CODEWITHSUNDEEP
rgoogle-bigquerybigrquery
hhh
hhh

Reputation: 52840

How to load large datasets to R from BigQuery?

I have tried two ways with Bigrquery package such that

library(bigrquery)
library(DBI)

con <- dbConnect(
  bigrquery::bigquery(),
  project = "YOUR PROJECT ID HERE",
  dataset = "YOUR DATASET"
)
test<- dbGetQuery(con, sql, n = 10000, max_pages = Inf)

and

sql <- `YOUR LARGE QUERY HERE` #long query saved to View and its select here
tb <- bigrquery::bq_project_query(project, sql)
bq_table_download(tb, max_results = 1000)

but failing to the error "Error: Requested Resource Too Large to Return [responseTooLarge]", potentially related issue here, but I am interested in any tool to get the job done: I tried already the solutions outlined here but they failed.

How can I load large datasets to R from BigQuery?

Upvotes: 5

Views: 4550

Answers (4)

stevec
stevec

Reputation: 52268

I see someone's created a way of making this easier. There's some setup involved, but then you can download using the Google Storage API like so:

## Auth is done automagically using Application Default Credentials.
## Use the following command once to set it up :
## gcloud auth application-default login --billing-project={project}
library(bigrquerystorage)

# TODO(developer): Set the project_id variable.
# project_id <- 'your-project-id'
#
# The read session is created in this project. This project can be
# different from that which contains the table.

rows <- bqs_table_download(
  x = "bigquery-public-data:usa_names.usa_1910_current"
  , parent = project_id
  # , snapshot_time = Sys.time() # a POSIX time
  , selected_fields = c("name", "number", "state"),
  , row_restriction = 'state = "WA"'
  # , as_tibble = TRUE # FALSE : arrow, TRUE : arrow->as.data.frame
)

sprintf("Got %d unique names in states: %s",
        length(unique(rows$name)),
        paste(unique(rows$state), collapse = " "))

# Replace bigrquery::bq_download_table
library(bigrquery)
rows <- bigrquery::bq_table_download("bigquery-public-data.usa_names.usa_1910_current")
# Downloading 6,122,890 rows in 613 pages.
overload_bq_table_download(project_id)
rows <- bigrquery::bq_table_download("bigquery-public-data.usa_names.usa_1910_current")
# Streamed 6122890 rows in 5980 messages.

Upvotes: 2

djfinnoy
djfinnoy

Reputation: 595

This did the trick for me.

# Make page_size some value greater than the default (10000)
x <- 50000

bq_table_download(tb, page_size=x)

Beware, if you set page_size to some arbitrarily high value (100000 in my case), you'll start seeing a lot of empty rows.

Still haven't found a good "rule of thumb" for what the correct page_size value should be for a given table size.

Upvotes: 0

ASH
ASH

Reputation: 20302

I just started using BigQuery too. I think it should be something like this.

The current bigrquery release can be installed from CRAN:

install.packages("bigrquery")

The newest development release can be installed from GitHub:

install.packages('devtools')
devtools::install_github("r-dbi/bigrquery")

Usage Low-level API

library(bigrquery)
billing <- bq_test_project() # replace this with your project ID 
sql <- "SELECT year, month, day, weight_pounds FROM `publicdata.samples.natality`"

tb <- bq_project_query(billing, sql)
#> Auto-refreshing stale OAuth token.
bq_table_download(tb, max_results = 10)

DBI

library(DBI)

con <- dbConnect(
  bigrquery::bigquery(),
  project = "publicdata",
  dataset = "samples",
  billing = billing
)
con 
#> <BigQueryConnection>
#>   Dataset: publicdata.samples
#>   Billing: bigrquery-examples

dbListTables(con)
#> [1] "github_nested"   "github_timeline" "gsod"            "natality"       
#> [5] "shakespeare"     "trigrams"        "wikipedia"

dbGetQuery(con, sql, n = 10)



library(dplyr)

natality <- tbl(con, "natality")

natality %>%
  select(year, month, day, weight_pounds) %>% 
  head(10) %>%
  collect()

Upvotes: 1

Temu
Temu

Reputation: 879

As @hrbrmstr kind of suggested you, the documentation mentions specifically:

> #' @param page_size The number of rows returned per page. Make this smaller
> #'   if you have many fields or large records and you are seeing a
> #'   'responseTooLarge' error.

In this documentation from r-project.org you will read a different advise in the explanation of this function (page 13):

This retrieves rows in chunks of page_size. It is most suitable for results of smaller queries (<100 MB, say). For larger queries, it is better to export the results to a CSV file stored on google cloud and use the bq command line tool to download locally.

Upvotes: 2

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