# # 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("calling sparkRSQL.init returns existing SQL context", { expect_equal(sparkRSQL.init(sc), sqlContext) }) 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") expect_equal(infer_type(list(1L, 2L)), "array") expect_equal(infer_type(listToStruct(list(a = 1L, b = "2"))), "struct") e <- new.env() assign("a", 1L, envir = e) expect_equal(infer_type(e), "map") expect_error(checkType("map"), "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$Species)) mtcarsdf <- createDataFrame(sqlContext, mtcars) expect_equivalent(collect(mtcarsdf), mtcars) bytes <- as.raw(c(1, 2, 3)) df <- createDataFrame(sqlContext, list(list(bytes))) expect_equal(collect(df)[[1]][[1]], bytes) }) test_that("create DataFrame with different data types", { l <- list(a = 1L, b = 2, c = TRUE, d = "ss", e = as.Date("2012-12-13"), f = as.POSIXct("2015-03-15 12:13:14.056")) df <- createDataFrame(sqlContext, list(l)) expect_equal(dtypes(df), list(c("a", "int"), c("b", "double"), c("c", "boolean"), c("d", "string"), c("e", "date"), c("f", "timestamp"))) expect_equal(count(df), 1) expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE)) }) test_that("create DataFrame with complex types", { e <- new.env() assign("n", 3L, envir = e) s <- listToStruct(list(a = "aa", b = 3L)) l <- list(as.list(1:10), list("a", "b"), e, s) df <- createDataFrame(sqlContext, list(l), c("a", "b", "c", "d")) expect_equal(dtypes(df), list(c("a", "array"), c("b", "array"), c("c", "map"), c("d", "struct"))) expect_equal(count(df), 1) ldf <- collect(df) expect_equal(names(ldf), c("a", "b", "c", "d")) expect_equal(ldf[1, 1][[1]], l[[1]]) expect_equal(ldf[1, 2][[1]], l[[2]]) e <- ldf$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$c3, list(list(1.0, 2.0, 3.0), list(4.0, 5.0, 6.0), list (7.0, 8.0, 9.0))) # MapType schema <- structType(structField("name", "string"), structField("info", "map")) df <- read.df(sqlContext, mapTypeJsonPath, "json", schema) expect_equal(dtypes(df), list(c("name", "string"), c("info", "map"))) ldf <- collect(df) expect_equal(nrow(ldf), 3) expect_equal(ncol(ldf), 2) expect_equal(names(ldf), c("name", "info")) expect_equal(ldf$name, c("Bob", "Alice", "David")) bob <- ldf$info[[1]] expect_equal(class(bob), "environment") expect_equal(bob$age, 16) expect_equal(bob$height, 176.5) # StructType df <- read.json(sqlContext, mapTypeJsonPath) expect_equal(dtypes(df), list(c("info", "struct"), c("name", "string"))) ldf <- collect(df) expect_equal(nrow(ldf), 3) expect_equal(ncol(ldf), 2) expect_equal(names(ldf), c("info", "name")) expect_equal(ldf$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 <- suppressWarnings(jsonRDD(sqlContext, rdd)) expect_is(df, "DataFrame") expect_equal(count(df), 3) rdd2 <- flatMap(rdd, function(x) c(x, x)) df <- suppressWarnings(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"))$name, "Bob") dropTempTable(sqlContext, "table1") unlink(jsonPath2) unlink(parquetPath2) }) test_that("tableToDF() returns a new DataFrame", { df <- read.json(sqlContext, jsonPath) registerTempTable(df, "table1") tabledf <- tableToDF(sqlContext, "table1") expect_is(tabledf, "DataFrame") expect_equal(count(tabledf), 3) tabledf2 <- tableToDF(sqlContext, "table1") expect_equal(count(tabledf2), 3) dropTempTable(sqlContext, "table1") }) test_that("toRDD() returns an RRDD", { df <- read.json(sqlContext, jsonPath) testRDD <- toRDD(df) expect_is(testRDD, "RDD") expect_equal(count(testRDD), 3) }) test_that("union on two RDDs created from DataFrames returns an RRDD", { df <- read.json(sqlContext, jsonPath) RDD1 <- toRDD(df) RDD2 <- toRDD(df) unioned <- unionRDD(RDD1, RDD2) expect_is(unioned, "RDD") expect_equal(getSerializedMode(unioned), "byte") expect_equal(collect(unioned)[[2]]$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(), unique() and dropDuplicates() on DataFrames", { lines <- c("{\"name\":\"Michael\"}", "{\"name\":\"Andy\", \"age\":30}", "{\"name\":\"Justin\", \"age\":19}", "{\"name\":\"Justin\", \"age\":19}") jsonPathWithDup <- tempfile(pattern="sparkr-test", fileext=".tmp") writeLines(lines, jsonPathWithDup) df <- read.json(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 dropDuplicates() df <- createDataFrame( sqlContext, list( list(2, 1, 2), list(1, 1, 1), list(1, 2, 1), list(2, 1, 2), list(2, 2, 2), list(2, 2, 1), list(2, 1, 1), list(1, 1, 2), list(1, 2, 2), list(1, 2, 1)), schema = c("key", "value1", "value2")) result <- collect(dropDuplicates(df)) expected <- rbind.data.frame( c(1, 1, 1), c(1, 1, 2), c(1, 2, 1), c(1, 2, 2), c(2, 1, 1), c(2, 1, 2), c(2, 2, 1), c(2, 2, 2)) names(expected) <- c("key", "value1", "value2") expect_equivalent( result[order(result$key, result$value1, result$value2),], expected) result <- collect(dropDuplicates(df, c("key", "value1"))) expected <- rbind.data.frame( c(1, 1, 1), c(1, 2, 1), c(2, 1, 2), c(2, 2, 2)) names(expected) <- c("key", "value1", "value2") expect_equivalent( result[order(result$key, result$value1, result$value2),], expected) result <- collect(dropDuplicates(df, "key")) expected <- rbind.data.frame( c(1, 1, 1), c(2, 1, 2)) names(expected) <- c("key", "value1", "value2") expect_equivalent( result[order(result$key, result$value1, result$value2),], expected) }) test_that("sample on a DataFrame", { df <- read.json(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) # nolint start # Test base::sample is working #expect_equal(length(sample(1:12)), 12) # nolint end }) 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) + hash(c) + exp(c) c4 <- explode(c) + expm1(c) + factorial(c) + first(c) + floor(c) + hex(c) c5 <- hour(c) + initcap(c) + last(c) + last_day(c) + length(c) c6 <- log(c) + (c) + log1p(c) + log2(c) + lower(c) + ltrim(c) + max(c) + md5(c) c7 <- mean(c) + min(c) + month(c) + negate(c) + 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$a, 8, "#")))[1, 1], "###aaads") # nolint expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###") # nolint 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("when(), otherwise() and ifelse() with column 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, lit(1))))[, 1], c(NA, 1)) expect_equal(collect(select(df, otherwise(when(df$a > 1, lit(1)), lit(0))))[, 1], c(0, 1)) expect_equal(collect(select(df, ifelse(df$a > 1 & df$b > 2, lit(0), lit(1))))[, 1], c(1, 0)) }) test_that("group by, agg functions", { df <- read.json(sqlContext, jsonPath) df1 <- agg(df, name = "max", age = "sum") expect_equal(1, count(df1)) df1 <- agg(df, age2 = max(df$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$age)) expect_is(df4, "DataFrame") expect_equal(3, count(df4)) expect_equal(columns(df4), c("name", "sumAge")) df5 <- sum(gd, "age") expect_is(df5, "DataFrame") expect_equal(3, count(df5)) expect_equal(3, count(mean(gd))) expect_equal(3, count(max(gd))) expect_equal(30, collect(max(gd))[2, 2]) expect_equal(1, collect(count(gd))[1, 2]) mockLines2 <- c("{\"name\":\"ID1\", \"value\": \"10\"}", "{\"name\":\"ID1\", \"value\": \"10\"}", "{\"name\":\"ID1\", \"value\": \"22\"}", "{\"name\":\"ID2\", \"value\": \"-3\"}") jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") writeLines(mockLines2, jsonPath2) gd2 <- groupBy(read.json(sqlContext, jsonPath2), "name") df6 <- agg(gd2, value = "sum") df6_local <- collect(df6) expect_equal(42, df6_local[df6_local$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)))) # nolint }) 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("read/write text files", { # Test write.df and read.df df <- read.df(sqlContext, jsonPath, "text") expect_is(df, "DataFrame") expect_equal(colnames(df), c("value")) expect_equal(count(df), 3) textPath <- tempfile(pattern = "textPath", fileext = ".txt") write.df(df, textPath, "text", mode="overwrite") # Test write.text and read.text textPath2 <- tempfile(pattern = "textPath2", fileext = ".txt") write.text(df, textPath2) df2 <- read.text(sqlContext, c(textPath, textPath2)) expect_is(df2, "DataFrame") expect_equal(colnames(df2), c("value")) expect_equal(count(df2), count(df) * 2) unlink(textPath) unlink(textPath2) }) 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) # nolint }) 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$age + 1 expect_is(age, "Column") rm(age) stat2 <- summary(age) expect_equal(collect(stat2)[5, "age"], "30") detach("df") stat3 <- summary(df[, "age"]) expect_equal(collect(stat3)[5, "age"], "30") expect_error(age) }) test_that("with() on a DataFrame", { df <- suppressWarnings(createDataFrame(sqlContext, iris)) expect_error(Sepal_Length) sum1 <- with(df, list(summary(Sepal_Length), summary(Sepal_Width))) expect_equal(collect(sum1[[1]])[1, "Sepal_Length"], "150") sum2 <- with(df, distinct(Sepal_Length)) expect_equal(nrow(sum2), 35) }) test_that("Method coltypes() to get and set R's data types of a DataFrame", { expect_equal(coltypes(irisDF), c(rep("numeric", 4), "character")) data <- data.frame(c1=c(1,2,3), c2=c(T,F,T), c3=c("2015/01/01 10:00:00", "2015/01/02 10:00:00", "2015/01/03 10:00:00")) schema <- structType(structField("c1", "byte"), structField("c3", "boolean"), structField("c4", "timestamp")) # Test primitive types DF <- createDataFrame(sqlContext, data, schema) expect_equal(coltypes(DF), c("integer", "logical", "POSIXct")) # Test complex types x <- createDataFrame(sqlContext, list(list(as.environment( list("a"="b", "c"="d", "e"="f"))))) expect_equal(coltypes(x), "map") df <- selectExpr(read.json(sqlContext, jsonPath), "name", "(age * 1.21) as age") expect_equal(dtypes(df), list(c("name", "string"), c("age", "double"))) df1 <- select(df, cast(df$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") }) test_that("Method str()", { # Structure of Iris iris2 <- iris colnames(iris2) <- c("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width", "Species") iris2$col <- TRUE irisDF2 <- createDataFrame(sqlContext, iris2) out <- capture.output(str(irisDF2)) expect_equal(length(out), 7) expect_equal(out[1], "'DataFrame': 6 variables:") expect_equal(out[2], " $ Sepal_Length: num 5.1 4.9 4.7 4.6 5 5.4") expect_equal(out[3], " $ Sepal_Width : num 3.5 3 3.2 3.1 3.6 3.9") expect_equal(out[4], " $ Petal_Length: num 1.4 1.4 1.3 1.5 1.4 1.7") expect_equal(out[5], " $ Petal_Width : num 0.2 0.2 0.2 0.2 0.2 0.4") expect_equal(out[6], paste0(" $ Species : chr \"setosa\" \"setosa\" \"", "setosa\" \"setosa\" \"setosa\" \"setosa\"")) expect_equal(out[7], " $ col : logi TRUE TRUE TRUE TRUE TRUE TRUE") # A random dataset with many columns. This test is to check str limits # the number of columns. Therefore, it will suffice to check for the # number of returned rows x <- runif(200, 1, 10) df <- data.frame(t(as.matrix(data.frame(x,x,x,x,x,x,x,x,x)))) DF <- createDataFrame(sqlContext, df) out <- capture.output(str(DF)) expect_equal(length(out), 103) # Test utils:::str expect_equal(capture.output(utils:::str(iris)), capture.output(str(iris))) }) unlink(parquetPath) unlink(jsonPath) unlink(jsonPathNa)