When duckdb meets dplyr!

R
DuckDB
dplyr
duckplyr
S3
Parquet
Author

Philippe Massicotte

Published

May 1, 2024

Introduction

I like DuckDB πŸ¦†. I am excited to see that it is now possible to use it with dplyr using the fantastic duckplyr package which gives us another way to bridge dplyr with DuckDB.

When duckdb meets dplyr! Photo by DuckDB

When duckdb meets dplyr! Photo by DuckDB

In this short post, I will show how duckplyr can be used to query parquet files hosted on an S3 bucket. I will use the duckplyr_df_from_parquet() function to read the data and then use dplyr verbs to summarize the data.

First, let’s install the duckplyr package and load the necessary libraries.

install.packages("duckplyr")
library(dplyr)
library(duckplyr)
library(duckdb)

options(duckdb.materialize_message = FALSE)

In order we need to follow these steps:

  1. Create a connection to a DuckDB database.
  2. Load the httpfs extension.
  3. Set the S3 region and endpoint to access the data.
con <- duckplyr:::get_default_duckdb_connection()

DBI::dbSendQuery(con, "INSTALL httpfs; LOAD httpfs;")
DBI::dbSendQuery(
  con,
  "SET s3_region='auto';SET s3_endpoint='s3.valeria.science';"
)
Note

Note the use of the triple colon (:::) to access the internal function.

Now, we can read the data from the parquet file stored on the S3 bucket with the duckplyr_df_from_parquet() function. We can also specify the class of the output data frame with class = class(tibble()). In this case, I will use tibble.

flights <- duckplyr_df_from_parquet(
  "s3://public/flights.parquet",
  class = class(tibble())
)

flights
#> # A tibble: 336,776 Γ— 19
#>     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#>  1  2013     1     1      517            515         2      830            819
#>  2  2013     1     1      533            529         4      850            830
#>  3  2013     1     1      542            540         2      923            850
#>  4  2013     1     1      544            545        -1     1004           1022
#>  5  2013     1     1      554            600        -6      812            837
#>  6  2013     1     1      554            558        -4      740            728
#>  7  2013     1     1      555            600        -5      913            854
#>  8  2013     1     1      557            600        -3      709            723
#>  9  2013     1     1      557            600        -3      838            846
#> 10  2013     1     1      558            600        -2      753            745
#> # β„Ή 336,766 more rows
#> # β„Ή 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#> #   hour <dbl>, minute <dbl>, time_hour <dttm>

From what I understand, all the data is pulled into the memory. This could be a problem if the data is too large. What we can do is to summarize the data to know how many rows are in the table.

duckplyr_df_from_parquet(
  "s3://public/flights.parquet",
  class = class(tibble())
) |>
  nrow()
#> [1] 336776

Or even have a glimpse() of the data.

duckplyr_df_from_parquet(
  "s3://public/flights.parquet",
  class = class(tibble())
) |>
  glimpse()
#> Rows: 336,776
#> Columns: 19
#> $ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
#> $ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
#> $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
#> $ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
#> $ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
#> $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
#> $ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
#> $ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
#> $ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
#> $ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
#> $ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
#> $ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
#> $ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
#> $ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
#> $ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
#> $ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
#> $ time_hour      <dttm> 2013-01-01 10:00:00, 2013-01-01 10:00:00, 2013-01-01 1…

Now, let’s summarize the data by calculating the average departure delay by carrier.

flights |>
  summarise(mean_dep_delay = mean(dep_delay), .by = "carrier")
#> # A tibble: 16 Γ— 2
#>    carrier mean_dep_delay
#>    <chr>            <dbl>
#>  1 9E               16.7 
#>  2 F9               20.2 
#>  3 YV               19.0 
#>  4 AA                8.59
#>  5 DL                9.26
#>  6 B6               13.0 
#>  7 EV               20.0 
#>  8 WN               17.7 
#>  9 FL               18.7 
#> 10 AS                5.80
#> 11 UA               12.1 
#> 12 MQ               10.6 
#> 13 VX               12.9 
#> 14 HA                4.90
#> 15 US                3.78
#> 16 OO               12.6

duckplyr_df_from_parquet(
  "s3://public/flights.parquet",
  class = class(tibble())
) |>
  summarise(mean_dep_delay = mean(dep_delay), .by = "carrier")
#> # A tibble: 16 Γ— 2
#>    carrier mean_dep_delay
#>    <chr>            <dbl>
#>  1 9E               16.7 
#>  2 F9               20.2 
#>  3 YV               19.0 
#>  4 AA                8.59
#>  5 DL                9.26
#>  6 B6               13.0 
#>  7 EV               20.0 
#>  8 WN               17.7 
#>  9 FL               18.7 
#> 10 AS                5.80
#> 11 UA               12.1 
#> 12 MQ               10.6 
#> 13 VX               12.9 
#> 14 HA                4.90
#> 15 US                3.78
#> 16 OO               12.6

Scaling Up: Analyzing a larger dataset

Let’s try with a much larger dataset. I will use the NYC taxi data from 2019. The data is partitioned by month and stored in parquet partitioning. But before we can process, we need to change the endpoint.

DBI::dbSendQuery(
  con,
  "SET s3_region='auto';SET s3_endpoint='';"
)

duckplyr_df_from_file(
  "s3://duckplyr-demo-taxi-data/taxi-data-2019-partitioned/*/*.parquet",
  "read_parquet",
  options = list(hive_partitioning = TRUE),
  class = class(tibble())
) |>
  count()
#> # A tibble: 1 Γ— 1
#>          n
#>      <int>
#> 1 84393604

Impressive, isn’t it? With DuckDB, analyzing over 80 million rows of data is a so fast πŸ˜€.

Warning

Unless you have a lot of memory, do not run the code below (i.e. without the count() function). It will load the entire dataset in memory!

duckplyr_df_from_file(
  "s3://duckplyr-demo-taxi-data/taxi-data-2019-partitioned/*/*.parquet",
  "read_parquet",
  options = list(hive_partitioning = TRUE),
  class = class(tibble())
)

Performance Benchmarking

How long it took to count the rows? Let’s find out ⌚.

system.time({
  duckplyr_df_from_file(
    "s3://duckplyr-demo-taxi-data/taxi-data-2019-partitioned/*/*.parquet",
    "read_parquet",
    options = list(hive_partitioning = TRUE),
    class = class(tibble())
  ) |>
    count()
})
#>    user  system elapsed 
#>   0.016   0.004   2.304

It took less than 3 seconds to count the rows. This is impressive!

To wrap up, let’s do an actual analysis. We will calculate the median tip percentage by the number of passengers in the taxi.

duckplyr_df_from_file(
  "s3://duckplyr-demo-taxi-data/taxi-data-2019-partitioned/*/*.parquet",
  "read_parquet",
  options = list(hive_partitioning = TRUE),
  class = class(tibble())
) |>
  filter(total_amount > 0) |>
  filter(!is.na(passenger_count)) |>
  mutate(tip_pct = 100 * tip_amount / total_amount) |>
  summarise(
    avg_tip_pct = median(tip_pct),
    n = n(),
    .by = passenger_count
  ) |>
  arrange(desc(passenger_count))
#> # A tibble: 10 Γ— 3
#>    passenger_count avg_tip_pct        n
#>              <dbl>       <dbl>    <int>
#>  1               9        12.8      221
#>  2               8        12.4      274
#>  3               7        10.2      412
#>  4               6        16.6  2035161
#>  5               5        16.7  3391426
#>  6               4        12.8  1706047
#>  7               3        13.7  3575542
#>  8               2        14.5 12755829
#>  9               1        15.3 58967003
#> 10               0        13.7  1525284

Conclusion

By integrating DuckDB with dplyr via duckplyr, we unlock a powerful toolset for data analysis. Whether it’s exploring small datasets or crunching numbers in massive datasets, DuckDB’s efficiency and dplyr’s versatility make for a winning combination.

Bonus: comparing the performance with dbplyr

I recently stumbled across this discussion addressing the difference between duckplyr and dbplyr. Intrigued, I decided to compare the performance of duckplyr against dbplyr.

library(dplyr)
library(dbplyr)
library(bench)

f_dbplyr <- function() {
  con <- dbConnect(duckdb())

  dbSendQuery(con, "INSTALL httpfs; LOAD httpfs;")
  dbSendQuery(con, "SET s3_region='auto';SET s3_endpoint='';")

  df <- tbl(
    con,
    "read_parquet('s3://duckplyr-demo-taxi-data/taxi-data-2019-partitioned/*/*.parquet')"
  )

  df |>
    filter(total_amount > 0) |>
    filter(!is.na(passenger_count)) |>
    mutate(tip_pct = 100 * tip_amount / total_amount) |>
    summarise(
      avg_tip_pct = median(tip_pct),
      n = n(),
      .by = passenger_count
    ) |>
    arrange(desc(passenger_count))

  dbDisconnect(con)
}

f_duckplyr <- function() {
  con <- duckplyr:::get_default_duckdb_connection()

  dbSendQuery(con, "INSTALL httpfs; LOAD httpfs;")
  dbSendQuery(
    con,
    "SET s3_region='auto';SET s3_endpoint='';"
  )

  duckplyr_df_from_file(
    "s3://duckplyr-demo-taxi-data/taxi-data-2019-partitioned/*/*.parquet",
    "read_parquet",
    options = list(hive_partitioning = TRUE),
    class = class(tibble())
  ) |>
    filter(total_amount > 0) |>
    filter(!is.na(passenger_count)) |>
    mutate(tip_pct = 100 * tip_amount / total_amount) |>
    summarise(
      avg_tip_pct = median(tip_pct),
      n = n(),
      .by = passenger_count
    ) |>
    arrange(desc(passenger_count))
}

mark(f_dbplyr(), f_duckplyr(), check = FALSE)
#> # A tibble: 2 Γ— 6
#>   expression        min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>   <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f_dbplyr()      1.25s    1.25s     0.803    5.31MB    0.803
#> 2 f_duckplyr()    6.93s    6.93s     0.144   51.34KB    0

The initial results show that using dbplyr is faster than duckplyr. This is interesting and I will need to investigate further to understand why.

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}

Citation

BibTeX citation:
@online{massicotte2024,
  author = {Massicotte, Philippe},
  title = {When Duckdb Meets Dplyr!},
  date = {2024-05-01},
  url = {https://www.pmassicotte.com/posts/2024-05-01-query-s3-duckplyr/},
  langid = {en}
}
For attribution, please cite this work as:
Massicotte, Philippe. 2024. β€œWhen Duckdb Meets Dplyr!” May 1, 2024. https://www.pmassicotte.com/posts/2024-05-01-query-s3-duckplyr/.