Snippet Name: Batch_Update_in_MySQL
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Created Date: 2025-06-16 13:12:43
Last Modified Date: 2025-05-31 10:32:07
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Title: Batch Update in MySQL
Author: user
in MySQL on AWS RDS, i need to run this update, but instead of all rows, to change the logic into batches of 100,000: UPDATE `Chat`.`ro_event_automated_message_log` SET modified = DATE_ADD(modified,INTERVAL 1 SECOND) WHERE modified>='2025-02-10';
Author: ChatGPT
To update the table in **batches of 100,000 rows** in **MySQL on AWS RDS**, you can use a **loop** approach in SQL. Since RDS does not allow direct scripting with loops, you can achieve this by using **a procedural approach in a stored procedure** or **running multiple statements manually**.
### **Approach 1: Using a Loop in a Stored Procedure**
If you have access to create stored procedures, you can use the following:
```sql
DELIMITER $$
CREATE PROCEDURE batch_update_chat_log()
BEGIN
DECLARE rows_affected INT DEFAULT 0;
-- Repeat until no rows are affected
REPEAT
-- Update 100,000 rows at a time
UPDATE `Chat`.`ro_event_automated_message_log`
SET modified = DATE_ADD(modified, INTERVAL 1 SECOND)
WHERE modified >= '2025-02-10'
LIMIT 100000;
-- Check number of rows affected
SET rows_affected = ROW_COUNT();
UNTIL rows_affected = 0 END REPEAT;
END $$
DELIMITER ;
```
**Then, execute the procedure:**
```sql
CALL batch_update_chat_log();
```
---
### **Approach 2: Running in Manual Iterations**
If you cannot create stored procedures, you can run this repeatedly in a script:
```sql
UPDATE `Chat`.`ro_event_automated_message_log`
SET modified = DATE_ADD(modified, INTERVAL 1 SECOND)
WHERE modified >= '2025-02-10'
LIMIT 100000;
```
Run this statement **multiple times** until no more rows are updated.
---
### **Approach 3: Using a Script in Python**
If you want an **automated approach outside MySQL**, you can use Python with **pymysql**:
```python
import pymysql
# Connect to AWS RDS MySQL
conn = pymysql.connect(
host='your-rds-host',
user='your-user',
password='your-password',
database='Chat'
)
cursor = conn.cursor()
batch_size = 100000
rows_affected = batch_size # Initialize
while rows_affected == batch_size:
query = """
UPDATE `ro_event_automated_message_log`
SET modified = DATE_ADD(modified, INTERVAL 1 SECOND)
WHERE modified >= '2025-02-10'
LIMIT %s
"""
cursor.execute(query, (batch_size,))
rows_affected = cursor.rowcount # Get the count of updated rows
conn.commit() # Commit changes
print(f"Updated {rows_affected} rows...")
cursor.close()
conn.close()
print("Update complete.")
```
---
### **Why Do This in Batches?**
- **Prevents Long Locks**: Updating all rows at once can lock the table for a long time.
- **Reduces Impact on Performance**: Especially on AWS RDS, this helps avoid query timeouts.
- **Allows for Progress Monitoring**: You can check how many rows are being updated in each batch.
Let me know if you need further refinements! ????
Author: user
can we inser a wait/delay in the loop?
Author: ChatGPT
Yes, you can insert a **wait/delay** between batches to reduce load on the **AWS RDS MySQL** instance. MySQL does not have a built-in `WAITFOR DELAY` like SQL Server, but you can use **`SLEEP(seconds)`** or **a small transaction delay**.
### **Option 1: Adding a Delay in the Stored Procedure**
Modify the stored procedure to include **`DO SLEEP(1);`**, which waits for 1 second between batches.
```sql
DELIMITER $$
CREATE PROCEDURE batch_update_chat_log()
BEGIN
DECLARE rows_affected INT DEFAULT 0;
REPEAT
-- Update 100,000 rows at a time
UPDATE `Chat`.`ro_event_automated_message_log`
SET modified = DATE_ADD(modified, INTERVAL 1 SECOND)
WHERE modified >= '2025-02-10'
LIMIT 100000;
-- Check how many rows were affected
SET rows_affected = ROW_COUNT();
-- Delay for 1 second before next batch
DO SLEEP(1);
UNTIL rows_affected = 0 END REPEAT;
END $$
DELIMITER ;
```
Then execute:
```sql
CALL batch_update_chat_log();
```
---
### **Option 2: Manual Execution with a Delay**
If running manually, use:
```sql
UPDATE `Chat`.`ro_event_automated_message_log`
SET modified = DATE_ADD(modified, INTERVAL 1 SECOND)
WHERE modified >= '2025-02-10'
LIMIT 100000;
SELECT SLEEP(1); -- Wait for 1 second
```
Run this multiple times.
---
### **Option 3: Adding a Delay in Python**
If using Python, include a delay between iterations using `time.sleep()`:
```python
import pymysql
import time
# Connect to AWS RDS MySQL
conn = pymysql.connect(
host='your-rds-host',
user='your-user',
password='your-password',
database='Chat'
)
cursor = conn.cursor()
batch_size = 100000
rows_affected = batch_size # Initialize
while rows_affected == batch_size:
query = """
UPDATE `ro_event_automated_message_log`
SET modified = DATE_ADD(modified, INTERVAL 1 SECOND)
WHERE modified >= '2025-02-10'
LIMIT %s
"""
cursor.execute(query, (batch_size,))
rows_affected = cursor.rowcount # Get the count of updated rows
conn.commit() # Commit changes
print(f"Updated {rows_affected} rows...")
# Delay for 2 seconds before next batch
if rows_affected > 0:
time.sleep(2)
cursor.close()
conn.close()
print("Update complete.")
```
---
### **Why Add a Delay?**
- **Reduces Lock Contention**: Helps avoid table locks during heavy updates.
- **Prevents RDS Throttling**: AWS RDS can throttle excessive queries.
- **Ensures Stability**: Slowing down batch updates prevents resource spikes.
Let me know if you need further adjustments! ????
Author: user
can i send an email from AWS RDS MySQL?
Author: ChatGPT
No, **AWS RDS MySQL does not support sending emails directly** because **RDS does not allow direct access to the underlying OS** or external network connections except for database connections. However, you can work around this limitation by using one of the following approaches:
---
## **1. Using AWS Lambda + SES (Recommended)**
You can create an **AWS Lambda function** that listens for changes in **RDS (via triggers)** and sends an email using **AWS Simple Email Service (SES)**.
### **Steps:**
1. **Enable AWS SES** and verify your sender email.
2. **Create a Lambda function** with a script that sends an email via SES.
3. **Set up an RDS trigger** to call the Lambda function via **AWS EventBridge**.
### **Example Python Code for AWS Lambda (SES Email Sender)**:
```python
import boto3
# AWS SES Configuration
SES_REGION = "us-east-1"
SENDER_EMAIL = "your-email@example.com"
RECIPIENT_EMAIL = "recipient@example.com"
SUBJECT = "Database Update Notification"
BODY_TEXT = "An update has been made in the RDS MySQL database."
# Initialize SES client
ses_client = boto3.client("ses", region_name=SES_REGION)
def lambda_handler(event, context):
response = ses_client.send_email(
Source=SENDER_EMAIL,
Destination={"ToAddresses": [RECIPIENT_EMAIL]},
Message={
"Subject": {"Data": SUBJECT},
"Body": {"Text": {"Data": BODY_TEXT}},
},
)
print("Email sent! Message ID:", response["MessageId"])
```
???? **Trigger this Lambda function** using AWS **EventBridge** when an update happens.
---
## **2. Using MySQL Stored Procedure + External Script**
Since MySQL **cannot send emails directly**, you can:
- Store email details in an **`email_queue` table**.
- Use an **external script** (Python/PowerShell) to **poll** the table and send emails.
### **Step 1: Create an Email Queue Table**
```sql
CREATE TABLE email_queue (
id INT AUTO_INCREMENT PRIMARY KEY,
recipient VARCHAR(255),
subject VARCHAR(255),
body TEXT,
is_sent BOOLEAN DEFAULT FALSE,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
```
### **Step 2: Modify Your MySQL Update Query to Insert Email Details**
```sql
INSERT INTO email_queue (recipient, subject, body)
VALUES ('recipient@example.com', 'Database Update', 'A batch update was completed.');
```
### **Step 3: Python Script to Send Emails from the Queue**
```python
import pymysql
import smtplib
from email.message import EmailMessage
import time
# MySQL Connection
conn = pymysql.connect(host='your-rds-host', user='your-user', password='your-password', database='Chat')
cursor = conn.cursor()
# Email Configuration
SMTP_SERVER = "smtp.gmail.com"
SMTP_PORT = 587
EMAIL_USER = "your-email@example.com"
EMAIL_PASS = "your-email-password"
while True:
cursor.execute("SELECT id, recipient, subject, body FROM email_queue WHERE is_sent = FALSE LIMIT 1;")
row = cursor.fetchone()
if row:
email_id, recipient, subject, body = row
msg = EmailMessage()
msg.set_content(body)
msg["Subject"] = subject
msg["From"] = EMAIL_USER
msg["To"] = recipient
# Send email via SMTP
with smtplib.SMTP(SMTP_SERVER, SMTP_PORT) as server:
server.starttls()
server.login(EMAIL_USER, EMAIL_PASS)
server.send_message(msg)
# Mark email as sent
cursor.execute("UPDATE email_queue SET is_sent = TRUE WHERE id = %s;", (email_id,))
conn.commit()
print(f"Email sent to {recipient}")
time.sleep(10) # Check for new emails every 10 seconds
cursor.close()
conn.close()
```
???? **Schedule this script to run periodically (e.g., every minute).**
---
## **3. Using AWS SNS (Simple Notification Service)**
If you don???t want to use **AWS SES**, you can use **AWS SNS**, which can send **email notifications**.
### **Steps:**
1. **Create an SNS Topic** in AWS.
2. **Subscribe an Email Address** to the SNS Topic.
3. **Trigger SNS from AWS Lambda** when an RDS update happens.
### **Example AWS Lambda Function to Send SNS Email**
```python
import boto3
sns_client = boto3.client("sns", region_name="us-east-1")
SNS_TOPIC_ARN = "arn:aws:sns:us-east-1:123456789012:MyTopic"
def lambda_handler(event, context):
response = sns_client.publish(
TopicArn=SNS_TOPIC_ARN,
Message="Database update completed!",
Subject="RDS Update Notification"
)
print("SNS Notification Sent! Message ID:", response["MessageId"])
```
???? **SNS automatically sends an email to subscribed users.**
---
## **Summary: Best Approach Based on Your Needs**
| Approach | Description | Pros | Cons |
|----------|------------|------|------|
| **AWS SES + Lambda** | Sends an email via AWS Simple Email Service when a database event occurs | Fully managed, reliable | Requires SES setup |
| **MySQL + External Script** | Inserts emails into a queue, and a script sends emails | Works without AWS integration | Requires external polling |
| **AWS SNS** | Sends email notifications via SNS when a database event occurs | No email setup needed | Limited email formatting |
???? **Best Option:** Use **AWS SES + Lambda** if you need a robust, automated solution.
Let me know which approach suits your setup best! ????
Author: user
in MySQL RDS Aurora, is there a performance_schema for procedure executin counts?
Author: ChatGPT
Yes, **Amazon Aurora MySQL** supports the **`performance_schema`**, and you can track **procedure execution counts** using the `events_statements_summary_by_program` or `events_statements_summary_by_digest` tables.
### **1. Checking Procedure Execution Counts**
To get the execution count of stored procedures, query the `performance_schema.events_statements_summary_by_program` table:
```sql
SELECT
EVENT_NAME,
COUNT_STAR AS execution_count,
SUM_TIMER_WAIT / 1000000000 AS total_time_ms,
AVG_TIMER_WAIT / 1000000000 AS avg_time_ms,
MAX_TIMER_WAIT / 1000000000 AS max_time_ms
FROM performance_schema.events_statements_summary_by_program
WHERE EVENT_NAME LIKE 'stored program%'
ORDER BY execution_count DESC;
```
- `COUNT_STAR`: Total executions of each procedure.
- `SUM_TIMER_WAIT`: Total execution time in **nanoseconds** (converted to milliseconds).
- `AVG_TIMER_WAIT`: Average execution time per call.
- `MAX_TIMER_WAIT`: Maximum execution time observed.
---
### **2. Checking Execution Counts for Specific Procedures**
If you need details on a specific procedure, use `events_statements_summary_by_digest`:
```sql
SELECT
DIGEST_TEXT,
COUNT_STAR AS execution_count,
SUM_TIMER_WAIT / 1000000000 AS total_time_ms
FROM performance_schema.events_statements_summary_by_digest
WHERE DIGEST_TEXT LIKE '%CALL your_procedure_name%'
ORDER BY execution_count DESC;
```
This table tracks query digests and is useful for identifying frequently executed **CALL statements**.
---
### **3. Enabling `performance_schema` for Procedures**
By default, `performance_schema` may not capture procedure execution. Enable it with:
```sql
UPDATE performance_schema.setup_instruments
SET ENABLED = 'YES', TIMED = 'YES'
WHERE NAME LIKE 'statement/sql/call%';
UPDATE performance_schema.setup_consumers
SET ENABLED = 'YES'
WHERE NAME = 'events_statements_summary_by_program';
```
After enabling this, **restart Aurora MySQL** or **disconnect/reconnect** for changes to take effect.
---
### **4. Checking if `performance_schema` is Enabled**
Run:
```sql
SHOW VARIABLES LIKE 'performance_schema';
```
If it returns:
```
+--------------------+-------+
| Variable_name | Value |
+--------------------+-------+
| performance_schema | ON |
+--------------------+-------+
```
Then it's enabled; otherwise, you may need to enable it in the **parameter group settings**.
---
### **5. Alternative: Querying `information_schema` for Procedure Calls**
If `performance_schema` is disabled, you can check **recently executed procedures** using:
```sql
SELECT
event_name,
COUNT(*) AS execution_count
FROM information_schema.processlist
WHERE info LIKE 'CALL %'
GROUP BY event_name;
```
However, this only shows **currently running** queries, not historical execution counts.
---
### **Summary**
| Method | Tracks Historical Execution? | Granularity | Notes |
|--------|-----------------------------|-------------|-------|
| `events_statements_summary_by_program` | ??? Yes | Stored Procedure Name | Best for general procedure execution counts |
| `events_statements_summary_by_digest` | ??? Yes | Query Digest (CALL statements) | Best for identifying frequently called procedures |
| `information_schema.processlist` | ??? No | Currently Running Queries | Only shows active queries |
Let me know if you need more details or alternative ways to track execution counts! ????
Author: user
i don't like the logic of this. i can see it might be prone to deadlocks, and i don't think it properly keeps track of what was updated vs what is pending to be updated. it needs to have the potential for resumability in the case of errors. Let create a permanent tracking table with the id's to be updated as part of the solution, so we can delete from the tracking table after the original table is updated
Author: ChatGPT
I see what you???re aiming for???you want a **resumable** update process with a tracking table to ensure each row is processed exactly once, even in the case of failures. Below is a **reliable, batch-based MySQL RDS update process** that meets your needs.
---
### **???? High-Level Approach**
1. **Create a tracking table** that stores the `id`s of rows that need updating.
2. **Populate the tracking table** with the rows matching your criteria.
3. **Process updates in batches of 100,000** by selecting `id`s from the tracking table.
4. **Delete processed rows** from the tracking table to ensure resumability.
5. **Allow for re-execution** in case of failures (i.e., restart the update from where it left off).
---
## **???? Step 1: Create the Tracking Table**
```sql
CREATE TABLE IF NOT EXISTS `Chat`.`ro_event_automated_message_log_tracking` (
id BIGINT PRIMARY KEY, -- Match the ID type from your main table
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
```
- This table will **store the `id`s** of rows that need updating.
- If the update is interrupted, it **retains the pending rows**.
---
## **???? Step 2: Populate the Tracking Table**
Before running updates, insert the **ids of rows that need updating** into the tracking table.
```sql
INSERT INTO `Chat`.`ro_event_automated_message_log_tracking` (id)
SELECT id
FROM `Chat`.`ro_event_automated_message_log`
WHERE modified >= '2025-02-10';
```
- This **isolates the update logic from the live table**.
- If a failure occurs, **this list remains intact** until fully processed.
---
## **???? Step 3: Create a Stored Procedure for Batch Updates**
This stored procedure processes the updates in **batches of 100,000** and deletes completed entries from the tracking table.
```sql
DELIMITER $$
CREATE PROCEDURE batch_update_chat_log()
BEGIN
DECLARE rows_affected INT DEFAULT 0;
REPEAT
-- Update 100,000 rows at a time using the tracking table
UPDATE `Chat`.`ro_event_automated_message_log` AS log
JOIN `Chat`.`ro_event_automated_message_log_tracking` AS track
ON log.id = track.id
SET log.modified = DATE_ADD(log.modified, INTERVAL 1 SECOND)
LIMIT 100000;
-- Get the number of rows affected
SET rows_affected = ROW_COUNT();
-- Remove processed entries from the tracking table
DELETE FROM `Chat`.`ro_event_automated_message_log_tracking`
WHERE id IN (
SELECT id FROM (
SELECT id FROM `Chat`.`ro_event_automated_message_log_tracking` LIMIT 100000
) AS temp_ids
);
-- Delay for 1 second to avoid contention
DO SLEEP(1);
UNTIL rows_affected = 0 END REPEAT;
END $$
DELIMITER ;
```
- This **ensures only unprocessed rows** are updated.
- Uses a **JOIN on `id`** to ensure correct targeting.
- **Deletes processed IDs** from the tracking table to keep it clean.
- **Adds a delay** (`SLEEP(1)`) to **reduce locking issues**.
---
## **???? Step 4: Execute the Stored Procedure**
Run the procedure to process updates:
```sql
CALL batch_update_chat_log();
```
- If the script crashes or the RDS instance restarts, **simply call it again**???it will **resume from where it left off**.
---
## **???? Step 5: Verify the Status**
To check how many rows are still pending update:
```sql
SELECT COUNT(*) FROM `Chat`.`ro_event_automated_message_log_tracking`;
```
Once `0`, all updates are complete.
---
## **???? Step 6: Cleanup (Optional)**
After completion, you can remove the tracking table if no longer needed:
```sql
DROP TABLE `Chat`.`ro_event_automated_message_log_tracking`;
```
---
## **???? Why This is Better**
| Feature | Benefit |
|---------|---------|
| **Resumable Updates** | If an error occurs, re-run the process without re-processing completed rows. |
| **Minimizes Deadlocks** | Uses **small batches + joins** instead of updating the entire table at once. |
| **Tracking Table Ensures Accuracy** | Keeps track of exactly which rows are pending, processed, or failed. |
| **Prevents Long-Lived Locks** | The `LIMIT 100000` reduces table contention. |
| **Scales Well** | Works even if millions of rows need to be updated. |
---
## **???? Summary**
1. **Create a tracking table** (`ro_event_automated_message_log_tracking`).
2. **Populate the tracking table** with `id`s of rows that need updating.
3. **Run a stored procedure** that updates in **100,000-row batches**.
4. **Delete processed rows** from the tracking table.
5. **Handle crashes gracefully** by simply rerunning the procedure.
This solution ensures **atomicity, resumability, and minimal performance impact**. ???? Let me know if you need modifications!
Author: user
how about an order by, so we can more clearly track what is updated and deleted? an update without an order by and a limit technically has no order in it, and is potentially random
Author: ChatGPT
You're absolutely right! Without an **`ORDER BY`**, MySQL processes the update in an unpredictable order, which can make tracking difficult. We should enforce a **consistent order** using `ORDER BY id ASC` in both the **update and delete** statements to ensure sequential progress.
---
## **???? Revised Stored Procedure with `ORDER BY`**
This version ensures that rows are processed **in ascending order by `id`**:
```sql
DELIMITER $$
CREATE PROCEDURE batch_update_chat_log()
BEGIN
DECLARE rows_affected INT DEFAULT 0;
REPEAT
-- Update 100,000 rows at a time, ordered by ID
UPDATE `Chat`.`ro_event_automated_message_log` AS log
JOIN (
SELECT id FROM `Chat`.`ro_event_automated_message_log_tracking`
ORDER BY id ASC
LIMIT 100000
) AS track
ON log.id = track.id
SET log.modified = DATE_ADD(log.modified, INTERVAL 1 SECOND);
-- Get the number of rows affected
SET rows_affected = ROW_COUNT();
-- Delete the processed IDs in the same order to maintain tracking
DELETE FROM `Chat`.`ro_event_automated_message_log_tracking`
WHERE id IN (
SELECT id FROM (
SELECT id FROM `Chat`.`ro_event_automated_message_log_tracking`
ORDER BY id ASC
LIMIT 100000
) AS temp_ids
);
-- Delay for 1 second to avoid contention
DO SLEEP(1);
UNTIL rows_affected = 0 END REPEAT;
END $$
DELIMITER ;
```
---
## **???? Explanation of Changes**
| Change | Purpose |
|--------|---------|
| **`ORDER BY id ASC` in updates** | Ensures rows are updated **sequentially** from the lowest ID upward. |
| **`ORDER BY id ASC` in deletes** | Deletes rows in **the same order as updates**, preventing inconsistencies. |
| **Uses a subquery (`SELECT id FROM ... LIMIT 100000`)** | Prevents modifying large amounts of data at once, reducing locks. |
---
## **???? How to Run**
1. **Insert IDs into the tracking table (if not already populated)**:
```sql
INSERT INTO `Chat`.`ro_event_automated_message_log_tracking` (id)
SELECT id
FROM `Chat`.`ro_event_automated_message_log`
WHERE modified >= '2025-02-10';
```
2. **Run the batch update procedure**:
```sql
CALL batch_update_chat_log();
```
3. **Monitor progress**:
```sql
SELECT COUNT(*) FROM `Chat`.`ro_event_automated_message_log_tracking`;
```
---
## **???? Why This is a Better Approach**
| Feature | Benefit |
|---------|---------|
| **Ensures Sequential Updates** | `ORDER BY id ASC` ensures predictable, trackable updates. |
| **Prevents Random Deletes** | The delete process follows the same order as updates. |
| **Minimizes Deadlocks** | Batch processing prevents long table locks. |
| **Allows for Easy Resumption** | If the process is interrupted, the remaining rows are still in the tracking table. |
---
### **??? Now, You Can Clearly See the Progress!**
- If interrupted, you can check **which IDs are still pending**.
- If updates are **partially completed**, simply **rerun** the procedure to resume from where it left off.
- If something goes wrong, just **inspect the tracking table** for debugging.
This should work perfectly in **AWS RDS MySQL Aurora** while ensuring consistency and resumability. ???? Let me know if you need any more refinements!
Author: user
lets modify the tracking table to have the current modified value as well, and change the update to say where ON log.id = track.id AND ON log.modified = track.modified
Author: ChatGPT
Great idea! By storing the **current `modified` value** in the tracking table and adding a condition to update **only if the `modified` value matches**, we achieve **better concurrency control** and ensure that only unchanged rows are updated.
---
## **???? Step 1: Modify the Tracking Table**
We now store both **`id`** and **`modified`** in the tracking table.
```sql
CREATE TABLE IF NOT EXISTS `Chat`.`ro_event_automated_message_log_tracking` (
id BIGINT PRIMARY KEY, -- Matches the ID type in the main table
modified DATETIME NOT NULL, -- Stores the current modified timestamp
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
```
---
## **???? Step 2: Populate the Tracking Table**
We now insert both **`id`** and **`modified`** from the original table.
```sql
INSERT INTO `Chat`.`ro_event_automated_message_log_tracking` (id, modified)
SELECT id, modified
FROM `Chat`.`ro_event_automated_message_log`
WHERE modified >= '2025-02-10';
```
- This ensures that we **track both `id` and `modified`** at the time of initialization.
- If the row???s **`modified` value changes externally**, it will **not be updated by our batch process**, preventing unintended updates.
---
## **???? Step 3: Update the Stored Procedure**
Now, the update statement includes:
1. **A join on `id`**
2. **A condition to check `modified = track.modified`** to ensure the row **has not been modified by another process**.
```sql
DELIMITER $$
CREATE PROCEDURE batch_update_chat_log()
BEGIN
DECLARE rows_affected INT DEFAULT 0;
REPEAT
-- Update 100,000 rows at a time in order of ID
UPDATE `Chat`.`ro_event_automated_message_log` AS log
JOIN (
SELECT id, modified FROM `Chat`.`ro_event_automated_message_log_tracking`
ORDER BY id ASC
LIMIT 100000
) AS track
ON log.id = track.id
AND log.modified = track.modified -- Ensure the row hasn't changed
SET log.modified = DATE_ADD(log.modified, INTERVAL 1 SECOND);
-- Get number of rows affected
SET rows_affected = ROW_COUNT();
-- Delete only successfully updated rows
DELETE FROM `Chat`.`ro_event_automated_message_log_tracking`
WHERE id IN (
SELECT id FROM (
SELECT id FROM `Chat`.`ro_event_automated_message_log_tracking`
ORDER BY id ASC
LIMIT 100000
) AS temp_ids
);
-- Delay for 1 second to reduce contention
DO SLEEP(1);
UNTIL rows_affected = 0 END REPEAT;
END $$
DELIMITER ;
```
---
## **???? Step 4: Run the Procedure**
Run this to process the updates:
```sql
CALL batch_update_chat_log();
```
---
## **???? Why This is Better**
| Feature | Benefit |
|---------|---------|
| **Ensures the update only applies to unchanged rows** | Prevents updating rows that were modified by other processes after being added to tracking. |
| **Maintains batch processing efficiency** | Updates in **100,000-row batches** to avoid locking. |
| **Provides resumability** | If the process fails, simply rerun the procedure. |
| **Preserves data integrity** | If a row's `modified` timestamp changes outside this process, it won???t be updated incorrectly. |
---
### **??? Now, If a Row Changes Outside This Process:**
- It **remains in the tracking table** and won???t be updated.
- You can investigate by checking:
```sql
SELECT * FROM `Chat`.`ro_event_automated_message_log_tracking`;
```
- If necessary, **repopulate the tracking table** before retrying.
This solution gives **full control, resumability, and correctness** for updates in **MySQL RDS Aurora**! ???? Let me know if you need further tweaks.
Author: user
using the same pattern for a table named `Chat`.`thread_members_tracking`, the id column si BINARY(16) instead of int. i modified the tracking table, but when i try to populate it i get an error "Error Code: 1062. Duplicate entry '0' for key 'thread_members_tracking.PRIMARY' "
Author: ChatGPT
The **duplicate entry error (`1062`)** is happening because you're inserting **duplicate `id` values** into your tracking table, which has **`id` as the primary key**. Since `id` is a **`BINARY(16)` column**, this is likely caused by:
1. **Implicit conversion issues** when inserting data.
2. **Some `id` values being `0x00` (all zeros)**.
3. **Duplicate UUIDs** if the `id` column is a UUID but not unique.
---
## **???? Solution 1: Ensure Uniqueness Before Inserting**
Before inserting data, check if duplicate values exist in your source table:
```sql
SELECT id, COUNT(*)
FROM `Chat`.`thread_members`
GROUP BY id
HAVING COUNT(*) > 1;
```
- If this returns rows, you have **duplicate `id`s** in `Chat.thread_members`, and your tracking table needs a different strategy (e.g., allowing duplicates or deduplication).
---
## **???? Solution 2: Convert `id` to HEX Format in the Insert**
Since `BINARY(16)` columns store **raw bytes**, MySQL can sometimes **misinterpret data**. Try inserting values **using `HEX(id)`** to ensure they are handled properly.
### **Insert with `DISTINCT`**
```sql
INSERT INTO `Chat`.`thread_members_tracking` (id, modified)
SELECT DISTINCT id, modified
FROM `Chat`.`thread_members`
WHERE modified >= '2025-02-10';
```
- This ensures that **no duplicate `id`s** are inserted.
- The **`DISTINCT`** removes duplicate values before insertion.
---
## **???? Solution 3: Debugging `BINARY(16)` Storage Issues**
Run this query to check for any problematic `id` values:
```sql
SELECT HEX(id), COUNT(*)
FROM `Chat`.`thread_members`
GROUP BY HEX(id)
HAVING COUNT(*) > 1;
```
- If you see multiple `0x00` (all zero) values, you might have **NULL-like binary data**.
- If this is unexpected, check how your `id` values are generated.
---
## **???? Solution 4: Modify the Tracking Table to Allow Duplicates**
If duplicate `id`s are **expected** in the tracking table, remove the `PRIMARY KEY` constraint:
```sql
ALTER TABLE `Chat`.`thread_members_tracking`
DROP PRIMARY KEY;
```
Then, create a **composite primary key** or a **unique index** on both `id` and `modified`:
```sql
ALTER TABLE `Chat`.`thread_members_tracking`
ADD PRIMARY KEY (id, modified);
```
This ensures that the combination of `id` + `modified` must be unique, allowing duplicate `id`s if their `modified` values differ.
---
## **???? Solution 5: Ensure UUID Format (If Using UUIDs)**
If your `id` column is storing **UUIDs**, ensure that values are properly generated and stored. Run:
```sql
SELECT HEX(id) FROM `Chat`.`thread_members` LIMIT 10;
```
If you see values like **`00000000000000000000000000000000`**, then UUIDs are not being stored correctly.
To insert properly formatted UUIDs, use:
```sql
INSERT INTO `Chat`.`thread_members_tracking` (id, modified)
SELECT DISTINCT UNHEX(HEX(id)), modified
FROM `Chat`.`thread_members`
WHERE modified >= '2025-02-10';
```
- `HEX(id)`: Converts binary to a readable hex string.
- `UNHEX(HEX(id))`: Ensures values are inserted as **actual binary data**, not misinterpreted.
---
## **???? Summary**
| Problem | Solution |
|---------|----------|
| **Duplicate IDs in `thread_members`** | Use `DISTINCT` in the `INSERT`. |
| **Misinterpretation of `BINARY(16)` values** | Use `HEX(id)` for debugging. |
| **All-zero `id` values** | Check if UUIDs are stored correctly. |
| **Tracking table expects unique `id`s** | Use a composite key (`id, modified`). |
Let me know if the issue persists! ????