Export a table to a compressed file

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "cloud.google.com/go/bigquery" 
 ) 
 // exportTableAsCompressedCSV demonstrates using an export job to 
 // write the contents of a table into Cloud Storage as compressed CSV. 
 func 
  
 exportTableAsCompressedCSV 
 ( 
 projectID 
 , 
  
 gcsURI 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // gcsURI := "gs://mybucket/shakespeare.csv" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 bigquery 
 . 
 NewClient 
 ( 
 ctx 
 , 
  
 projectID 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "bigquery.NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 srcProject 
  
 := 
  
 "bigquery-public-data" 
  
 srcDataset 
  
 := 
  
 "samples" 
  
 srcTable 
  
 := 
  
 "shakespeare" 
  
 gcsRef 
  
 := 
  
 bigquery 
 . 
  NewGCSReference 
 
 ( 
 gcsURI 
 ) 
  
 gcsRef 
 . 
  Compression 
 
  
 = 
  
 bigquery 
 . 
  Gzip 
 
  
 extractor 
  
 := 
  
 client 
 . 
  DatasetInProject 
 
 ( 
 srcProject 
 , 
  
 srcDataset 
 ). 
 Table 
 ( 
 srcTable 
 ). 
 ExtractorTo 
 ( 
 gcsRef 
 ) 
  
 extractor 
 . 
 DisableHeader 
  
 = 
  
 true 
  
 // You can choose to run the job in a specific location for more complex data locality scenarios. 
  
 // Ex: In this example, source dataset and GCS bucket are in the US. 
  
 extractor 
 . 
  Location 
 
  
 = 
  
 "US" 
  
 job 
 , 
  
 err 
  
 := 
  
 extractor 
 . 
 Run 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 status 
 , 
  
 err 
  
 := 
  
 job 
 . 
 Wait 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 if 
  
 err 
  
 := 
  
 status 
 . 
  Err 
 
 (); 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 return 
  
 nil 
 } 
 

  import 
  
 com.google.cloud.bigquery. BigQuery 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryException 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryOptions 
 
 ; 
 import 
  
 com.google.cloud.bigquery. ExtractJobConfiguration 
 
 ; 
 import 
  
 com.google.cloud.bigquery. Job 
 
 ; 
 import 
  
 com.google.cloud.bigquery. JobInfo 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TableId 
 
 ; 
 // Sample to extract a compressed table 
 public 
  
 class 
 ExtractTableCompressed 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectName 
  
 = 
  
 "MY_PROJECT_NAME" 
 ; 
  
 String 
  
 datasetName 
  
 = 
  
 "MY_DATASET_NAME" 
 ; 
  
 String 
  
 tableName 
  
 = 
  
 "MY_TABLE_NAME" 
 ; 
  
 String 
  
 bucketName 
  
 = 
  
 "MY-BUCKET-NAME" 
 ; 
  
 String 
  
 destinationUri 
  
 = 
  
 "gs://" 
  
 + 
  
 bucketName 
  
 + 
  
 "/path/to/file" 
 ; 
  
 // For more information on export formats available see: 
  
 // https://cloud.google.com/bigquery/docs/exporting-data#export_formats_and_compression_types 
  
 String 
  
 compressed 
  
 = 
  
 "gzip" 
 ; 
  
 // For more information on Job see: 
  
 // https://googleapis.dev/java/google-cloud-clients/latest/index.html?com/google/cloud/bigquery/package-summary.html 
  
 String 
  
 dataFormat 
  
 = 
  
 "CSV" 
 ; 
  
 extractTableCompressed 
 ( 
  
 projectName 
 , 
  
 datasetName 
 , 
  
 tableName 
 , 
  
 destinationUri 
 , 
  
 dataFormat 
 , 
  
 compressed 
 ); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 extractTableCompressed 
 ( 
  
 String 
  
 projectName 
 , 
  
 String 
  
 datasetName 
 , 
  
 String 
  
 tableName 
 , 
  
 String 
  
 destinationUri 
 , 
  
 String 
  
 dataFormat 
 , 
  
 String 
  
 compressed 
 ) 
  
 { 
  
 try 
  
 { 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. 
  
  BigQuery 
 
  
 bigquery 
  
 = 
  
  BigQueryOptions 
 
 . 
 getDefaultInstance 
 (). 
 getService 
 (); 
  
  TableId 
 
  
 tableId 
  
 = 
  
  TableId 
 
 . 
 of 
 ( 
 projectName 
 , 
  
 datasetName 
 , 
  
 tableName 
 ); 
  
  ExtractJobConfiguration 
 
  
 extractConfig 
  
 = 
  
  ExtractJobConfiguration 
 
 . 
 newBuilder 
 ( 
 tableId 
 , 
  
 destinationUri 
 ) 
  
 . 
 setCompression 
 ( 
 compressed 
 ) 
  
 . 
  setFormat 
 
 ( 
 dataFormat 
 ) 
  
 . 
 build 
 (); 
  
  Job 
 
  
 job 
  
 = 
  
 bigquery 
 . 
  create 
 
 ( 
 JobInfo 
 . 
 of 
 ( 
 extractConfig 
 )); 
  
 // Blocks until this job completes its execution, either failing or succeeding. 
  
  Job 
 
  
 completedJob 
  
 = 
  
 job 
 . 
  waitFor 
 
 (); 
  
 if 
  
 ( 
 completedJob 
  
 == 
  
 null 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Job not executed since it no longer exists." 
 ); 
  
 return 
 ; 
  
 } 
  
 else 
  
 if 
  
 ( 
 completedJob 
 . 
 getStatus 
 (). 
 getError 
 () 
  
 != 
  
 null 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
  
 "BigQuery was unable to extract due to an error: \n" 
  
 + 
  
 job 
 . 
 getStatus 
 (). 
 getError 
 ()); 
  
 return 
 ; 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Table extract compressed successful" 
 ); 
  
 } 
  
 catch 
  
 ( 
  BigQueryException 
 
  
 | 
  
 InterruptedException 
  
 e 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Table extraction job was interrupted. \n" 
  
 + 
  
 e 
 . 
 toString 
 ()); 
  
 } 
  
 } 
 } 
 

  // Import the Google Cloud client libraries 
 const 
  
 { 
 BigQuery 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/bigquery 
' 
 ); 
 const 
  
 { 
 Storage 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/storage 
' 
 ); 
 const 
  
 bigquery 
  
 = 
  
 new 
  
  BigQuery 
 
 (); 
 const 
  
 storage 
  
 = 
  
 new 
  
 Storage 
 (); 
 async 
  
 function 
  
 extractTableCompressed 
 () 
  
 { 
  
 // Exports my_dataset:my_table to gcs://my-bucket/my-file as a compressed file. 
  
 /** 
 * TODO(developer): Uncomment the following lines before running the sample. 
 */ 
  
 // const datasetId = "my_dataset"; 
  
 // const tableId = "my_table"; 
  
 // const bucketName = "my-bucket"; 
  
 // const filename = "file.csv"; 
  
 // Location must match that of the source table. 
  
 const 
  
 options 
  
 = 
  
 { 
  
 location 
 : 
  
 'US' 
 , 
  
 gzip 
 : 
  
 true 
 , 
  
 }; 
  
 // Export data from the table into a Google Cloud Storage file 
  
 const 
  
 [ 
 job 
 ] 
  
 = 
  
 await 
  
 bigquery 
  
 . 
 dataset 
 ( 
 datasetId 
 ) 
  
 . 
 table 
 ( 
 tableId 
 ) 
  
 . 
 extract 
 ( 
 storage 
 . 
 bucket 
 ( 
 bucketName 
 ). 
 file 
 ( 
 filename 
 ), 
  
 options 
 ); 
  
 console 
 . 
 log 
 ( 
 `Job 
 ${ 
  job 
 
 . 
 id 
 } 
 created.` 
 ); 
  
 // Check the job's status for errors 
  
 const 
  
 errors 
  
 = 
  
  job 
 
 . 
 status 
 . 
 errors 
 ; 
  
 if 
  
 ( 
 errors 
 && 
 errors 
 . 
 length 
 > 
 0 
 ) 
  
 { 
  
 throw 
  
 errors 
 ; 
  
 } 
 } 
 

  # from google.cloud import bigquery 
 # client = bigquery.Client() 
 # bucket_name = 'my-bucket' 
 destination_uri 
 = 
 "gs:// 
 {} 
 / 
 {} 
 " 
 . 
 format 
 ( 
 bucket_name 
 , 
 "shakespeare.csv.gz" 
 ) 
 dataset_ref 
 = 
 bigquery 
 . 
 DatasetReference 
 ( 
 project 
 , 
 dataset_id 
 ) 
 table_ref 
 = 
 dataset_ref 
 . 
 table 
 ( 
 "shakespeare" 
 ) 
 job_config 
 = 
 bigquery 
 . 
 job 
 . 
 ExtractJobConfig 
 () 
 job_config 
 . 
 compression 
 = 
 bigquery 
 . 
 Compression 
 . 
 GZIP 
 extract_job 
 = 
 client 
 . 
 extract_table 
 ( 
 table_ref 
 , 
 destination_uri 
 , 
 # Location must match that of the source table. 
 location 
 = 
 "US" 
 , 
 job_config 
 = 
 job_config 
 , 
 ) 
 # API request 
 extract_job 
 . 
 result 
 () 
 # Waits for job to complete.