I'm currently using a provisioned Redshift cluster, and managing database migrations with Flyway. I'm thinking on migrating to Redshift Serverless, but I'm not sure if can still use Flyway to manage the migrations.
Already added the rule in my security group to allow my IP (I'm trying to run Flyway migrations locally), and also have the Publicly accessible parameter set to On, following the steps in this document, and using the endpoint given by the workspace.
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I have a .NET application that is using EF Core as ORM, and all db modifications are done using Db Migrations in EF.
The application is hosted on the cloud on multiple VMs in production, after do all testing, a rolling deployment is initiated to take one VM at a time, deploy the new application, and so on.
The database itself hosted on managed Db service (Like aws RDS, Azure SQL) with multi-az/replication setup.
The main goal, is to make sure there is no downtime (0 downtime), and rollback if any issue happened (or manually distribute canary weighted requests accordingly)
the main issue is, if application successfully deployed to one instance, and that instance receives a connection, this will cause the database to be migrated to the new version, causing all other instances requests to fail (as EF will have different migrated db in the old instances)
My current project have the following structure:
Starts with a script in jupyter notebook which dowloads data from a CRM API to put in a local PostgressSql database I run with PgAdmin. After that it runs cluster analysis, return some scoring values, creates a table in database with the results and updates this values in the CRM with another API call. This process will take between 10 to 20 hours (the API only allows 400 requests per minute).
The second notebook reads the database, detects last update, runs api call to update database since the last call, runs kmeans analysis to cluster the data, compare results with the previous call, updates the new ones and the CRM via API. This second process takes less than 2 hours in my estimation and I want this script to run every 24 hours.
After testing, this works fine. Now I'm evaluating how to put this in production in AWS. I understand for the notebooks I need Sagemaker and from I have seen is not that complicated, my only doubt here is if I can call the API without implementing aditional code or need some configuration. My second problem is database. I don't understand the difference between RDS which is the one I think I have to use for this and Aurora or S3. My goal is to write the less code as possible, but a have try some tutorial of RDS like this one: [1]: https://www.youtube.com/watch?v=6fDTre5gikg&t=10s, and I understand this connect my local postgress to AWS but I can't find the data in the amazon page, only creates an instance?? and how to connect to it to analysis this data from SageMaker. My final goal is to run the notebooks in the cloud and connect to my postgres in the cloud. Just some orientation about how to use this tools would be appreciated.
I don't understand the difference between RDS which is the one I think I have to use for this and Aurora or S3
RDS and Aurora are relational databases fully managed by AWS. "Regular" RDS allows you to launch the existing popular databases such as MySQL, PostgreSQSL and other which you can launch at home/work as well.
Aurora is in-house, cloud-native implementation databases compatible with MySQL and PosrgreSQL. It can store the same data as RDS MySQL or PosrgreSQL, but provides a number of features not available for RDS, such as more read replicas, distributed storage, global databases and more.
S3 is not a database, but an object storage, where you can store your files, such as images, csv, excels, similarly like you would store them on your computer.
I understand this connect my local postgress to AWS but I can't find the data in the amazon page, only creates an instance??
You can migrate your data from your local postgress to RDS or Aurora if you wish. But RDS nor Aurora will not connect to your existing local database, as they are databases themselfs.
My final goal is to run the notebooks in the cloud and connect to my postgres in the cloud.
I don't see a reason why you wouldn't be able to connect to the database. You can try to make it work, and if you encounter difficulties you can make new question on SO with RDS/Aurora setup details.
I have a database in PostgreSQL. Now we need to import whole database into DynamoDB. Data Migration service need to be used for this purpose or any other service can be used. Please explain in detail.
What is the strategy to be followed? I have studied many blogs but I couldn't get any proper way to migrate the whole database from PostgreSQL to DynamoDB. Only through DMS Service is it possible to migrate or any other service can be used to migrate or any script should be run to migrate the PostgreSQL to to DynamoDB.
The AWS Database Migration Service offers everything you would need to migrate data from any relational DB into AWS - whether the target is DynamoDB (NoSQL offering from AWS) or any of the DB flavors from AWS RDS service.
You can find multiple migration playbooks and step-by-step guides on the Resources page of this AWS service
how to setup tables of aws aurora mysql using aws cloudformation or aws cdk?
in my setup i have a serverless app using lambda for various microservices. the datebase is a serverless aurora mysql database. to provision the aws infrastructure i will use aws CDK. i like to setup the database using some migration tools like liquibase or sequelize.
for the moment i am using a separat lambda function. the lambda function executes liquibase to setup db changes. but i have to execute the function separately after deployment with CDK is succeded.
an execution triggered after the execution of the cloudformation stack (cdk stack) would be optimal?! I would like to avoid a CI / CD stack via code pipeline.
does anyone has best practice to setup database at provision time?
Cloud watch rules
Cloud watch rules based on cloudformation events can be used to route events for processing lambda. Cloud watch rules can be a part of the CDK deployment description.
The triggered function can then execute liquibase, flyway, sequelize or something else to spinup or change db.
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Cloudformation custom resource
AWS cloudformation custom ressource can execute a lambda function during cloudformation lifecycle.
The triggered function can then execute liquibase, flyway, sequelize or something else to spinup or change db.
I use Cloudformation custom resources for running database migrations and initial database setup scripts at deployment time.
This is the recommended way for running DB migrations for serverless applications if you don't want to rely on a CI/CD pipeline to do it for you.
Here's a well written blog post by Alex DeBrie about CF custom resources: https://www.alexdebrie.com/posts/cloudformation-custom-resources/
I have read that you can replicate a Cloud SQL database to MySQL. Instead, I want to replicate from a MySQL database (that the business uses to keep inventory) to Cloud SQL so it can have up-to-date inventory levels for use on a web site.
Is it possible to replicate MySQL to Cloud SQL. If so, how do I configure that?
This is something that is not yet possible in CloudSQL.
I'm using DBSync to do it, and working fine.
http://dbconvert.com/mysql.php
The Sync version do the service that you want.
It work well with App Engine and Cloud SQL. You must authorize external conections first.
This is a rather old question, but it might be worth noting that this seems now possible by Configuring External Masters.
The high level steps are:
Create a dump of the data from the master and upload the file to a storage bucket
Create a master instance in CloudSQL
Setup a replica of that instance, using the external master IP, username and password. Also provide the dump file location
Setup additional replicas if needed
Voilà!