What are relational database (and schema) migration patterns on production in continuous delivery?
In many traditional developments the DBA arranges a big migration script out of the many smaller scripts created in the current release cycle. But in CD the developer may want to push the change now to production, not wait to compile them with other scripts.
I know on rails-migration but to me it looks more reasonable to use raw sql scripts.
I've also seen tools like flyway to manage migrations but I have not read of many people using them in production. This is why I wonder what are the common practices here.
Flyway works great for continuous delivery/deployment. Many clients use it across all environments, including production.
The single most important thing for cascading DB migrations across environments is to have a 3 step process:
Step 1
Old application code works together with old DB.
Step 2
New application code get deployed, and migrates DB on startup. This migration must be backwards-compatible so that old application code still works with the new DB. This is essential because:
you can then do rolling upgrades, upgrading one node at a time until all nodes have the new application code
rollback immediately to the old application code if the new one is broken
This step may involve compatibility views and triggers to do the job.
Step 3
After the changes have been proven to work, the next version of the application code gets
deployed together with the necessary DB migrations to discard any remaining outdated (from step 1) and compatibility (from step 2) structures.
Implement changes to your database as single (raw) sql files, then use sqlpatch to build a migration script.
I usually have a single git repository for the database alone and a cd environment attached to it. I usually have a production and a development database that are automatically migrated when I push to corresponding branches.
This setup makes is very easy to setup another database for a feature branch and to experiment with it. Sqlpatch takes care of all the dependencies in the separate sql files so that I can easily merge a feature branch in another branch.
Related
I have a .NET core app which I'am running on AWS Elastic Container Services (ECS).
- The app runs on two different instances.
- Database is SQL server
The app runs the database migrations on startup, which has worked really well. But then i had to migrate a lot of data which meant that the migration took longer time. This resulted in duplicates of the data being moved.
This happens because both apps first checks the database if the migration has been executed, both finds out that it hasn't, then both starts running the migration which takes time. After it is done it adds the migration to the database.
How do people solve this?
Possible solutions I and others have thought of
Start with only one instance of the app, then scale up.
this would work, but then I will have to manually scale down and up for each time there is a migration. (It is possible to do it automatically, but it would take time)
Wrap long running migrations in transactions and at the start set the migration as done in the database. Check if it is in the database before commiting the change. If the transaction fails, remove the migration from the database.
Lock the database? EF Core lock the database during migration . Seems weird.
Make the migration a part of the deployment process. This seems to be best practice, but it would mean that the Build server would need to know the Database secrets. I'am not to afraid to give it, but it would mean i would have to maintain a duplicate set.
What does people out there do? Am I missing some obvious solution?
Thanks
We also used to have our applications perform the migration, but even Microsoft recommends avoiding this in a multi-instance environment:
We recommend production apps should not call Database.Migrate at application startup. Migrate shouldn't be called from an app in server farm. For example, if the app has been cloud deployed with scale-out (multiple instances of the app are running).
Database migration should be done as part of deployment, and in a controlled way.
Like everything there are different ways to go about solving the problem. Our team is small and so we generate migration scripts through the EF CLI tooling and then run them manually as part of a deployment/maintenance routine. This could of course be automated if your process warrants it.
When I debug against a (central) database, Flyway can update the database schema. This happens when my local application runs on a development branch which is further ahead than the deployed application.
Running the local application will then invoke migration scripts on the central database. In the worst case, this might update a production database of course.
Another scenario is one where 2 developers work against 1 development database with test-data. Both developers are working on different features and both are modifying the schema. When one developer updates the db, the other is (probably) confronted with a checksum issue, and otherwise surprised by the changes made.
I'm thinking about a solution to such problems.
Of course, there are some solutions outside of Flyway, like only connecting to throw-away databases, or preventing access to important db's.
I'm interested in which options Flyway offers.
There are some options, which seems to be useful in your cases.
For the first one, you can set the target property with the same version your DB has, to prevent Flyway to update it.
For the second case with 2 developers working simultaneously with the same DB instance, you can try to turn off the validateOnMigrate property to avoid validation failures or the ignoreMissingMigrations to ignore the migrations some of the developers doesn't have yet.
Here you can find all available via console properties for migration task. You didn't specify, haw exactly you run the Flyway, if it's done vie Spring Boot, then not all this properties are available, just some of them - you can find it here under the Flyway section.
But for most cases, I think, the best solution is to simply turn Flyway migrations off during the development and debugging if it's possible and use for the delivery of ready features.
My development team of four people has been facing this issue for some time now:
Sometimes we need to be working off the same set of data. So while we develop on our local computers, the dev database is connected to remotely.
However, sometimes we need to run operations on the db that will step on other developers' data, ie we break associations. For this a local db would be nice.
Is there a best practice for getting around this dilemma? Is there something like an "SCM for data" tool?
In a weird way, keeping a text file of SQL insert/delete/update queries in the git repo would be useful, but I think this could get very slow very quickly.
How do you guys deal with this?
You may find my question How Do You Build Your Database From Source Control useful.
Fundamentally, effective management of shared resources (like a database) is hard. It's hard because it requires balancing the needs of multiple people, including other developers, testers, project managers, etc.
Often, it's more effective to give individual developers their own sandboxed environment in which they can perform development and unit testing without affecting other developers or testers. This isn't a panacea though, because you now have to provide a mechanism to keep these multiple separate environments in sync with one another over time. You need to make sure that developers have a reasonable way of picking up each other changes (both data, schema, and code). This isn't necesarily easier. A good SCM practice can help, but it still requires a considerable level of cooperation and coordination to pull it off. Not only that, but providing each developer with their own copy of an entire environment can introduce costs for storage, and additional DBA resource to assist in the management and oversight of those environments.
Here are some ideas for you to consider:
Create a shared, public "environment whiteboard" (it could be electronic) where developers can easily see which environments are available and who is using them.
Identify an individual or group to own database resources. They are responsible for keeping track of environments, and helping resolve the conflicting needs of different groups (developers, testers, etc).
If time and budgets allow, consider creating sandbox environments for all of your developers.
If you don't already do so, consider separating developer "play areas", from your integration, testing, and acceptance testing environments.
Make sure you version control critical database objects - particularly those that change often like triggers, stored procedures, and views. You don't want to lose work if someone overwrites someone else's changes.
We use local developer databases and a single, master database for integration testing. We store creation scripts in SCM. One developer is responsible for updating the SQL scripts based on the "golden master" schema. A developer can make changes as necessary to their local database, populating as necessary from the data in the integration DB, using an import process, or generating data using a tool (Red Gate Data Generator, in our case). If necessary, developers wipe out their local copy and can refresh from the creation script and integration data as needed. Typically databases are only used for integration testing and we mock them out for unit tests so the amount of work keeping things synchronized is minimized.
I recommend that you take a look at Scott Allen´s views on this matter. He wrote a series of blogs which are, in my opinion, excellent.
Three Rules for Database Work,
The Baseline,
Change scripts,
Views, stored procs etc,
Branching and Merging.
I use these guidelines more or less, with personal changes and they work.
In the past, I've dealt with this several ways.
One is the SQL Script repository that creates and populates the database. It's not a bad option at all and can keep everything in sync (even if you're not using this method, you should still maintain these scripts so that your DB is in Source Control).
The other (which I prefer) was having a single instance of a "clean" dev database on the server that nobody connected to. When developers needed to refresh their dev databases, they ran a SSIS package that copied the "clean" database onto their dev copy. We could then modify our dev databases as needed without stepping on the feet of other developers.
We have a database maintenance tool that we use that creates/updates our tables and our procs. we have a server that has an up-to-date database populated with data.
we keep local databases that we can play with as we choose, but when we need to go back to "baseline" we get a backup of the "master" from the server and restore it locally.
if/when we add columns/tables/procs we update the dbMaintenance tool which is kept in source control.
sometimes, its a pain, but it works reasonably well.
If you use an ORM such as nHibernate, create a script that generate both the schema & the data in the LOCAL development database of your developers.
Improve that script during the development to include typical data.
Test on a staging database before deployment.
We do replicate production database to UAT database for the end users. That database is not accessible by developers.
It takes less than few seconds to drop all tables, create them again and inject test data.
If you are using an ORM that generates the schema, you don't have to maintain the creation script.
Previously, I worked on a product that was data warehouse-related, and designed to be installed at client sites if desired. Consequently, the software knew how to go about "installation" (mainly creation of the required database schema and population of static data such as currency/country codes, etc.).
Because we had this information in the code itself, and because we had pluggable SQL adapters, it was trivial to get this code to work with an in-memory database (we used HSQL). Consequently we did most of our actual development work and performance testing against "real" local servers (Oracle or SQL Server), but all of the unit testing and other automated tasks against process-specific in-memory DBs.
We were quite fortunate in this respect that if there was a change to the centralised static data, we needed to include it in the upgrade part of the installation instructions, so by default it was stored in the SCM repository, checked out by the developers and installed as part of their normal workflow. On reflection this is very similar to your proposed DB changelog idea, except a little more formalised and with a domain-specific abstraction layer around it.
This scheme worked very well, because anyone could build a fully working DB with up-to-date static data in a few minutes, without stepping on anyone else's toes. I couldn't say if it's worthwhile if you don't need the install/upgrade functionality, but I would consider it anyway because it made the database dependency completely painless.
What about this approach:
Maintain a separate repo for a "clean db". The repo will be a sql file with table creates/inserts, etc.
Using Rails (I'm sure could be adapted for any git repo), maintain the "clean db" as a submodule within the application. Write a script (rake task, perhaps) that queries a local dev db with the SQL statements.
To clean your local db (and replace with fresh data):
git submodule init
git submodule update
then
rake dev_db:update ......... (or something like that!)
I've done one of two things. In both cases, developers working on code that might conflict with others run their own database locally, or get a separate instance on the dev database server.
Similar to what #tvanfosson recommended, you keep a set of SQL scripts that can build the database from scratch, or
On a well defined, regular basis, all of the developer databases are overwritten with a copy of production data, or with a scaled down/deidentified copy of production, depending on what kind of data we're using.
I would agree with all the LBushkin has said in his answer. If you're using SQL Server, we've got a solution here at Red Gate that should allow you to easily share changes between multiple development environments.
http://www.red-gate.com/products/sql_source_control/index.htm
If there are storage concerns that make it hard for your DBA to allow multiple development environments, Red Gate has a solution for this. With Red Gate's HyperBac technology you can create virtual databases for each developer. These appear to be exactly the same as ordinary database, but in the background, the common data is being shared between the different databases. This allows developers to have their own databases without taking up an impractical amount of storage space on your SQL Server.
We're re-evaluating our database upgrade process for our application to try and remove the pain of having to generate all the upgrade scripts for a release at the end of the release cycle. We're looking to move towards a more evolutionary process, using migrations which are checked in alongside features with a tool such as migratordotnet, and this seems like a very clean way of managing schema changes.
However, the default data which is shipped with our database is quite regularly subject to change and some of these data updates do not fit well with the migration process. For example, inserts to tables which have an Identity primary key are not easily identifiable and so cannot be reversed on downgrading.
So I'm wondering how people manage the migration of default data? Do they managed it outside of the scheme migration process? Or are inserts performed during migrations but the removal of the data is not performed during downgrades?
For us, DB migration is part of the daily development process. Developers have to either commit a program or a script which performs the necessary changes. This happens as soon the related feature is implemented and never "at the end of the release cycle".
Downgrade is rarely an issue but if you have it, create a column with a version information. When the upgrade succeeds and the customer decides to stay with the new version, drop the column again (or keep it).
The key for success is that we have extensive test cases which create the database from scratch (using an In-Memory DB like H2 or a DB which is installed on each developer machine) including all data and then migrate it all the way through and back. We can import anonymized data from the productions servers into the test cases to track down bugs and improve tests without violating the privacy of our customers or getting in the way of the developers.
Just a question about best-practices when upgrading an existing database. Assuming there will be all kinds of modifications to the data itself, the structure, the relations, additional columns, disappearing columns and whatever more.
My problem is a simple one. I'm working on a project that will use SQL Server. No problem there, since I'm enough of an expert to handle this. But this project will be upgraded later on and I need to specify a protocol that needs to be followed by the upgrade mechanism. Basically, this protocol needs to be followed when creating upgrade scripts...
Right now, I have these simple steps:
Add the new columns to the tables.
Add constraints to the new columns.
Add new tables.
Drop constraints where needed.
Drop columns that need to be removed.
Drop tables that need to be removed.
Somehow, this list feels incomplete. Is there a more extended list somewhere describing the proper steps which needs to be followed during an upgrade?
Also, is it always possible to do a complete upgrade within a single database transaction (with SQL Server) or are there breakpoints that need to be included within the protocol where one transaction should end and another one starts?While automated tools will provide a nice, automated solution, I still can't really use them. The development team working on this system has 4 developers, each with their own database on their local system. Every developer keeps track of their own updates to the structure and keeps track of them by generating both an Upgrade and Downgrade script for his own modifications, both for structural changes and data changes. These scripts can then be used by the other developers to keep their own system up-to-date. Whenever the system is going to be released, those scripts are all merged into one big script.
The system does not include any stored procedures or other "special" features. The database is just that: a data storage with just tables and relations between them. No roles, no users, no stored procedures, no triggers, no complex datatypes...The DB is used by an application where users work from 9-to-5 so shutting down can be done easily, including upgrades for the clients. We also add a version number to the database and applications will check if they're linked to the correct database version.
During development, all developers use their own database instance, which they can fully control. Since we're not the ones who use the application, we tend to develop for the Express edition, not any more expensive one. To be honest, we don't develop our application to support a lot of users, but we'll inform our users that since it uses SQL Server, they could install the system on a bigger SQL Server platform, according to their own needs. They will need their own DBA for this, though. We do have a bigger SQL Server available for ourselves, which we also use for our own web interface, but this server is located in a special dataserver where it is being maintained for us, not by us.
The project previously used MS Access for it's data storage and was intended for single-user development, but as it turned out, many users still decided to share their databases and this had shown that the datamodel itself is reliable enough for multi-user environments. So we migrated to SQL Server to support smaller offices with 3 or more users and some big organisation who will have 500 or more users at the same time.
Since we need to keep the cost of the software low, we don't have a big budget to spend on expensive tools or a more expensive server.
Check out Red-Gate's SQL Compare (structure comparison), SQL Data Compare (data comparison), and SQL Packager (for packaging up updates scripts into a C# project or a .NET executable).
They provide a nice, clean, fully functional and easy-to-use solution for all your database upgrade needs. They're well worth their license fees - that pays for itself in a few weeks or months.
Highly recommended!
In my opinion, it's an absolute bear doing these manually. For Microsoft SQL Server, I'd recommend using the Database editiion of Team System, since it includes complete source control capabilities for your database, and can automatically build your scripts for upgrading/downgrading versions.
Another option is SQLCompare with Redgate, which can also handle these kinds of upgrades/downgrades, and will result in a very nice SQL script. I've used both, and keeping the historic scripts has helped us troubleshoot issues and resolve many a mystery.
If you are working with a manual script as above, don't forget to also account for SP changes in your scripts. Also, any hand-edited script should be able to be executed multiple times on a database - i.e. if your script includes a table creation or drop, be sure to check for existance first, otherwise your script will fail if executed back to back.
Again, while it's possible to build a manual protocol I'd still fall back on using one of the purpose-built tools out there, and both Team System and SQL Compare will be able to output scripts that you could include as part of an installation/upgrade package.
With database updates I always believe it should be all or nothing. If any of the DB updates fail your application will be left in an unknown state that could be harmful to the data so I think it is best practice to either apply them all or none (1 transaction around them all).
I also like to backup the database before applying updates so that if anything does go wrong the database can be rolled back (this has saved me numerous times when working with live data).
Hope this helps.
Best practices for upgrading a production database schema actually look pretty bad on the surface. Unless you can completely shut down your system for the upgrade, which is often not possible, your changes all need to be backwards compatible. If you have many clients accessing the database, you can't update them all simultaneously, so any schema changes you make need to allow old code to run.
That means never renaming a column, and making all new columns nullable. This doesn't mean you leave it like that forever. You write two scripts, one for the initial change, which is backwards compatible, then another to clean things up after all clients have been updated.
Automated tools are great for validation of schemas, but they are not so good when it comes to actually modifying a complex system. You should break your changes up into many small, discrete change scripts so each can be run manually. If there's a failure, it's easier to pinpoint the cause and fix it. Basically, each feature gets its own script. Give each a unique name and then store that name in the database itself when you run the script so you can query the database to find out what's been run and what hasn't. This is invaluable when you have instances on developer's machines, test servers, production, etc.