BigQuery jobs: perform a job
These functions are low-level functions designed to be used by experts. Each of these low-level functions is paired with a high-level function that you should use instead:
bq_perform_copy()
: bq_table_copy()
.
bq_perform_query()
: bq_dataset_query()
, bq_project_query()
.
bq_perform_upload()
: bq_table_upload()
.
bq_perform_load()
: bq_table_load()
.
bq_perform_extract()
: bq_table_save()
.
bq_perform_extract( x, destination_uris, destination_format = "NEWLINE_DELIMITED_JSON", compression = "NONE", ..., print_header = TRUE, billing = x$project ) bq_perform_upload( x, values, fields = NULL, create_disposition = "CREATE_IF_NEEDED", write_disposition = "WRITE_EMPTY", ..., billing = x$project ) bq_perform_load( x, source_uris, billing = x$project, source_format = "NEWLINE_DELIMITED_JSON", fields = NULL, nskip = 0, create_disposition = "CREATE_IF_NEEDED", write_disposition = "WRITE_EMPTY", ... ) bq_perform_query( query, billing, ..., parameters = NULL, destination_table = NULL, default_dataset = NULL, create_disposition = "CREATE_IF_NEEDED", write_disposition = "WRITE_EMPTY", use_legacy_sql = FALSE, priority = "INTERACTIVE" ) bq_perform_query_dry_run( query, billing, ..., default_dataset = NULL, parameters = NULL, use_legacy_sql = FALSE ) bq_perform_copy( src, dest, create_disposition = "CREATE_IF_NEEDED", write_disposition = "WRITE_EMPTY", ..., billing = NULL )
x |
A bq_table |
destination_uris |
A character vector of fully-qualified Google Cloud
Storage URIs where the extracted table should be written. Can export
up to 1 Gb of data per file. Use a wild card URI (e.g.
|
destination_format |
The exported file format. Possible values include "CSV", "NEWLINE_DELIMITED_JSON" and "AVRO". Tables with nested or repeated fields cannot be exported as CSV. |
compression |
The compression type to use for exported files. Possible values include "GZIP", "DEFLATE", "SNAPPY", and "NONE". "DEFLATE" and "SNAPPY" are only supported for Avro. |
... |
Additional arguments passed on to the underlying API call. snake_case names are automatically converted to camelCase. |
print_header |
Whether to print out a header row in the results. |
billing |
Identifier of project to bill. |
values |
Data frame of values to insert. |
fields |
A bq_fields specification, or something coercible to it
(like a data frame). Leave as |
create_disposition |
Specifies whether the job is allowed to create new tables. The following values are supported:
|
write_disposition |
Specifies the action that occurs if the destination table already exists. The following values are supported:
|
source_uris |
The fully-qualified URIs that point to your data in Google Cloud. For Google Cloud Storage URIs: Each URI can contain one ''*'“ wildcard character and it must come after the 'bucket' name. Size limits related to load jobs apply to external data sources. For Google Cloud Bigtable URIs: Exactly one URI can be specified and it has be a fully specified and valid HTTPS URL for a Google Cloud Bigtable table. For Google Cloud Datastore backups: Exactly one URI can be specified. Also, the '*' wildcard character is not allowed. |
source_format |
The format of the data files:
|
nskip |
For |
query |
SQL query string. |
parameters |
Named list of parameters match to query parameters.
Parameter Generally, you can supply R vectors and they will be automatically
converted to the correct type. If you need greater control, you can call
See https://cloud.google.com/bigquery/docs/parameterized-queries for more details. |
destination_table |
A bq_table where results should be stored. If not supplied, results will be saved to a temporary table that lives in a special dataset. You must supply this parameter for large queries (> 128 MB compressed). |
default_dataset |
A bq_dataset used to automatically qualify table names. |
use_legacy_sql |
If |
priority |
Specifies a priority for the query. Possible values include "INTERACTIVE" and "BATCH". Batch queries do not start immediately, but are not rate-limited in the same way as interactive queries. |
A bq_job.
Additional information at:
if (bq_testable()) { ds <- bq_test_dataset() bq_mtcars <- bq_table(ds, "mtcars") job <- bq_perform_upload(bq_mtcars, mtcars) bq_table_exists(bq_mtcars) bq_job_wait(job) bq_table_exists(bq_mtcars) head(bq_table_download(bq_mtcars)) }
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