Export RDS logs to S3 bucket robotically

Earlier than we learn to Export RDS logs to S3, do you know AWS CloudWatch robotically shops RDS DB logs? However, wait! It’ll solely retailer the final logs on your DB occasion. However, what if you wish to know which question was run in your DB desk? Which person carried out a selected question or from which IP Handle? Who deleted sure data out of your DB tables?

Additionally, in some way you’ve got managed to export all audit logs of your RDS occasion to CloudWatch, which is nice. However, are you aware? CloudWatch will retain these logs for 30 days solely!!

You can even export CloudWatch logs to the S3 bucket. However, you’ll have to do it manually from the CloudWatch dashboard. However, what if you wish to have automation for this?

This information will stroll you thru all of the steps required to log all such occasions robotically!!

This information contains:

1) Tweak RDS settings to ship audit logs to CloudWatch
2) Create an S3 Bucket (To Retailer CloudWatch Audit Logs)
3) Create IAM Function (We’ll use this for Lambda automation)
4) Lambda (Operate to automate CloudWatch Logs export to S3)

Let’s start!

Tweak RDS Settings to Ship Audit Logs to CloudWatch

To start with, we have to tweak the RDS to ship particular logs to CloudWatch. To do that, we are going to create one Choice Group and one Parameter Group.

Create Choice Group

⇒ Go to your Amazon RDS Dashboard.
⇒ Go to the Choice Group.
⇒ Click on on the Create Group button.

⇒ Enter the Title and Description for this group.
⇒ Choose Engine: mysql and Engine Model: 8.0. Click on on Create.

⇒ Click on on the Choice Group that you’ve got created.
⇒ Underneath Choices part, click on on Add possibility button.
⇒ Underneath “SERVER_AUDIT_EVENTS*” possibility, enter the values that you simply need to audit i.e. CONNECT, QUERY, QUERY_DDL, QUERY_DML, QUERY_DCL, QUERY_DML_NO_SELECT. If you wish to log all of the queries run in your tables, simply enter QUERY on this subject.
⇒ Set “Sure” for Apply Instantly. Click on on Add Choice.

Create Parameter Group

⇒ Let’s open the Amazon RDS Dashboard.
⇒ Click on on the Parameter Group.
⇒ Click on on the Create Parameter Group button.

⇒ Choose “Parameter group household”: mysql8.0
⇒ Choose “Kind”: DB Parameter Group
⇒ Enter Group Title and Description. Click on on Create.

⇒ Now, click on on the parameter Group that you’ve got created.
⇒ Underneath Parameter, we are going to edit the sure parameters and set the next values:
⇒ Edit parameter: “log_output” ⇒ Change its worth from TABLE to FILE
⇒ Edit parameter: “slow_query_log” ⇒ Change its worth from BLANK to 1
⇒ Edit parameter: “general_log” ⇒ Change its worth from BLANK to 1

Now, we’re all set with our Choice Group and Parameter Group. Now, let’s allocate these teams to our RDS.

⇒ Go to your RDS Dashboard.
⇒ Click on on the RDS that you’ve got created.
⇒ Click on on Modify.
⇒ Scroll down the web page and go to the “Database choices” part.
⇒ Underneath “DB parameter group”, choose the parameter group that you’ve got created.
⇒ Underneath “Choice group”, choose the choice group that you’ve got created.

We at the moment are all set with the RDS. As soon as you might be executed with the above steps, kindly enable 20-Half-hour to populate the information in CloudWatch (NOTE: It might take extra time relying on the dimensions of the logs).

As soon as CloudWatch has gathered all of the logs, you will notice the audit logs underneath your CloudWatch Dashboard. It’ll appear to be the next picture:

Create an S3 Bucket (To Retailer CloudWatch Audit Logs)

⇒ Go to Amazon S3 Dashboard.
⇒ Create a brand new Bucket.
⇒ Upon getting created a bucket, open it and navigate to the Permissions tab.
⇒ We might want to enable CloudWatch to place objects to the bucket (Write Entry)
⇒ Click on on Edit button for Bucket coverage. Enter the next code:

{
“Model”: “2012-10-17”,
“Assertion”: [
{
“Effect”: “Allow”,
“Principal”: {
“Service”: “logs.YOUR-REGION.amazonaws.com” // i.e. logs.us-east-1.amazonaws.com
},
“Action”: “s3:GetBucketAcl”,
“Resource”: “arn:aws:s3:::BUCKET_NAME_HERE”
},
{
“Effect”: “Allow”,
“Principal”: {
“Service”: “logs.YOUR-REGION.amazonaws.com”
},
“Action”: “s3:PutObject”,
“Resource”: “arn:aws:s3:::BUCKET_NAME_HERE/*”,
“Condition”: {
“StringEquals”: {
“s3:x-amz-acl”: “bucket-owner-full-control”
}
}
}
]
}

Create IAM Function (We’ll use this for Lambda automation)

Now, we are going to create the IAM position that will likely be used within the Lambda perform arrange. The AWS Lambda service would require permission to log occasions and write to the S3 bucket we have now created.

We’ll create the IAM Function “Export-RDS-CloudWatch-to-S3-Lambda” with “AmazonS3FullAccess”, “CloudWatchLogsFullAccess”, and “CloudWatchEventsFullAccess” insurance policies.

⇒ Open your AWS IAM Dashboard.
⇒ Swap to the Roles and click on on the Create Function button.
⇒ Underneath “Use case”, choose Lambda and click on on Subsequent.
⇒ Seek for “AmazonS3FullAccess” and choose it.
⇒ Seek for “CloudWatchLogsFullAccess” and choose it.
⇒ Seek for “CloudWatchEventsFullAccess” and choose it.
⇒ Set Function Title: “Export-RDS-CloudWatch-to-S3-Lambda” and click on on Create position.

Lambda (Operate to automate CloudWatch Logs export to S3)

Lambda perform lets you put your code underneath the perform and run it on triggers. You do not want to have any server or arrange for this. Very straightforward and environment friendly!

⇒ Swap to AWS Lambda Dashboard.
⇒ Click on on the Features after which click on on the Create perform button.

⇒ Preserve “Writer for Scratch” chosen.
⇒ Set “Operate title”: Export-RDS-CloudWatch-Logs-To-S3
⇒ Underneath “Runtime”, choose Python 3.x.
⇒ Underneath “Permissions”, choose “Use an present position” and choose the IAM position that we created within the earlier step.

⇒ Click on on the Create perform and navigate to Code view and enter the next script:

import boto3
import os
import datetime

GROUP_NAME = os.environ[‘GROUP_NAME’]
DESTINATION_BUCKET = os.environ[‘DESTINATION_BUCKET’]
PREFIX = os.environ[‘PREFIX’]
NDAYS = os.environ[‘NDAYS’]
nDays = int(NDAYS)

currentTime = datetime.datetime.now()
StartDate = currentTime – datetime.timedelta(days=nDays)
EndDate = currentTime – datetime.timedelta(days=nDays – 1)

fromDate = int(StartDate.timestamp() * 1000)
toDate = int(EndDate.timestamp() * 1000)

BUCKET_PREFIX = os.path.be a part of(PREFIX, StartDate.strftime(‘%Y{0}%m{0}%d’).format(os.path.sep))

def lambda_handler(occasion, context):
consumer = boto3.consumer(‘logs’)
consumer.create_export_task(
logGroupName=GROUP_NAME,
fromTime=fromDate,
to=toDate,
vacation spot=DESTINATION_BUCKET,
destinationPrefix=BUCKET_PREFIX
)

⇒ Now, click on on Configuration ⇒ Atmosphere Variables.
⇒ We have to create 4 variables:
⇒ DESTINATION_BUCKET: <Title of your S3 bucket>
⇒ GROUP_NAME: <Log’s group title that you’re exporting>
⇒ NDAYS: 1
⇒ PREFIX: exported-logs

⇒ Alright, you might be set now. Save the perform.

Now, let’s set automation to run this lambda perform.

⇒ Now, go to your CloudWatch dashboard.
⇒ Go to the Occasions ⇒ Guidelines.
⇒ Click on on Create Rule.
⇒ Underneath Occasion Supply, choose Schedule.
⇒ Set Mounted charge or cron expression to robotically run our lambda perform.
⇒ Underneath Goal, choose Lambda Operate.
⇒ Underneath Operate, choose the perform we have now created within the earlier step.

That is all. Now, you’ve got arrange an surroundings to Export RDS logs to S3 robotically. You’ll be able to retain the logs so long as you need. However, for those who nonetheless face any problem, be at liberty to put up a remark right here. We might love to listen to from you!!

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