모든 BigQuery 코드 샘플

BigQuery 코드 샘플

This page contains code samples for BigQuery. To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser.

Add a column using a load job

Add a new column to a BigQuery table while appending rows using a load job.

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• Modifying table schemas

Add a column using a query job

Add a new column to a BigQuery table while appending rows using a query job with an explicit destination table.

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• Modifying table schemas

Add a label

Add a label to a table.

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• Adding labels to resources
• Updating labels

Add an empty column

Manually add an empty column.

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• Modifying table schemas

Array parameters

Run a query with array parameters.

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• Running parameterized queries

Authorize a BigQuery Dataset

Authorize a BigQuery Dataset

Java Ruby

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Cancel a job

Attempt to cancel a job.

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• Managing jobs

Check dataset existence

A function to check whether a dataset exists.

Java Python

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• Listing datasets

Clustered table

Load data from a CSV file on Cloud Storage to a clustered table.

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• Create and use clustered tables

Column-based time partitioning

Create a table that uses column-based time partitioning.

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• Creating partitioned tables

Copy a single-source table

Copy a single-source table to a given destination.

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• Manage tables

Copy a table

Copy a table with customer-managed encryption keys.

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• Customer-managed Cloud KMS keys

Copy multiple tables

Copy multiple source tables to a given destination.

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• Manage tables

Create a client with a service account key file

Create a BigQuery client using a service account key file.

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• Authenticating with a service account key file

Create a client with application default credentials

Create a BigQuery client using application default credentials.

Java Node.js PHP Python

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• Getting started with authentication

Create a clustered table

Create a clustered table.

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• Create and use clustered tables

Create a dataset in BigQuery.

Sample demonstrating how to create a dataset in BigQuery. Give the dataset a unique name. Note, that the geographic location for the location can not be changed once the dataset is created.

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• Creating datasets
• Getting started with authentication

Create a dataset with a customer-managed encryption key

The following example creates a dataset named `mydataset`, and also uses the `google_kms_crypto_key` and `google_kms_key_ring` resources to specify a Cloud Key Management Service key for the dataset. You must enable the Cloud Key Management Service API before running this example.

Terraform

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• Creating datasets

Create a job

Run a BigQuery job (query, load, extract, or copy) in a specified location with additional configuration.

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• Running jobs programmatically

Create a model

Create a model within an existing dataset.

Java Node.js

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Create a routine

Create a routine within an existing dataset.

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Create a routine with DDL

Create a routine using a DDL query.

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Create a table

Create a table with customer-managed encryption keys.

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• Customer-managed Cloud KMS keys

Create a table using a template

Create a table using the properties of one table (schema, partitioning, clustering) to create a new empty table with the same configuration.

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Create a view

Create a view within a dataset.

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• Create views

Create a view with DDL

Create a view using a DDL query.

Java Node.js Python

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• Data definition language (DDL) statements in GoogleSQL

Create an authorized view

Create an authorized view using GitHub public data.

Java Node.js Python

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• Create an authorized view

Create an integer-range partitioned table

Create a new integer-range partitioned table in an existing dataset.

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• Creating partitioned tables

Create credentials with scopes

Create credentials with Drive and BigQuery API scopes.

Java Python

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• Create Google Drive external tables

Create external table with hive partitioning

Create an external table using hive partitioning.

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Create IAM policy

Create an IAM policy for a table.

Java

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• Control access to resources with IAM

Create materialized view

Create a materialized view.

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• Create materialized views

Create table with schema

Create a table with a schema.

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• Create and use tables
• Specifying a schema

Delete a dataset

Delete a dataset from a project.

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• Manage datasets

Delete a dataset and its contents

Delete a dataset and its contents from a project.

C# Java Ruby

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Delete a label from a dataset

Remove a label from a dataset.

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• Deleting labels

Delete a label from a table

Remove a label from a table.

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• Deleting labels

Delete a model

Delete a model from a dataset.

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• Delete models

Delete a routine

Delete a routine from a dataset.

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Delete a table

Delete a table from a dataset.

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• Manage tables
• Managing views

Delete materialized view

Delete a materialized view.

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• Manage materialized views

Disable query cache

Query disables the use of the cache.

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• Using cached query results

Download public table data to DataFrame

Use the BigQuery Storage API to speed up downloads of large tables to DataFrame.

Python

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Download public table data to DataFrame from the sandbox

Use the BigQuery Storage API to download query results to DataFrame.

Python

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Download query results to a GeoPandas GeoDataFrame

Download query results to a GeoPandas GeoDataFrame.

Python

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Download query results to DataFrame

Get query results as a Pandas DataFrame.

Python

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Download table data to DataFrame

Download table data to a Pandas DataFrame.

Python

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Dry run query

Run a dry run query.

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• Control costs in BigQuery
• Estimate query costs
• Issue a query dry run

Enable large results

Query enables large result sets using legacy SQL.

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• Writing query results

Export a model

Export an existing model to an existing Cloud Storage bucket.

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• Export models

Export a table to a compressed file

Exports a table to a compressed file in a Cloud Storage bucket.

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• Export table data

Export a table to a CSV file

Exports a table to a CSV file in a Cloud Storage bucket.

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• Export table data

Export a table to a JSON file

Exports a table to a newline-delimited JSON file in a Cloud Storage bucket.

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Get a model

Get a model resource for a given model ID.

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• Get model metadata

Get a routine

Get a routine resource for a given routine ID.

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Get dataset labels

Retrieve the labels of a dataset for a given dataset ID.

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• Viewing labels

Get dataset properties

Retrieve the properties of a dataset.

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• Listing datasets

Get job properties

Retrieve the properties of a job for a given job ID.

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• Managing jobs

Get table labels

Retrieve the labels of a table for a given table ID.

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• Viewing labels

Get table properties

Retrieve the properties of a table for a given table ID.

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• Create and use tables

Get total rows

Run a query and get total rows.

Java Python

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Get view properties

Retrieve the properties of a view for a given view ID.

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• Getting information about views

Grant view access

Authorize and grant access to a view.

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• Authorized views and materialized views

Import a local file

Import a local file into a table.

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• Batch loading data

Insert GeoJSON data

Streaming insert into GEOGRAPHY column with GeoJSON data.

Python Ruby

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• Working with geospatial data

Insert rows with no IDs

Insert rows without row IDs in a table.

Java PHP Python

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• Use the legacy streaming API

Insert WKT data

Streaming insert into GEOGRAPHY column with WKT data.

Python Ruby

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• Working with geospatial data

List by label

List datasets, filtering by labels.

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• Filtering resources using labels

List datasets

Lists all existing datasets in a project.

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• Listing datasets

List jobs

List all jobs in a project.

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• Managing jobs

List models

Lists all existing models in a dataset.

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• List models

List models using streaming

Lists all existing models in the dataset using streaming.

Node.js

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List routines

Lists all existing routines in a dataset.

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List tables

List tables in a given dataset.

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• Create and use tables
• Getting information about views
• Manage materialized views

Load a CSV file

Load a CSV file from Cloud Storage using an explicit schema.

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• Loading CSV data from Cloud Storage

Load a CSV file to replace a table

Load a CSV file from Cloud Storage, replacing a table.

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• Loading CSV data from Cloud Storage

Load a CSV file with autodetect schema

Load a CSV file from Cloud Storage using an autodetected schema.

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• Using schema auto-detection

Load a DataFrame to BigQuery with pandas-gbq

Use the pandas-gbq package to load a DataFrame to BigQuery.

Python

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Load a JSON file

Loads a JSON file from Cloud Storage using an explicit schema.

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• Loading JSON data from Cloud Storage
• Specifying a schema

Load a JSON file to replace a table

Load a JSON file from Cloud Storage, replacing a table.

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• Loading JSON data from Cloud Storage

Load a JSON file with autodetect schema

Load a JSON file from Cloud Storage using autodetect schema.

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• Using schema auto-detection

Load a Parquet file

Load a Parquet file from Cloud Storage into a new table.

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• Loading Parquet data from Cloud Storage

Load a Parquet to replace a table

Load a Parquet file from Cloud Storage, replacing a table.

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• Loading Parquet data from Cloud Storage

Load a table in JSON format

Load a table with customer-managed encryption keys to Cloud Storage in JSON format.

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• Customer-managed Cloud KMS keys

Load an Avro file

Load an Avro file from Cloud Storage into a new table.

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• Loading Avro data from Cloud Storage

Load an Avro file to replace a table

Load an Avro file from Cloud Storage, replacing existing table data.

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• Loading Avro data from Cloud Storage

Load an ORC file

Load an ORC file from Cloud Storage into a new table.

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• Loading ORC data from Cloud Storage

Load an ORC file to replace a table

Load an ORC file from Cloud Storage, replacing a table.

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• Loading ORC data from Cloud Storage

Load data from DataFrame

Load contents of a pandas DataFrame to a table.

Python

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Load data into a column-based time partitioning table

Load data into a table that uses column-based time partitioning.

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• Loading CSV data from Cloud Storage

Migration Guide: pandas-gbq

Samples for a guide for migrating from pandas-gbq to google-cloud-bigquery

Python

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• Comparison with pandas-gbq

Migration Guide: pandas-gbq

Samples for a guide for migrating from pandas-gbq to google-cloud-bigquery

Python

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• Comparison with pandas-gbq

Named parameters

Run a query with named parameters.

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• Running parameterized queries

Named parameters and provided types

Run a query with named parameters and provided parameter types.

Java Node.js

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Nested repeated schema

Specify nested and repeated columns in schema.

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• Specify nested and repeated columns in table schemas

Positional parameters

Run a query with positional parameters.

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• Running parameterized queries

Positional parameters and provided types

Run a query with positional parameters and provided parameter types.

Java Node.js

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Preview table data

Retrieve selected row data from a table.

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• Managing table data
• Paging through table data

Query a clustered table

Query a table that has a clustering specification.

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• Querying clustered tables

Query a column-based time-partitioned table

Query a table that uses column-based time partitioning.

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Query a table

Query a table with customer-managed encryption keys.

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• Customer-managed Cloud KMS keys

Query Bigtable using a permanent table

Query data from a Bigtable instance by creating a permanent table.

Java

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• Create a Bigtable external table

Query Bigtable using a temporary table

Query data from a Bigtable instance by creating a temporary table.

Java

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• Query Bigtable data

Query Cloud Storage with a permanent table

Query data from a file on Cloud Storage by creating a permanent table.

Java Node.js Python

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• Create Cloud Storage external tables

Query Cloud Storage with a temporary table

Query data from a file on Cloud Storage by creating a temporary table.

Java Node.js Python

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• Query Cloud Storage data in external tables

Query materialized view

Query an existing materialized view.

Java

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Query pagination

Run a query and get rows using automatic pagination.

Java Node.js Python

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• Paging through table data

Query script

Run a query script.

Java Python

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Query Sheets with a permanent table

Query data from a Google Sheets file by creating a permanent table.

Java Python

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• Create Google Drive external tables

Query Sheets with a temporary table

Query data from a Google Sheets file by creating a temporary table.

Java Python

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• Query Drive data

Query with the BigQuery API

Create a simple app with BigQuery.

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• BigQuery API Client Libraries
• Getting started with authentication
• Query a public dataset with the BigQuery C# client library
• Query a public dataset with the BigQuery client libraries
• Query a public dataset with the BigQuery Go client library
• Query a public dataset with the BigQuery Java client library
• Query a public dataset with the BigQuery Node.js client library
• Query a public dataset with the BigQuery PHP client library
• Query a public dataset with the BigQuery Python client library
• Query a public dataset with the BigQuery Ruby client library

Relax a column

Change columns from required to nullable.

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• Modifying table schemas

Relax a column in a load append job

Change a column from required to nullable in a load append job.

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• Modifying table schemas

Relax a column in a query append job

Change a column from required to nullable in a query append job.

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• Modifying table schemas

Revoke access to a dataset

Remove a user or group's permissions to access a BigQuery dataset.

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• Control access to resources with IAM

Run a legacy SQL query with pandas-gbq

Use the pandas-gbq package to run a query using legacy SQL syntax.

Python

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Run a query with batch priority

Run a query job using batch priority.

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• Run interactive and batch query jobs

Run a query with GoogleSQL

Run a query with GoogleSQL.

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• BigQuery API Client Libraries
• Run interactive and batch query jobs

Run a query with legacy SQL

Runs a query with legacy SQL.

C# Go Java Node.js