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22-arrow.Rmd
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22-arrow.Rmd
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# Arrow
**Learning objectives:**
- Use {arrow} to load large data files into R efficiently
- Partition large data files into parquet files for quicker access, less memory usage, and quicker wrangling
- Wrangle {arrow} data using existing {dplyr} operations
## Why learn {arrow}? {-}
- CSV files = very common for ease of access and use
- Big/messy CSVs = slow
- {arrow} 📦 reads large datasets quickly & uses {dplyr} syntax
## Packages used {-}
```{r arrow-library, warning=F, message=F}
library(arrow)
library(curl)
library(duckdb)
library(tidyverse)
```
## Download data {-}
- Case study: [item checkouts dataset from Seattle libraries](https://data.seattle.gov/Community/Checkouts-by-Title/tmmm-ytt6)
- **DON'T `download.file()`!!!** (41,389,465 rows of data)
```{r, eval=F}
dir.create("data", showWarnings = FALSE)
curl::multi_download(
"https://r4ds.s3.us-west-2.amazonaws.com/seattle-library-checkouts.csv",
"data/seattle-library-checkouts.csv",
resume = TRUE
)
```
## Open the data {-}
- Size in memory ≈ 2 × size on disk
- ~~`read_csv()`~~ ➡️ `arrow::open_dataset()`
- Scans a few thousand rows to determine dataset structure
- `ISBN` is empty for 80k rows, so we specify
- Does NOT load entire dataset into memory
```{r, eval=F}
seattle_csv <- open_dataset(
sources = "data/seattle-library-checkouts.csv",
col_types = schema(ISBN = string()),
format = "csv"
)
```
## Glimpse the data {-}
```{r, eval=F}
seattle_csv |> glimpse()
#> FileSystemDataset with 1 csv file
#> 41,389,465 rows x 12 columns
#> $ UsageClass <string> "Physical", "Physical", "Digital", "Physical", "Ph…
#> $ CheckoutType <string> "Horizon", "Horizon", "OverDrive", "Horizon", "Hor…
#> $ MaterialType <string> "BOOK", "BOOK", "EBOOK", "BOOK", "SOUNDDISC", "BOO…
#> $ CheckoutYear <int64> 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 20…
#> $ CheckoutMonth <int64> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
#> $ Checkouts <int64> 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 2, 3, 2, 1, 3, 2,…
#> $ Title <string> "Super rich : a guide to having it all / Russell S…
#> $ ISBN <string> "", "", "", "", "", "", "", "", "", "", "", "", ""…
#> $ Creator <string> "Simmons, Russell", "Barclay, James, 1965-", "Tim …
#> $ Subjects <string> "Self realization, Conduct of life, Attitude Psych…
#> $ Publisher <string> "Gotham Books,", "Pyr,", "Random House, Inc.", "Di…
#> $ PublicationYear <string> "c2011.", "2010.", "2015", "2005.", "c2004.", "c20…
```
## Manipulate the data {-}
- Can use {dplyr} functions on data
```{r, eval=F}
seattle_csv |>
group_by(CheckoutYear) |>
summarise(Checkouts = sum(Checkouts)) |>
arrange(CheckoutYear) |>
collect()
#> # A tibble: 18 × 2
#> CheckoutYear Checkouts
#> <int> <int>
#> 1 2005 3798685
#> 2 2006 6599318
#> 3 2007 7126627
#> 4 2008 8438486
#> 5 2009 9135167
#> 6 2010 8608966
#> # ℹ 12 more rows
```
## Parquet > CSV {-}
- Slow: Manipulating large CSV datasets with {readr}
- Faster: Manipulating large CSV datasets with {arrow}
- Much faster: Manipulating large `parquet` datasets with {arrow}
- Data subdivided into multiple files
## Benefits of parquet {-}
- Smaller files than CSV (efficient encodings + compression)
- Stores datatypes (vs CSV storing all as character & guessing)
- "Column-oriented" ("thinks" like a dataframe)
- Splits data into chunks you can (often) skip (faster)
**But:**
- Not human-readable
## Partitioning {-}
- Split data across files so analyses can skip unused data
- Experiment to find best partition for your data
- Recommendations:
- 20 MB < Filesize < 2 GB
- <= 10,000 files
## Seattle library CSV to parquet {-}
- `dplyr::group_by()` to define partitions
- `arrow::write_dataset()` to save as parquet
```{r, eval=F}
pq_path <- "data/seattle-library-checkouts"
seattle_csv |>
group_by(CheckoutYear) |>
write_dataset(path = pq_path, format = "parquet")
```
## Seattle library parquet files {-}
- [Apache Hive](https://hive.apache.org/) "self-describing" directory/file names
```{r, eval=F}
tibble(
files = list.files(pq_path, recursive = TRUE),
size_MB = file.size(file.path(pq_path, files)) / 1024^2
)
#> # A tibble: 18 × 2
#> files size_MB
#> <chr> <dbl>
#> 1 CheckoutYear=2005/part-0.parquet 109.
#> 2 CheckoutYear=2006/part-0.parquet 164.
#> 3 CheckoutYear=2007/part-0.parquet 178.
#> 4 CheckoutYear=2008/part-0.parquet 195.
#> 5 CheckoutYear=2009/part-0.parquet 214.
#> 6 CheckoutYear=2010/part-0.parquet 222.
#> # ℹ 12 more rows
```
## parquet + {arrow} + {dplyr} {-}
```{r, eval=F}
seattle_pq <- open_dataset(pq_path)
query <- seattle_pq |>
filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
group_by(CheckoutYear, CheckoutMonth) |>
summarize(TotalCheckouts = sum(Checkouts)) |>
arrange(CheckoutYear, CheckoutMonth)
```
## Results (uncollected) {-}
```{r, eval=F}
query
#> FileSystemDataset (query)
#> CheckoutYear: int32
#> CheckoutMonth: int64
#> TotalCheckouts: int64
#>
#> * Grouped by CheckoutYear
#> * Sorted by CheckoutYear [asc], CheckoutMonth [asc]
#> See $.data for the source Arrow object
```
## Results (collected) {-}
```{r, eval=F}
query |> collect()
#> # A tibble: 58 × 3
#> # Groups: CheckoutYear [5]
#> CheckoutYear CheckoutMonth TotalCheckouts
#> <int> <int> <int>
#> 1 2018 1 355101
#> 2 2018 2 309813
#> 3 2018 3 344487
#> 4 2018 4 330988
#> 5 2018 5 318049
#> 6 2018 6 341825
#> # ℹ 52 more rows
```
## Available verbs {-}
- [`?arrow-dplyr`](https://arrow.apache.org/docs/r/reference/acero.html) for supported functions
- (book uses `?acero` but that's way harder to remember)
## Performance {-}
```{r, eval=F}
x |>
filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
group_by(CheckoutMonth) |>
summarize(TotalCheckouts = sum(Checkouts)) |>
arrange(desc(CheckoutMonth)) |>
collect() |>
system.time()
```
- CSV: 11.951s
- Parquet: **0.263s**
## Using {duckdb} with {arrow} {-}
- `arrow::to_duckdb()` translates {arrow} dataset to {duckdb}
- Outside R/memory
- Discussion: Why?
- More tidyverse verbs?
## Meeting Videos {-}
### Cohort 7 {-}
`r knitr::include_url("https://www.youtube.com/embed/qsgZAgmmpt4")`
<details>
<summary> Meeting chat log </summary>
```
00:06:25 Oluwafemi Oyedele: Hi Tim, Good Evening!!!
00:06:41 Tim Newby: Hi Oluwafemi
00:07:10 Oluwafemi Oyedele: We will start the meeting in 5 minute time!!!
00:08:15 Tim Newby: Reacted to "We will start the me..." with 👍
00:11:45 Oluwafemi Oyedele: https://github.com/hadley/r4ds/issues/1374
00:12:27 Oluwafemi Oyedele: start
00:13:56 Oluwafemi Oyedele: https://parquet.apache.org/
00:14:53 Oluwafemi Oyedele: https://data.seattle.gov/Community/Checkouts-by-Title/tmmm-ytt6
00:28:35 Oluwafemi Oyedele: https://data.seattle.gov/Community/Checkouts-by-Title/tmmm-ytt6
00:40:49 Oluwafemi Oyedele: stop
```
</details>
### Cohort 8 {-}
`r knitr::include_url("https://www.youtube.com/embed/dUQ3vCNejbI")`