One of the primary advantages of piggyback is the
ability to store a lot of fairly large files. This is also potentially
the source of some frustrations: piggyback assets may potentially be
quite large (too large to fit in RAM) and difficult to work with once
they have been uploaded to the release.
There are a substantial and rapidly growing number of packages that
are able to work with data on-disk without reading the whole thing into
memory, including terra, stars, and sf for
large spatial assets, as well as arrow and duckdb for
tabular data.
Going a step further, such libraries now also make it possible to not only skip the ‘read twice’ pattern of downloading once to disk and reading to disk, but can let you skip ever reading the whole data file into R at all - for instance, spatial packages can use GDAL’s virtual file system.
arrow and duckdb can do similar tricks on
parquet and csv files, allowing users to leverage functions like
dplyr::select() and dplyr::filter() directly
on the remote data source to access only the subset of rows/columns they
need. Subsetting data directly from a URL in this manner thus has the
performance benefit of reading directly into memory while also having
the added benefit of allowing more efficient and bigger-than-RAM
workflows. This is sometimes referred to as
cloud-native workflows.
This vignette shows some examples of using duckdb for
querying larger datasets, using example data from the nflverse project for
NFL football analytics. (Consult the nflverse’s nflreadr package if looking to
work with NFL data beyond this example)
The nflverse/nflverse-data data repository is organized into one release for a specific dataframe and typically sharded into multiple files (and file formats) by season. Here’s a brief glimpse at how this looks under the piggyback lens:
pb_releases("nflverse/nflverse-data")
#> # A data.frame: 20 × 10
#> release_name release_id release_body tag_name draft created_at published_at
#> <chr> <int> <chr> <chr> <lgl> <chr> <chr>
#> 1 rosters 58152863 "Roster data, acce… rosters FALSE 2022-01-2… 2022-01-28T…
#> 2 player_stats 58152881 "Play by play data… player_… FALSE 2022-01-2… 2022-01-28T…
#> 3 pbp 58152862 "Play by play data… pbp FALSE 2022-01-2… 2022-01-28T…
#> 4 pfr_advstats 58152981 "PFR Adv Stats dat… pfr_adv… FALSE 2022-01-2… 2022-01-28T…
#> 5 depth_charts 58152948 "Depth chart data,… depth_c… FALSE 2022-01-2… 2022-01-28T…
#> # ℹ 15 more rows
#> # ℹ 3 more variables: html_url <chr>, upload_url <chr>, n_assets <int>
#> # ℹ Use `print(n = ...)` to see more rows
pb_list(repo = "nflverse/nflverse-data", tag = "pbp")
#> # A data.frame: 148 × 6
#> file_name size timestamp tag owner repo
#> <chr> <int> <dttm> <chr> <chr> <chr>
#> 1 play_by_play_2023.rds 12308832 2023-12-26 17:10:52 pbp nflv… nflv…
#> 2 play_by_play_2023.parquet 17469950 2023-12-26 17:11:02 pbp nflv… nflv…
#> 3 play_by_play_2023.csv 84490319 2023-12-26 17:10:58 pbp nflv… nflv…
#> 4 play_by_play_2022.rds 14387514 2023-02-28 09:25:26 pbp nflv… nflv…
#> 5 play_by_play_2022.parquet 20003378 2023-02-28 09:25:35 pbp nflv… nflv…
#> 6 play_by_play_2022.csv 97205016 2023-02-28 09:25:31 pbp nflv… nflv…
#> # ℹ 143 more rows
#> # ℹ Use `print(n = ...)` to see more rows
pb_download_url(
"play_by_play_2023.csv",
repo = "nflverse/nflverse-data",
tag = "pbp"
) |>
read.csv() |>
dplyr::glimpse()
#> Rows: 42,066
#> Columns: 372
#> $ play_id <int> 1, 39, 55, 77, 102, 124, 147…
#> $ game_id <chr> "2023_01_ARI_WAS", "2023_01_…
#> $ home_team <chr> "WAS", "WAS", "WAS", "WAS", …
#> $ away_team <chr> "ARI", "ARI", "ARI", "ARI", …
#> $ season_type <chr> "REG", "REG", "REG", "REG", …
#> $ week <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ posteam <chr> "", "WAS", "WAS", "WAS", "WA…
#> $ posteam_type <chr> "", "home", "home", "home", …
#> $ defteam <chr> "", "ARI", "ARI", "ARI", "AR…
#> $ yardline_100 <int> NA, 35, 75, 72, 66, 64, 64, …
#> $ down <int> NA, NA, 1, 2, 3, 1, 2, 1, 2,…
#> $ play_type <chr> "", "kickoff", "run", "pass"…We’ll look at the play by play release data and try to calculate some summary statistics, without downloading it or reading it all into RAM…
Packages used in this section:
First, initialize duckdb and install/load httpfs (short
for http file system)
Next, we’ll need to get all of the relevant play-by-play URLs from
the release - we can do this with pb_download_url - and
pass it into duckdb’s read_parquet
function
tictoc::tic()
pbp_urls <- pb_download_url(repo = "nflverse/nflverse-data", tag = "pbp")
# keep only the ones matching the desired regex pattern, "play_by_play_####.parquet"
pbp_urls <- pbp_urls[grepl("play_by_play_\\d+.parquet", pbp_urls)]
query <- glue::glue_sql("SELECT COUNT(*) as row_count FROM read_parquet([{pbp_urls *}])", .con = conn)
DBI::dbGetQuery(conn = conn, query)
#> row_count
#> 1 1190783
tictoc::toc()
#> 2.845 sec elapsedNow, we can construct a SQL query that summarizes the data:
tictoc::tic()
query <- glue::glue_sql(
"
SELECT
season,
posteam,
play_type,
COUNT(play_id) AS n_plays,
AVG(epa) AS epa_per_play
FROM read_parquet([{pbp_urls *}], filename = true)
WHERE filename SIMILAR TO '.*(2021|2022|2023).*'
AND (pass = 1 OR rush = 1)
GROUP BY season, posteam, play_type
ORDER BY season DESC, posteam ASC, n_plays DESC
",
.con = conn
)
DBI::dbGetQuery(conn = conn, query)
#> # A data.frame: 288 × 5
#> season posteam play_type n_plays epa_per_play
#> <int> <chr> <chr> <dbl> <dbl>
#> 1 2023 ARI pass 539 -0.231
#> 2 2023 ARI run 391 0.0351
#> 3 2023 ARI no_play 48 0.191
#> 4 2023 ATL pass 499 -0.0738
#> 5 2023 ATL run 465 -0.103
#> # ℹ 283 more rows
#> # ℹ Use `print(n = ...)` to see more rows
tictoc::toc()
#> 3.343 sec elapsedYou can also turn this into a view and query it with dbplyr/dplyr instead:
query <- glue::glue_sql(
"
CREATE VIEW pbp AS
SELECT
*
FROM read_parquet([{pbp_urls *}], filename = true)
",
.con = conn
)
DBI::dbExecute(conn, query)
pbp <- dplyr::tbl(conn, "pbp")
tictoc::tic()
pbp |>
dplyr::filter(grepl("2021|2022|2023", filename), pass == 1 | rush == 1) |>
dplyr::summarise(
n_plays = dplyr::n(),
epa_per_play = mean(epa, na.rm = TRUE),
.by = c(season, posteam, play_type)
) |>
dplyr::arrange(
desc(season), posteam, desc(n_plays)
) |>
dplyr::collect()
#> # A tibble: 288 × 5
#> season posteam play_type n_plays epa_per_play
#> <int> <chr> <chr> <dbl> <dbl>
#> 1 2023 ARI pass 539 -0.231
#> 2 2023 ARI run 391 0.0351
#> 3 2023 ARI no_play 48 0.191
#> 4 2023 ATL pass 499 -0.0738
#> 5 2023 ATL run 465 -0.103
#> # ℹ 283 more rows
#> # ℹ Use `print(n = ...)` to see more rows
tictoc::toc()
#> 3.491 sec elapsedUsing duckdb certainly adds a little verbosity - in exchange, we’ve managed to query and summarize the 20+ parquet files summing 1M+ rows without having to load it all into memory!
duckdbfs was developed to wrap this latter workflow into a single function call that accepts a vector of URLs:
library(duckdbfs)
pbp <- duckdbfs::open_dataset(pbp_urls, filename = TRUE)
tictoc::tic()
pbp |>
dplyr::filter(grepl("2021|2022|2023", filename), pass == 1 | rush == 1) |>
dplyr::summarise(
n_plays = dplyr::n(),
epa_per_play = mean(epa, na.rm = TRUE),
.by = c(season, posteam, play_type)
) |>
dplyr::arrange(
desc(season), posteam, desc(n_plays)
) |>
dplyr::collect()
#> # A tibble: 288 × 5
#> season posteam play_type n_plays epa_per_play
#> <int> <chr> <chr> <dbl> <dbl>
#> 1 2023 ARI pass 539 -0.231
#> 2 2023 ARI run 391 0.0351
#> 3 2023 ARI no_play 48 0.191
#> 4 2023 ATL pass 499 -0.0738
#> 5 2023 ATL run 465 -0.103
#> # ℹ 283 more rows
#> # ℹ Use `print(n = ...)` to see more rows
tictoc::toc()
#> 3.492 sec elapsed