Deletes

This document describes how to delete data stored in Bigtable tables, discusses when you should use each approach, and provides examples. Before you read this page, you should be familiar with the Bigtable overview and understand the concepts involved in schema design .

For consistency, descriptions on this page refer to the API methods that are used for each type of request. However, we strongly recommend that you always use one of the Bigtable client libraries to access the Bigtable APIs instead of using REST or RPC.

Examples on this page use sample data similar to the data that you might store in Bigtable.

To learn the number of times that you can use the operations described on this page per day, see Quotas and limits .

How Bigtable deletes data

When you send a delete request, cells are marked for deletion and cannot be read. The data is removed up to a week later during compaction , a background process that continuously optimizes the table. Deletion metadata can cause your data to take up slightly more space (several kb per row) for a few days after you send a delete request, until the next compaction occurs.

You can always send a delete request, even if your cluster has exceeded the storage limit and reads and writes are blocked.

Delete a range of rows

If you want to delete a large amount of data stored in contiguous rows, use dropRowRange . This operation deletes all rows for a range of rows identified by a starting and ending row or a row key prefix.

The row key values that you provide when you delete a range of rows are treated as service data. For information about how service data is handled, see the Google Cloud Privacy Notice .

After a successful deletion is complete and you receive a response, you can safely write data to the same row range.

The dropRowRange operation has the following restrictions:

You can't drop a range of rows from an authorized view.
You can't call the dropRowRange method asynchronously. If you send a dropRowRange request to a table while another request is in progress, Bigtable returns an UNAVAILABLE error with the message A DropRowRange operation is already ongoing . To resolve the error, send the request again.
With instances that use replication, be aware that Bigtable might take a long time to complete the operation due to increased replication latency and CPU usage. To delete data from an instance that uses replication, use the Data API to read and then delete your data .

The following code samples show how to drop a range of rows that start with the row key prefix phone#5c10102 :

Java

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  import 
  
 com.google.cloud.bigtable.admin.v2. BigtableTableAdminClient 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 DropRowRangeExample 
  
 { 
  
 public 
  
 void 
  
 dropRowRange 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 instanceId 
 , 
  
 String 
  
 tableId 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 try 
  
 ( 
  BigtableTableAdminClient 
 
  
 tableAdminClient 
  
 = 
  
  BigtableTableAdminClient 
 
 . 
 create 
 ( 
 projectId 
 , 
  
 instanceId 
 )) 
  
 { 
  
 tableAdminClient 
 . 
  dropRowRange 
 
 ( 
 tableId 
 , 
  
 "phone#4c410523" 
 ); 
  
 } 
  
 } 
 }

Python

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  def 
  
 drop_row_range 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable 
  
 import 
  Client 
 
 client 
 = 
 Client 
 ( 
 project 
 = 
 project_id 
 , 
 admin 
 = 
 True 
 ) 
 instance 
 = 
  client 
 
 . 
  instance 
 
 ( 
 instance_id 
 ) 
 table 
 = 
 instance 
 . 
 table 
 ( 
 table_id 
 ) 
 row_key_prefix 
 = 
 "phone#4c410523" 
 table 
 . 
  drop_by_prefix 
 
 ( 
 row_key_prefix 
 , 
 timeout 
 = 
 200 
 )

Node.js

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  await 
  
 table 
 . 
 deleteRows 
 ( 
 'phone#5c10102' 
 ); 
 await 
  
 printRows 
 ();

Delete data using Data API methods

If you need to delete small amounts of non-contiguous data, deleting data using a method that calls the Cloud Bigtable API (Data API) is often the best choice. Use these methods if you are deleting MB, not GB, of data in a request. Using the Data API is the only way to delete data from a column (not column family).

Data API methods call MutateRows with one of three mutation types:

DeleteFromColumn
DeleteFromFamily
DeleteFromRow

A delete request using the Data API is atomic: either the request succeeds and all data is deleted, or the request fails and no data is removed.

In most cases, avoid using CheckAndMutate methods to delete data. In the rare event that you require strong consistency, you might want to use this approach, but be aware that it is resource-intensive and performance might be affected.

To use MutateRows to delete data, you send a readRows request with a filter to determine what you want to delete, and then you send the deletion request. For a list of the filters that are available, see Filters .

Samples in this section assume that you have already determined what data to delete.

Delete from a column

The following code samples demonstrate how to delete all the cells from a column in a row:

Java

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  import 
  
 com.google.cloud.bigtable.data.v2. BigtableDataClient 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. Mutation 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. RowMutation 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. TableId 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 DeleteFromColumnExample 
  
 { 
  
 public 
  
 void 
  
 deleteFromColumnCells 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 instanceId 
 , 
  
 String 
  
 tableId 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 try 
  
 ( 
  BigtableDataClient 
 
  
 dataClient 
  
 = 
  
  BigtableDataClient 
 
 . 
 create 
 ( 
 projectId 
 , 
  
 instanceId 
 )) 
  
 { 
  
  Mutation 
 
  
 mutation 
  
 = 
  
  Mutation 
 
 . 
 create 
 (). 
 deleteCells 
 ( 
 "cell_plan" 
 , 
  
 "data_plan_01gb" 
 ); 
  
 dataClient 
 . 
  mutateRow 
 
 ( 
  
  RowMutation 
 
 . 
 create 
 ( 
  TableId 
 
 . 
 of 
 ( 
 tableId 
 ), 
  
 "phone#4c410523#20190501" 
 , 
  
 mutation 
 )); 
  
 } 
  
 } 
 }

Python

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  def 
  
 delete_from_column 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable 
  
 import 
  Client 
 
 client 
 = 
 Client 
 ( 
 project 
 = 
 project_id 
 , 
 admin 
 = 
 True 
 ) 
 instance 
 = 
  client 
 
 . 
  instance 
 
 ( 
 instance_id 
 ) 
 table 
 = 
 instance 
 . 
 table 
 ( 
 table_id 
 ) 
 row 
 = 
 table 
 . 
 row 
 ( 
 "phone#4c410523#20190501" 
 ) 
 row 
 . 
 delete_cell 
 ( 
 column_family_id 
 = 
 "cell_plan" 
 , 
 column 
 = 
 "data_plan_01gb" 
 ) 
 row 
 . 
 commit 
 ()

Python asyncio

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  async 
 def 
  
 delete_from_column 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable.data 
  
 import 
 BigtableDataClientAsync 
 from 
  
 google.cloud.bigtable.data 
  
 import 
  DeleteRangeFromColumn 
 
 client 
 = 
 BigtableDataClientAsync 
 ( 
 project 
 = 
 project_id 
 ) 
 table 
 = 
 client 
 . 
 get_table 
 ( 
 instance_id 
 , 
 table_id 
 ) 
 await 
 table 
 . 
 mutate_row 
 ( 
 "phone#4c410523#20190501" 
 , 
 DeleteRangeFromColumn 
 ( 
 family 
 = 
 "cell_plan" 
 , 
 qualifier 
 = 
 b 
 "data_plan_01gb" 
 ), 
 ) 
 await 
 table 
 . 
 close 
 () 
 await 
 client 
 . 
 close 
 ()

Node.js

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  await 
  
 table 
 . 
 mutate 
 ({ 
  
 key 
 : 
  
 'phone#4c410523#20190501' 
 , 
  
 method 
 : 
  
 'delete' 
 , 
  
 data 
 : 
  
 { 
  
 column 
 : 
  
 'cell_plan:data_plan_05gb' 
 , 
  
 }, 
 }); 
 await 
  
 printRows 
 ();

Delete from a column family

The following code samples demonstrate how to delete cells from a column family in a row:

Java

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  import 
  
 com.google.cloud.bigtable.data.v2. BigtableDataClient 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. RowMutation 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. TableId 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 DeleteFromColumnFamilyExample 
  
 { 
  
 public 
  
 void 
  
 deleteFromColumnFamily 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 instanceId 
 , 
  
 String 
  
 tableId 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 try 
  
 ( 
  BigtableDataClient 
 
  
 dataClient 
  
 = 
  
  BigtableDataClient 
 
 . 
 create 
 ( 
 projectId 
 , 
  
 instanceId 
 )) 
  
 { 
  
 dataClient 
 . 
  mutateRow 
 
 ( 
  
  RowMutation 
 
 . 
 create 
 ( 
  TableId 
 
 . 
 of 
 ( 
 tableId 
 ), 
  
 "phone#5c10102#20190501" 
 ) 
  
 . 
 deleteFamily 
 ( 
 "stats_summary" 
 )); 
  
 } 
  
 } 
 }

Python

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  def 
  
 delete_from_column_family 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable 
  
 import 
  Client 
 
 client 
 = 
 Client 
 ( 
 project 
 = 
 project_id 
 , 
 admin 
 = 
 True 
 ) 
 instance 
 = 
  client 
 
 . 
  instance 
 
 ( 
 instance_id 
 ) 
 table 
 = 
 instance 
 . 
 table 
 ( 
 table_id 
 ) 
 row 
 = 
 table 
 . 
 row 
 ( 
 "phone#4c410523#20190501" 
 ) 
 row 
 . 
 delete_cells 
 ( 
 column_family_id 
 = 
 "cell_plan" 
 , 
 columns 
 = 
 row 
 . 
 ALL_COLUMNS 
 ) 
 row 
 . 
 commit 
 ()

Python asyncio

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  async 
 def 
  
 delete_from_column_family 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable.data 
  
 import 
 BigtableDataClientAsync 
 from 
  
 google.cloud.bigtable.data 
  
 import 
  DeleteAllFromFamily 
 
 client 
 = 
 BigtableDataClientAsync 
 ( 
 project 
 = 
 project_id 
 ) 
 table 
 = 
 client 
 . 
 get_table 
 ( 
 instance_id 
 , 
 table_id 
 ) 
 await 
 table 
 . 
 mutate_row 
 ( 
 "phone#4c410523#20190501" 
 , 
 DeleteAllFromFamily 
 ( 
 "cell_plan" 
 )) 
 await 
 table 
 . 
 close 
 () 
 await 
 client 
 . 
 close 
 ()

Node.js

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  await 
  
 table 
 . 
 mutate 
 ({ 
  
 key 
 : 
  
 'phone#4c410523#20190501' 
 , 
  
 method 
 : 
  
 'delete' 
 , 
  
 data 
 : 
  
 { 
  
 column 
 : 
  
 'cell_plan' 
 , 
  
 }, 
 }); 
 await 
  
 printRows 
 ();

Delete from a row

The following code snippets demonstrate how to delete all the cells from a row:

Java

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  import 
  
 com.google.cloud.bigtable.data.v2. BigtableDataClient 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. Mutation 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. RowMutation 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. TableId 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 DeleteFromRowExample 
  
 { 
  
 public 
  
 void 
  
 deleteFromRow 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 instanceId 
 , 
  
 String 
  
 tableId 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 try 
  
 ( 
  BigtableDataClient 
 
  
 dataClient 
  
 = 
  
  BigtableDataClient 
 
 . 
 create 
 ( 
 projectId 
 , 
  
 instanceId 
 )) 
  
 { 
  
  Mutation 
 
  
 mutation 
  
 = 
  
  Mutation 
 
 . 
 create 
 (). 
 deleteRow 
 (); 
  
 dataClient 
 . 
  mutateRow 
 
 ( 
  
  RowMutation 
 
 . 
 create 
 ( 
  TableId 
 
 . 
 of 
 ( 
 tableId 
 ), 
  
 "phone#4c410523#20190501" 
 , 
  
 mutation 
 )); 
  
 } 
  
 } 
 }

Python

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  def 
  
 delete_from_row 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable 
  
 import 
  Client 
 
 client 
 = 
 Client 
 ( 
 project 
 = 
 project_id 
 , 
 admin 
 = 
 True 
 ) 
 instance 
 = 
  client 
 
 . 
  instance 
 
 ( 
 instance_id 
 ) 
 table 
 = 
 instance 
 . 
 table 
 ( 
 table_id 
 ) 
 row 
 = 
 table 
 . 
 row 
 ( 
 "phone#4c410523#20190501" 
 ) 
 row 
 . 
 delete 
 () 
 row 
 . 
 commit 
 ()

Python asyncio

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  async 
 def 
  
 delete_from_row 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable.data 
  
 import 
 BigtableDataClientAsync 
 from 
  
 google.cloud.bigtable.data 
  
 import 
  DeleteAllFromRow 
 
 client 
 = 
 BigtableDataClientAsync 
 ( 
 project 
 = 
 project_id 
 ) 
 table 
 = 
 client 
 . 
 get_table 
 ( 
 instance_id 
 , 
 table_id 
 ) 
 await 
 table 
 . 
 mutate_row 
 ( 
 "phone#4c410523#20190501" 
 , 
 DeleteAllFromRow 
 ()) 
 await 
 table 
 . 
 close 
 () 
 await 
 client 
 . 
 close 
 ()

Node.js

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  const 
  
 row 
  
 = 
  
 table 
 . 
 row 
 ( 
 'phone#4c410523#20190501' 
 ); 
 await 
  
 row 
 . 
 delete 
 (); 
 await 
  
 printRows 
 ();

Delete by streaming and batching

Streaming and batching your delete requests is often the best way to delete large amounts of data. This strategy can be useful when you have finer-grained data retention requirements than garbage collection policies allow.

If your application is written in Java, you can enable batch write flow control when you send batch deletes to Bigtable. For more information, see Batch write flow control and Enable batch write flow control .

The following code samples start a stream of data (reading rows), batch them, and then go through the batch and delete all the cells in column data_plan_01gb1 in the cell_plan column family:

Go

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  // Copyright 2025 Google LLC 
 // 
 // Licensed under the Apache License, Version 2.0 (the "License"); 
 // you may not use this file except in compliance with the License. 
 // You may obtain a copy of the License at 
 // 
 //     https://www.apache.org/licenses/LICENSE-2.0 
 // 
 // Unless required by applicable law or agreed to in writing, software 
 // distributed under the License is distributed on an "AS IS" BASIS, 
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
 // See the License for the specific language governing permissions and 
 // limitations under the License. 
 package 
  
 deletes 
 import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 "cloud.google.com/go/bigtable" 
 ) 
 // streamingAndBatching starts a stream of data (reading rows), batches them, and then goes through the batch and deletes all the cells in column 
 func 
  
 streamingAndBatching 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
 , 
  
 instanceID 
  
 string 
 , 
  
 tableName 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // instanceID := "my-instance-id" 
  
 // tableName := "mobile-time-series" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 bigtable 
 . 
  NewClient 
 
 ( 
 ctx 
 , 
  
 projectID 
 , 
  
 instanceID 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "bigtable.NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 tbl 
  
 := 
  
 client 
 . 
  Open 
 
 ( 
 tableName 
 ) 
  
 // Slices to hold the row keys and the corresponding mutations. 
  
 var 
  
 rowKeys 
  
 [] 
 string 
  
 var 
  
 mutations 
  
 [] 
 * 
 bigtable 
 . 
 Mutation 
  
 // Read all rows from the table. 
  
 err 
  
 = 
  
 tbl 
 . 
 ReadRows 
 ( 
 ctx 
 , 
  
 bigtable 
 . 
  InfiniteRange 
 
 ( 
 "" 
 ), 
  
 func 
 ( 
 row 
  
 bigtable 
 . 
 Row 
 ) 
  
 bool 
  
 { 
  
 // For each row, create a mutation to delete the specified cell. 
  
 mut 
  
 := 
  
 bigtable 
 . 
  NewMutation 
 
 () 
  
 mut 
 . 
  DeleteCellsInColumn 
 
 ( 
 "cell_plan" 
 , 
  
 "data_plan_01gb" 
 ) 
  
 // Append the row key and mutation to the slices. 
  
 rowKeys 
  
 = 
  
 append 
 ( 
 rowKeys 
 , 
  
 row 
 . 
  Key 
 
 ()) 
  
 mutations 
  
 = 
  
 append 
 ( 
 mutations 
 , 
  
 mut 
 ) 
  
 // Continue processing rows. 
  
 return 
  
 true 
  
 }) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "tbl.ReadRows: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 if 
  
 len 
 ( 
 mutations 
 ) 
  
 == 
  
 0 
  
 { 
  
 return 
  
 nil 
  
 } 
  
 // If there are mutations to apply, apply them in a single bulk request. 
  
 // ApplyBulk returns a slice of errors, one for each mutation. 
  
 var 
  
 errs 
  
 [] 
 error 
  
 if 
  
 errs 
 , 
  
 err 
  
 = 
  
 tbl 
 . 
  ApplyBulk 
 
 ( 
 ctx 
 , 
  
 rowKeys 
 , 
  
 mutations 
 ); 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "tbl.ApplyBulk: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 if 
  
 errs 
  
 != 
  
 nil 
  
 { 
  
 // Log any individual errors that occurred during the bulk operation. 
  
 var 
  
 errorCount 
  
 int 
  
 for 
  
 _ 
 , 
  
 individualErr 
  
 := 
  
 range 
  
 errs 
  
 { 
  
 if 
  
 individualErr 
  
 != 
  
 nil 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Error applying mutation: %v\n" 
 , 
  
 individualErr 
 ) 
  
 errorCount 
 ++ 
  
 } 
  
 } 
  
 if 
  
 errorCount 
 > 
 0 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "encountered %d error(s) out of %d mutations" 
 , 
  
 errorCount 
 , 
  
 len 
 ( 
 errs 
 )) 
  
 } 
  
 } 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Successfully deleted cells from all rows" 
 ) 
  
 return 
  
 nil 
 }

Java

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  import 
  
 com.google.api.gax. batching 
. Batcher 
 
 ; 
 import 
  
 com.google.api.gax.rpc. ServerStream 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2. BigtableDataClient 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. Query 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. Row 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. RowMutationEntry 
 
 ; 
 import 
  
 com.google.cloud.bigtable.data.v2.models. TableId 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 BatchDeleteExample 
  
 { 
  
 public 
  
 void 
  
 batchDelete 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 instanceId 
 , 
  
 String 
  
 tableId 
 ) 
  
 throws 
  
 InterruptedException 
 , 
  
 IOException 
  
 { 
  
 try 
  
 ( 
  BigtableDataClient 
 
  
 dataClient 
  
 = 
  
  BigtableDataClient 
 
 . 
 create 
 ( 
 projectId 
 , 
  
 instanceId 
 )) 
  
 { 
  
 try 
  
 ( 
 Batcher<RowMutationEntry 
 , 
  
 Void 
>  
 batcher 
  
 = 
  
 dataClient 
 . 
  newBulkMutationBatcher 
 
 ( 
  TableId 
 
 . 
 of 
 ( 
 tableId 
 ))) 
  
 { 
  
 ServerStream<Row> 
  
 rows 
  
 = 
  
 dataClient 
 . 
  readRows 
 
 ( 
  Query 
 
 . 
 create 
 ( 
  TableId 
 
 . 
 of 
 ( 
 tableId 
 ))); 
  
 for 
  
 ( 
  Row 
 
  
 row 
  
 : 
  
 rows 
 ) 
  
 { 
  
 batcher 
 . 
 add 
 ( 
  
  RowMutationEntry 
 
 . 
 create 
 ( 
 row 
 . 
 getKey 
 ()). 
 deleteCells 
 ( 
 "cell_plan" 
 , 
  
 "data_plan_05gb" 
 )); 
  
 } 
  
 // Blocks until mutations are applied on all submitted row entries. 
  
 batcher 
 . 
 flush 
 (); 
  
 } 
  
 } 
  
 } 
 }

Python

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  def 
  
 streaming_and_batching 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable 
  
 import 
  Client 
 
 client 
 = 
 Client 
 ( 
 project 
 = 
 project_id 
 , 
 admin 
 = 
 True 
 ) 
 instance 
 = 
  client 
 
 . 
  instance 
 
 ( 
 instance_id 
 ) 
 table 
 = 
 instance 
 . 
 table 
 ( 
 table_id 
 ) 
 batcher 
 = 
 table 
 . 
 mutations_batcher 
 ( 
 flush_count 
 = 
 2 
 ) 
 rows 
 = 
 table 
 . 
 read_rows 
 () 
 for 
 row 
 in 
 rows 
 : 
 row 
 = 
 table 
 . 
 row 
 ( 
 row 
 . 
 row_key 
 ) 
 row 
 . 
 delete_cell 
 ( 
 column_family_id 
 = 
 "cell_plan" 
 , 
 column 
 = 
 "data_plan_01gb" 
 ) 
  batcher 
 
 . 
 mutate_rows 
 ( 
 rows 
 )

Python asyncio

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  async 
 def 
  
 streaming_and_batching 
 ( 
 project_id 
 , 
 instance_id 
 , 
 table_id 
 ): 
 from 
  
 google.cloud.bigtable.data 
  
 import 
 BigtableDataClientAsync 
 from 
  
 google.cloud.bigtable.data 
  
 import 
  DeleteRangeFromColumn 
 
 from 
  
 google.cloud.bigtable.data 
  
 import 
  RowMutationEntry 
 
 from 
  
 google.cloud.bigtable.data 
  
 import 
  ReadRowsQuery 
 
 client 
 = 
 BigtableDataClientAsync 
 ( 
 project 
 = 
 project_id 
 ) 
 table 
 = 
 client 
 . 
 get_table 
 ( 
 instance_id 
 , 
 table_id 
 ) 
 async 
 with 
 table 
 . 
 mutations_batcher 
 () 
 as 
 batcher 
 : 
 async 
 for 
 row 
 in 
 await 
 table 
 . 
 read_rows_stream 
 ( 
 ReadRowsQuery 
 ( 
 limit 
 = 
 10 
 )): 
 await 
 batcher 
 . 
 append 
 ( 
 RowMutationEntry 
 ( 
  row 
 
 . 
 row_key 
 , 
 DeleteRangeFromColumn 
 ( 
 family 
 = 
 "cell_plan" 
 , 
 qualifier 
 = 
 b 
 "data_plan_01gb" 
 ), 
 ) 
 ) 
 await 
 table 
 . 
 close 
 () 
 await 
 client 
 . 
 close 
 ()

Node.js

To learn how to install and use the client library for Bigtable, see Bigtable client libraries .

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

  const 
  
 rows 
  
 = 
  
 ( 
 await 
  
 table 
 . 
 getRows 
 ({ 
 limit 
 : 
  
 2 
 }))[ 
 0 
 ]; 
 const 
  
 entries 
  
 = 
  
 rows 
 . 
 map 
 ( 
 row 
  
 = 
>  
 { 
  
 return 
  
 { 
  
 key 
 : 
  
 row 
 . 
 id 
 , 
  
 method 
 : 
  
 'delete' 
 , 
  
 data 
 : 
  
 { 
  
 column 
 : 
  
 'cell_plan:data_plan_05gb' 
 , 
  
 }, 
  
 }; 
 }); 
 await 
  
 table 
 . 
 mutate 
 ( 
 entries 
 ); 
 await 
  
 printRows 
 ();

Delete data in an authorized view

You can delete table data by sending a delete request to an authorized view. You must use one of the following:

gcloud CLI
Bigtable client for Java

When you delete data from an authorized view, you supply the authorized view ID in addition to the table ID.

The data that you can delete from an authorized view is limited by the authorized view definition. You can only delete data that is included in the authorized view. If you attempt to delete data that is outside of the authorized view definition or is subject to the following rules, an error of PERMISSION_DENIED is returned:

Deleting a range of rows from an authorized view using DropRowRange in the admin API is not supported.
Deleting from a row is not supported.
Deleting from a column is supported as long as it's for rows that are in the authorized view.
Deleting from a column family is only permitted if the specified column family is configured to allow all column qualifier prefixes ( qualifier_prefixes="" ) in the authorized view.

For example, if you attempt to delete from a specified row, and that row contains columns in the underlying table that are not in your authorized view, then the request fails.

Deletes

How Bigtable deletes data

Delete a range of rows

Java

Python

Node.js

Delete data using Data API methods

Delete from a column

Java

Python

Python asyncio

Node.js

Delete from a column family

Java

Python

Python asyncio

Node.js

Delete from a row

Java

Python

Python asyncio

Node.js

Delete by streaming and batching

Go

Java

Python

Python asyncio

Node.js

Delete data in an authorized view

What's next