Intro to gbifdbSource:
The goal of
gbifdb is to provide a relational database interface to a
parquet based serializations of
gbif data. Instead of requiring custom functions for filtering and selecting data from the central GBIF server (as in
gbifdb users can take advantage of the full array of
tidyr functions which can be automatically translated to SQL by
dbplyr. Users already familiar with SQL can construct SQL queries directly with
gbifdb sends these queries to
duckdb, a high-performance, columnar-oriented database engine which runs entirely inside the client, (unlike server-client databases such as MySQL or Postgres, no additional setup is needed outside of installing
duckdb is able to execute these SQL queries directly on-disk against the Parquet data files, side-stepping limitations of available RAM or the need to import the data. It’s highly optimized implementation can be faster even than in-memory operations in
duckdb supports the full set of SQL instructions, including windowed operations like
summarise as well as table joins.
gbifdb has two mechanisms for providing database connections: one which the Parquet snapshot of GBIF must first be downloaded locally, and a second where the GBIF parquet snapshot can be accessed directly from an Amazon Public Data Registry S3 bucket without downloading a copy. The latter approach will be faster for one-off operations and is also suitable when using a cloud-based computing provider in the same region.
gbifdb currently requires the dev version of
duckdb, which you can install using:
install.packages("https://github.com/duckdb/duckdb/releases/download/master-builds/duckdb_r_src.tar.gz", repos = NULL)
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("cboettig/gbifdb")
gbifdb has few dependencies: only
DBI are required.
To begin working with GBIF data directly without downloading the data first, simply establish a remote connection using
gbif <- gbif_remote()
We can now perform most
By default, this relies on an
arrow connection, which currently lacks support for some more complex windowed operations in
dplyr. A user can specify the option
to_duckdb = TRUE in
gbif_remote() (or simply pass the connection to
arrow::to_duckdb()) to create a
duckdb connection. This is slightly slower at this time. Keep in mind that as with any database connection, to use non-
dplyr functions the user will generally need to call
dplyr::collect(), which pulls the data into working memory.
Be sure to subset the data appropriately first (e.g. with
summarise, etc), as attempting to
collect() a large table will probably exceed available RAM and crash your R session!
When using a
gbif_remote() connection, all I/O operations will be conducted over the network storage instead of your local disk, without downloading the full dataset first. These operations will be considerably slower than they would be if you download the entire dataset first (see below, unless you are on an AWS cloud instance in the same region as the remote host), but this does avoid the download step all-together, which may be necessary if you do not have 100+ GB free storage space or the time to download the whole dataset first (e.g. for one-off queries).
For extended analysis of GBIF, users may prefer to download the entire GBIF parquet data first. This requires over 100 GB free disk space, and will be a time-consuming process the first time. However, once downloaded, future queries will run much much faster, particularly if you are network-limited. Users can download the current release of GBIF to local storage like so:
By default, this will download to the dir given by
An alternative directory can be set as an argument, or by setting the path in the environmental variable,
Once you have downloaded the parquet-formatted GBIF data, simply point
gbif_conn() at the directory containing your parquet files to initialize a connection. (By default,
gbif_conn() will look for data in the configurable directory given by
conn <- gbif_conn()
The resulting connection can be used with
dplyr::tbl() to access the full gbif data:
gbif <- tbl(conn, "gbif") gbif #> # Source: table<gbif> [?? x 50] #> # Database: duckdb_connection #> gbifid datasetkey occurrenceid kingdom phylum class order family genus #> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 1851456555 b234abf6-4… 03871B11FFA43… Animal… Echin… Holo… Dend… Cucum… Acti… #> 2 1851456618 b234abf6-4… 03871B11FFAA3… Animal… Echin… Holo… Dend… Scler… Clad… #> 3 1851456554 b234abf6-4… 03871B11FFA53… Animal… Echin… Holo… Dend… Cucum… Acti… #> 4 1851456620 b234abf6-4… 03871B11FFA03… Animal… Echin… Holo… Dend… Phyll… Tria… #> 5 1851456556 b234abf6-4… 03871B11FFA83… Animal… Echin… Holo… Dend… Scler… Glob… #> 6 1851456619 b234abf6-4… 03871B11FFA33… Animal… Echin… Holo… Dend… Phyll… Mass… #> 7 1851456623 b234abf6-4… 03871B11FFA03… Animal… Echin… Holo… Dend… Phyll… Tria… #> 8 1851456714 b234abf6-4… 03871B11FFAC3… Animal… Echin… Holo… Dend… Phyll… Mass… #> 9 1851456553 b234abf6-4… 03871B11FFAA3… Animal… Echin… Holo… Dend… Scler… Clad… #> 10 1851456622 b234abf6-4… 03871B11FFA53… Animal… Echin… Holo… Dend… Cucum… Acti… #> # … with more rows, and 41 more variables: species <chr>, #> # infraspecificepithet <chr>, taxonrank <chr>, scientificname <chr>, #> # verbatimscientificname <chr>, verbatimscientificnameauthorship <chr>, #> # countrycode <chr>, locality <chr>, stateprovince <chr>, #> # occurrencestatus <chr>, individualcount <int>, publishingorgkey <chr>, #> # decimallatitude <dbl>, decimallongitude <dbl>, #> # coordinateuncertaintyinmeters <dbl>, coordinateprecision <dbl>, …
colnames(gbif) #>  "gbifid" "datasetkey" #>  "occurrenceid" "kingdom" #>  "phylum" "class" #>  "order" "family" #>  "genus" "species" #>  "infraspecificepithet" "taxonrank" #>  "scientificname" "verbatimscientificname" #>  "verbatimscientificnameauthorship" "countrycode" #>  "locality" "stateprovince" #>  "occurrencestatus" "individualcount" #>  "publishingorgkey" "decimallatitude" #>  "decimallongitude" "coordinateuncertaintyinmeters" #>  "coordinateprecision" "elevation" #>  "elevationaccuracy" "depth" #>  "depthaccuracy" "eventdate" #>  "day" "month" #>  "year" "taxonkey" #>  "specieskey" "basisofrecord" #>  "institutioncode" "collectioncode" #>  "catalognumber" "recordnumber" #>  "identifiedby" "dateidentified" #>  "license" "rightsholder" #>  "recordedby" "typestatus" #>  "establishmentmeans" "lastinterpreted" #>  "mediatype" "issue"
Now, we can use
dplyr to perform standard queries:
growth <- gbif %>% filter(phylum == "Chordata", year > 1990) %>% count(class, year) %>% arrange(year) growth #> # Source: lazy query [?? x 3] #> # Database: duckdb_connection #> # Groups: class #> # Ordered by: year #> class year n #> <chr> <int> <dbl> #> 1 Aves 1991 3183184 #> 2 Mammalia 1991 100931 #> 3 Actinopterygii 1991 363791 #> 4 Amphibia 1991 18443 #> 5 Reptilia 1991 29806 #> 6 Elasmobranchii 1991 17521 #> 7 Holocephali 1991 1048 #> 8 Cephalaspidomorphi 1991 1152 #> 9 Thaliacea 1991 669 #> 10 Sarcopterygii 1991 13 #> # … with more rows
Recall that when database connections in
dplyr, the data remains in the database (i.e. on disk, not in working RAM).
This is fine for any further operations using
tidyr functions which can be translated into SQL.
Using such functions we can usually reduce our resulting table to something much smaller, which can then be pulled into memory in R for further analysis using