README.Rmd

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# duckdbfs

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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. 

## Installation

You can install the development version of duckdbfs from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("cboettig/duckdbfs")
```

## Quickstart

```{r setup}
library(duckdbfs)
library(dplyr)
```

Imagine 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()`.  


```{r example}
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.

```{r}
ds <- open_dataset(urls, unify_schemas = TRUE)
ds
```

Use `filter()`, `select()`, etc from dplyr to subset and process data -- [any method supported by dbpylr](https://dbplyr.tidyverse.org/reference/index.html). Then use `dplyr::collect()` to trigger evaluation and ingest results of the query into R.

## S3-based access

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)

```{r}
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](https://min.io)).  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:

```{r}
efi <- open_dataset("s3://anonymous@neon4cast-scores/parquet/aquatics?endpoint_override=data.ecoforecast.org")
```


## Spatial data

`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](https://github.com/duckdb/duckdb-spatial#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).  

### Generating spatial data from tabular

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`:

```{r}
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)
```

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](https://github.com/duckdb/duckdb-spatial)

### Reading spatial vector files

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.  


```{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`,

```{r}
countries_meta <- st_read_meta(url)
countries_meta
```

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:

```{r}
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. 

```{r}
library(sf)
melbourne <- st_point(c(144.9633, -37.814)) |> st_as_text()

countries |> 
  filter(st_contains(geom, ST_GeomFromText({melbourne})))

```

As before, we use `to_sf()` to read in the query results as a native (in-memory) `sf` object:

```{r}
sf_obj <- countries |> filter(continent == "Africa") |> to_sf() 
plot(sf_obj["name"])
```

## Spatial joins

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:

```{r}
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:

```{r}
countries_meta$proj4
st_read_meta(url_cities)$proj4
```

For instance, we can return all points (cities) within a collection of polygons (all country boundaries in Oceania continent):

```{r}
countries |>
   dplyr::filter(continent == "Oceania") |>
   spatial_join(cities, by = "st_intersects", join="inner") |>
   select(name_long, sovereignt, pop2020) 

```


Possible [spatial joins](https://postgis.net/workshops/postgis-intro/spatial_relationships.html) 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.

## Writing datasets

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:

```{r message=FALSE}
mtcars |> group_by(cyl, gear) |> write_dataset(tempfile())
```


## Local files

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). 

```{r}
write.csv(mtcars, "mtcars.csv", row.names=FALSE)
lazy_cars <- open_dataset("mtcars.csv", format = "csv")
```


```{r include = FALSE}
unlink("mtcars.csv")
```


## Mechanism / motivation

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).


## Advanced notes

This is very similar to the behavior of `arrow::open_dataset()`, with a few exceptions: 

- at this time, `arrow` does not support access over HTTP -- remote sources must be in an S3 or GC-based object store.
- With local file system or S3 paths, `duckdb` can support "globbing" at any point in the path, e.g. `open_dataset(data/*/subdir)`. (Like arrow, `duckdbfs::open_dataset` will assume recursive path discovery on directories). Note that http(s) URLs will always require the full vector since a `ls()` method is not possible.  Even with URLs or vector-based paths, `duckdb` can automatically populate column names given only by hive structure when `hive_style=TRUE` (default).  Note that passing a vector of paths can be significantly faster than globbing with S3 sources where the `ls()` operation is relatively expensive when there are many partitions.


## Performance notes

- In some settings, `duckdbfs::open_dataset` can give substantially better performance (orders of magnitude) than `arrow::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 default `cached_connection()` unless available RAM is not limiting. 
- `unify_schema` is very computationally expensive. Ensuring all files/partitions match schema in advance or processing different files separately can greatly improve performance.