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.
open_dataset(
sources,
schema = NULL,
partitioning = hive_partition(),
hive_style = NA,
unify_schemas = NULL,
format = c("parquet", "arrow", "ipc", "feather", "csv", "tsv", "text"),
...
)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 for the Dataset. If NULL (the default), the schema
will be inferred from the data sources.
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.
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.
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).
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".
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).
A Dataset R6 object. Use dplyr methods on it to query the data,
or call $NewScan() to construct a query directly.
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 partioning 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.
# 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)
#> FileSystemDataset with 3 Parquet files
#> mpg: double
#> disp: double
#> hp: double
#> drat: double
#> wt: double
#> qsec: double
#> vs: double
#> am: double
#> gear: double
#> carb: double
#> cyl: int32
# 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
#> mpg: double
#> disp: double
#> hp: double
#> drat: double
#> wt: double
#> qsec: double
#> vs: double
#> am: double
#> gear: double
#> carb: double
## 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
if (FALSE) {
open_dataset(tf2)
}
# This line will work
open_dataset(tf2, format = "ipc")
#> FileSystemDataset with 3 Feather files
#> mpg: double
#> disp: double
#> hp: double
#> drat: double
#> wt: double
#> qsec: double
#> vs: double
#> am: double
#> gear: double
#> carb: double
#> cyl: int32
## 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
#> 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
#> 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
#> Ozone: int32
#> Solar.R: int32
#> Wind: double
#> Temp: int32
#> Month: int8
#> Day: int8
#>
#> See $metadata for additional Schema metadata