There are several techniques to delete large amounts of data in CockroachDB.
The simplest method is to use TTL expressions on rows to define when the data is expired, and then let CockroachDB handle the delete operations.
To manually 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.
If you want to delete all of the rows in a table (and not just a large subset of the rows), use a TRUNCATE statement.
Batch-delete "expired" data
CockroachDB has support for Time to Live ("TTL") expiration on table rows, also known as Row-Level TTL. Row-Level TTL is a mechanism whereby rows from a table are considered "expired" and can be automatically deleted once those rows have been stored longer than a specified expiration time.
For more information, see Batch delete expired data with Row-Level TTL.
Manually delete data using batches
This section 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.
Exercise caution when batch deleting rows from tables with foreign key constraints and explicit ON DELETE foreign key actions. To preserve DELETE performance on tables with foreign key actions, we recommend using smaller batch sizes, as additional rows updated or deleted due to ON DELETE actions can make batch loops significantly slower.
Before you begin
Before reading this page, do the following:
- Create a CockroachDB Serverless cluster or start a local cluster.
- Install a Driver or ORM Framework.
- Connect to the database.
Insert data that you now want to delete.
For the example on this page, we load a cluster with the
tpccdatabase and data fromcockroach workload.
Batch delete on an indexed column
For high-performance batch deletes, we recommending filtering the DELETE query on an indexed column.
Having an indexed filtering column can make delete operations faster, but it might lead to bottlenecks in execution, especially if the filtering column is a timestamp. To reduce bottlenecks, we recommend using a hash-sharded index.
Each iteration of a batch-delete loop should execute a transaction containing a single DELETE query. When writing this DELETE query:
- Use a
WHEREclause 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
DELETEquery, add anORDER BYclause on the filtering column. - Use a
LIMITclause 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
RETURNINGclause 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 theDELETEfilter during the time that it takes to perform the delete.
Examples
Choose the language for the example code.
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 query 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 using the psycopg2 driver, 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('DATABASE_URL'))
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()
A simple JDBC application that bulk deletes rows in batches of 5000 would look like this:
package com.cockroachlabs.bulkdelete;
import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.Statement;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.Optional;
import org.postgresql.ds.PGSimpleDataSource;
public class App {
public static void deleteData(Connection conn) {
boolean cont = true;
// the initial warehouse ID we will delete orders from
int warehouseId = 4;
try {
do {
System.out.println("Deleting data from warehouses <= to " + warehouseId);
String sql = "DELETE FROM new_order WHERE no_w_id <= ? ORDER BY no_w_id DESC LIMIT 5000 RETURNING no_w_id";
// use a prepared statement and the current warehouse ID
PreparedStatement ps = conn.prepareStatement(sql, ResultSet.TYPE_SCROLL_SENSITIVE, ResultSet.CONCUR_READ_ONLY);
ps.setInt(1, warehouseId);
ResultSet results = ps.executeQuery();
if (!results.next()) {
cont = false;
} else {
results.last();
System.out.println("Deleted " + results.getRow() + " rows.");
// get the warehouse ID of the last row of this batch
warehouseId = results.getInt(1);
System.out.println("Warehouse ID is now " + warehouseId);
}
} while (cont);
}
catch(Exception e) {
return;
}
}
public static void main(String[] args) throws SQLException {
// create the datasource for the JDBC driver
PGSimpleDataSource ds = new PGSimpleDataSource();
ds.setApplicationName("docs_bulk_delete_java");
// get the cluster JDBC URL from an environment variable
ds.setUrl(Optional.ofNullable(System.getenv("JDBC_DATABASE_URL")).orElseThrow(
() -> new IllegalArgumentException("JDBC_DATABASE_URL is not set.")));
try (Connection connection = ds.getConnection()) {
// call the method to perform the deletes
deleteData(connection);
} catch (SQLException e) {
e.printStackTrace();
}
}
}
A simple C# Npgsql application that bulk deletes rows in batches of 5000 would look like this:
using System;
using System.Data;
using System.Net.Security;
using Npgsql;
namespace Cockroach
{
class MainClass
{
static void Main(string[] args)
{
// create the connection string from the connection parameters
var connStringBuilder = new NpgsqlConnectionStringBuilder();
connStringBuilder.Host = "cluster-name-743.g95.cockroachlabs.cloud";
connStringBuilder.Port = 26257;
connStringBuilder.SslMode = SslMode.VerifyFull;
connStringBuilder.Username = "maxroach";
connStringBuilder.Password = "notAGoodPassword";
connStringBuilder.Database = "tpcc";
connStringBuilder.ApplicationName = "docs_bulk_delete_csharp";
// call the method to perform the deletes
DeleteRows(connStringBuilder.ConnectionString);
}
static void DeleteRows(string connString)
{
// create the data source with the connection string
using var dataSource = NpgsqlDataSource.Create(connString);
{
using var connection = dataSource.OpenConnection();
bool cont = true;
// the initial warehouse ID we will delete orders from
int warehouseId = 4;
do {
Console.WriteLine("Deleting data from warehouse <= to " + warehouseId);
using var cmd = new NpgsqlCommand("DELETE FROM new_order WHERE no_w_id <= (@p1) ORDER BY no_w_id DESC LIMIT 5000 RETURNING no_w_id", connection)
{
Parameters =
{
// using a prepared statement and the current warehouse ID
new("p1", warehouseId)
}
};
using (var reader = cmd.ExecuteReader())
{
if (reader.HasRows)
{
while (reader.Read())
{
// Get the warehouse ID. When the result set is empty this will be
// the warehouse ID of the final row of this batch.
warehouseId = reader.GetInt32(0);
}
Console.WriteLine("Warehouse ID is now " + warehouseId);
}
else {
// All the rows have been deleted, so break out of the loop
cont = false;
}
Console.WriteLine("Deleted " + reader.RecordsAffected + " rows.");
}
} while (cont);
}
}
}
}
This example 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:
Execute a
SELECTquery that returns the primary key values for the rows that you want to delete. When writing theSELECTquery:- Use a
WHEREclause that filters on the column identifying the rows. - If you need to avoid transaction contention you can use an
AS OF SYSTEM TIMEclause at the end of the selection subquery, or run the selection query in a separate, read-only transaction withSET TRANSACTION AS OF SYSTEM TIME. If you add anAS OF SYSTEM TIMEclause, make sure your selection query to get the batches of rows is run outside of the window of theAS OF SYSTEM TIMEclause. That is, if you useAS OF SYSTEM TIME '-5s'to find the rows to delete, you should wait at least 5 seconds before rerunning the select query. Otherwise you will retrieve rows that have already been deleted. - Use a
LIMITclause to limit the number of rows queried to a subset of the rows that you want to delete. To determine the optimalSELECTbatch size, try out different sizes (10,000 rows, 100,000 rows, 1,000,000 rows, etc.), and monitor the change in performance. Note that thisSELECTbatch size can be much larger than the batch size of rows that are deleted in the subsequentDELETEquery. - To ensure that rows are efficiently scanned in the subsequent
DELETEquery, include anORDER BYclause on the primary key.
- Use a
Write a nested
DELETEloop over the primary key values returned by theSELECTquery, in batches smaller than the initialSELECTbatch size. To determine the optimalDELETEbatch 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 eachDELETEin 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.
Examples
Choose the language for the example code.
In Python, the script would look similar to the following:
#!/usr/bin/env python3
import psycopg2
import os
import time
import logging
def main():
try:
dsn = os.environ.get("DATABASE_URL")
conn = psycopg2.connect(dsn)
except Exception as e:
logging.fatal("database connection failed")
logging.fatal(e)
exit
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 tpcc.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 tpcc.history WHERE (h_w_id, rowid) = ANY %s", (batch,))
print(cur.statusmessage)
del batch
del pkvals
time.sleep(5)
conn.close()
if __name__ == "__main__":
main()
In Java, the code would look similar to:
public static void deleteDataNonindexed(Connection conn) {
boolean cont = true;
try {
do {
// select the rows using the primary key
String select = "SELECT h_w_id, rowid FROM history WHERE h_date < current_date() - INTERVAL '1 MONTH' ORDER BY h_w_id, rowid LIMIT 20000";
Statement st = conn.createStatement();
ResultSet results = st.executeQuery(select);
List<KeyFields> pkeys = new ArrayList<>();
if (!results.isBeforeFirst()) {
cont = false;
} else {
System.out.println("Found results, deleting rows.");
while (results.next()) {
KeyFields kf = new KeyFields();
kf.hwid = results.getInt(1);
kf.rowId = UUID.fromString(results.getString(2));
pkeys.add(kf);
}
}
while (pkeys.size() > 0) {
// check the size of the list of primary keys
// if it is smaller than the batch size, set the last
// index of the batch size to the size of the list
int size = pkeys.size();
int lastIndex;
if (size > 5000) {
lastIndex = 5000;
} else {
lastIndex = size;
}
// slice the list of primary keys to the batch size
String pkeyList = String.join(",", pkeys.subList(0, lastIndex).toString());
String deleteStatement = new String("DELETE FROM history WHERE (h_w_id, rowid) = ANY ( ARRAY " + pkeyList + ")");
int deleteCount = conn.createStatement().executeUpdate(deleteStatement);
System.out.println("Deleted " + deleteCount + " rows.");
// remove the deleted rows primary keys
pkeys.subList(0, lastIndex).clear();
}
} while (cont);
} catch(Exception e) {
e.printStackTrace();
return;
}
}
// inner class to store the primary key data
public static class KeyFields {
public int hwid;
public UUID rowId;
@Override
public String toString() {
return "( " + hwid + ", '" + rowId.toString() + "' )";
}
}
The KeyFields class encapsulates the compound primary key for the history table, and is used in the typed collection of primary keys returned by the SELECT query.
In C# the code would look similar to:
public class KeyFields {
public Int32 hwid;
public Guid rowId;
public override String ToString()
{
return "( " + hwid.ToString() + ", '" + rowId.ToString() + "' )";
}
}
static void DeleteRows(string connString)
{
using var dataSource = NpgsqlDataSource.Create(connString);
{
using var connection = dataSource.OpenConnection();
bool cont = true;
do
{
using (var cmdSelect = new NpgsqlCommand("SELECT h_w_id, rowid FROM history WHERE h_date < current_date() - INTERVAL '1 MONTH' ORDER BY h_w_id, rowid LIMIT 20000", connection))
{
List<KeyFields> pkeys = new List<KeyFields>();
using (var reader = cmdSelect.ExecuteReader())
{
if (reader.HasRows)
{
while (reader.Read())
{
KeyFields kf = new KeyFields();
kf.hwid = reader.GetInt32(0);
kf.rowId = reader.GetGuid(1);
pkeys.Add(kf);
}
}
else
{
// All the rows have been deleted, so break out of the loop
cont = false;
}
}
while (pkeys.Count > 0) {
// get the size of the list of primary keys
// if it is smaller than the batch size, set the last
// index of the batch size to the size of the list
Int32 size = pkeys.Count;
Int32 lastIndex;
if (size > 5000)
{
lastIndex = 5000;
} else
{
lastIndex = size;
}
List<KeyFields> batch = pkeys.GetRange(0, lastIndex);
String pkeyList = String.Join(',', batch);
String deleteStatement = new String("DELETE FROM history WHERE (h_w_id, rowid) = ANY ( ARRAY [ " + pkeyList + "])");
using (var cmdDelete = new NpgsqlCommand(deleteStatement, connection))
{
Int32 deleteCount = cmdDelete.ExecuteNonQuery();
Console.WriteLine("Deleted " + deleteCount + " rows.");
}
pkeys.RemoveRange(0, lastIndex);
}
}
} while (cont);
}
}
The KeyFields class encapsulates the compound primary key for the history table, and is used in the typed collection of primary keys returned by the SELECT query.
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.
CockroachDB records the timestamp of each row created in a table in the crdb_internal_mvcc_timestamp metadata column. In the absence of an explicit timestamp column in your table, you can use crdb_internal_mvcc_timestamp to filter expired data.
crdb_internal_mvcc_timestamp cannot be indexed. If you plan to use crdb_internal_mvcc_timestamp as a filter for large deletes, you must follow the non-indexed column pattern.
Exercise caution when using crdb_internal_mvcc_timestamp in production, as the column is subject to change without prior notice in new releases of CockroachDB. Instead, we recommend creating a column with an ON UPDATE expression to avoid any conflicts due to internal changes to crdb_internal_mvcc_timestamp.
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.
Examples
Choose the language for the example code.
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")
In Java, the code would look similar to this:
public static void truncateTable (Connection conn) {
try {
int results = conn.createStatement().executeUpdate("TRUNCATE new_order");
System.out.println("Truncated table new_order. Result: " + results);
} catch (Exception e) {
e.printStackTrace();
return;
}
}
In C# the code would look similar to this:
static void TruncateTable(string connString)
{
using var dataSource = NpgsqlDataSource.Create(connString);
using var connection = dataSource.OpenConnection();
using (var cmdTruncate = new NpgsqlCommand("TRUNCATE new_order", connection))
{
Int32 results = cmdTruncate.ExecuteNonQuery();
Console.WriteLine("Dropped " + results + " rows in new_order");
}
}
For detailed reference documentation on the TRUNCATE statement, including additional examples, see the TRUNCATE syntax page.
See also
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