R: Reading data from Google BigQuery

spark_read_bigquery {sparkbq}

R Documentation

Reading data from Google BigQuery

Description

This function reads data stored in a Google BigQuery table.

Usage

spark_read_bigquery(sc, name,
  billingProjectId = default_billing_project_id(),
  projectId = billingProjectId, datasetId = NULL, tableId = NULL,
  sqlQuery = NULL, type = default_bigquery_type(),
  gcsBucket = default_gcs_bucket(),
  serviceAccountKeyFile = default_service_account_key_file(),
  additionalParameters = NULL, memory = FALSE, ...)

Arguments

`sc`	`spark_connection` provided by sparklyr.
`name`	The name to assign to the newly generated table (see also `spark_read_source`).
`billingProjectId`	Google Cloud Platform project ID for billing purposes. This is the project on whose behalf to perform BigQuery operations. Defaults to `default_billing_project_id()`.
`projectId`	Google Cloud Platform project ID of BigQuery dataset. Defaults to `billingProjectId`.
`datasetId`	Google BigQuery dataset ID (may contain letters, numbers and underscores). Either both of `datasetId` and `tableId` or `sqlQuery` must be specified.
`tableId`	Google BigQuery table ID (may contain letters, numbers and underscores). Either both of `datasetId` and `tableId` or `sqlQuery` must be specified.
`sqlQuery`	Google BigQuery SQL query. Either both of `datasetId` and `tableId` or `sqlQuery` must be specified. The query must be specified in standard SQL (SQL-2011). Legacy SQL is not supported. Tables are specified as '<project_id>.<dataset_id>.<table_id>'.
`type`	BigQuery import type to use. Options include "direct", "avro", "json" and "csv". Defaults to `default_bigquery_type()`. See bigquery_defaults for more details about the supported types.
`gcsBucket`	Google Cloud Storage (GCS) bucket to use for storing temporary files. Temporary files are used when importing through BigQuery load jobs and exporting through BigQuery extraction jobs (i.e. when using data extracts such as Parquet, Avro, ORC, ...). The service account specified in `serviceAccountKeyFile` needs to be given appropriate rights. This should be the name of an existing storage bucket.
`serviceAccountKeyFile`	Google Cloud service account key file to use for authentication with Google Cloud services. The use of service accounts is highly recommended. Specifically, the service account will be used to interact with BigQuery and Google Cloud Storage (GCS).
`additionalParameters`	Additional spark-bigquery options. See https://github.com/miraisolutions/spark-bigquery for more information.
`memory`	`logical` specifying whether data should be loaded eagerly into memory, i.e. whether the table should be cached. Note that eagerly caching prevents predicate pushdown (e.g. in conjunction with `filter`) and therefore the default is `FALSE`. See also `spark_read_source`.
`...`	Additional arguments passed to `spark_read_source`.

Value

A tbl_spark which provides a dplyr-compatible reference to a Spark DataFrame.

References

Examples

## Not run: 
config <- spark_config()

sc <- spark_connect(master = "local", config = config)

bigquery_defaults(
  billingProjectId = "<your_billing_project_id>",
  gcsBucket = "<your_gcs_bucket>",
  datasetLocation = "US",
  serviceAccountKeyFile = "<your_service_account_key_file>",
  type = "direct")

# Reading the public shakespeare data table
# https://cloud.google.com/bigquery/public-data/
# https://cloud.google.com/bigquery/sample-tables
shakespeare <-
  spark_read_bigquery(
    sc,
    name = "shakespeare",
    projectId = "bigquery-public-data",
    datasetId = "samples",
    tableId = "shakespeare")

## End(Not run)

[Package sparkbq version 0.1.1 Index]