| # Licensed to the Apache Software Foundation (ASF) under one |
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| # 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. |
| |
| #' Open a multi-file dataset |
| #' |
| #' Arrow Datasets allow you to query against data that has been split across |
| #' multiple files. This sharding of data may indicate partitioning, which |
| #' can accelerate queries that only touch some partitions (files). Call |
| #' `open_dataset()` to point to a directory of data files and return a |
| #' `Dataset`, then use `dplyr` methods to query it. |
| #' |
| #' @param sources One of: |
| #' * a string path or URI to a directory containing data files |
| #' * a string path or URI to a single file |
| #' * a character vector of paths or URIs to individual data files |
| #' * a list of `Dataset` objects as created by this function |
| #' * a list of `DatasetFactory` objects as created by [dataset_factory()]. |
| #' |
| #' When `sources` is a vector of file URIs, they must all use the same protocol |
| #' and point to files located in the same file system and having the same |
| #' format. |
| #' @param schema [Schema] for the `Dataset`. If `NULL` (the default), the schema |
| #' will be inferred from the data sources. |
| #' @param partitioning When `sources` is a directory path/URI, one of: |
| #' * a `Schema`, in which case the file paths relative to `sources` will be |
| #' parsed, and path segments will be matched with the schema fields. For |
| #' example, `schema(year = int16(), month = int8())` would create partitions |
| #' for file paths like `"2019/01/file.parquet"`, `"2019/02/file.parquet"`, |
| #' etc. |
| #' * a character vector that defines the field names corresponding to those |
| #' path segments (that is, you're providing the names that would correspond |
| #' to a `Schema` but the types will be autodetected) |
| #' * a `HivePartitioning` or `HivePartitioningFactory`, as returned |
| #' by [hive_partition()] which parses explicit or autodetected fields from |
| #' Hive-style path segments |
| #' * `NULL` for no partitioning |
| #' |
| #' The default is to autodetect Hive-style partitions. When `sources` is not a |
| #' directory path/URI, `partitioning` is ignored. |
| #' @param unify_schemas logical: should all data fragments (files, `Dataset`s) |
| #' be scanned in order to create a unified schema from them? If `FALSE`, only |
| #' the first fragment will be inspected for its schema. Use this fast path |
| #' when you know and trust that all fragments have an identical schema. |
| #' The default is `FALSE` when creating a dataset from a directory path/URI or |
| #' vector of file paths/URIs (because there may be many files and scanning may |
| #' be slow) but `TRUE` when `sources` is a list of `Dataset`s (because there |
| #' should be few `Dataset`s in the list and their `Schema`s are already in |
| #' memory). |
| #' @param format A [FileFormat] object, or a string identifier of the format of |
| #' the files in `x`. This argument is ignored when `sources` is a list of `Dataset` objects. |
| #' Currently supported values: |
| #' * "parquet" |
| #' * "ipc"/"arrow"/"feather", all aliases for each other; for Feather, note that |
| #' only version 2 files are supported |
| #' * "csv"/"text", aliases for the same thing (because comma is the default |
| #' delimiter for text files |
| #' * "tsv", equivalent to passing `format = "text", delimiter = "\t"` |
| #' |
| #' Default is "parquet", unless a `delimiter` is also specified, in which case |
| #' it is assumed to be "text". |
| #' @param ... additional arguments passed to `dataset_factory()` when `sources` |
| #' is a directory path/URI or vector of file paths/URIs, otherwise ignored. |
| #' These may include `format` to indicate the file format, or other |
| #' format-specific options. |
| #' @return A [Dataset] R6 object. Use `dplyr` methods on it to query the data, |
| #' or call [`$NewScan()`][Scanner] to construct a query directly. |
| #' @export |
| #' @seealso `vignette("dataset", package = "arrow")` |
| #' @include arrow-package.R |
| #' @examplesIf arrow_with_dataset() & arrow_with_parquet() |
| #' # Set up directory for examples |
| #' tf <- tempfile() |
| #' dir.create(tf) |
| #' on.exit(unlink(tf)) |
| #' |
| #' data <- dplyr::group_by(mtcars, cyl) |
| #' write_dataset(data, tf) |
| #' |
| #' # You can specify a directory containing the files for your dataset and |
| #' # open_dataset will scan all files in your directory. |
| #' open_dataset(tf) |
| #' |
| #' # You can also supply a vector of paths |
| #' open_dataset(c(file.path(tf, "cyl=4/part-1.parquet"), file.path(tf,"cyl=8/part-2.parquet"))) |
| #' |
| #' ## You must specify the file format if using a format other than parquet. |
| #' tf2 <- tempfile() |
| #' dir.create(tf2) |
| #' on.exit(unlink(tf2)) |
| #' write_dataset(data, tf2, format = "ipc") |
| #' # This line will results in errors when you try to work with the data |
| #' \dontrun{open_dataset(tf2)} |
| #' # This line will work |
| #' open_dataset(tf2, format = "ipc") |
| #' |
| #' ## You can specify file partitioning to include it as a field in your dataset |
| #' # Create a temporary directory and write example dataset |
| #' tf3 <- tempfile() |
| #' dir.create(tf3) |
| #' on.exit(unlink(tf3)) |
| #' write_dataset(airquality, tf3, partitioning = c("Month", "Day"), hive_style = FALSE) |
| #' |
| #' # View files - you can see the partitioning means that files have been written |
| #' # to folders based on Month/Day values |
| #' list.files(tf3, recursive = TRUE) |
| #' |
| #' # With no partitioning specified, dataset contains all files but doesn't include |
| #' # directory names as field names |
| #' open_dataset(tf3) |
| #' |
| #' # Now that partitioning has been specified, your dataset contains columns for Month and Day |
| #' open_dataset(tf3, partitioning = c("Month", "Day")) |
| #' |
| #' # If you want to specify the data types for your fields, you can pass in a Schema |
| #' open_dataset(tf3, partitioning = schema(Month = int8(), Day = int8())) |
| open_dataset <- function(sources, |
| schema = NULL, |
| partitioning = hive_partition(), |
| unify_schemas = NULL, |
| format = c("parquet", "arrow", "ipc", "feather", "csv", "tsv", "text"), |
| ...) { |
| if (is_list_of(sources, "Dataset")) { |
| if (is.null(schema)) { |
| if (is.null(unify_schemas) || isTRUE(unify_schemas)) { |
| # Default is to unify schemas here |
| schema <- unify_schemas(schemas = map(sources, ~.$schema)) |
| } else { |
| # Take the first one. |
| schema <- sources[[1]]$schema |
| } |
| } |
| # Enforce that all datasets have the same schema |
| assert_is(schema, "Schema") |
| sources <- lapply(sources, function(x) { |
| x$schema <- schema |
| x |
| }) |
| return(dataset___UnionDataset__create(sources, schema)) |
| } |
| |
| factory <- DatasetFactory$create(sources, partitioning = partitioning, format = format, ...) |
| tryCatch( |
| # Default is _not_ to inspect/unify schemas |
| factory$Finish(schema, isTRUE(unify_schemas)), |
| error = function(e){ |
| handle_parquet_io_error(e, format) |
| } |
| ) |
| } |
| |
| #' Multi-file datasets |
| #' |
| #' @description |
| #' Arrow Datasets allow you to query against data that has been split across |
| #' multiple files. This sharding of data may indicate partitioning, which |
| #' can accelerate queries that only touch some partitions (files). |
| #' |
| #' A `Dataset` contains one or more `Fragments`, such as files, of potentially |
| #' differing type and partitioning. |
| #' |
| #' For `Dataset$create()`, see [open_dataset()], which is an alias for it. |
| #' |
| #' `DatasetFactory` is used to provide finer control over the creation of `Dataset`s. |
| #' |
| #' @section Factory: |
| #' `DatasetFactory` is used to create a `Dataset`, inspect the [Schema] of the |
| #' fragments contained in it, and declare a partitioning. |
| #' `FileSystemDatasetFactory` is a subclass of `DatasetFactory` for |
| #' discovering files in the local file system, the only currently supported |
| #' file system. |
| #' |
| #' For the `DatasetFactory$create()` factory method, see [dataset_factory()], an |
| #' alias for it. A `DatasetFactory` has: |
| #' |
| #' - `$Inspect(unify_schemas)`: If `unify_schemas` is `TRUE`, all fragments |
| #' will be scanned and a unified [Schema] will be created from them; if `FALSE` |
| #' (default), only the first fragment will be inspected for its schema. Use this |
| #' fast path when you know and trust that all fragments have an identical schema. |
| #' - `$Finish(schema, unify_schemas)`: Returns a `Dataset`. If `schema` is provided, |
| #' it will be used for the `Dataset`; if omitted, a `Schema` will be created from |
| #' inspecting the fragments (files) in the dataset, following `unify_schemas` |
| #' as described above. |
| #' |
| #' `FileSystemDatasetFactory$create()` is a lower-level factory method and |
| #' takes the following arguments: |
| #' * `filesystem`: A [FileSystem] |
| #' * `selector`: Either a [FileSelector] or `NULL` |
| #' * `paths`: Either a character vector of file paths or `NULL` |
| #' * `format`: A [FileFormat] |
| #' * `partitioning`: Either `Partitioning`, `PartitioningFactory`, or `NULL` |
| #' @section Methods: |
| #' |
| #' A `Dataset` has the following methods: |
| #' - `$NewScan()`: Returns a [ScannerBuilder] for building a query |
| #' - `$schema`: Active binding that returns the [Schema] of the Dataset; you |
| #' may also replace the dataset's schema by using `ds$schema <- new_schema`. |
| #' This method currently supports only adding, removing, or reordering |
| #' fields in the schema: you cannot alter or cast the field types. |
| #' |
| #' `FileSystemDataset` has the following methods: |
| #' - `$files`: Active binding, returns the files of the `FileSystemDataset` |
| #' - `$format`: Active binding, returns the [FileFormat] of the `FileSystemDataset` |
| #' |
| #' `UnionDataset` has the following methods: |
| #' - `$children`: Active binding, returns all child `Dataset`s. |
| #' |
| #' @export |
| #' @seealso [open_dataset()] for a simple interface to creating a `Dataset` |
| Dataset <- R6Class("Dataset", inherit = ArrowObject, |
| public = list( |
| # @description |
| # Start a new scan of the data |
| # @return A [ScannerBuilder] |
| NewScan = function() dataset___Dataset__NewScan(self), |
| ToString = function() self$schema$ToString() |
| ), |
| active = list( |
| schema = function(schema) { |
| if (missing(schema)) { |
| dataset___Dataset__schema(self) |
| } else { |
| assert_is(schema, "Schema") |
| invisible(dataset___Dataset__ReplaceSchema(self, schema)) |
| } |
| }, |
| metadata = function() self$schema$metadata, |
| num_rows = function() self$NewScan()$Finish()$CountRows(), |
| num_cols = function() length(self$schema), |
| # @description |
| # Return the Dataset's type. |
| type = function() dataset___Dataset__type_name(self) |
| ) |
| ) |
| Dataset$create <- open_dataset |
| |
| #' @name FileSystemDataset |
| #' @rdname Dataset |
| #' @export |
| FileSystemDataset <- R6Class("FileSystemDataset", inherit = Dataset, |
| public = list( |
| .class_title = function() { |
| nfiles <- length(self$files) |
| file_type <- self$format$type |
| pretty_file_type <- list( |
| parquet = "Parquet", |
| ipc = "Feather" |
| )[[file_type]] |
| |
| paste( |
| class(self)[[1]], |
| "with", |
| nfiles, |
| pretty_file_type %||% file_type, |
| ifelse(nfiles == 1, "file", "files") |
| ) |
| } |
| ), |
| active = list( |
| # @description |
| # Return the files contained in this `FileSystemDataset` |
| files = function() dataset___FileSystemDataset__files(self), |
| # @description |
| # Return the format of files in this `Dataset` |
| format = function() { |
| dataset___FileSystemDataset__format(self) |
| }, |
| # @description |
| # Return the filesystem of files in this `Dataset` |
| filesystem = function() { |
| dataset___FileSystemDataset__filesystem(self) |
| } |
| ) |
| ) |
| |
| #' @name UnionDataset |
| #' @rdname Dataset |
| #' @export |
| UnionDataset <- R6Class("UnionDataset", inherit = Dataset, |
| active = list( |
| # @description |
| # Return the UnionDataset's child `Dataset`s |
| children = function() { |
| dataset___UnionDataset__children(self) |
| } |
| ) |
| ) |
| |
| #' @name InMemoryDataset |
| #' @rdname Dataset |
| #' @export |
| InMemoryDataset <- R6Class("InMemoryDataset", inherit = Dataset) |
| InMemoryDataset$create <- function(x) { |
| if (!inherits(x, "Table")) { |
| x <- Table$create(x) |
| } |
| dataset___InMemoryDataset__create(x) |
| } |
| |
| |
| #' @export |
| names.Dataset <- function(x) names(x$schema) |
| |
| #' @export |
| dim.Dataset <- function(x) c(x$num_rows, x$num_cols) |
| |
| #' @export |
| c.Dataset <- function(...) Dataset$create(list(...)) |
| |
| #' @export |
| head.Dataset <- function(x, n = 6L, ...) { |
| assert_that(n > 0) # For now |
| scanner <- Scanner$create(ensure_group_vars(x)) |
| dataset___Scanner__head(scanner, n) |
| } |
| |
| #' @export |
| tail.Dataset <- function(x, n = 6L, ...) { |
| assert_that(n > 0) # For now |
| result <- list() |
| batch_num <- 0 |
| scanner <- Scanner$create(ensure_group_vars(x)) |
| for (batch in rev(dataset___Scanner__ScanBatches(scanner))) { |
| batch_num <- batch_num + 1 |
| result[[batch_num]] <- tail(batch, n) |
| n <- n - nrow(batch) |
| if (n <= 0) break |
| } |
| Table$create(!!!rev(result)) |
| } |
| |
| #' @export |
| `[.Dataset` <- function(x, i, j, ..., drop = FALSE) { |
| if (nargs() == 2L) { |
| # List-like column extraction (x[i]) |
| return(x[, i]) |
| } |
| if (!missing(j)) { |
| x <- select.Dataset(x, j) |
| } |
| |
| if (!missing(i)) { |
| x <- take_dataset_rows(x, i) |
| } |
| x |
| } |
| |
| take_dataset_rows <- function(x, i) { |
| if (!is.numeric(i) || any(i < 0)) { |
| stop("Only slicing with positive indices is supported", call. = FALSE) |
| } |
| scanner <- Scanner$create(ensure_group_vars(x)) |
| i <- Array$create(i - 1) |
| dataset___Scanner__TakeRows(scanner, i) |
| } |