Column-based time partitioning

Create a table that uses column-based time partitioning.

Explore further

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

Creating partitioned tables

Code sample

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" 
  
 "time" 
  
 "cloud.google.com/go/bigquery" 
 ) 
 // createTablePartitioned demonstrates creating a table and specifying a time partitioning configuration. 
 func 
  
 createTablePartitioned 
 ( 
 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 
 : 
  
 "name" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  StringFieldType 
 
 }, 
  
 { 
 Name 
 : 
  
 "post_abbr" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  IntegerFieldType 
 
 }, 
  
 { 
 Name 
 : 
  
 "date" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  DateFieldType 
 
 }, 
  
 } 
  
 metadata 
  
 := 
  
& bigquery 
 . 
  TableMetadata 
 
 { 
  
 TimePartitioning 
 : 
  
& bigquery 
 . 
  TimePartitioning 
 
 { 
  
 Field 
 : 
  
 "date" 
 , 
  
 Expiration 
 : 
  
 90 
  
 * 
  
 24 
  
 * 
  
 time 
 . 
 Hour 
 , 
  
 }, 
  
 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. 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 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TimePartitioning 
 
 ; 
 // Sample to create a partition table 
 public 
  
 class 
 CreatePartitionedTable 
  
 { 
  
 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 
 ( 
 "name" 
 , 
  
  StandardSQLTypeName 
 
 . 
 STRING 
 ), 
  
  Field 
 
 . 
 of 
 ( 
 "post_abbr" 
 , 
  
  StandardSQLTypeName 
 
 . 
 STRING 
 ), 
  
  Field 
 
 . 
 of 
 ( 
 "date" 
 , 
  
  StandardSQLTypeName 
 
 . 
 DATE 
 )); 
  
 createPartitionedTable 
 ( 
 datasetName 
 , 
  
 tableName 
 , 
  
 schema 
 ); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 createPartitionedTable 
 ( 
 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 
 ); 
  
  TimePartitioning 
 
  
 partitioning 
  
 = 
  
  TimePartitioning 
 
 . 
 newBuilder 
 ( 
  TimePartitioning 
 
 . 
 Type 
 . 
 DAY 
 ) 
  
 . 
 setField 
 ( 
 "date" 
 ) 
  
 //  name of column to use for partitioning 
  
 . 
  setExpirationMs 
 
 ( 
 7776000000L 
 ) 
  
 // 90 days 
  
 . 
 build 
 (); 
  
  StandardTableDefinition 
 
  
 tableDefinition 
  
 = 
  
  StandardTableDefinition 
 
 . 
 newBuilder 
 () 
  
 . 
 setSchema 
 ( 
 schema 
 ) 
  
 . 
 setTimePartitioning 
 ( 
 partitioning 
 ) 
  
 . 
 build 
 (); 
  
  TableInfo 
 
  
 tableInfo 
  
 = 
  
  TableInfo 
 
 . 
 newBuilder 
 ( 
 tableId 
 , 
  
 tableDefinition 
 ). 
 build 
 (); 
  
 bigquery 
 . 
  create 
 
 ( 
 tableInfo 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Partitioned table created successfully" 
 ); 
  
 } 
  
 catch 
  
 ( 
  BigQueryException 
 
  
 e 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "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 
  
 createTablePartitioned 
 () 
  
 { 
  
 // Creates a new 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:string, Post_Abbr:string, Date:date' 
 ; 
  
 // For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource 
  
 const 
  
 options 
  
 = 
  
 { 
  
 schema 
 : 
  
 schema 
 , 
  
 location 
 : 
  
 'US' 
 , 
  
 timePartitioning 
 : 
  
 { 
  
 type 
 : 
  
 'DAY' 
 , 
  
 expirationMs 
 : 
  
 '7776000000' 
 , 
  
 field 
 : 
  
 ' date 
' 
 , 
  
 }, 
  
 }; 
  
 // Create a new table in the dataset 
  
 const 
  
 [ 
 table 
 ] 
  
 = 
  
 await 
  
 bigquery 
  
 . 
 dataset 
 ( 
 datasetId 
 ) 
  
 . 
  createTable 
 
 ( 
 tableId 
 , 
  
 options 
 ); 
  
 console 
 . 
 log 
 ( 
 `Table 
 ${ 
 table 
 . 
 id 
 } 
 created with partitioning: ` 
 ); 
  
 console 
 . 
 log 
 ( 
 table 
 . 
 metadata 
 . 
 timePartitioning 
 ); 
 }

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 
 
 client 
 = 
  bigquery 
 
 . 
  Client 
 
 () 
 # Use format "your-project.your_dataset.your_table_name" for table_id 
 table_id 
 = 
 your_fully_qualified_table_id 
 schema 
 = 
 [ 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "name" 
 , 
 "STRING" 
 ), 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "post_abbr" 
 , 
 "STRING" 
 ), 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "date" 
 , 
 "DATE" 
 ), 
 ] 
 table 
 = 
  bigquery 
 
 . 
  Table 
 
 ( 
 table_id 
 , 
 schema 
 = 
 schema 
 ) 
 table 
 . 
 time_partitioning 
 = 
  bigquery 
 
 . 
  TimePartitioning 
 
 ( 
 type_ 
 = 
  bigquery 
 
 . 
  TimePartitioningType 
 
 . 
  DAY 
 
 , 
 field 
 = 
 "date" 
 , 
 # name of column to use for partitioning 
 expiration_ms 
 = 
 1000 
 * 
 60 
 * 
 60 
 * 
 24 
 * 
 90 
 , 
 ) 
 # 90 days 
 table 
 = 
 client 
 . 
  create_table 
 
 ( 
 table 
 ) 
 print 
 ( 
 f 
 "Created table 
 { 
 table 
 . 
 project 
 } 
 . 
 { 
 table 
 . 
 dataset_id 
 } 
 . 
 { 
 table 
 . 
 table_id 
 } 
 , " 
 f 
 "partitioned on column 
 { 
 table 
 . 
 time_partitioning 
 . 
 field 
 } 
 ." 
 )

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 
  
 time_partitioning 
  
 { 
  
 type 
  
 = 
  
 "DAY" 
  
 field 
  
 = 
  
 "Created" 
  
 expiration_ms 
  
 = 
  
 432000000 
 # 5 days 
  
 } 
  
 require_partition_filter 
  
 = 
  
 true 
  
 schema 
  
 = 
  
<< EOF 
 [ 
  
 { 
  
 "name" 
 : 
  
 "ID" 
 , 
  
 "type" 
 : 
  
 "INT64" 
 , 
  
 "mode" 
 : 
  
 "NULLABLE" 
 , 
  
 "description" 
 : 
  
 "Item ID" 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Created" 
 , 
  
 "type" 
 : 
  
 "TIMESTAMP" 
 , 
  
 "description" 
 : 
  
 "Record creation timestamp" 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Item" 
 , 
  
 "type" 
 : 
  
 "STRING" 
 , 
  
 "mode" 
 : 
  
 "NULLABLE" 
  
 } 
 ] 
 EOF 
 }

What's next

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

Column-based time partitioning Stay organized with collections Save and categorize content based on your preferences.

Explore further

Code sample

Go

Java

Node.js

Python

Terraform

What's next

Column-based time partitioning