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.

Usage

open_dataset(
  sources,
  schema = NULL,
  partitioning = hive_partition(),
  hive_style = NA,
  unify_schemas = NULL,
  format = c("parquet", "arrow", "ipc", "feather", "csv", "tsv", "text", "json"),
  factory_options = list(),
  ...
)

Arguments

sources

One of:

a string path or URI to a directory containing data files
a FileSystem that references a directory containing data files (such as what is returned by s3_bucket())
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.

schema

Schema for the Dataset. If NULL (the default), the schema will be inferred from the data sources.

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.
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 Partitioning or PartitioningFactory, such as returned by hive_partition()
NULL for no partitioning

The default is to autodetect Hive-style partitions unless hive_style = FALSE. See the "Partitioning" section for details. When sources is not a directory path/URI, partitioning is ignored.

hive_style

Logical: should partitioning be interpreted as Hive-style? Default is NA, which means to inspect the file paths for Hive-style partitioning and behave accordingly.

unify_schemas

logical: should all data fragments (files, Datasets) 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 Datasets (because there should be few Datasets in the list and their Schemas are already in memory).

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"
"json", for JSON format datasets Note: only newline-delimited JSON (aka ND-JSON) datasets are currently supported Default is "parquet", unless a delimiter is also specified, in which case it is assumed to be "text".

factory_options

list of optional FileSystemFactoryOptions:

partition_base_dir: string path segment prefix to ignore when discovering partition information with DirectoryPartitioning. Not meaningful (ignored with a warning) for HivePartitioning, nor is it valid when providing a vector of file paths.
exclude_invalid_files: logical: should files that are not valid data files be excluded? Default is FALSE because checking all files up front incurs I/O and thus will be slower, especially on remote filesystems. If false and there are invalid files, there will be an error at scan time. This is the only FileSystemFactoryOption that is valid for both when providing a directory path in which to discover files and when providing a vector of file paths.
selector_ignore_prefixes: character vector of file prefixes to ignore when discovering files in a directory. If invalid files can be excluded by a common filename prefix this way, you can avoid the I/O cost of exclude_invalid_files. Not valid when providing a vector of file paths (but if you're providing the file list, you can filter invalid files yourself).

...

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 (see read_csv_arrow(), read_parquet() and read_feather() on how to specify these).

Value

A Dataset R6 object. Use dplyr methods on it to query the data, or call $NewScan() to construct a query directly.

Partitioning

Data is often split into multiple files and nested in subdirectories based on the value of one or more columns in the data. It may be a column that is commonly referenced in queries, or it may be time-based, for some examples. Data that is divided this way is "partitioned," and the values for those partitioning columns are encoded into the file path segments. These path segments are effectively virtual columns in the dataset, and because their values are known prior to reading the files themselves, we can greatly speed up filtered queries by skipping some files entirely.

Arrow supports reading partition information from file paths in two forms:

"Hive-style", deriving from the Apache Hive project and common to some database systems. Partitions are encoded as "key=value" in path segments, such as "year=2019/month=1/file.parquet". While they may be awkward as file names, they have the advantage of being self-describing.
"Directory" partitioning, which is Hive without the key names, like "2019/01/file.parquet". In order to use these, we need know at least what names to give the virtual columns that come from the path segments.

The default behavior in open_dataset() is to inspect the file paths contained in the provided directory, and if they look like Hive-style, parse them as Hive. If your dataset has Hive-style partitioning in the file paths, you do not need to provide anything in the partitioning argument to open_dataset() to use them. If you do provide a character vector of partition column names, they will be ignored if they match what is detected, and if they don't match, you'll get an error. (If you want to rename partition columns, do that using select() or rename() after opening the dataset.). If you provide a Schema and the names match what is detected, it will use the types defined by the Schema. In the example file path above, you could provide a Schema to specify that "month" should be int8() instead of the int32() it will be parsed as by default.

If your file paths do not appear to be Hive-style, or if you pass hive_style = FALSE, the partitioning argument will be used to create Directory partitioning. A character vector of names is required to create partitions; you may instead provide a Schema to map those names to desired column types, as described above. If neither are provided, no partitioning information will be taken from the file paths.

Examples

# Set up directory for examples
tf <- tempfile()
dir.create(tf)
on.exit(unlink(tf))

write_dataset(mtcars, tf, partitioning = "cyl")

# You can specify a directory containing the files for your dataset and
# open_dataset will scan all files in your directory.
open_dataset(tf)
#> FileSystemDataset with 3 Parquet files
#> 11 columns
#> mpg: double
#> disp: double
#> hp: double
#> drat: double
#> wt: double
#> qsec: double
#> vs: double
#> am: double
#> gear: double
#> carb: double
#> cyl: int32
#> 
#> See $metadata for additional Schema metadata

# You can also supply a vector of paths
open_dataset(c(file.path(tf, "cyl=4/part-0.parquet"), file.path(tf, "cyl=8/part-0.parquet")))
#> FileSystemDataset with 2 Parquet files
#> 10 columns
#> mpg: double
#> disp: double
#> hp: double
#> drat: double
#> wt: double
#> qsec: double
#> vs: double
#> am: double
#> gear: double
#> carb: double
#> 
#> See $metadata for additional Schema metadata

## You must specify the file format if using a format other than parquet.
tf2 <- tempfile()
dir.create(tf2)
on.exit(unlink(tf2))
write_dataset(mtcars, tf2, format = "ipc")
# This line will results in errors when you try to work with the data
if (FALSE) { # \dontrun{
open_dataset(tf2)
} # }
# This line will work
open_dataset(tf2, format = "ipc")
#> FileSystemDataset with 1 Feather file
#> 11 columns
#> mpg: double
#> cyl: double
#> disp: double
#> hp: double
#> drat: double
#> wt: double
#> qsec: double
#> vs: double
#> am: double
#> gear: double
#> carb: double
#> 
#> See $metadata for additional Schema metadata

## 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
tf3_files <- 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)
#> FileSystemDataset with 153 Parquet files
#> 4 columns
#> Ozone: int32
#> Solar.R: int32
#> Wind: double
#> Temp: int32
#> 
#> See $metadata for additional Schema metadata

# Now that partitioning has been specified, your dataset contains columns for Month and Day
open_dataset(tf3, partitioning = c("Month", "Day"))
#> FileSystemDataset with 153 Parquet files
#> 6 columns
#> Ozone: int32
#> Solar.R: int32
#> Wind: double
#> Temp: int32
#> Month: int32
#> Day: int32
#> 
#> See $metadata for additional Schema metadata

# 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()))
#> FileSystemDataset with 153 Parquet files
#> 6 columns
#> Ozone: int32
#> Solar.R: int32
#> Wind: double
#> Temp: int32
#> Month: int8
#> Day: int8
#> 
#> See $metadata for additional Schema metadata

Usage

Arguments

Value

Partitioning

See also

Examples