Bulk-delete Data

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To delete a large number of rows (i.e., tens of thousands of rows or more), we recommend iteratively deleting subsets of the rows that you want to delete, until all of the unwanted rows have been deleted. You can write a script to do this, or you can write a loop into your application.

This page provides guidance on batch deleting with the DELETE query filter on an indexed column and on a non-indexed column. Filtering on an indexed column is both simpler to implement and more efficient, but adding an index to a table can slow down insertions to the table and may cause bottlenecks. Queries that filter on a non-indexed column must perform at least one full-table scan, a process that takes time proportional to the size of the entire table.

Before you begin

Before reading this page, do the following:

• Install CockroachDB.
• Start a local cluster, or create a CockroachDB Cloud cluster.

• Install a Postgres client.

  For the example on this page, we use the psycopg2 Python driver.

• Connect to the database.

• Insert data that you now want to delete.

  For the example on this page, we load a cluster with the tpcc database and data from cockroach workload.

Batch delete on an indexed column

For high-performance batch deletes, we recommending filtering the DELETE query on an indexed column.

Each iteration of a batch-delete loop should execute a transaction containing a single DELETE query. When writing this DELETE query:

• Use a WHERE clause to filter on a column that identifies the unwanted rows. If the filtering column is not the primary key, the column should have a secondary index. Note that if the filtering column is not already indexed, it is not beneficial to add an index just to speed up batch deletes. Instead, consider batch deleting on non-indexed columns.
• To ensure that rows are efficiently scanned in the DELETE query, add an ORDER BY clause on the filtering column.
• Use a LIMIT clause to limit the number of rows to the desired batch size. To determine the optimal batch size, try out different batch sizes (1,000 rows, 10,000 rows, 100,000 rows, etc.) and monitor the change in performance.
• Add a RETURNING clause to the end of the query that returns the filtering column values of the deleted rows. Then, using the values of the deleted rows, update the filter to match only the subset of remaining rows to delete. This narrows each query's scan to the fewest rows possible, and preserves the performance of the deletes over time. This pattern assumes that no new rows are generated that match on the DELETE filter during the time that it takes to perform the delete.

For example, suppose that you want to delete all rows in the tpcc new_order table where no_w_id is less than 5, in batches of 5,000 rows. To do this, you can write a script that loops over batches of 5,000 rows, following the DELETE query guidance provided above. Note that in this case, no_w_id is the first column in the primary index, and, as a result, you do not need to create a secondary index on the column.

In Python, the script would look similar to the following:

#!/usr/bin/env python3

import psycopg2
import psycopg2.sql
import os

conn = psycopg2.connect(os.environ.get('DB_URI'))
filter = 4
lastrow = None

while True:
  with conn:
    with conn.cursor() as cur:
        if lastrow:
            filter = lastrow[0]
        query = psycopg2.sql.SQL("DELETE FROM new_order WHERE no_w_id <= %s ORDER BY no_w_id DESC LIMIT 5000 RETURNING no_w_id")
        cur.execute(query, (filter,))
        print(cur.statusmessage)
        if cur.rowcount == 0:
            break
        lastrow = cur.fetchone()

conn.close()

This script iteratively deletes rows in batches of 5,000, until all of the rows where no_w_id <= 4 are deleted. Note that at each iteration, the filter is updated to match a narrower subset of rows.

Batch delete on a non-indexed column

If you cannot index the column that identifies the unwanted rows, we recommend defining the batch loop to execute separate read and write operations at each iteration:

1. Execute a SELECT query that returns the primary key values for the rows that you want to delete. When writing the SELECT query:

   • Use a WHERE clause that filters on the column identifying the rows.
   • Add an AS OF SYSTEM TIME clause to the end of the selection subquery, or run the selection query in a separate, read-only transaction with SET TRANSACTION AS OF SYSTEM TIME. This helps to reduce transaction contention.
   • Use a LIMIT clause to limit the number of rows queried to a subset of the rows that you want to delete. To determine the optimal SELECT batch size, try out different sizes (10,000 rows, 100,000 rows, 1,000,000 rows, etc.), and monitor the change in performance. Note that this SELECT batch size can be much larger than the batch size of rows that are deleted in the subsequent DELETE query.
   • To ensure that rows are efficiently scanned in the subsequent DELETE query, include an ORDER BY clause on the primary key.

2. Write a nested DELETE loop over the primary key values returned by the SELECT query, in batches smaller than the initial SELECT batch size. To determine the optimal DELETE batch size, try out different sizes (1,000 rows, 10,000 rows, 100,000 rows, etc.), and monitor the change in performance. Where possible, we recommend executing each DELETE in a separate transaction.

For example, suppose that you want to delete all rows in the tpcc history table that are older than a month. You can create a script that loops over the data and deletes unwanted rows in batches, following the query guidance provided above.

In Python, the script would look similar to the following:

#!/usr/bin/env python3

import psycopg2
import os
import time

conn = psycopg2.connect(os.environ.get('DB_URI'))

while True:
    with conn:
        with conn.cursor() as cur:
            cur.execute("SET TRANSACTION AS OF SYSTEM TIME '-5s'")
            cur.execute("SELECT h_w_id, rowid FROM history WHERE h_date < current_date() - INTERVAL '1 MONTH' ORDER BY h_w_id, rowid LIMIT 20000")
            pkvals = list(cur)
    if not pkvals:
        return
    while pkvals:
        batch = pkvals[:5000]
        pkvals = pkvals[5000:]
        with conn:
            with conn.cursor() as cur:
                cur.execute("DELETE FROM history WHERE (h_w_id, rowid) = ANY %s", (batch,))
                print(cur.statusmessage)
    del batch
    del pkvals
    time.sleep(5)

conn.close()

At each iteration, the selection query returns the primary key values of up to 20,000 rows of matching historical data from 5 seconds in the past, in a read-only transaction. Then, a nested loop iterates over the returned primary key values in smaller batches of 5,000 rows. At each iteration of the nested DELETE loop, a batch of rows is deleted. After the nested DELETE loop deletes all of the rows from the initial selection query, a time delay ensures that the next selection query reads historical data from the table after the last iteration's DELETE final delete.

Batch-delete "expired" data

CockroachDB does not support Time to Live (TTL) on table rows. To delete "expired" rows, we recommend automating a batch delete process using a job scheduler like cron.

For example, suppose that every morning you want to delete all rows in the tpcc history table that are older than a month. To do this, you could use the example Python script that batch deletes on the non-indexed h_date column.

To run the script with a daily cron job:

1. Make the file executable:

   $ chmod +x cleanup.py
   

2. Create a new cron job:

   $ crontab -e
   

   30 10 * * * DB_URI='cockroachdb://user@host:26257/bank' cleanup.py >> ~/cron.log 2>&1
   

Saving the cron file will install a new job that runs the cleanup.py file every morning at 10:30 A.M., writing the results to the cron.log file.

Delete all of the rows in a table

To delete all of the rows in a table, use a TRUNCATE statement.

For example, to delete all rows in the tpcc new_order table, execute the following SQL statement:

TRUNCATE new_order;

You can execute the statement from a compatible SQL client (e.g., the CockroachDB SQL client), or in a script or application.

For example, in Python, using the psycopg2 client driver:

#!/usr/bin/env python3

import psycopg2
import os

conn = psycopg2.connect(os.environ.get('DB_URI'))

with conn:
  with conn.cursor() as cur:
      cur.execute("TRUNCATE new_order")

See also

• Delete data
• DELETE
• TRUNCATE

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