duckdbfs is a simple wrapper around the duckdb package to facilitate
working with the construction of a single lazy table (SQL connection)
from a set of file paths, URLs, or S3 URIs.
You can install the development version of duckdbfs from GitHub with:
# install.packages("devtools")
devtools::install_github("cboettig/duckdbfs")library(duckdbfs)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionImagine we have a collection of URLs to files we want to combine into a
single tibble in R. The files could be parquet or csv, and some files
may have additional columns not present in other files. The combined
data may be very large, potentially bigger than available RAM or slow to
download completely, but we may only want a subset using methods like
dplyr::filter() or dplyr::summarise().
base <- paste0("https://github.com/duckdb/duckdb/raw/main/",
"data/parquet-testing/hive-partitioning/union_by_name/")
f1 <- paste0(base, "x=1/f1.parquet")
f2 <- paste0(base, "x=1/f2.parquet")
f3 <- paste0(base, "x=2/f2.parquet")
urls <- c(f1,f2,f3)We can easily access this data without downloading by passing a vector
of URLs. Note that if schemas (column names) do not match, we must
explicitly request duckdb join the two schemas. Leave this as default,
FALSE when not required to achieve much better performance.
ds <- open_dataset(urls, unify_schemas = TRUE)
ds
#> # Source: table<f1> [3 x 4]
#> # Database: DuckDB v0.10.1 [unknown@Linux 6.6.10-76060610-generic:R 4.3.2/:memory:]
#> i j x k
#> <int> <int> <dbl> <int>
#> 1 42 84 1 NA
#> 2 42 84 1 NA
#> 3 NA 128 2 33Use filter(), select(), etc from dplyr to subset and process data –
any method supported by
dbpylr. Then use
dplyr::collect() to trigger evaluation and ingest results of the query
into R.
We can also access remote data over the S3 protocol. An advantage of S3 is that unlike https, it can discover all files in a given folder, so we don’t have to list them individually. This is particularly convenient for accessing large, partitioned datasets, like GBIF: (nearly 200 GB of data split across more than 2000 parquet files)
parquet <- "s3://gbif-open-data-us-east-1/occurrence/2023-06-01/occurrence.parquet"
duckdb_s3_config()
gbif <- open_dataset(parquet, anonymous = TRUE, s3_region="us-east-1")The additional configuration arguments are passed to the helper function
duckdb_s3_config() to set access credentials and configure other
settings, like alternative endpoints (for use with S3-compliant systems
like minio). Of course it also possible to set these
ahead of time by calling duckdb_s3_config() directly. Many of these
settings can also be passed along more compactly using the URI query
notation found in the arrow package. For instance, we can request
anonymous access to a bucket on an alternative endpoint as:
efi <- open_dataset("s3://anonymous@neon4cast-scores/parquet/aquatics?endpoint_override=data.ecoforecast.org")duckdb can also understand a wide array of spatial data queries for
spatial vector data, similar to operations found in the popular sf
package. See the list of supported
functions
for details. Most spatial query operations require an geometry column
that expresses the simple feature geometry in duckdb’s internal
geometry format (nearly but not exactly WKB).
A common pattern will first generate the geometry column from raw
columns, such as latitude and lognitude columns, using the duckdb
implementation of the a method familiar to postgis, st_point:
spatial_ex <- paste0("https://raw.githubusercontent.com/cboettig/duckdbfs/",
"main/inst/extdata/spatial-test.csv") |>
open_dataset(format = "csv")
spatial_ex |>
mutate(geometry = st_point(longitude, latitude)) |>
mutate(dist = st_distance(geometry, st_point(0,0))) |>
to_sf(crs = 4326)
#> Simple feature collection with 10 features and 4 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 1 ymin: 1 xmax: 10 ymax: 10
#> Geodetic CRS: WGS 84
#> site latitude longitude dist geom
#> 1 a 1 1 1.414214 POINT (1 1)
#> 2 b 2 2 2.828427 POINT (2 2)
#> 3 c 3 3 4.242641 POINT (3 3)
#> 4 d 4 4 5.656854 POINT (4 4)
#> 5 e 5 5 7.071068 POINT (5 5)
#> 6 f 6 6 8.485281 POINT (6 6)
#> 7 g 7 7 9.899495 POINT (7 7)
#> 8 h 8 8 11.313708 POINT (8 8)
#> 9 i 9 9 12.727922 POINT (9 9)
#> 10 j 10 10 14.142136 POINT (10 10)Note that when coercing generic tabular such as CSV into spatial data, the user is responsible for specifying the coordinate reference system (crs) used by the columns. For instance, in this case our data is latitude-longitude, so we specify the corresponding EPSG code. This is optional (sf allows objects to have unknown CRS), but advisable.
Recall that when used against any sort of external database like
duckdb, most dplyr functions like dplyr::mutate() are being
transcribed into SQL by dbplyr, and not actually ever run in R. This
allows us to seamlessly pass along spatial functions like st_point,
despite this not being an available R function. (Also note that SQL is
not case-sensitive, so this function is also written as ST_Point).
Optionally, we can do additional operations on this geometry column,
such as computing distances (st_distance shown here), spatial filters,
and so forth. The to_sf() coercion will parse its input into a SQL
query that gets passed to duckdb, and the return object will be
collected through sf::st_read, returning an (in-memory) sf object.
For more details including a complete list of the dozens of spatial operations currently supported and notes on performance and current limitations, see the duckdb spatial docs
The duckdb spatial package can also use GDAL to read large spatial
vector files. This includes support for remote files. This means that we
can easily subset columns from a wide array of potentially remote file
types and filter on rows and columns, and perform many spatial
operations without ever reading the entire objects into memory in R.
url <- "https://github.com/cboettig/duckdbfs/raw/main/inst/extdata/world.fgb"
countries <- open_dataset(url, format = "sf")Note that open_dataset() always returns a lazy remote table – we have
not yet downloaded the data, let alone read it into R. We simply have a
connection allowing us to stream the data.
We can examine the spatial metadata associated with this remote dataset
using the duckdbfs spatial helper function, st_read_meta,
countries_meta <- st_read_meta(url)
countries_meta
#> # A tibble: 1 × 7
#> feature_count geom_column_name geom_type name code wkt proj4
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 177 geom Multi Polygon EPSG 4326 "GEOGCS[\"WGS … +pro…Because this is a small dataset, we can bring the entire data into R (in
memory) using to_sf(), specifying the CRS indicated in this metadata:
in_mem <- countries |> to_sf(crs = countries_meta$wkt)However, we can also do a wide range of spatial observations without
importing the data. This can be particularly helpful when working with
very large datasets. For example: which country polygon contains
Melbourne?
Note the result is still a lazy read, we haven’t downloaded or read in
the full spatial data object.
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
melbourne <- st_point(c(144.9633, -37.814)) |> st_as_text()
countries |>
filter(st_contains(geom, ST_GeomFromText({melbourne})))
#> # Source: SQL [1 x 16]
#> # Database: DuckDB v0.10.1 [unknown@Linux 6.6.10-76060610-generic:R 4.3.2/:memory:]
#> iso_a3 name sovereignt continent area pop_est pop_est_dens economy
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 AUS Australia Australia Oceania 7682300 21262641 2.77 2. Develo…
#> # ℹ 8 more variables: income_grp <chr>, gdp_cap_est <dbl>, life_exp <dbl>,
#> # well_being <dbl>, footprint <dbl>, inequality <dbl>, HPI <dbl>, geom <list>As before, we use to_sf() to read in the query results as a native
(in-memory) sf object:
sf_obj <- countries |> filter(continent == "Africa") |> to_sf()
plot(sf_obj["name"])
One very common operation are spatial joins, which can be a very powerful way to subset large data. Lets consider a set of point geometries representing the coordinates of major cities around the world:
url_cities <- "https://github.com/cboettig/duckdbfs/raw/spatial-read/inst/extdata/metro.fgb"
cities <- open_dataset(url_cities, format="sf")Note that metadata must be read directly from the source file, it is not embedded into the duckdb table view. Before combining this data with the countries data, we confirm that the CRS is the same for both datasets:
countries_meta$proj4
#> [1] "+proj=longlat +datum=WGS84 +no_defs"
st_read_meta(url_cities)$proj4
#> [1] "+proj=longlat +datum=WGS84 +no_defs"For instance, we can return all points (cities) within a collection of polygons (all country boundaries in Oceania continent):
countries |>
dplyr::filter(continent == "Oceania") |>
spatial_join(cities, by = "st_intersects", join="inner") |>
select(name_long, sovereignt, pop2020)
#> # Source: SQL [6 x 3]
#> # Database: DuckDB v0.10.1 [unknown@Linux 6.6.10-76060610-generic:R 4.3.2/:memory:]
#> name_long sovereignt pop2020
#> <chr> <chr> <dbl>
#> 1 Brisbane Australia 2388517
#> 2 Perth Australia 2036118
#> 3 Sydney Australia 4729406
#> 4 Adelaide Australia 1320783
#> 5 Auckland New Zealand 1426070
#> 6 Melbourne Australia 4500501Possible spatial joins include:
| Function | Description |
|---|---|
| st_intersects | Geometry A intersects with geometry B |
| st_disjoint | The complement of intersects |
| st_within | Geometry A is within geometry B (complement of contains) |
| st_dwithin | Geometries are within a specified distance, expressed in the same units as the coordinate reference system. |
| st_touches | Two polygons touch if the that have at least one point in common, even if their interiors do not touch. |
| st_contains | Geometry A entirely contains to geometry B. (complement of within) |
| st_containsproperly | stricter version of st_contains (boundary counts as external) |
| st_covers | geometry B is inside or on boundary of A. (A polygon covers a point on its boundary but does not contain it.) |
| st_overlaps | geometry A intersects but does not completely contain geometry B |
| st_equals | geometry A is equal to geometry B |
| st_crosses | Lines or points in geometry A cross geometry B. |
Note that while SQL functions are not case-sensitive, spatial_join
expects lower-case names.
Like arrow::write_dataset(), duckdbfs::write_dataset() can write
partitioned parquet files to local disks and also directly to an S3
bucket. Partitioned writes should take advantage of threading. Partition
variables can be specified explicitly, or any dplyr grouping variables
will be used by default:
mtcars |> group_by(cyl, gear) |> write_dataset(tempfile())Of course, open_dataset() and write_dataset() also be used with
local files. Remember that parquet format is not required, we can read
csv files (including multiple and hive-partitioned csv files).
write.csv(mtcars, "mtcars.csv", row.names=FALSE)
lazy_cars <- open_dataset("mtcars.csv", format = "csv")This package simply creates a duckdb connection, ensures the httpfs
and spatial extensions are installed if necessary, sets the S3
configuration, and then constructs a VIEW using duckdb’s
parquet_scan() or read_csv_auto() methods and associated options. It
then returns a dplyr::tbl() for the resulting view. Though
straightforward, this process is substantially more verbose than the
analogous single function call provided by arrow::open_dataset() due
mostly to the necessary string manipulation to construct the VIEW as a
SQL statement. I’ve used this pattern a lot, especially when arrow is
not an option (http data) or has substantially worse performance (many
S3 URIs).
This is very similar to the behavior of arrow::open_dataset(), with a
few exceptions:
- at this time,
arrowdoes not support access over HTTP – remote sources must be in an S3 or GC-based object store. - With local file system or S3 paths,
duckdbcan support “globbing” at any point in the path, e.g.open_dataset(data/*/subdir). (Like arrow,duckdbfs::open_datasetwill assume recursive path discovery on directories). Note that http(s) URLs will always require the full vector since als()method is not possible. Even with URLs or vector-based paths,duckdbcan automatically populate column names given only by hive structure whenhive_style=TRUE(default). Note that passing a vector of paths can be significantly faster than globbing with S3 sources where thels()operation is relatively expensive when there are many partitions.
- In some settings,
duckdbfs::open_datasetcan give substantially better performance (orders of magnitude) thanarrow::open_dataset(), while in other settings it may be comparable or even slower. Package versions, system libraries, network architecture, remote storage performance, network traffic, and other factors can all influence performance, making precise benchmark comparisons in real-world contexts difficult. - On slow network connections or when accessing a remote table
repeatedly, it may improve performance to create a local copy of the
table rather than perform all operations over the network. The
simplest way to do this is by setting the
mode = "TABLE"instead of “VIEW” on open dataset. It is probably desirable to pass a duckdb connection backed by persistent disk location in this case instead of the defaultcached_connection()unless available RAM is not limiting. unify_schemais very computationally expensive. Ensuring all files/partitions match schema in advance or processing different files separately can greatly improve performance.