Automating AWS Data Migration Service (DMS) Tasks

Overview

Main objective of this script is to automate DMS task creation by reading a CSV file that has got table configurations. The script generates required JSON configuration files that will be fed to DMS service. so that, manual task creation using Web console can be avoided, thus saves time and effort.

Configuration

config.py

• First configure config.py file with ARNs of Source end point, Target endpoint, and Replication Instance.
• The script can optionally publish messages to given SNS topic after tasks have been created, and deleted. If an SNS topic is created, specify the topic's ARN in sns_topic_arn attribute. This is optional feature though. If this notification feature is not required, simply leave the empty quotes.

csv_files_location = '.'
replication_task_settings = 'task_settings.json'
replication_instance_arn = ''
source_endpoint_arn = ''
target_endpoint_arn = ''
sns_topic_arn = ''

include.csv

ADMIN,EMPLOYEES,HIREDATE,GTE,1995-05-01,JOB_ID,EQ,PU_CLERK
ADMIN,JOBS
ADMIN,LOCATIONS
ADMIN,REGIONS
ADMIN,JOB_HISTORY,START_DATE,BETWEEN,1998-01-01~1999-12-31,JOB_ID,EQ,ST_CLERK

---

Sample Execution

cd DMS_Automation/src
python app.py -h

usage: app.py [-h] [--profile PROFILE] [--region REGION]
              [--action {
                  generate_json_files,
                  create_dms_tasks,
                  list_dms_tasks,
                  delete_dms_tasks,
                  start_dms_tasks,
                  test_db_connection_from_replication_instance,
                  describe_table_statistics,
                  create_iam_role_for_dms_cloudwatch_logs,
                  fetch_cloudwatch_logs_for_a_task,
                  describe_endpoints,
                  describe_db_log_files,
                  validate_table_structures,
                  validate_data,
                  prepare_include_file_for_a_schema,
                  delete_all_dms_tasks
                  }
              ]
              [--task_arn TASK_ARN] [--table_name TABLE_NAME]

Actions

Following are different types of actions the script supports.

Action ID	Action	Description
1	generate_json_files	Generate DMS JSON Files
2	create_dms_tasks	Create JSON Files, and then creates DMS tasks
3	list_dms_tasks	List DMS Tasks
4	delete_dms_tasks	Delete DMS tasks
5	run_dms_tasks	Run DMS tasks
6	test_db_connection_from_replication_instance	Test DB Connections
7	describe_table_statistics	Describe Table stats
8	create_iam_role_for_dms_cloudwatch_logs	Creates IAM role needed by DMS to log
9	fetch_cloudwatch_logs_for_a_task	Fetch Cloudwatch logs for the task
10	describe_endpoints	Describe DMS Endpoints
11	describe_db_log_files	Fetch DB Log files
12	validate_table_structures	Compares table structures of the Source & Target DB
13	validate_data	Compares data in the Source & Target DB
14	prepare_include_file_for_a_schema	Creates a file with all the tables in a schema (Currently suppports Oracle)
15	delete_all_dms_tasks	Delete all DMS tasks.

---

For Quick run

python app.py --action generate_json_files
python app.py --action create_dms_tasks
python app.py --action list_dms_tasks
python app.py --action delete_dms_tasks
python app.py --action run_dms_tasks
python app.py --action test_db_connection_from_replication_instance
python app.py --action describe_table_statistics
python app.py --action create_iam_role_for_dms_cloudwatch_logs
python app.py --action fetch_cloudwatch_logs_for_a_task --task_arn <task_arn>
python app.py --action describe_endpoints
python app.py --action describe_db_log_files
python app.py --action validate_table_structures --table_name <schema.table>
python app.py --action validate_table_structures --table_name all
python app.py --action prepare_include_file_for_a_schema
python app.py --action delete_all_dms_tasks

Rather than passing text based actions, the tool supports numeric IDs dedicated to each action.

python app.py --action 1
python app.py --action 2
python app.py --action 3
python app.py --action 4
python app.py --action 5
python app.py --action 6
python app.py --action 7
python app.py --action 8
python app.py --action 9
python app.py --action 10
python app.py --action 11
python app.py --action 12
python app.py --action 13
python app.py --action 14
python app.py --action 15

---

How to use the tool?

Generate JSON files.

• Update config/include*.csv file.

Generate JSON files & Create DMS tasks

This step creates JSON files and also creates DMS tasks. To use this:

• Update config/include*.csv file.
• update the src/config.py file to configure replication_instance_arn, source_endpoint_arn, target_endpoint_arn.

The generated DMS tasks will have the options specified in src/task_settings.py file.

CLI Profile

By default the too looks for default command line profile already configured and have the privilege to create DMS tasks. To use a different profile name use --profile <profile-name> option.

Region

By default, the tasks are created in US-EAST-2 region. To use a different region, update the DEFAULT_REGION parameter src/config.py

Verifying the JSON files

The generated JSON file are stored at json_files directory in the project root.

List DMS tasks

Run DMS tasks

---

Configuration

---

To perform Data validation for Oracle Databases

For Windows

• Download Oracle Instance client from Basic Package from https://www.oracle.com/database/technologies/instant-client/winx64-64-downloads.html.

• Help

  • https://oracle.github.io/odpi/doc/installation.html#windows

For MacOS

1. Downloaded Basic Package from this link: https://www.oracle.com/au/database/technologies/instant-client/macos-intel-x86-downloads.html

2. Unzipped the files

3. MacOS does not allow to open them. So, changed their permissions (for the files that gave an error):

   xattr -d com.apple.quarantine libclntsh.dylib.19.1
   xattr -d com.apple.quarantine libnnz19.dylib
   ...

4. Created a symbolic link:

   ln -s $HOME/Downloads/instantclient_19_8/libclntsh.dylib /Users/rk/anaconda/anaconda3/lib/python3.8/site-packages

5. Verified using this:

   python
   >> import cx_Oracle
   >> cx_Oracle.init_oracle_client(lib_dir=r"/Users/rk/Downloads/instantclient_19_8")
   

   It did not throw an error.

6. This link is helpful https://stackoverflow.com/questions/69165050/python-dpi-1047-cannot-locate-dlopenlibclntsh-dylib-on-macos/69169723#69169723

---

Other Python packages required

cd DMS_Automation/config
pip install -r requirements.txt

---

Using this tool

Before running the script:

• Configure DMS Endpoints using AWS Console
• Update the src/config.py file
  • oracle_instance_client_path - Update this to point to Oracle client path.
  • Update the following:
    • replication_instance_arn
    • source_endpoint_arn
    • target_endpoint_arn

Setting up passwords

The tool needs to connect to databases for the following actions.

• 12. validate_table_structures
• 13. validate_data
• 14. prepare_include_file_for_a_schema

In general, to connect to a database, following properties are needed:

• Host
• Port
• User
• Database
• password

The tool fetches these attributes (Except password) from the End points configured in DMS. DB Password can be supplied in two ways:

1. Harding in the config file (By populating SOURCE_DB_PWD & TARGET_DB_PWD)

2. By storing them in AWS Secrets Manager. Store them as Key/Value. Configure the following parameters in config.py:

   • SECRET_MANAGER_SECRET_NAME (Should be same as the one used in AWS Secrets Manager)
   • SOURCE_DB_SECRET_KEY (Should be same as the one used in AWS Secrets Manager)
   • TARGET_DB_SECRET_KEY (Should be same as the one used in AWS Secrets Manager)

---

Demo

Data Validation

Table structure validation

fetch_cloudwatch_logs_for_a_task