Create an integer-range partitioned table

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

Explore further

For detailed documentation that includes this code sample, see the following:

Creating partitioned tables

Code sample

C#

Before trying this sample, follow the C# setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery C# API reference documentation .

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

  using 
  
 Google.Apis.Bigquery.v2.Data 
 ; 
 using 
  
  Google.Cloud.BigQuery.V2 
 
 ; 
 public 
  
 class 
  
 BigQueryCreateTableRangePartitioned 
 { 
  
 public 
  
 BigQueryTable 
  
 CreateTable 
 ( 
 string 
  
 projectId 
 , 
  
 string 
  
 datasetId 
 , 
  
 string 
  
 tableId 
 ) 
  
 { 
  
  BigQueryClient 
 
  
 client 
  
 = 
  
  BigQueryClient 
 
 . 
  Create 
 
 ( 
 projectId 
 ); 
  
 var 
  
 dataset 
  
 = 
  
 client 
 . 
  GetDataset 
 
 ( 
 datasetId 
 ); 
  
 // Note: The field must be a top- level, NULLABLE/REQUIRED field. 
  
 // The only supported type is INTEGER/INT64. 
  
 var 
  
 partitioning 
  
 = 
  
 new 
  
 RangePartitioning 
  
 { 
  
 Field 
  
 = 
  
 "integerField" 
 , 
  
 Range 
  
 = 
  
 new 
  
 RangePartitioning 
 . 
 RangeData 
  
 { 
  
 Start 
  
 = 
  
 1 
 , 
  
 Interval 
  
 = 
  
 2 
 , 
  
 End 
  
 = 
  
 10 
  
 } 
  
 }; 
  
 var 
  
 schema 
  
 = 
  
 new 
  
  TableSchemaBuilder 
 
  
 { 
  
 { 
  
 "integerField" 
 , 
  
  BigQueryDbType 
 
 . 
  Int64 
 
  
 }, 
  
 { 
  
 "stringField" 
 , 
  
  BigQueryDbType 
 
 . 
  String 
 
  
 }, 
  
 { 
  
 "booleanField" 
 , 
  
  BigQueryDbType 
 
 . 
  Bool 
 
  
 }, 
  
 { 
  
 "dateField" 
 , 
  
  BigQueryDbType 
 
 . 
  Date 
 
  
 } 
  
 }. 
 Build 
 (); 
  
 var 
  
 table 
  
 = 
  
 new 
  
 Table 
  
 { 
  
 RangePartitioning 
  
 = 
  
 partitioning 
 , 
  
 Schema 
  
 = 
  
 schema 
  
 }; 
  
 return 
  
 dataset 
 . 
 CreateTable 
 ( 
 tableId 
 , 
  
 table 
 ); 
  
 } 
 }

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Go API reference documentation .

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "cloud.google.com/go/bigquery" 
 ) 
 // createTableRangeParitioned demonstrates creating a table and specifying a 
 // range partitioning configuration. 
 func 
  
 createTableRangePartitioned 
 ( 
 projectID 
 , 
  
 datasetID 
 , 
  
 tableID 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // datasetID := "mydatasetid" 
  
 // tableID := "mytableid" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 bigquery 
 . 
 NewClient 
 ( 
 ctx 
 , 
  
 projectID 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "bigquery.NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 sampleSchema 
  
 := 
  
 bigquery 
 . 
  Schema 
 
 { 
  
 { 
 Name 
 : 
  
 "full_name" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  StringFieldType 
 
 }, 
  
 { 
 Name 
 : 
  
 "city" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  StringFieldType 
 
 }, 
  
 { 
 Name 
 : 
  
 "zipcode" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  IntegerFieldType 
 
 }, 
  
 } 
  
 metadata 
  
 := 
  
& bigquery 
 . 
  TableMetadata 
 
 { 
  
 RangePartitioning 
 : 
  
& bigquery 
 . 
  RangePartitioning 
 
 { 
  
 Field 
 : 
  
 "zipcode" 
 , 
  
 Range 
 : 
  
& bigquery 
 . 
  RangePartitioningRange 
 
 { 
  
 Start 
 : 
  
 0 
 , 
  
 End 
 : 
  
 100000 
 , 
  
 Interval 
 : 
  
 10 
 , 
  
 }, 
  
 }, 
  
 Schema 
 : 
  
 sampleSchema 
 , 
  
 } 
  
 tableRef 
  
 := 
  
 client 
 . 
 Dataset 
 ( 
 datasetID 
 ). 
 Table 
 ( 
 tableID 
 ) 
  
 if 
  
 err 
  
 := 
  
 tableRef 
 . 
 Create 
 ( 
 ctx 
 , 
  
 metadata 
 ); 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 return 
  
 nil 
 }

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Java API reference documentation .

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

  import 
  
 com.google.cloud.bigquery. BigQuery 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryException 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryOptions 
 
 ; 
 import 
  
 com.google.cloud.bigquery. Field 
 
 ; 
 import 
  
 com.google.cloud.bigquery. RangePartitioning 
 
 ; 
 import 
  
 com.google.cloud.bigquery. Schema 
 
 ; 
 import 
  
 com.google.cloud.bigquery. StandardSQLTypeName 
 
 ; 
 import 
  
 com.google.cloud.bigquery. StandardTableDefinition 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TableId 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TableInfo 
 
 ; 
 // Sample to create a range partitioned table 
 public 
  
 class 
 CreateRangePartitionedTable 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 datasetName 
  
 = 
  
 "MY_DATASET_NAME" 
 ; 
  
 String 
  
 tableName 
  
 = 
  
 "MY_TABLE_NAME" 
 ; 
  
  Schema 
 
  
 schema 
  
 = 
  
  Schema 
 
 . 
 of 
 ( 
  
  Field 
 
 . 
 of 
 ( 
 "integerField" 
 , 
  
  StandardSQLTypeName 
 
 . 
 INT64 
 ), 
  
  Field 
 
 . 
 of 
 ( 
 "stringField" 
 , 
  
  StandardSQLTypeName 
 
 . 
 STRING 
 ), 
  
  Field 
 
 . 
 of 
 ( 
 "booleanField" 
 , 
  
  StandardSQLTypeName 
 
 . 
 BOOL 
 ), 
  
  Field 
 
 . 
 of 
 ( 
 "dateField" 
 , 
  
  StandardSQLTypeName 
 
 . 
 DATE 
 )); 
  
 createRangePartitionedTable 
 ( 
 datasetName 
 , 
  
 tableName 
 , 
  
 schema 
 ); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 createRangePartitionedTable 
 ( 
  
 String 
  
 datasetName 
 , 
  
 String 
  
 tableName 
 , 
  
  Schema 
 
  
 schema 
 ) 
  
 { 
  
 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 
 ( 
 datasetName 
 , 
  
 tableName 
 ); 
  
 // Note: The field must be a top- level, NULLABLE/REQUIRED field. 
  
 // The only supported type is INTEGER/INT64 
  
  RangePartitioning 
 
  
 partitioning 
  
 = 
  
  RangePartitioning 
 
 . 
 newBuilder 
 () 
  
 . 
 setField 
 ( 
 "integerField" 
 ) 
  
 . 
 setRange 
 ( 
  
  RangePartitioning 
 
 . 
 Range 
 . 
 newBuilder 
 () 
  
 . 
 setStart 
 ( 
 1L 
 ) 
  
 . 
 setInterval 
 ( 
 2L 
 ) 
  
 . 
 setEnd 
 ( 
 10L 
 ) 
  
 . 
 build 
 ()) 
  
 . 
 build 
 (); 
  
  StandardTableDefinition 
 
  
 tableDefinition 
  
 = 
  
  StandardTableDefinition 
 
 . 
 newBuilder 
 () 
  
 . 
 setSchema 
 ( 
 schema 
 ) 
  
 . 
 setRangePartitioning 
 ( 
 partitioning 
 ) 
  
 . 
 build 
 (); 
  
  TableInfo 
 
  
 tableInfo 
  
 = 
  
  TableInfo 
 
 . 
 newBuilder 
 ( 
 tableId 
 , 
  
 tableDefinition 
 ). 
 build 
 (); 
  
 bigquery 
 . 
  create 
 
 ( 
 tableInfo 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Range partitioned table created successfully" 
 ); 
  
 } 
  
 catch 
  
 ( 
  BigQueryException 
 
  
 e 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Range partitioned table was not created. \n" 
  
 + 
  
 e 
 . 
 toString 
 ()); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Node.js API reference documentation .

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

  // Import the Google Cloud client library 
 const 
  
 { 
 BigQuery 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/bigquery 
' 
 ); 
 const 
  
 bigquery 
  
 = 
  
 new 
  
  BigQuery 
 
 (); 
 async 
  
 function 
  
 createTableRangePartitioned 
 () 
  
 { 
  
 // Creates a new integer range partitioned table named "my_table" 
  
 // in "my_dataset". 
  
 /** 
 * TODO(developer): Uncomment the following lines before running the sample. 
 */ 
  
 // const datasetId = "my_dataset"; 
  
 // const tableId = "my_table"; 
  
 const 
  
 schema 
  
 = 
  
 [ 
  
 { 
 name 
 : 
  
 'fullName' 
 , 
  
 type 
 : 
  
 'STRING' 
 }, 
  
 { 
 name 
 : 
  
 'city' 
 , 
  
 type 
 : 
  
 'STRING' 
 }, 
  
 { 
 name 
 : 
  
 'zipcode' 
 , 
  
 type 
 : 
  
 ' INTEGER 
' 
 }, 
  
 ]; 
  
 // To use integer range partitioning, select a top-level REQUIRED or 
  
 // NULLABLE column with INTEGER / INT64 data type. Values that are 
  
 // outside of the range of the table will go into the UNPARTITIONED 
  
 // partition. Null values will be in the NULL partition. 
  
 const 
  
 rangePartition 
  
 = 
  
 { 
  
 field 
 : 
  
 'zipcode' 
 , 
  
 range 
 : 
  
 { 
  
 start 
 : 
  
 0 
 , 
  
 end 
 : 
  
 100000 
 , 
  
 interval 
 : 
  
 10 
 , 
  
 }, 
  
 }; 
  
 // For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource 
  
 const 
  
 options 
  
 = 
  
 { 
  
 schema 
 : 
  
 schema 
 , 
  
 rangePartitioning 
 : 
  
 rangePartition 
 , 
  
 }; 
  
 // Create a new table in the dataset 
  
 const 
  
 [ 
 table 
 ] 
  
 = 
  
 await 
  
 bigquery 
  
 . 
 dataset 
 ( 
 datasetId 
 ) 
  
 . 
  createTable 
 
 ( 
 tableId 
 , 
  
 options 
 ); 
  
 console 
 . 
 log 
 ( 
 `Table 
 ${ 
 table 
 . 
 id 
 } 
 created with integer range partitioning: ` 
 ); 
  
 console 
 . 
 log 
 ( 
 table 
 . 
 metadata 
 . 
 rangePartitioning 
 ); 
 }

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries . For more information, see the BigQuery Python API reference documentation .

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

  from 
  
 google.cloud 
  
 import 
  bigquery 
 
 # Construct a BigQuery client object. 
 client 
 = 
  bigquery 
 
 . 
  Client 
 
 () 
 # TODO(developer): Set table_id to the ID of the table to create. 
 # table_id = "your-project.your_dataset.your_table_name" 
 schema 
 = 
 [ 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "full_name" 
 , 
 "STRING" 
 ), 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "city" 
 , 
 "STRING" 
 ), 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "zipcode" 
 , 
 "INTEGER" 
 ), 
 ] 
 table 
 = 
  bigquery 
 
 . 
  Table 
 
 ( 
 table_id 
 , 
 schema 
 = 
 schema 
 ) 
 table 
 . 
 range_partitioning 
 = 
  bigquery 
 
 . 
  RangePartitioning 
 
 ( 
 # To use integer range partitioning, select a top-level REQUIRED / 
 # NULLABLE column with INTEGER / INT64 data type. 
 field 
 = 
 "zipcode" 
 , 
 range_ 
 = 
  bigquery 
 
 . 
  PartitionRange 
 
 ( 
 start 
 = 
 0 
 , 
 end 
 = 
 100000 
 , 
 interval 
 = 
 10 
 ), 
 ) 
 table 
 = 
 client 
 . 
  create_table 
 
 ( 
 table 
 ) 
 # Make an API request. 
 print 
 ( 
 "Created table 
 {} 
 . 
 {} 
 . 
 {} 
 " 
 . 
 format 
 ( 
 table 
 . 
 project 
 , 
 table 
 . 
 dataset_id 
 , 
 table 
 . 
 table_id 
 ) 
 )

Terraform

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands . For more information, see the Terraform provider reference documentation .

  resource 
  
 "google_bigquery_dataset" 
  
 "default" 
  
 { 
  
 dataset_id 
  
 = 
  
 "mydataset" 
  
 default_partition_expiration_ms 
  
 = 
  
 2592000000 
 # 30 days 
  
 default_table_expiration_ms 
  
 = 
  
 31536000000 
 # 365 days 
  
 description 
  
 = 
  
 "dataset description" 
  
 location 
  
 = 
  
 "US" 
  
 max_time_travel_hours 
  
 = 
  
 96 
 # 4 days 
  
 labels 
  
 = 
  
 { 
  
 billing_group 
  
 = 
  
 "accounting" 
 , 
  
 pii 
  
 = 
  
 "sensitive" 
  
 } 
 } 
 resource 
  
 "google_bigquery_table" 
  
 "default" 
  
 { 
  
 dataset_id 
  
 = 
  
 google_bigquery_dataset.default.dataset_id 
  
 table_id 
  
 = 
  
 "mytable" 
  
 deletion_protection 
  
 = 
  
 false 
 # set to "true" in production 
  
 range_partitioning 
  
 { 
  
 field 
  
 = 
  
 "ID" 
  
 range 
  
 { 
  
 start 
  
 = 
  
 0 
  
 end 
  
 = 
  
 1000 
  
 interval 
  
 = 
  
 10 
  
 } 
  
 } 
  
 require_partition_filter 
  
 = 
  
 true 
  
 schema 
  
 = 
  
<< EOF 
 [ 
  
 { 
  
 "name" 
 : 
  
 "ID" 
 , 
  
 "type" 
 : 
  
 "INT64" 
 , 
  
 "description" 
 : 
  
 "Item ID" 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Item" 
 , 
  
 "type" 
 : 
  
 "STRING" 
 , 
  
 "mode" 
 : 
  
 "NULLABLE" 
  
 } 
 ] 
 EOF 
 }

What's next

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Explore further

Code sample

C#

Go

Java

Node.js

Python

Terraform

What's next

Create an integer-range partitioned table