I have a small set of scripts that manage the build/test/deployment of an app. Recently I decided I wanted to switch to Liquibase for db schema management. This script will be working both on the developer machines where it regularly blow away and rebuild their database and also on deployed environment where we will only be adding new changesets.
When this program first runs on a deployed environment I need to detect if Liquibase has run or not and then run changelogSync to sync with the existing tables.
Other than manually checking if the database changelog table exists is there a way for the Liquibase API to let me know that it has already run at least once?
I'm using the Java core library in Groovy
The easiest way is probably ((StandardChangeLogHistoryService) ChangeLogHistoryServiceFactory.getInstance().getChangeLogService(database)).hasDatabaseChangeLogTable()
The ChangeLogHistoryService interface returned by liquibase.changelog.ChangeLogHistoryServiceFactory doesn't have a method to check if the table exists, but the StandardChangeLogHistoryService implementation does.
I'm writing a suite of black-box automated tests for our application. I keep bumping into the same design problem, so I was wondering what people here think about it.
Basically, it's a simple CRUD system. For argument's sake, let's see you're testing the screens to create, view, edit and delete user accounts. What I would like to do is write one test which tests that user creation works correctly, another test that checks that viewing a user shows you the same data as you originally typed in, another test that checks that editing a user works, and finally a test that deleting a user is OK.
The trouble is, if I do that, then the tests must be run in a certain order, or they won't work. (E.g., you can't delete a user that hasn't been created yet.) Now some say that the test setup should create everything that the test needs, and the teardown should put the system back into a consistent state. But think about it... the Create User test is going to need to delete that user afterwards, and the Delete User test will have to create a user first... so the two tests now have identical code, and the only difference is whether that code is in the setup / body / teardown. That just seems wrong.
In short, I seem to be faced with several alternatives, all of which seem broken:
Use setup to create users and teardown to delete them. This duplicates all of the Create User and Delete User test code as setup / teardown code.
Force the tests to run in a specific order. This violates the principle that tests should work in isolation and be runnable in any order.
Write one giant test which creates a user, views the user, edits the user, and then deletes the user, all as one huge monolithic block.
Note that creating a user is not a trivial matter; there's quite a lot of steps involved. Similarly, when deleting a user you have to specify what to do with their assigned projects, etc. It's not a trivial operation by any means.
Now, if this were a white-box test, I could mock the user account objects, or mock the database that holds them, or even prod the real database on disk. But these are black box tests, which test only the external, user-visible interface. (I.e., clicking buttons on a screen.) The idea is to test the whole system from end to end, without modifying it [except through GUI commands, obviously].
We have the same issue. We've taken two paths. In one style of test, we use the setup and teardown as you suggest to create the data (users, tickets, whatever) that the test needs. In the other style, we use pre-existing test data in the database. So, for example, if the test is AdminShouldBeAbleToCreateUser, we don't do either of those, because that's the test itself. But if the test is ExistingUserShouldBeAbleToCreateTicket, we use a pre-defined user in the test data, and if the test is UserShouldBeAbleToDeleteOwnTicket, we use a pre-defined user and create the ticket in the setup.
Before updating production, I need to ensure, that all manipulations, performed with seeds are working correctly.
How do you test seeds.rb with RSpec?
A seed is intended to be run first on an empty database, to correctly give the correct state. If that initial state needs to change (e.g. domain tables) you have to adapt the seed accordingly that it can add non-existing elements or change existing elements. A good way to achieve this is to do something like:
admin = Operator.find_or_create_by_login!(:admin) do |adm|
adm.name = 'admin'
adm.is_administrator = true
end
Before running the tests, we also load the seeds, so then it is easy to create a spec that will verify that the needed data is there (in case you wouldn't trust it).
If you need to manipulate existing data the preferred way is to use a migration. I generally do not write specs for migrations, but test them on my development database, and on a copy of my production database (before running it on top of actual production).
Hope this helps.
I'm using SQL-Server 2008 with Visual Studio Database Edition.
With this setup, keeping your schema in sync is very easy. Basically, there's a 'compare schema' tool that allow me to sync the schema of two databases and/or a database schema with a source-controlled creation script folder.
However, the situation is less clear when it comes to data, which can be of three different kind :
static data referenced in the code. typical example : my users can change their setting, and their configuration is stored on the server. However, there's a system-wide default value for each setting that is used in case the user didn't override it. The table containing those default settings grows as more options are added to the program. This means that when a new feature/option is checked in, the system-wide default setting is usually created in the database as well.
static data. eg. a product list populating a dropdown list. The program doesn't rely on the existence of a specific product in the list to work. This can be for example a list of unicode-encoded products that should be deployed in production when the new "unicode version" of the program is deployed.
other data, ie everything else (logs, user accounts, user data, etc.)
It seems obvious to me that my third item shouldn't be source-controlled (of course, it should be backuped on a regular basis)
But regarding the static data, I'm wondering what to do.
Should I append the insert scripts to the creation scripts? or maybe use separate scripts?
How do I (as a developer) warn the people doing the deployment that they should execute an insert statement ?
Should I differentiate my two kind of data? (the first one being usually created by a dev, while the second one is usually created by a non-dev)
How do you manage your DB static data ?
I have explained the technique I used in my blog Version Control and Your Database. I use database metadata (in this case SQL Server extended properties) to store the deployed application version. I only have scripts that upgrade from version to version. At startup the application reads the deployed version from the database metadata (lack of metadata is interpreted as version 0, ie. nothing is yet deployed). For each version there is an application function that upgrades to the next version. Usually this function runs an internal resource T-SQL script that does the upgrade, but it can be something else, like deploying a CLR assembly in the database.
There is no script to deploy the 'current' database schema. New installments iterate trough all intermediate versions, from version 1 to current version.
There are several advantages I enjoy by this technique:
Is easy for me to test a new version. I have a backup of the previous version, I apply the upgrade script, then I can revert to the previous version, change the script, try again, until I'm happy with the result.
My application can be deployed on top of any previous version. Various clients have various deployed version. When they upgrade, my application supports upgrade from any previous version.
There is no difference between a fresh install and an upgrade, it runs the same code, so I have fewer code paths to maintain and test.
There is no difference between DML and DDL changes (your original question). they all treated the same way, as script run to change from one version to next. When I need to make a change like you describe (change a default), I actually increase the schema version even if no other DDL change occurs. So at version 5.1 the default was 'foo', in 5.2 the default is 'bar' and that is the only difference between the two versions, and the 'upgrade' step is simply an UPDATE statement (followed of course by the version metadata change, ie. sp_updateextendedproperty).
All changes are in source control, part of the application sources (T-SQL scripts mostly).
I can easily get to any previous schema version, eg. to repro a customer complaint, simply by running the upgrade sequence and stopping at the version I'm interested in.
This approach saved my skin a number of times and I'm a true believer now. There is only one disadvantage: there is no obvious place to look in source to find 'what is the current form of procedure foo?'. Because the latest version of foo might have been upgraded 2 or 3 versions ago and it wasn't changed since, I need to look at the upgrade script for that version. I usually resort to just looking into the database and see what's in there, rather than searching through the upgrade scripts.
One final note: this is actually not my invention. This is modeled exactly after how SQL Server itself upgrades the database metadata (mssqlsystemresource).
If you are changing the static data (adding a new item to the table that is used to generate a drop-down list) then the insert should be in source control and deployed with the rest of the code. This is especially true if the insert is needed for the rest of the code to work. Otherwise, this step may be forgotten when the code is deployed and not so nice things happen.
If static data comes from another source (such as an import of the current airport codes in the US), then you may simply need to run an already documented import process. The import process itself should be in source control (we do this with all our SSIS packages), but the data need not be.
Here at Red Gate we recently added a feature to SQL Data Compare allowing static data to be stored as DML (one .sql file for each table) alongside the schema DDL that is currently supported by SQL Compare.
To understand how this works, here is a diagram that explains how it works.
The idea is that when you want to push changes to your target server, you do a comparison using the scripts as the source data source, which generates the necessary DML synchronization script to update the target. This means you don't have to assume that the target is being recreated from scratch each time. In time we hope to support static data in our upcoming SQL Source Control tool.
David Atkinson, Product Manager, Red Gate Software
I have come across this when developing CMS systems.
I went with appending the static data (the stuff referenced in the code) to the database creation scripts, then a separate script to add in any 'initialisation data' (like countries, initial product population etc).
For the first two steps, you could consider using an intermediate format (ie XML) for the data, then using a home grown tool, or something like CodeSmith to generate the SQL, and possible source files as well, if (for example) you have lookup tables which relate to enumerations used in the code - this helps enforce consistency.
This has another benefit that if the schema changes, in many cases you don't have to regenerate all your INSERT statements - you just change the tool.
I really like your distinction of the three types of data.
I agree for the third.
In our application, we try to avoid putting in the database the first, because it is duplicated (as it has to be in the code, the database is a duplicate). A secondary benefice is that we need no join or query to get access to that value from the code, so this speed things up.
If there is additional information that we would like to have in the database, for example if it can be changed per customer site, we separate the two. Other tables can still reference that data (either by index ex: 0, 1, 2, 3 or by code ex: EMPTY, SIMPLE, DOUBLE, ALL).
For the second, the scripts should be in source-control. We separate them from the structure (I think they typically are replaced as time goes, while the structures keeps adding deltas).
How do I (as a developer) warn the people doing the deployment that they should execute an insert statement ?
We have a complete procedure for that, and a readme coming with each release, with scripts and so on...
First off, I have never used Visual Studio Database Edition. You are blessed (or cursed) with whatever tools this utility gives you. Hopefully that includes a lot of flexibility.
I don't know that I'd make that big a difference between your type 1 and type 2 static data. Both are sets of data that are defined once and then never updated, barring subsequent releases and updates, right? In which case the main difference is in how or why the data is as it is, and not so much in how it is stored or initialized. (Unless the data is environment-specific, as in "A" for development, "B" for Production. This would be "type 4" data, and I shall cheerfully ignore it in this post, because I've solved it useing SQLCMD variables and they give me a headache.)
First, I would make a script to create all the tables in the database--preferably only one script, otherwise you can have a LOT of scripts lying about (and find-and-replace when renaming columns becomes very awkward). Then, I would make a script to populate the static data in these tables. This script could be appended to the end of the table script, or made it's own script, or even made one script per table, a good idea if you have hundreds or thousands of rows to load. (Some folks make a csv file and then issue a BULK INSERT on it, but I'd avoid that is it just gives you two files and a complex process [configuring drive mappings on deployment] to manage.)
The key thing to remember is that data (as stored in databases) can and will change over time. Rarely (if ever!) will you have the luxury of deleting your Production database and replacing it with a fresh, shiny, new one devoid of all that crufty data from the past umpteen years. Databases are all about changes over time, and that's where scripts come into their own. You start with the scripts to create the database, and then over time you add scripts that modify the database as changes come along -- and this applies to your static data (of any type) as well.
(Ultimately, my methodology is analogous to accounting: you have accounts, and as changes come in you adjust the accounts with journal entries. If you find you made a mistake, you never go back and modify your entries, you just make a subsequent entries to reverse and fix them. It's only an analogy, but the logic is sound.)
The solution I use is to have create and change scripts in source control, coupled with version information stored in the database.
Then, I have an install wizard that can detect whether it needs to create or update the db - the update process is managed by picking appropriate scripts based on the stored version information in the database.
See this thread's answer. Static data from your first two points should be in source control, IMHO.
Edit: *new
all-in-one or a separate script? it does not really matter as long as you (dev team) agree with your deployment team. I prefer to separate files, but I still can always create all-in-one.sql from those in the proper order [Logins, Roles, Users; Tables; Views; Stored Procedures; UDFs; Static Data; (Audit Tables, Audit Triggers)]
how do you make sure they execute it: well, make it another step in your application/database deployment documentation. If you roll out application which really needs specific (new) static data in the database, then you might want to perform a DB version check in your application. and you update the DB_VERSION to your new release number as part of that script. Then your application on a start-up should check it and report an error if the new DB version is required.
dev and non-dev static data: I have never seen this case actually. More often there is real static data, which you might call "dev", which is major configuration, ISO static data etc. The other type is default lookup data, which is there for users to start with, but they might add more. The mechanism to INSERT these data might be different, because you need to ensure you do not destoy (power-)user-created data.