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Changelog

Source: NEWS.md

bigrquery 1.5.1

  • Forward compatibility with upcoming dbplyr release (#601).

bigrquery 1.5.0

CRAN release: 2024-01-22

Major changes

  • bigrquery is now MIT licensed (#453).

  • Deprecated functions (i.e. those not starting with bq_) have been removed (#551). These have been superseded for a long time and were formally deprecated in bigrquery 1.3.0 (2020).

  • bq_table_download() now returns unknown fields as character vectors. This means that BIGNUMERIC (#435) and JSON (#544) data is downloaded into R for you to process as you wish.

    It now parses dates using the clock package. This leads to a considerable performance improvement (#430) and ensures that dates prior to 1970-01-01 are parsed correctly (#285).

Significant DBI improvements

Significant dbplyr improvements

  • bigrquery now uses 2nd edition of dbplyr interface (#508) and is compatible with dbplyr 2.4.0 (#550).

  • Joins now work correctly across bigrquery connections (#433).

  • grepl(pattern, x) is now correctly translated to REGEXP_CONTAINS(x, pattern) (#416).

  • median() gets a translation that works in summarise() and a clear error if you use it in mutate() (#419).

  • tbl() now works with views (#519), including the views found in the INFORMATION_SCHEMA schema (#468).

  • tbl(con, sql("...")) now works robustly once more (#540), fixing the “URL using bad/illegal format or missing URL” error.

  • runif(n()) gains a translation so that slice_sample() can work (@mgirlich, #448).

Minor improvements and bug fixes

  • Google API URLs have been aligned with the Google Cloud Discovery docs. This enables support for Private and Restricted Google APIs configurations (@husseyd, #541)

  • Functions generally try to do a better job of telling you when you’ve supplied the wrong type of input. Additionally, if you supply SQL() to a query, you no longer get a weird warning (#498).

  • If bq_job_wait() receives a 503 response, it now waits for 2 seconds and tries again (#535).

  • dbFetch() now respects the quiet setting from the connection (#463).

  • dbGetRowCount() and dbHasComplete() now return correct values when you try to fetch more rows than actually exist (#501).

  • New dbQuoteLiteral() method for logicals reverts breaking change introduced by DBI 1.1.2 (@meztez, #478).

  • dbWriteTable() now correct uses the billing value set in the connection (#486).

bigrquery 1.4.2

CRAN release: 2023-04-20

  • Sync up with the current release of gargle (1.4.0). Recently gargle introduced some changes around OAuth and bigrquery is syncing with up that:

    • bq_oauth_client() is a new function to replace the now-deprecated bq_oauth_app().
    • The new client argument of bq_auth_configure() replaces the now-deprecated client argument.
    • The documentation of bq_auth_configure() emphasizes that the preferred way to “bring your own OAuth client” is by providing the JSON downloaded from Google Developers Console.

  • op_table.lazy_select_query() now returns a string instead of a list, which fixes an error seen when printing or using functions like head() or dplyr::glimpse() (@clente, #509).

bigrquery 1.4.1

CRAN release: 2022-10-27

bigrquery 1.4.0

CRAN release: 2021-08-05

  • bq_table_download() has been heavily refactored (#412):

    • It should now return the requested results, in full, in most situations. However, when there is a “row shortage”, it throws an error instead of silently returning incomplete results.
    • The max_results argument has been deprecated in favor of n_max, which reflects what we actually do with this number and is consistent with the n_max argument elsewhere, e.g., readr::read_csv().
    • The default value of page_size is no longer fixed and, instead, is determined empirically. Users are strongly recommended to let bigrquery select page_size automatically, unless there’s a specific reason to do otherwise.

  • The BigQueryResult object gains a billing slot (@meztez, #423).

  • collect.tbl_BigQueryConnection() honours the bigint field found in a connection object created with DBI::dbConnect() and passes bigint along to bq_table_download(). This improves support for 64-bit integers when reading BigQuery tables with dplyr syntax (@zoews, #439, #437).

bigrquery 1.3.2

CRAN release: 2020-10-05

  • BigQuery BYTES and GEOGRAPHY column types are now supported via the blob and wk packages, respectively (@paleolimbot, #354, #388).

  • When used with dbplyr >= 2.0.0, ambiguous variables in joins will get suffixes _x and _y (instead of .x and .y which don’t work with BigQuery) (#403).

  • bq_table_download() works once again with large row counts (@gjuggler, #395). Google’s API has stopped accepting startIndex parameters with scientific formatting, which was happening for large values (>1e5) by default.

  • New bq_perform_query_dry_run() to retrieve the estimated cost of performing a query (@Ka2wei, #316).

bigrquery 1.3.1

CRAN release: 2020-05-15

  • Now requires gargle 0.5.0

bigrquery 1.3.0

CRAN release: 2020-05-08

  • Old functions (not starting with bq_) are deprecated (@byapparov, #335)

  • When bq_perform_*() fails, you now see all errors, not just the first (#355).

  • bq_perform_query() can now execute parameterised query with parameters of ARRAY type (@byapparov, #303). Vectors of length > 1 will be automatically converted to ARRAY type, or use bq_param_array() to be explicit.

  • bq_perform_upload() works once again (#361). It seems like the generated JSON was always incorrect, but Google’s type checking only recently become strict enough to detect the problem.

  • dbExecute() is better supported. It no longer fails with a spurious error for DDL queries, and it returns the number of affected rows for DML queries (#375).

  • dbSendQuery() (and hence dbGetQuery()) and collect() passes on ... to bq_perform_query(). collect() gains page_size and max_connection arguments that are passed on to bq_table_download() (#374).

  • copy_to() now works with BigQuery (although it doesn’t support temporary tables so application is somewhat limited) (#337).

  • str_detect() now correctly translated to REGEXP_CONTAINS
    (@jimmyg3g, #369).

  • Error messages include hints for common problems (@deflaux, #353).

bigrquery 1.2.0

CRAN release: 2019-07-02

Auth from gargle

bigrquery’s auth functionality now comes from the gargle package, which provides R infrastructure to work with Google APIs, in general. The same transition is underway in several other packages, such as googledrive. This will make user interfaces more consistent and makes two new token flows available in bigrquery:

  • Application Default Credentials
  • Service account tokens from the metadata server available to VMs running on GCE

Where to learn more:

Changes that a user will notice

Temporary files are now deleted after table download. (@meztez, #343)

OAuth2 tokens are now cached at the user level, by default, instead of in .httr-oauth in the current project. The default OAuth app has also changed. This means you will need to re-authorize bigrquery (i.e. get a new token). You may want to delete any vestigial .httr-oauth files lying around your bigrquery projects.

The OAuth2 token key-value store now incorporates the associated Google user when indexing, which makes it easier to switch between Google identities.

bq_user() is a new function that reveals the email of the user associated with the current token.

If you previously used set_service_token() to use a service account token, it still works. But you’ll get a deprecation warning. Switch over to bq_auth(path = "/path/to/your/service-account.json"). Several other functions are similarly soft-deprecated.

Dependency changes

R 3.1 is no longer explicitly supported or tested. Our general practice is to support the current release (3.6), devel, and the 4 previous versions of R (3.5, 3.4, 3.3, 3.2).

gargle and rlang are newly Imported.

bigrquery 1.1.1

CRAN release: 2019-05-16

bigrquery 1.1.0

CRAN release: 2019-02-05

Improved type support

SQL translation

Minor bug fixes and improvements

  • Jobs now print their ids while running (#252)

  • bq_job() tracks location so bigrquery now works painlessly with non-US/EU locations (#274).

  • bq_perform_upload() will only autodetect a schema if the table does not already exist.

  • bq_table_download() correctly computes page ranges if both max_results and start_index are supplied (#248)

  • Unparseable date times return NA (#285)

bigrquery 1.0.0

CRAN release: 2018-04-24

Improved downloads

The system for downloading data from BigQuery into R has been rewritten from the ground up to give considerable improvements in performance and flexibility.

  • The two steps, downloading and parsing, now happen in sequence, rather than interleaved. This means that you’ll now see two progress bars: one for downloading JSON from BigQuery and one for parsing that JSON into a data frame.

  • Downloads now occur in parallel, using up to 6 simultaneous connections by default.

  • The parsing code has been rewritten in C++. As well as considerably improving performance, this also adds support for nested (record/struct) and repeated (array) columns (#145). These columns will yield list-columns in the following forms:

    • Repeated values become list-columns containing vectors.
    • Nested values become list-columns containing named lists.
    • Repeated nested values become list-columns containing data frames.

  • Results are now returned as tibbles, not data frames, because the base print method does not handle list columns well.

I can now download the first million rows of publicdata.samples.natality in about a minute. This data frame is about 170 MB in BigQuery and 140 MB in R; a minute to download this much data seems reasonable to me. The bottleneck for loading BigQuery data is now parsing BigQuery’s json format. I don’t see any obvious way to make this faster as I’m already using the fastest C++ json parser, RapidJson. If this is still too slow for you (i.e. you’re downloading GBs of data), see ?bq_table_download for an alternative approach.

New features

dplyr

  • dplyr::compute() now works (@realAkhmed, #52).

  • tbl() now accepts fully (or partially) qualified table names, like “publicdata.samples.shakespeare” or “samples.shakespeare”. This makes it possible to join tables across datasets (#219).

DBI

  • dbConnect() now defaults to standard SQL, rather than legacy SQL. Use use_legacy_sql = TRUE if you need the previous behaviour (#147).

  • dbConnect() now allows dataset to be omitted; this is natural when you want to use tables from multiple datasets.

  • dbWriteTable() and dbReadTable() now accept fully (or partially) qualified table names.

  • dbi_driver() is deprecated; please use bigquery() instead.

Low-level API

The low-level API has been completely overhauled to make it easier to use. The primary motivation was to make bigrquery development more enjoyable for me, but it should also be helpful to you when you need to go outside of the features provided by higher-level DBI and dplyr interfaces. The old API has been soft-deprecated - it will continue to work, but no further development will occur (including bug fixes). It will be formally deprecated in the next version, and then removed in the version after that.

  • Consistent naming scheme: All API functions now have the form bq_object_verb(), e.g.  bq_table_create(), or bq_dataset_delete().

  • S3 classes: bq_table(), bq_dataset(), bq_job(), bq_field() and bq_fields() constructor functions create S3 objects corresponding to important BigQuery objects (#150). These are paired with as_ coercion functions and used throughout the new API.

  • Easier local testing: New bq_test_project() and bq_test_dataset() make it easier to run bigrquery tests locally. To run the tests yourself, you need to create a BigQuery project, and then follow the instructions in ?bq_test_project.

  • More efficient data transfer: The new API makes extensive use of the fields query parameter, ensuring that functions only download data that they actually use (#153).

  • Tighter GCS connection: New bq_table_load() loads data from a Google Cloud Storage URI, pairing with bq_table_save() which saves data to a GCS URI (#155).

Bug fixes and minor improvements

dplyr

Low-level

Version 0.4.1

CRAN release: 2017-06-26

  • Fix SQL translation omissions discovered by dbplyr 1.1.0

Version 0.4.0

CRAN release: 2017-06-23

New features

  • dplyr support has been updated to require dplyr 0.7.0 and use dbplyr. This means that you can now more naturally work directly with DBI connections. dplyr now also uses modern BigQuery SQL which supports a broader set of translations. Along the way I’ve also fixed some SQL generation bugs (#48).

  • The DBI driver gets a new name: bigquery().

  • New insert_extract_job() make it possible to extract data and save in google storage (@realAkhmed, #119).

  • New insert_table() allows you to insert empty tables into a dataset.

  • All POST requests (inserts, updates, copies and query_exec) now take .... This allows you to add arbitrary additional data to the request body making it possible to use parts of the BigQuery API that are otherwise not exposed (#149). snake_case argument names are automatically converted to camelCase so you can stick consistently to snake case in your R code.

  • Full support for DATE, TIME, and DATETIME types (#128).

Big fixes and minor improvements

  • All bigrquery requests now have a custom user agent that specifies the versions of bigrquery and httr that are used (#151).

  • dbConnect() gains new use_legacy_sql, page_size, and quiet arguments that are passed onto query_exec(). These allow you to control query options at the connection level.

  • insert_upload_job() now sends data in newline-delimited JSON instead of csv (#97). This should be considerably faster and avoids character encoding issues (#45). POSIXlt columns are now also correctly coerced to TIMESTAMPS (#98).

  • insert_query_job() and query_exec() gain new arguments:

    • quiet = TRUE will suppress the progress bars if needed.
    • use_legacy_sql = FALSE option allows you to opt-out of the legacy SQL system (#124, @backlin)

  • list_tables() (#108) and list_datasets() (#141) are now paginated. By default they retrieve 50 items per page, and will iterate until they get everything.

  • list_tabledata() and query_exec() now give a nicer progress bar, including estimated time remaining (#100).

  • query_exec() should be considerably faster because profiling revealed that ~40% of the time taken by was a single line inside a function that helps parse BigQuery’s json into an R data frame. I replaced the slow R code with a faster C function.

  • set_oauth2.0_cred() allows user to supply their own Google OAuth application when setting credentials (#130, @jarodmeng)

  • wait_for() uses now reports the query total bytes billed, which is more accurate because it takes into account caching and other factors.

  • list_tabledata returns empty table on max_pages=0 (#184, @ras44 @byapparov)

Version 0.3.0

CRAN release: 2016-06-28

  • New set_service_token() allows you to use OAuth service token instead of interactive authentication.from

  • ^ is correctly translated to pow() (#110).

  • Provide full DBI compliant interface (@krlmlr).

  • Backend now translates iflese() to IF (@realAkhmed, #53).

Version 0.2.0.

CRAN release: 2016-03-03

  • Compatible with latest httr.

  • Computation of the SQL data type that corresponds to a given R object is now more robust against unknown classes. (#95, @krlmlr)

  • A data frame with full schema information is returned for zero-row results. (#88, @krlmlr)

  • New exists_table(). (#91, @krlmlr)

  • New arguments create_disposition and write_disposition to insert_upload_job(). (#92, @krlmlr)

  • Renamed option bigquery.quiet to bigrquery.quiet. (#89, @krlmlr)

  • New format_dataset() and format_table(). (#81, @krlmlr)

  • New list_tabledata_iter() that allows fetching a table in chunks of varying size. (#77, #87, @krlmlr)

  • Add support for API keys via the BIGRQUERY_API_KEY environment variable. (#49)