, unix_timestamp()))[1, 1], 0)
expect_more_than(collect(select(df2, unix_timestamp(df2$b)))[1, 1], 0)
expect_more_than(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(sqlContext, 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(sqlContext, 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("when(), otherwise() and ifelse() on a DataFrame", {
l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
df <- createDataFrame(sqlContext, 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$b > 2, 0, 1)))[, 1], c(1, 0))
})
test_that("group by, agg functions", {
df <- read.json(sqlContext, jsonPath)
df1 <- agg(df, name = "max", age = "sum")
expect_equal(1, count(df1))
df1 <- agg(df, age2 = max(df$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, "DataFrame")
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$age))
expect_is(df_summarized, "DataFrame")
expect_equal(3, count(df_summarized))
df3 <- agg(gd, age = "stddev")
expect_is(df3, "DataFrame")
df3_local <- collect(df3)
expect_true(is.nan(df3_local[df3_local$name == "Andy",][1, 2]))
df4 <- agg(gd, sumAge = sum(df$age))
expect_is(df4, "DataFrame")
expect_equal(3, count(df4))
expect_equal(columns(df4), c("name", "sumAge"))
df5 <- sum(gd, "age")
expect_is(df5, "DataFrame")
expect_equal(3, count(df5))
expect_equal(3, count(mean(gd)))
expect_equal(3, count(max(gd)))
expect_equal(30, collect(max(gd))[1, 2])
expect_equal(1, collect(count(gd))[1, 2])
mockLines2 <- c("{\"name\":\"ID1\", \"value\": \"10\"}",
"{\"name\":\"ID1\", \"value\": \"10\"}",
"{\"name\":\"ID1\", \"value\": \"22\"}",
"{\"name\":\"ID2\", \"value\": \"-3\"}")
jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(mockLines2, jsonPath2)
gd2 <- groupBy(read.json(sqlContext, jsonPath2), "name")
df6 <- agg(gd2, value = "sum")
df6_local <- collect(df6)
expect_equal(42, df6_local[df6_local$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$name == "ID2",][1, 2]))
mockLines3 <- c("{\"name\":\"Andy\", \"age\":30}",
"{\"name\":\"Andy\", \"age\":30}",
"{\"name\":\"Justin\", \"age\":19}",
"{\"name\":\"Justin\", \"age\":1}")
jsonPath3 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(mockLines3, jsonPath3)
df8 <- read.json(sqlContext, jsonPath3)
gd3 <- groupBy(df8, "name")
gd3_local <- collect(sum(gd3))
expect_equal(60, gd3_local[gd3_local$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)
unlink(jsonPath2)
unlink(jsonPath3)
})
test_that("arrange() and orderBy() on a DataFrame", {
df <- read.json(sqlContext, 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)$name, "Michael")
expect_equal(collect(sorted4)[3,"name"], "Andy")
sorted5 <- arrange(df, "age", "name", decreasing = TRUE)
expect_equal(collect(sorted5)[1,2], "Andy")
sorted6 <- arrange(df, "age","name", decreasing = c(T, F))
expect_equal(collect(sorted6)[1,2], "Andy")
sorted7 <- arrange(df, "name", decreasing = FALSE)
expect_equal(collect(sorted7)[2,"age"], 19)
})
test_that("filter() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
filtered <- filter(df, "age > 20")
expect_equal(count(filtered), 1)
expect_equal(collect(filtered)$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 stats::filter is working
#expect_true(is.ts(filter(1:100, rep(1, 3))))
})
test_that("join() and merge() on a DataFrame", {
df <- read.json(sqlContext, 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(sqlContext, jsonPath2)
joined <- join(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"))$"name_x"[1], "Andy")
merged <- merge(df, df2, by = NULL)
expect_equal(count(merged), 12)
expect_equal(names(merged), c("age", "name", "name", "test"))
mockLines3 <- c("{\"name\":\"Michael\", \"name_y\":\"Michael\", \"test\": \"yes\"}",
"{\"name\":\"Andy\", \"name_y\":\"Andy\", \"test\": \"no\"}",
"{\"name\":\"Justin\", \"name_y\":\"Justin\", \"test\": \"yes\"}",
"{\"name\":\"Bob\", \"name_y\":\"Bob\", \"test\": \"yes\"}")
jsonPath3 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(mockLines3, jsonPath3)
df3 <- read.json(sqlContext, jsonPath3)
expect_error(merge(df, df3),
paste("The following column name: name_y occurs more than once in the 'DataFrame'.",
"Please use different suffixes for the intersected columns.", sep = ""))
unlink(jsonPath2)
unlink(jsonPath3)
})
test_that("toJSON() returns an RDD of the correct values", {
df <- read.json(sqlContext, jsonPath)
testRDD <- toJSON(df)
expect_is(testRDD, "RDD")
expect_equal(getSerializedMode(testRDD), "string")
expect_equal(collect(testRDD)[[1]], mockLines[1])
})
test_that("showDF()", {
df <- read.json(sqlContext, jsonPath)
s <- capture.output(showDF(df))
expected <- paste("+----+-------+\n",
"| age| name|\n",
"+----+-------+\n",
"|null|Michael|\n",
"| 30| Andy|\n",
"| 19| Justin|\n",
"+----+-------+\n", sep="")
expect_output(s , expected)
})
test_that("isLocal()", {
df <- read.json(sqlContext, jsonPath)
expect_false(isLocal(df))
})
test_that("unionAll(), rbind(), except(), and intersect() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
lines <- c("{\"name\":\"Bob\", \"age\":24}",
"{\"name\":\"Andy\", \"age\":30}",
"{\"name\":\"James\", \"age\":35}")
jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(lines, jsonPath2)
df2 <- read.df(sqlContext, jsonPath2, "json")
unioned <- arrange(unionAll(df, df2), df$age)
expect_is(unioned, "DataFrame")
expect_equal(count(unioned), 6)
expect_equal(first(unioned)$name, "Michael")
unioned2 <- arrange(rbind(unioned, df, df2), df$age)
expect_is(unioned2, "DataFrame")
expect_equal(count(unioned2), 12)
expect_equal(first(unioned2)$name, "Michael")
excepted <- arrange(except(df, df2), desc(df$age))
expect_is(unioned, "DataFrame")
expect_equal(count(excepted), 2)
expect_equal(first(excepted)$name, "Justin")
intersected <- arrange(intersect(df, df2), df$age)
expect_is(unioned, "DataFrame")
expect_equal(count(intersected), 1)
expect_equal(first(intersected)$name, "Andy")
# Test base::rbind is working
expect_equal(length(rbind(1:4, c = 2, a = 10, 10, deparse.level = 0)), 16)
# Test base::intersect is working
expect_equal(length(intersect(1:20, 3:23)), 18)
unlink(jsonPath2)
})
test_that("withColumn() and withColumnRenamed()", {
df <- read.json(sqlContext, jsonPath)
newDF <- withColumn(df, "newAge", df$age + 2)
expect_equal(length(columns(newDF)), 3)
expect_equal(columns(newDF)[3], "newAge")
expect_equal(first(filter(newDF, df$name != "Michael"))$newAge, 32)
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(sqlContext, 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"))$newAge, 32)
newDF2 <- rename(df, newerAge = df$age)
expect_equal(length(columns(newDF2)), 2)
expect_equal(columns(newDF2)[1], "newerAge")
names(newDF2) <- c("newerName", "evenNewerAge")
expect_equal(length(names(newDF2)), 2)
expect_equal(names(newDF2)[1], "newerName")
transformedDF <- transform(df, newAge = -df$age, newAge2 = df$age / 2)
expect_equal(length(columns(transformedDF)), 4)
expect_equal(columns(transformedDF)[3], "newAge")
expect_equal(columns(transformedDF)[4], "newAge2")
expect_equal(first(filter(transformedDF, transformedDF$name == "Andy"))$newAge, -30)
# test if base::transform on local data frames works
# ensure the proper signature is used - otherwise this will fail to run
attach(airquality)
result <- transform(Ozone, logOzone = log(Ozone))
expect_equal(nrow(result), 153)
expect_equal(ncol(result), 2)
detach(airquality)
})
test_that("read/write Parquet files", {
df <- read.df(sqlContext, jsonPath, "json")
# Test write.df and read.df
write.df(df, parquetPath, "parquet", mode="overwrite")
df2 <- read.df(sqlContext, parquetPath, "parquet")
expect_is(df2, "DataFrame")
expect_equal(count(df2), 3)
# Test write.parquet/saveAsParquetFile and read.parquet/parquetFile
parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet")
write.parquet(df, parquetPath2)
parquetPath3 <- tempfile(pattern = "parquetPath3", fileext = ".parquet")
suppressWarnings(saveAsParquetFile(df, parquetPath3))
parquetDF <- read.parquet(sqlContext, c(parquetPath2, parquetPath3))
expect_is(parquetDF, "DataFrame")
expect_equal(count(parquetDF), count(df) * 2)
parquetDF2 <- suppressWarnings(parquetFile(sqlContext, parquetPath2, parquetPath3))
expect_is(parquetDF2, "DataFrame")
expect_equal(count(parquetDF2), count(df) * 2)
# Test if varargs works with variables
saveMode <- "overwrite"
mergeSchema <- "true"
parquetPath4 <- tempfile(pattern = "parquetPath3", fileext = ".parquet")
write.df(df, parquetPath3, "parquet", mode = saveMode, mergeSchema = mergeSchema)
unlink(parquetPath2)
unlink(parquetPath3)
unlink(parquetPath4)
})
test_that("describe() and summarize() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
stats <- describe(df, "age")
expect_equal(collect(stats)[1, "summary"], "count")
expect_equal(collect(stats)[2, "age"], "24.5")
expect_equal(collect(stats)[3, "age"], "7.7781745930520225")
stats <- describe(df)
expect_equal(collect(stats)[4, "name"], "Andy")
expect_equal(collect(stats)[5, "age"], "30")
stats2 <- summary(df)
expect_equal(collect(stats2)[4, "name"], "Andy")
expect_equal(collect(stats2)[5, "age"], "30")
# Test base::summary is working
expect_equal(length(summary(attenu, digits = 4)), 35)
})
test_that("dropna() and na.omit() on a DataFrame", {
df <- read.json(sqlContext, jsonPathNa)
rows <- collect(df)
# drop with columns
expected <- rows[!is.na(rows$name),]
actual <- collect(dropna(df, cols = "name"))
expect_identical(expected, actual)
actual <- collect(na.omit(df, cols = "name"))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age),]
actual <- collect(dropna(df, cols = "age"))
row.names(expected) <- row.names(actual)
# identical on two dataframes does not work here. Don't know why.
# use identical on all columns as a workaround.
expect_identical(expected$age, actual$age)
expect_identical(expected$height, actual$height)
expect_identical(expected$name, actual$name)
actual <- collect(na.omit(df, cols = "age"))
expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
actual <- collect(dropna(df, cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, cols = c("age", "height")))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
actual <- collect(dropna(df))
expect_identical(expected, actual)
actual <- collect(na.omit(df))
expect_identical(expected, actual)
# drop with how
expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
actual <- collect(dropna(df))
expect_identical(expected, actual)
actual <- collect(na.omit(df))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) | !is.na(rows$height) | !is.na(rows$name),]
actual <- collect(dropna(df, "all"))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "all"))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
actual <- collect(dropna(df, "any"))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "any"))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
actual <- collect(dropna(df, "any", cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "any", cols = c("age", "height")))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) | !is.na(rows$height),]
actual <- collect(dropna(df, "all", cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "all", cols = c("age", "height")))
expect_identical(expected, actual)
# drop with threshold
expected <- rows[as.integer(!is.na(rows$age)) + as.integer(!is.na(rows$height)) >= 2,]
actual <- collect(dropna(df, minNonNulls = 2, cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, minNonNulls = 2, cols = c("age", "height")))
expect_identical(expected, actual)
expected <- rows[as.integer(!is.na(rows$age)) +
as.integer(!is.na(rows$height)) +
as.integer(!is.na(rows$name)) >= 3,]
actual <- collect(dropna(df, minNonNulls = 3, cols = c("name", "age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, minNonNulls = 3, cols = c("name", "age", "height")))
expect_identical(expected, actual)
# Test stats::na.omit is working
expect_equal(nrow(na.omit(data.frame(x = c(0, 10, NA)))), 2)
})
test_that("fillna() on a DataFrame", {
df <- read.json(sqlContext, jsonPathNa)
rows <- collect(df)
# fill with value
expected <- rows
expected$age[is.na(expected$age)] <- 50
expected$height[is.na(expected$height)] <- 50.6
actual <- collect(fillna(df, 50.6))
expect_identical(expected, actual)
expected <- rows
expected$name[is.na(expected$name)] <- "unknown"
actual <- collect(fillna(df, "unknown"))
expect_identical(expected, actual)
expected <- rows
expected$age[is.na(expected$age)] <- 50
actual <- collect(fillna(df, 50.6, "age"))
expect_identical(expected, actual)
expected <- rows
expected$name[is.na(expected$name)] <- "unknown"
actual <- collect(fillna(df, "unknown", c("age", "name")))
expect_identical(expected, actual)
# fill with named list
expected <- rows
expected$age[is.na(expected$age)] <- 50
expected$height[is.na(expected$height)] <- 50.6
expected$name[is.na(expected$name)] <- "unknown"
actual <- collect(fillna(df, list("age" = 50, "height" = 50.6, "name" = "unknown")))
expect_identical(expected, actual)
})
test_that("crosstab() on a DataFrame", {
rdd <- lapply(parallelize(sc, 0:3), function(x) {
list(paste0("a", x %% 3), paste0("b", x %% 2))
})
df <- toDF(rdd, list("a", "b"))
ct <- crosstab(df, "a", "b")
ordered <- ct[order(ct$a_b),]
row.names(ordered) <- NULL
expected <- data.frame("a_b" = c("a0", "a1", "a2"), "b0" = c(1, 0, 1), "b1" = c(1, 1, 0),
stringsAsFactors = FALSE, row.names = NULL)
expect_identical(expected, ordered)
})
test_that("cov() and corr() on a DataFrame", {
l <- lapply(c(0:9), function(x) { list(x, x * 2.0) })
df <- createDataFrame(sqlContext, l, c("singles", "doubles"))
result <- cov(df, "singles", "doubles")
expect_true(abs(result - 55.0 / 3) < 1e-12)
result <- corr(df, "singles", "doubles")
expect_true(abs(result - 1.0) < 1e-12)
result <- corr(df, "singles", "doubles", "pearson")
expect_true(abs(result - 1.0) < 1e-12)
# Test stats::cov is working
#expect_true(abs(max(cov(swiss)) - 1739.295) < 1e-3)
})
test_that("freqItems() on a DataFrame", {
input <- 1:1000
rdf <- data.frame(numbers = input, letters = as.character(input),
negDoubles = input * -1.0, stringsAsFactors = F)
rdf[ input %% 3 == 0, ] <- c(1, "1", -1)
df <- createDataFrame(sqlContext, rdf)
multiColResults <- freqItems(df, c("numbers", "letters"), support=0.1)
expect_true(1 %in% multiColResults$numbers[[1]])
expect_true("1" %in% multiColResults$letters[[1]])
singleColResult <- freqItems(df, "negDoubles", support=0.1)
expect_true(-1 %in% head(singleColResult$negDoubles)[[1]])
l <- lapply(c(0:99), function(i) {
if (i %% 2 == 0) { list(1L, -1.0) }
else { list(i, i * -1.0) }})
df <- createDataFrame(sqlContext, l, c("a", "b"))
result <- freqItems(df, c("a", "b"), 0.4)
expect_identical(result[[1]], list(list(1L, 99L)))
expect_identical(result[[2]], list(list(-1, -99)))
})
test_that("sampleBy() on a DataFrame", {
l <- lapply(c(0:99), function(i) { as.character(i %% 3) })
df <- createDataFrame(sqlContext, l, "key")
fractions <- list("0" = 0.1, "1" = 0.2)
sample <- sampleBy(df, "key", fractions, 0)
result <- collect(orderBy(count(groupBy(sample, "key")), "key"))
expect_identical(as.list(result[1, ]), list(key = "0", count = 3))
expect_identical(as.list(result[2, ]), list(key = "1", count = 7))
})
test_that("SQL error message is returned from JVM", {
retError <- tryCatch(sql(sqlContext, "select * from blah"), error = function(e) e)
expect_equal(grepl("Table not found: blah", retError), TRUE)
})
irisDF <- suppressWarnings(createDataFrame(sqlContext, iris))
test_that("Method as.data.frame as a synonym for collect()", {
expect_equal(as.data.frame(irisDF), collect(irisDF))
irisDF2 <- irisDF[irisDF$Species == "setosa", ]
expect_equal(as.data.frame(irisDF2), collect(irisDF2))
})
test_that("attach() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
expect_error(age)
attach(df)
expect_is(age, "DataFrame")
expected_age <- data.frame(age = c(NA, 30, 19))
expect_equal(head(age), expected_age)
stat <- summary(age)
expect_equal(collect(stat)[5, "age"], "30")
age <- age$age + 1
expect_is(age, "Column")
rm(age)
stat2 <- summary(age)
expect_equal(collect(stat2)[5, "age"], "30")
detach("df")
stat3 <- summary(df[, "age"])
expect_equal(collect(stat3)[5, "age"], "30")
expect_error(age)
})
test_that("with() on a DataFrame", {
df <- suppressWarnings(createDataFrame(sqlContext, iris))
expect_error(Sepal_Length)
sum1 <- with(df, list(summary(Sepal_Length), summary(Sepal_Width)))
expect_equal(collect(sum1[[1]])[1, "Sepal_Length"], "150")
sum2 <- with(df, distinct(Sepal_Length))
expect_equal(nrow(sum2), 35)
})
test_that("Method coltypes() to get and set R's data types of a DataFrame", {
expect_equal(coltypes(irisDF), c(rep("numeric", 4), "character"))
data <- data.frame(c1=c(1,2,3),
c2=c(T,F,T),
c3=c("2015/01/01 10:00:00", "2015/01/02 10:00:00", "2015/01/03 10:00:00"))
schema <- structType(structField("c1", "byte"),
structField("c3", "boolean"),
structField("c4", "timestamp"))
# Test primitive types
DF <- createDataFrame(sqlContext, data, schema)
expect_equal(coltypes(DF), c("integer", "logical", "POSIXct"))
# Test complex types
x <- createDataFrame(sqlContext, list(list(as.environment(
list("a"="b", "c"="d", "e"="f")))))
expect_equal(coltypes(x), "map<string,string>")
df <- selectExpr(read.json(sqlContext, jsonPath), "name", "(age * 1.21) as age")
expect_equal(dtypes(df), list(c("name", "string"), c("age", "decimal(24,2)")))
df1 <- select(df, cast(df$age, "integer"))
coltypes(df) <- c("character", "integer")
expect_equal(dtypes(df), list(c("name", "string"), c("age", "int")))
value <- collect(df[, 2])[[3, 1]]
expect_equal(value, collect(df1)[[3, 1]])
expect_equal(value, 22)
coltypes(df) <- c(NA, "numeric")
expect_equal(dtypes(df), list(c("name", "string"), c("age", "double")))
expect_error(coltypes(df) <- c("character"),
"Length of type vector should match the number of columns for DataFrame")
expect_error(coltypes(df) <- c("environment", "list"),
"Only atomic type is supported for column types")
})
unlink(parquetPath)
unlink(jsonPath)
unlink(jsonPathNa)
akzaidi/SparkR documentation built on May 10, 2019, 8:32 a.m.
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#
# 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
}
# Tests for SparkSQL functions in SparkR
sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
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")
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)
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("create DataFrame from RDD", {
rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
df <- createDataFrame(sqlContext, rdd, list("a", "b"))
dfAsDF <- as.DataFrame(sqlContext, rdd, list("a", "b"))
expect_is(df, "DataFrame")
expect_is(dfAsDF, "DataFrame")
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(sqlContext, rdd)
dfAsDF <- as.DataFrame(sqlContext, rdd)
expect_is(df, "DataFrame")
expect_is(dfAsDF, "DataFrame")
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(sqlContext, rdd, schema)
expect_is(df, "DataFrame")
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(sqlContext, rdd)
expect_is(df, "DataFrame")
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(sqlContext, jsonPathNa, "json", schema)
df2 <- createDataFrame(sqlContext, toRDD(df), schema)
df2AsDF <- as.DataFrame(sqlContext, 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(sqlContext, localDF, schema)
expect_is(df, "DataFrame")
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$name == "John"))),
list(name = "John", age = 19L, height = 176.5))
ssc <- callJMethod(sc, "sc")
hiveCtx <- tryCatch({
newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc)
},
error = function(err) {
skip("Hive is not build with SparkSQL, skipped")
})
sql(hiveCtx, "CREATE TABLE people (name string, age double, height float)")
df <- read.df(hiveCtx, jsonPathNa, "json", schema)
invisible(insertInto(df, "people"))
expect_equal(collect(sql(hiveCtx, "SELECT age from people WHERE name = 'Bob'"))$age,
c(16))
expect_equal(collect(sql(hiveCtx, "SELECT height from people WHERE name ='Bob'"))$height,
c(176.5))
})
test_that("convert NAs to null type in DataFrames", {
rdd <- parallelize(sc, list(list(1L, 2L), list(NA, 4L)))
df <- createDataFrame(sqlContext, 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(sqlContext, 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(sqlContext, 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(sqlContext, l)
expect_equal(collect(df)[2, "x"], 1)
expect_true(is.na(collect(df)[2, "y"]))
l <- list("a", "b", NA, "d")
df <- createDataFrame(sqlContext, 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(sqlContext, l)
expect_true(is.na(collect(df)[3, "_1"]))
expect_equal(collect(df)[4, "_1"], "d")
l <- list(TRUE, FALSE, NA, TRUE)
df <- createDataFrame(sqlContext, l)
expect_true(is.na(collect(df)[3, "_1"]))
expect_equal(collect(df)[4, "_1"], TRUE)
})
test_that("toDF", {
rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) })
df <- toDF(rdd, list("a", "b"))
expect_is(df, "DataFrame")
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, "DataFrame")
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, "DataFrame")
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, "DataFrame")
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(sqlContext, 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(sqlContext, l)
expect_equal(columns(df), c("a", "b"))
a <- 1:3
b <- c("a", "b", "c")
ldf <- data.frame(a, b)
df <- createDataFrame(sqlContext, 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$a)
irisdf <- suppressWarnings(createDataFrame(sqlContext, iris))
iris_collected <- collect(irisdf)
expect_equivalent(iris_collected[,-5], iris[,-5])
expect_equal(iris_collected$Species, as.character(iris$Species))
mtcarsdf <- createDataFrame(sqlContext, mtcars)
expect_equivalent(collect(mtcarsdf), mtcars)
bytes <- as.raw(c(1, 2, 3))
df <- createDataFrame(sqlContext, list(list(bytes)))
expect_equal(collect(df)[[1]][[1]], bytes)
})
test_that("create DataFrame with different data types", {
l <- list(a = 1L, b = 2, c = TRUE, d = "ss", e = as.Date("2012-12-13"),
f = as.POSIXct("2015-03-15 12:13:14.056"))
df <- createDataFrame(sqlContext, list(l))
expect_equal(dtypes(df), list(c("a", "int"), c("b", "double"), c("c", "boolean"),
c("d", "string"), c("e", "date"), c("f", "timestamp")))
expect_equal(count(df), 1)
expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE))
})
test_that("create DataFrame with complex types", {
e <- new.env()
assign("n", 3L, envir = e)
s <- listToStruct(list(a = "aa", b = 3L))
l <- list(as.list(1:10), list("a", "b"), e, s)
df <- createDataFrame(sqlContext, list(l), c("a", "b", "c", "d"))
expect_equal(dtypes(df), list(c("a", "array<int>"),
c("b", "array<string>"),
c("c", "map<string,int>"),
c("d", "struct<a:string,b:int>")))
expect_equal(count(df), 1)
ldf <- collect(df)
expect_equal(names(ldf), c("a", "b", "c", "d"))
expect_equal(ldf[1, 1][[1]], l[[1]])
expect_equal(ldf[1, 2][[1]], l[[2]])
e <- ldf$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", {
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(sqlContext, ldf)
collected <- collect(sdf)
expect_identical(ldf[, 1, FALSE], collected[, 1, FALSE])
expect_equal(ldf$an_envir, collected$an_envir)
})
# 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)
test_that("Collect DataFrame with complex types", {
# ArrayType
df <- read.json(sqlContext, 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$c3, list(list(1.0, 2.0, 3.0), list(4.0, 5.0, 6.0), list (7.0, 8.0, 9.0)))
# MapType
schema <- structType(structField("name", "string"),
structField("info", "map<string,double>"))
df <- read.df(sqlContext, mapTypeJsonPath, "json", schema)
expect_equal(dtypes(df), list(c("name", "string"),
c("info", "map<string,double>")))
ldf <- collect(df)
expect_equal(nrow(ldf), 3)
expect_equal(ncol(ldf), 2)
expect_equal(names(ldf), c("name", "info"))
expect_equal(ldf$name, c("Bob", "Alice", "David"))
bob <- ldf$info[[1]]
expect_equal(class(bob), "environment")
expect_equal(bob$age, 16)
expect_equal(bob$height, 176.5)
# StructType
df <- read.json(sqlContext, mapTypeJsonPath)
expect_equal(dtypes(df), list(c("info", "struct<age:bigint,height:double>"),
c("name", "string")))
ldf <- collect(df)
expect_equal(nrow(ldf), 3)
expect_equal(ncol(ldf), 2)
expect_equal(names(ldf), c("info", "name"))
expect_equal(ldf$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", {
# Test read.df
df <- read.df(sqlContext, jsonPath, "json")
expect_is(df, "DataFrame")
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(sqlContext, jsonPath, "json", schema)
expect_is(df1, "DataFrame")
expect_equal(dtypes(df1), list(c("name", "string"), c("age", "double")))
# Test loadDF
df2 <- loadDF(sqlContext, jsonPath, "json", schema)
expect_is(df2, "DataFrame")
expect_equal(dtypes(df2), list(c("name", "string"), c("age", "double")))
# Test read.json
df <- read.json(sqlContext, jsonPath)
expect_is(df, "DataFrame")
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(sqlContext, c(jsonPath2, jsonPath3))
expect_is(jsonDF1, "DataFrame")
expect_equal(count(jsonDF1), 6)
# Suppress warnings because jsonFile is deprecated
jsonDF2 <- suppressWarnings(jsonFile(sqlContext, c(jsonPath2, jsonPath3)))
expect_is(jsonDF2, "DataFrame")
expect_equal(count(jsonDF2), 6)
unlink(jsonPath2)
unlink(jsonPath3)
})
test_that("jsonRDD() on a RDD with json string", {
rdd <- parallelize(sc, mockLines)
expect_equal(count(rdd), 3)
df <- jsonRDD(sqlContext, rdd)
expect_is(df, "DataFrame")
expect_equal(count(df), 3)
rdd2 <- flatMap(rdd, function(x) c(x, x))
df <- jsonRDD(sqlContext, rdd2)
expect_is(df, "DataFrame")
expect_equal(count(df), 6)
})
test_that("test cache, uncache and clearCache", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
cacheTable(sqlContext, "table1")
uncacheTable(sqlContext, "table1")
clearCache(sqlContext)
dropTempTable(sqlContext, "table1")
})
test_that("test tableNames and tables", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
expect_equal(length(tableNames(sqlContext)), 1)
df <- tables(sqlContext)
expect_equal(count(df), 1)
dropTempTable(sqlContext, "table1")
})
test_that("registerTempTable() results in a queryable table and sql() results in a new DataFrame", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
newdf <- sql(sqlContext, "SELECT * FROM table1 where name = 'Michael'")
expect_is(newdf, "DataFrame")
expect_equal(count(newdf), 1)
dropTempTable(sqlContext, "table1")
})
test_that("insertInto() on a registered table", {
df <- read.df(sqlContext, jsonPath, "json")
write.df(df, parquetPath, "parquet", "overwrite")
dfParquet <- read.df(sqlContext, 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(sqlContext, jsonPath2, "json")
write.df(df2, parquetPath2, "parquet", "overwrite")
dfParquet2 <- read.df(sqlContext, parquetPath2, "parquet")
registerTempTable(dfParquet, "table1")
insertInto(dfParquet2, "table1")
expect_equal(count(sql(sqlContext, "select * from table1")), 5)
expect_equal(first(sql(sqlContext, "select * from table1 order by age"))$name, "Michael")
dropTempTable(sqlContext, "table1")
registerTempTable(dfParquet, "table1")
insertInto(dfParquet2, "table1", overwrite = TRUE)
expect_equal(count(sql(sqlContext, "select * from table1")), 2)
expect_equal(first(sql(sqlContext, "select * from table1 order by age"))$name, "Bob")
dropTempTable(sqlContext, "table1")
unlink(jsonPath2)
unlink(parquetPath2)
})
test_that("table() returns a new DataFrame", {
df <- read.json(sqlContext, jsonPath)
registerTempTable(df, "table1")
tabledf <- table(sqlContext, "table1")
expect_is(tabledf, "DataFrame")
expect_equal(count(tabledf), 3)
dropTempTable(sqlContext, "table1")
# Test base::table is working
#a <- letters[1:3]
#expect_equal(class(table(a, sample(a))), "table")
})
test_that("toRDD() returns an RRDD", {
df <- read.json(sqlContext, jsonPath)
testRDD <- toRDD(df)
expect_is(testRDD, "RDD")
expect_equal(count(testRDD), 3)
})
test_that("union on two RDDs created from DataFrames returns an RRDD", {
df <- read.json(sqlContext, jsonPath)
RDD1 <- toRDD(df)
RDD2 <- toRDD(df)
unioned <- unionRDD(RDD1, RDD2)
expect_is(unioned, "RDD")
expect_equal(getSerializedMode(unioned), "byte")
expect_equal(collect(unioned)[[2]]$name, "Andy")
})
test_that("union on mixed serialization types correctly returns a byte RRDD", {
# 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(sqlContext, jsonPath)
dfRDD <- toRDD(df)
unionByte <- unionRDD(rdd, dfRDD)
expect_is(unionByte, "RDD")
expect_equal(getSerializedMode(unionByte), "byte")
expect_equal(collect(unionByte)[[1]], 1)
expect_equal(collect(unionByte)[[12]]$name, "Andy")
unionString <- unionRDD(textRDD, dfRDD)
expect_is(unionString, "RDD")
expect_equal(getSerializedMode(unionString), "byte")
expect_equal(collect(unionString)[[1]], "Michael")
expect_equal(collect(unionString)[[5]]$name, "Andy")
})
test_that("objectFile() works with row serialization", {
objectPath <- tempfile(pattern="spark-test", fileext=".tmp")
df <- read.json(sqlContext, jsonPath)
dfRDD <- toRDD(df)
saveAsObjectFile(coalesce(dfRDD, 1L), objectPath)
objectIn <- objectFile(sc, objectPath)
expect_is(objectIn, "RDD")
expect_equal(getSerializedMode(objectIn), "byte")
expect_equal(collect(objectIn)[[2]]$age, 30)
})
test_that("lapply() on a DataFrame returns an RDD with the correct columns", {
df <- read.json(sqlContext, jsonPath)
testRDD <- lapply(df, function(row) {
row$newCol <- row$age + 5
row
})
expect_is(testRDD, "RDD")
collected <- collect(testRDD)
expect_equal(collected[[1]]$name, "Michael")
expect_equal(collected[[2]]$newCol, 35)
})
test_that("collect() returns a data.frame", {
df <- read.json(sqlContext, jsonPath)
rdf <- collect(df)
expect_true(is.data.frame(rdf))
expect_equal(names(rdf)[1], "age")
expect_equal(nrow(rdf), 3)
expect_equal(ncol(rdf), 2)
# collect() returns data correctly from a DataFrame with 0 row
df0 <- limit(df, 0)
rdf <- collect(df0)
expect_true(is.data.frame(rdf))
expect_equal(names(rdf)[1], "age")
expect_equal(nrow(rdf), 0)
expect_equal(ncol(rdf), 2)
# collect() correctly handles multiple columns with same name
df <- createDataFrame(sqlContext, list(list(1, 2)), schema = c("name", "name"))
ldf <- collect(df)
expect_equal(names(ldf), c("name", "name"))
})
test_that("limit() returns DataFrame with the correct number of rows", {
df <- read.json(sqlContext, jsonPath)
dfLimited <- limit(df, 2)
expect_is(dfLimited, "DataFrame")
expect_equal(count(dfLimited), 2)
})
test_that("collect() and take() on a DataFrame return the same number of rows and columns", {
df <- read.json(sqlContext, jsonPath)
expect_equal(nrow(collect(df)), nrow(take(df, 10)))
expect_equal(ncol(collect(df)), ncol(take(df, 10)))
})
test_that("collect() support Unicode characters", {
lines <- c("{\"name\":\"안녕하세요\"}",
"{\"name\":\"您好\", \"age\":30}",
"{\"name\":\"こんにちは\", \"age\":19}",
"{\"name\":\"Xin chào\"}")
jsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(lines, jsonPath)
df <- read.df(sqlContext, jsonPath, "json")
rdf <- collect(df)
expect_true(is.data.frame(rdf))
expect_equal(rdf$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$name[4], markUtf8("Xin chào"))
df1 <- createDataFrame(sqlContext, rdf)
expect_equal(collect(where(df1, df1$name == markUtf8("您好")))$name, markUtf8("您好"))
})
test_that("multiple pipeline transformations result in an RDD with the correct values", {
df <- read.json(sqlContext, jsonPath)
first <- lapply(df, function(row) {
row$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(count(second), 3)
expect_equal(collect(second)[[2]]$age, 35)
expect_true(collect(second)[[2]]$testCol)
expect_false(collect(second)[[3]]$testCol)
})
test_that("cache(), persist(), and unpersist() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
expect_false(df@env$isCached)
cache(df)
expect_true(df@env$isCached)
unpersist(df)
expect_false(df@env$isCached)
persist(df, "MEMORY_AND_DISK")
expect_true(df@env$isCached)
unpersist(df)
expect_false(df@env$isCached)
# make sure the data is collectable
expect_true(is.data.frame(collect(df)))
})
test_that("schema(), dtypes(), columns(), names() return the correct values/format", {
df <- read.json(sqlContext, 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(sqlContext, jsonPath)
names(df) <- c("col1", "col2")
expect_equal(colnames(df)[2], "col2")
colnames(df) <- c("col3", "col4")
expect_equal(names(df)[1], "col3")
# 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_that("head() and first() return the correct data", {
df <- read.json(sqlContext, 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() and unique 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(sqlContext, jsonPathWithDup)
uniques <- distinct(df)
expect_is(uniques, "DataFrame")
expect_equal(count(uniques), 3)
uniques2 <- unique(df)
expect_is(uniques2, "DataFrame")
expect_equal(count(uniques2), 3)
})
test_that("sample on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
sampled <- sample(df, FALSE, 1.0)
expect_equal(nrow(collect(sampled)), count(df))
expect_is(sampled, "DataFrame")
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)
# Test base::sample is working
#expect_equal(length(sample(1:12)), 12)
})
test_that("select operators", {
df <- select(read.json(sqlContext, jsonPath), "name", "age")
expect_is(df$name, "Column")
expect_is(df[[2]], "Column")
expect_is(df[["age"]], "Column")
expect_is(df[,1], "DataFrame")
expect_equal(columns(df[,1]), c("name"))
expect_equal(columns(df[,"age"]), c("age"))
df2 <- df[,c("age", "name")]
expect_is(df2, "DataFrame")
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$age2 <- NULL
expect_equal(columns(df), c("name", "age"))
df$age3 <- NULL
expect_equal(columns(df), c("name", "age"))
})
test_that("select with column", {
df <- read.json(sqlContext, jsonPath)
df1 <- select(df, "name")
expect_equal(columns(df1), c("name"))
expect_equal(count(df1), 3)
df2 <- select(df, df$age)
expect_equal(columns(df2), c("age"))
expect_equal(count(df2), 3)
df3 <- select(df, lit("x"))
expect_equal(columns(df3), c("x"))
expect_equal(count(df3), 3)
expect_equal(collect(select(df3, "x"))[[1, 1]], "x")
df4 <- select(df, c("name", "age"))
expect_equal(columns(df4), c("name", "age"))
expect_equal(count(df4), 3)
expect_error(select(df, c("name", "age"), "name"),
"To select multiple columns, use a character vector or list for col")
})
test_that("subsetting", {
# read.json returns columns in random order
df <- select(read.json(sqlContext, jsonPath), "name", "age")
filtered <- df[df$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]
expect_is(df2, "DataFrame")
expect_equal(count(df2), 1)
expect_equal(columns(df2), c("name"))
expect_equal(collect(df2)$name, "Justin")
df3 <- df[df$age > 20, 2]
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$age %in% c(30), c(1,2))
expect_equal(count(df6), 1)
expect_equal(columns(df6), c("name", "age"))
df7 <- subset(df, select = "name")
expect_equal(count(df7), 3)
expect_equal(columns(df7), c("name"))
# Test base::subset is working
expect_equal(nrow(subset(airquality, Temp > 80, select = c(Ozone, Temp))), 68)
})
test_that("selectExpr() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
selected <- selectExpr(df, "age * 2")
expect_equal(names(selected), "(age * 2)")
expect_equal(collect(selected), collect(select(df, df$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(sqlContext, jsonPath)
expect_equal(collect(select(df, expr("abs(-123)")))[1, 1], 123)
})
test_that("column calculation", {
df <- read.json(sqlContext, 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, "DataFrame")
expect_equal(count(df2), 3)
})
test_that("test HiveContext", {
ssc <- callJMethod(sc, "sc")
hiveCtx <- tryCatch({
newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc)
},
error = function(err) {
skip("Hive is not build with SparkSQL, skipped")
})
df <- createExternalTable(hiveCtx, "json", jsonPath, "json")
expect_is(df, "DataFrame")
expect_equal(count(df), 3)
df2 <- sql(hiveCtx, "select * from json")
expect_is(df2, "DataFrame")
expect_equal(count(df2), 3)
jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp")
invisible(saveAsTable(df, "json2", "json", "append", path = jsonPath2))
df3 <- sql(hiveCtx, "select * from json2")
expect_is(df3, "DataFrame")
expect_equal(count(df3), 3)
unlink(jsonPath2)
})
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
})
test_that("column functions", {
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) + 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) + quarter(c)
c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + sha1(c)
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)
# 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(sqlContext, jsonPath)
df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))
expect_equal(collect(df2)[[2, 1]], TRUE)
expect_equal(collect(df2)[[2, 2]], FALSE)
expect_equal(collect(df2)[[3, 1]], FALSE)
expect_equal(collect(df2)[[3, 2]], TRUE)
df3 <- select(df, between(df$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$age)))[1, 1], 30.25)
df5 <- createDataFrame(sqlContext, list(list(a = "010101")))
expect_equal(collect(select(df5, conv(df5$a, 2, 16)))[1, 1], "15")
# Test array_contains() and sort_array()
df <- createDataFrame(sqlContext, 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(sqlContext,
list(list(1L, 2L, 3L), list(4L, 5L, 6L)),
schema = c("a", "b", "c"))
result <- collect(select(df, struct("a", "c")))
expected <- data.frame(row.names = 1:2)
expected$"struct(a,c)" <- list(listToStruct(list(a = 1L, c = 3L)),
listToStruct(list(a = 4L, c = 6L)))
expect_equal(result, expected)
result <- collect(select(df, struct(df$a, df$b)))
expected <- data.frame(row.names = 1:2)
expected$"struct(a,b)" <- 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(sqlContext,
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_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(sqlContext, 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$b, 1)))[4, 1], 4)
expect_equal(class(collect(select(df, rand()))[2, 1]), "numeric")
expect_equal(collect(select(df, rand(1)))[1, 1], 0.134, tolerance = 0.01)
expect_equal(class(collect(select(df, randn()))[2, 1]), "numeric")
expect_equal(collect(select(df, randn(1)))[1, 1], -1.03, tolerance = 0.01)
})
test_that("string operators", {
df <- read.json(sqlContext, jsonPath)
expect_equal(count(where(df, like(df$name, "A%"))), 1)
expect_equal(count(where(df, startsWith(df$name, "A"))), 1)
expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi")
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$name, "(n.y)", "ydn")))[2, 1], "Aydn")
l2 <- list(list(a = "aaads"))
df2 <- createDataFrame(sqlContext, l2)
expect_equal(collect(select(df2, locate("aa", df2$a)))[1, 1], 1)
expect_equal(collect(select(df2, locate("aa", df2$a, 1)))[1, 1], 2)
expect_equal(collect(select(df2, lpad(df2$a, 8, "#")))[1, 1], "###aaads")
expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###")
l3 <- list(list(a = "a.b.c.d"))
df3 <- createDataFrame(sqlContext, l3)
expect_equal(collect(select(df3, substring_index(df3$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")
})
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(sqlContext, 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$b, 1)))[, 1],
c(as.Date("2012-12-12"), as.Date("2013-12-13"), as.Date("2014-12-14")))
l2 <- list(list(a = 1L, b = as.POSIXlt("2012-12-13 12:34:00", tz = "UTC")),
list(a = 2L, b = as.POSIXlt("2014-12-15 01:24:34", tz = "UTC")))
df2 <- createDataFrame(sqlContext, l2)
expect_equal(collect(select(df2, minute(df2$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$b, "JST")))[, 1],
c(as.POSIXlt("2012-12-13 03:34:00 UTC"), as.POSIXlt("2014-12-14 16:24:34 UTC")))
expect_more_than(collect(select(df2, unix_timestamp()))[1, 1], 0)
expect_more_than(collect(select(df2, unix_timestamp(df2$b)))[1, 1], 0)
expect_more_than(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(sqlContext, 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(sqlContext, 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("when(), otherwise() and ifelse() on a DataFrame", {
l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
df <- createDataFrame(sqlContext, 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$b > 2, 0, 1)))[, 1], c(1, 0))
})
test_that("group by, agg functions", {
df <- read.json(sqlContext, jsonPath)
df1 <- agg(df, name = "max", age = "sum")
expect_equal(1, count(df1))
df1 <- agg(df, age2 = max(df$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, "DataFrame")
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$age))
expect_is(df_summarized, "DataFrame")
expect_equal(3, count(df_summarized))
df3 <- agg(gd, age = "stddev")
expect_is(df3, "DataFrame")
df3_local <- collect(df3)
expect_true(is.nan(df3_local[df3_local$name == "Andy",][1, 2]))
df4 <- agg(gd, sumAge = sum(df$age))
expect_is(df4, "DataFrame")
expect_equal(3, count(df4))
expect_equal(columns(df4), c("name", "sumAge"))
df5 <- sum(gd, "age")
expect_is(df5, "DataFrame")
expect_equal(3, count(df5))
expect_equal(3, count(mean(gd)))
expect_equal(3, count(max(gd)))
expect_equal(30, collect(max(gd))[1, 2])
expect_equal(1, collect(count(gd))[1, 2])
mockLines2 <- c("{\"name\":\"ID1\", \"value\": \"10\"}",
"{\"name\":\"ID1\", \"value\": \"10\"}",
"{\"name\":\"ID1\", \"value\": \"22\"}",
"{\"name\":\"ID2\", \"value\": \"-3\"}")
jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(mockLines2, jsonPath2)
gd2 <- groupBy(read.json(sqlContext, jsonPath2), "name")
df6 <- agg(gd2, value = "sum")
df6_local <- collect(df6)
expect_equal(42, df6_local[df6_local$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$name == "ID2",][1, 2]))
mockLines3 <- c("{\"name\":\"Andy\", \"age\":30}",
"{\"name\":\"Andy\", \"age\":30}",
"{\"name\":\"Justin\", \"age\":19}",
"{\"name\":\"Justin\", \"age\":1}")
jsonPath3 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(mockLines3, jsonPath3)
df8 <- read.json(sqlContext, jsonPath3)
gd3 <- groupBy(df8, "name")
gd3_local <- collect(sum(gd3))
expect_equal(60, gd3_local[gd3_local$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)
unlink(jsonPath2)
unlink(jsonPath3)
})
test_that("arrange() and orderBy() on a DataFrame", {
df <- read.json(sqlContext, 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)$name, "Michael")
expect_equal(collect(sorted4)[3,"name"], "Andy")
sorted5 <- arrange(df, "age", "name", decreasing = TRUE)
expect_equal(collect(sorted5)[1,2], "Andy")
sorted6 <- arrange(df, "age","name", decreasing = c(T, F))
expect_equal(collect(sorted6)[1,2], "Andy")
sorted7 <- arrange(df, "name", decreasing = FALSE)
expect_equal(collect(sorted7)[2,"age"], 19)
})
test_that("filter() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
filtered <- filter(df, "age > 20")
expect_equal(count(filtered), 1)
expect_equal(collect(filtered)$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 stats::filter is working
#expect_true(is.ts(filter(1:100, rep(1, 3))))
})
test_that("join() and merge() on a DataFrame", {
df <- read.json(sqlContext, 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(sqlContext, jsonPath2)
joined <- join(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"))$"name_x"[1], "Andy")
merged <- merge(df, df2, by = NULL)
expect_equal(count(merged), 12)
expect_equal(names(merged), c("age", "name", "name", "test"))
mockLines3 <- c("{\"name\":\"Michael\", \"name_y\":\"Michael\", \"test\": \"yes\"}",
"{\"name\":\"Andy\", \"name_y\":\"Andy\", \"test\": \"no\"}",
"{\"name\":\"Justin\", \"name_y\":\"Justin\", \"test\": \"yes\"}",
"{\"name\":\"Bob\", \"name_y\":\"Bob\", \"test\": \"yes\"}")
jsonPath3 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(mockLines3, jsonPath3)
df3 <- read.json(sqlContext, jsonPath3)
expect_error(merge(df, df3),
paste("The following column name: name_y occurs more than once in the 'DataFrame'.",
"Please use different suffixes for the intersected columns.", sep = ""))
unlink(jsonPath2)
unlink(jsonPath3)
})
test_that("toJSON() returns an RDD of the correct values", {
df <- read.json(sqlContext, jsonPath)
testRDD <- toJSON(df)
expect_is(testRDD, "RDD")
expect_equal(getSerializedMode(testRDD), "string")
expect_equal(collect(testRDD)[[1]], mockLines[1])
})
test_that("showDF()", {
df <- read.json(sqlContext, jsonPath)
s <- capture.output(showDF(df))
expected <- paste("+----+-------+\n",
"| age| name|\n",
"+----+-------+\n",
"|null|Michael|\n",
"| 30| Andy|\n",
"| 19| Justin|\n",
"+----+-------+\n", sep="")
expect_output(s , expected)
})
test_that("isLocal()", {
df <- read.json(sqlContext, jsonPath)
expect_false(isLocal(df))
})
test_that("unionAll(), rbind(), except(), and intersect() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
lines <- c("{\"name\":\"Bob\", \"age\":24}",
"{\"name\":\"Andy\", \"age\":30}",
"{\"name\":\"James\", \"age\":35}")
jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp")
writeLines(lines, jsonPath2)
df2 <- read.df(sqlContext, jsonPath2, "json")
unioned <- arrange(unionAll(df, df2), df$age)
expect_is(unioned, "DataFrame")
expect_equal(count(unioned), 6)
expect_equal(first(unioned)$name, "Michael")
unioned2 <- arrange(rbind(unioned, df, df2), df$age)
expect_is(unioned2, "DataFrame")
expect_equal(count(unioned2), 12)
expect_equal(first(unioned2)$name, "Michael")
excepted <- arrange(except(df, df2), desc(df$age))
expect_is(unioned, "DataFrame")
expect_equal(count(excepted), 2)
expect_equal(first(excepted)$name, "Justin")
intersected <- arrange(intersect(df, df2), df$age)
expect_is(unioned, "DataFrame")
expect_equal(count(intersected), 1)
expect_equal(first(intersected)$name, "Andy")
# Test base::rbind is working
expect_equal(length(rbind(1:4, c = 2, a = 10, 10, deparse.level = 0)), 16)
# Test base::intersect is working
expect_equal(length(intersect(1:20, 3:23)), 18)
unlink(jsonPath2)
})
test_that("withColumn() and withColumnRenamed()", {
df <- read.json(sqlContext, jsonPath)
newDF <- withColumn(df, "newAge", df$age + 2)
expect_equal(length(columns(newDF)), 3)
expect_equal(columns(newDF)[3], "newAge")
expect_equal(first(filter(newDF, df$name != "Michael"))$newAge, 32)
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(sqlContext, 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"))$newAge, 32)
newDF2 <- rename(df, newerAge = df$age)
expect_equal(length(columns(newDF2)), 2)
expect_equal(columns(newDF2)[1], "newerAge")
names(newDF2) <- c("newerName", "evenNewerAge")
expect_equal(length(names(newDF2)), 2)
expect_equal(names(newDF2)[1], "newerName")
transformedDF <- transform(df, newAge = -df$age, newAge2 = df$age / 2)
expect_equal(length(columns(transformedDF)), 4)
expect_equal(columns(transformedDF)[3], "newAge")
expect_equal(columns(transformedDF)[4], "newAge2")
expect_equal(first(filter(transformedDF, transformedDF$name == "Andy"))$newAge, -30)
# test if base::transform on local data frames works
# ensure the proper signature is used - otherwise this will fail to run
attach(airquality)
result <- transform(Ozone, logOzone = log(Ozone))
expect_equal(nrow(result), 153)
expect_equal(ncol(result), 2)
detach(airquality)
})
test_that("read/write Parquet files", {
df <- read.df(sqlContext, jsonPath, "json")
# Test write.df and read.df
write.df(df, parquetPath, "parquet", mode="overwrite")
df2 <- read.df(sqlContext, parquetPath, "parquet")
expect_is(df2, "DataFrame")
expect_equal(count(df2), 3)
# Test write.parquet/saveAsParquetFile and read.parquet/parquetFile
parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet")
write.parquet(df, parquetPath2)
parquetPath3 <- tempfile(pattern = "parquetPath3", fileext = ".parquet")
suppressWarnings(saveAsParquetFile(df, parquetPath3))
parquetDF <- read.parquet(sqlContext, c(parquetPath2, parquetPath3))
expect_is(parquetDF, "DataFrame")
expect_equal(count(parquetDF), count(df) * 2)
parquetDF2 <- suppressWarnings(parquetFile(sqlContext, parquetPath2, parquetPath3))
expect_is(parquetDF2, "DataFrame")
expect_equal(count(parquetDF2), count(df) * 2)
# Test if varargs works with variables
saveMode <- "overwrite"
mergeSchema <- "true"
parquetPath4 <- tempfile(pattern = "parquetPath3", fileext = ".parquet")
write.df(df, parquetPath3, "parquet", mode = saveMode, mergeSchema = mergeSchema)
unlink(parquetPath2)
unlink(parquetPath3)
unlink(parquetPath4)
})
test_that("describe() and summarize() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
stats <- describe(df, "age")
expect_equal(collect(stats)[1, "summary"], "count")
expect_equal(collect(stats)[2, "age"], "24.5")
expect_equal(collect(stats)[3, "age"], "7.7781745930520225")
stats <- describe(df)
expect_equal(collect(stats)[4, "name"], "Andy")
expect_equal(collect(stats)[5, "age"], "30")
stats2 <- summary(df)
expect_equal(collect(stats2)[4, "name"], "Andy")
expect_equal(collect(stats2)[5, "age"], "30")
# Test base::summary is working
expect_equal(length(summary(attenu, digits = 4)), 35)
})
test_that("dropna() and na.omit() on a DataFrame", {
df <- read.json(sqlContext, jsonPathNa)
rows <- collect(df)
# drop with columns
expected <- rows[!is.na(rows$name),]
actual <- collect(dropna(df, cols = "name"))
expect_identical(expected, actual)
actual <- collect(na.omit(df, cols = "name"))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age),]
actual <- collect(dropna(df, cols = "age"))
row.names(expected) <- row.names(actual)
# identical on two dataframes does not work here. Don't know why.
# use identical on all columns as a workaround.
expect_identical(expected$age, actual$age)
expect_identical(expected$height, actual$height)
expect_identical(expected$name, actual$name)
actual <- collect(na.omit(df, cols = "age"))
expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
actual <- collect(dropna(df, cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, cols = c("age", "height")))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
actual <- collect(dropna(df))
expect_identical(expected, actual)
actual <- collect(na.omit(df))
expect_identical(expected, actual)
# drop with how
expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
actual <- collect(dropna(df))
expect_identical(expected, actual)
actual <- collect(na.omit(df))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) | !is.na(rows$height) | !is.na(rows$name),]
actual <- collect(dropna(df, "all"))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "all"))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
actual <- collect(dropna(df, "any"))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "any"))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
actual <- collect(dropna(df, "any", cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "any", cols = c("age", "height")))
expect_identical(expected, actual)
expected <- rows[!is.na(rows$age) | !is.na(rows$height),]
actual <- collect(dropna(df, "all", cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, "all", cols = c("age", "height")))
expect_identical(expected, actual)
# drop with threshold
expected <- rows[as.integer(!is.na(rows$age)) + as.integer(!is.na(rows$height)) >= 2,]
actual <- collect(dropna(df, minNonNulls = 2, cols = c("age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, minNonNulls = 2, cols = c("age", "height")))
expect_identical(expected, actual)
expected <- rows[as.integer(!is.na(rows$age)) +
as.integer(!is.na(rows$height)) +
as.integer(!is.na(rows$name)) >= 3,]
actual <- collect(dropna(df, minNonNulls = 3, cols = c("name", "age", "height")))
expect_identical(expected, actual)
actual <- collect(na.omit(df, minNonNulls = 3, cols = c("name", "age", "height")))
expect_identical(expected, actual)
# Test stats::na.omit is working
expect_equal(nrow(na.omit(data.frame(x = c(0, 10, NA)))), 2)
})
test_that("fillna() on a DataFrame", {
df <- read.json(sqlContext, jsonPathNa)
rows <- collect(df)
# fill with value
expected <- rows
expected$age[is.na(expected$age)] <- 50
expected$height[is.na(expected$height)] <- 50.6
actual <- collect(fillna(df, 50.6))
expect_identical(expected, actual)
expected <- rows
expected$name[is.na(expected$name)] <- "unknown"
actual <- collect(fillna(df, "unknown"))
expect_identical(expected, actual)
expected <- rows
expected$age[is.na(expected$age)] <- 50
actual <- collect(fillna(df, 50.6, "age"))
expect_identical(expected, actual)
expected <- rows
expected$name[is.na(expected$name)] <- "unknown"
actual <- collect(fillna(df, "unknown", c("age", "name")))
expect_identical(expected, actual)
# fill with named list
expected <- rows
expected$age[is.na(expected$age)] <- 50
expected$height[is.na(expected$height)] <- 50.6
expected$name[is.na(expected$name)] <- "unknown"
actual <- collect(fillna(df, list("age" = 50, "height" = 50.6, "name" = "unknown")))
expect_identical(expected, actual)
})
test_that("crosstab() on a DataFrame", {
rdd <- lapply(parallelize(sc, 0:3), function(x) {
list(paste0("a", x %% 3), paste0("b", x %% 2))
})
df <- toDF(rdd, list("a", "b"))
ct <- crosstab(df, "a", "b")
ordered <- ct[order(ct$a_b),]
row.names(ordered) <- NULL
expected <- data.frame("a_b" = c("a0", "a1", "a2"), "b0" = c(1, 0, 1), "b1" = c(1, 1, 0),
stringsAsFactors = FALSE, row.names = NULL)
expect_identical(expected, ordered)
})
test_that("cov() and corr() on a DataFrame", {
l <- lapply(c(0:9), function(x) { list(x, x * 2.0) })
df <- createDataFrame(sqlContext, l, c("singles", "doubles"))
result <- cov(df, "singles", "doubles")
expect_true(abs(result - 55.0 / 3) < 1e-12)
result <- corr(df, "singles", "doubles")
expect_true(abs(result - 1.0) < 1e-12)
result <- corr(df, "singles", "doubles", "pearson")
expect_true(abs(result - 1.0) < 1e-12)
# Test stats::cov is working
#expect_true(abs(max(cov(swiss)) - 1739.295) < 1e-3)
})
test_that("freqItems() on a DataFrame", {
input <- 1:1000
rdf <- data.frame(numbers = input, letters = as.character(input),
negDoubles = input * -1.0, stringsAsFactors = F)
rdf[ input %% 3 == 0, ] <- c(1, "1", -1)
df <- createDataFrame(sqlContext, rdf)
multiColResults <- freqItems(df, c("numbers", "letters"), support=0.1)
expect_true(1 %in% multiColResults$numbers[[1]])
expect_true("1" %in% multiColResults$letters[[1]])
singleColResult <- freqItems(df, "negDoubles", support=0.1)
expect_true(-1 %in% head(singleColResult$negDoubles)[[1]])
l <- lapply(c(0:99), function(i) {
if (i %% 2 == 0) { list(1L, -1.0) }
else { list(i, i * -1.0) }})
df <- createDataFrame(sqlContext, l, c("a", "b"))
result <- freqItems(df, c("a", "b"), 0.4)
expect_identical(result[[1]], list(list(1L, 99L)))
expect_identical(result[[2]], list(list(-1, -99)))
})
test_that("sampleBy() on a DataFrame", {
l <- lapply(c(0:99), function(i) { as.character(i %% 3) })
df <- createDataFrame(sqlContext, l, "key")
fractions <- list("0" = 0.1, "1" = 0.2)
sample <- sampleBy(df, "key", fractions, 0)
result <- collect(orderBy(count(groupBy(sample, "key")), "key"))
expect_identical(as.list(result[1, ]), list(key = "0", count = 3))
expect_identical(as.list(result[2, ]), list(key = "1", count = 7))
})
test_that("SQL error message is returned from JVM", {
retError <- tryCatch(sql(sqlContext, "select * from blah"), error = function(e) e)
expect_equal(grepl("Table not found: blah", retError), TRUE)
})
irisDF <- suppressWarnings(createDataFrame(sqlContext, iris))
test_that("Method as.data.frame as a synonym for collect()", {
expect_equal(as.data.frame(irisDF), collect(irisDF))
irisDF2 <- irisDF[irisDF$Species == "setosa", ]
expect_equal(as.data.frame(irisDF2), collect(irisDF2))
})
test_that("attach() on a DataFrame", {
df <- read.json(sqlContext, jsonPath)
expect_error(age)
attach(df)
expect_is(age, "DataFrame")
expected_age <- data.frame(age = c(NA, 30, 19))
expect_equal(head(age), expected_age)
stat <- summary(age)
expect_equal(collect(stat)[5, "age"], "30")
age <- age$age + 1
expect_is(age, "Column")
rm(age)
stat2 <- summary(age)
expect_equal(collect(stat2)[5, "age"], "30")
detach("df")
stat3 <- summary(df[, "age"])
expect_equal(collect(stat3)[5, "age"], "30")
expect_error(age)
})
test_that("with() on a DataFrame", {
df <- suppressWarnings(createDataFrame(sqlContext, iris))
expect_error(Sepal_Length)
sum1 <- with(df, list(summary(Sepal_Length), summary(Sepal_Width)))
expect_equal(collect(sum1[[1]])[1, "Sepal_Length"], "150")
sum2 <- with(df, distinct(Sepal_Length))
expect_equal(nrow(sum2), 35)
})
test_that("Method coltypes() to get and set R's data types of a DataFrame", {
expect_equal(coltypes(irisDF), c(rep("numeric", 4), "character"))
data <- data.frame(c1=c(1,2,3),
c2=c(T,F,T),
c3=c("2015/01/01 10:00:00", "2015/01/02 10:00:00", "2015/01/03 10:00:00"))
schema <- structType(structField("c1", "byte"),
structField("c3", "boolean"),
structField("c4", "timestamp"))
# Test primitive types
DF <- createDataFrame(sqlContext, data, schema)
expect_equal(coltypes(DF), c("integer", "logical", "POSIXct"))
# Test complex types
x <- createDataFrame(sqlContext, list(list(as.environment(
list("a"="b", "c"="d", "e"="f")))))
expect_equal(coltypes(x), "map<string,string>")
df <- selectExpr(read.json(sqlContext, jsonPath), "name", "(age * 1.21) as age")
expect_equal(dtypes(df), list(c("name", "string"), c("age", "decimal(24,2)")))
df1 <- select(df, cast(df$age, "integer"))
coltypes(df) <- c("character", "integer")
expect_equal(dtypes(df), list(c("name", "string"), c("age", "int")))
value <- collect(df[, 2])[[3, 1]]
expect_equal(value, collect(df1)[[3, 1]])
expect_equal(value, 22)
coltypes(df) <- c(NA, "numeric")
expect_equal(dtypes(df), list(c("name", "string"), c("age", "double")))
expect_error(coltypes(df) <- c("character"),
"Length of type vector should match the number of columns for DataFrame")
expect_error(coltypes(df) <- c("environment", "list"),
"Only atomic type is supported for column types")
})
unlink(parquetPath)
unlink(jsonPath)
unlink(jsonPathNa)
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