I am new in this and try to found information in the web have not got any success. I need to create some log tables but have no idea what information should this table contains and how to organize them.
For example:
LogErrorTabble, LogChangesTable, etc..
Could anyone give me some articles about this or link to site with example solutions that he has used?
First of all what log library do you use? If you're on java got for log4j, if you're on .NET go for log4net. Both of these frameworks provide db log appenders that log to the database out of the box.
In case you're not using a log library: use a log library :)
In case you really want to do that on your own I can recommend a layout I used in a project where log messages were stored in a table logs and exceptions associated with an entry in the logs table were stored in an exceptions table but that highly depends on your platform.
You can find a lot of useful information on how to design your log tables in the log4net and log4j documentation. For example take a look at the log4net AdoNetAppender Class.
I have a Django project with multiple apps. They all share a db with engine = django.db.backends.postgresql_psycopg2. Now I want some functionality of GeoDjango and decided I want to integrate it into my existing project. I read through the tutorial, and it looks like I have to create a separate spartial database for GeoDjango. I wonder if there is anyway around. I tried to add this into one of my apps' models.py without changing my db settings :
from django.contrib.gis.db.models import PointField
class Location(models.Model):
location = PointField()
But when I run syncdb, I got this error.
File "/home/virtual/virtual-env/lib/python2.7/site-packages/django/contrib/gis/db/models/fields.py", line 200, in db_type
return connection.ops.geo_db_type(self)
Actually, as i recall, django.contrib.gis.db.backends.postgis is extension of postgresql_psycopg2 so you could change db driver in settings, create new db with spatial template and then migrate data to new db (South is great for this). By itself geodjango is highly dependent on DB inner methods thus, unfortunately, you couldn't use it with regular db.
Other way - you could make use of django's multi-db ability, and create extra db for geodjango models.
Your error looks like it comes from not changing the database extension in your settings file. You don't technically need to create a new database using the spatial template, you can simply run the PostGIS scripts on your existing database to get all of the geospatial goodies. As always, you should backup your existing database before doing this though.
I'm not 100%, but I think that you can pipe postgis.sql and spatial_ref_sys.sql into your existing database, grant permissions to the tables, and change the db setting to "django.contrib.gis.db.backends.postgis". (After you have installed the deps of course)
https://docs.djangoproject.com/en/dev/ref/contrib/gis/install/#spatialdb-template
I'd be interested to see what you find. Be careful, postgis installation can build some character but you don't want it to build too much.
From the docs (django 3.1) https://docs.djangoproject.com/en/3.1/ref/databases/#migration-operation-for-adding-extensions :
If you need to add a PostgreSQL extension (like hstore, postgis, etc.) using a migration, use the CreateExtension operation.
Are there way to using data generation plans in VS 2010's database projects to create a set of default data? Or am I barking up the wrong tree i.e. are data generation plans best suited to create dummy example data?
We have a bunch of data (default settings, default users etc etc) that needs to be created for each database deployment. It would be nice to have tooling to help us with this, so it can be source controlled and better managed.
I'm guessing that there are probably third party alternatives, but I'm hoping there is a built-in Visual-Studio-Way of doing things, so it can integrate nicely with TFS etc.
There are probably different ways to do this, but the basics are to:
create a script that inserts your default data
Edit the Script.PostDeployment.sql script to use your script for inserting the default values
For example, I created a new folder, DefaultData, under the Scripts->Post-Deployment folder of my project and added my script for inserting the default data here, InsertDefaultData.sql. Then, I added the following line to Script.PostDeployment.sql ":r ..\DefaultData\InsertDefaultData.sql".
I'd like to know your approach/experiences when it's time to initially populate the Grails DB that will hold your app data. Assuming you have CSVs with data, is is "safer" to create a script (with whatever tool fits you) that:
1.-Generates the Bootstrap commands with the domain classes, run it in test or dev environment and then use the native db commands to export it to prod?
2.-Create the DB's insert script assuming GORM's version = 0 and incrementing manually the soon-to-be autogenerated IDs ?
My fear is that the second approach may lead to inconsistencies for hibernate will have the responsability for the IDs generation and there may be something else I'm missing.
Thanks in advance.
Take a look at this link. This allows you to run groovy scripts in the normal grails context giving you access to all grails features including GORM. I'm currently importing data from a legacy database and have found that writing a Groovy script using the Groovy SQL interface to pull out the data then putting that data in domain objects appears to be the easiest thing to do. Once you have the data imported you just use the commands specific to your database system to move that data to the production database.
Update:
Apparently the updated entry referenced from the blog entry I link to no longer exists. I was able to get this working using code at the following link which is also referenced in the comments.
http://pastie.org/180868
Finally it seems that the simplest solution is to consider that GORM as of the current release (1.2) uses a single sequence for all auto-generated ids. So considering this when creating whatever scripts you need (in the language of your preference) should suffice. I understand it's planned for 1.3 release that every table has its own sequence.
We are currently reviewing how we store our database scripts (tables, procs, functions, views, data fixes) in subversion and I was wondering if there is any consensus as to what is the best approach?
Some of the factors we'd need to consider include:
Should we checkin 'Create' scripts or checkin incremental changes with 'Alter' scripts
How do we keep track of the state of the database for a given release
It should be easy to build a database from scratch for any given release version
Should a table exist in the database listing the scripts that have run against it, or the version of the database etc.
Obviously it's a pretty open ended question, so I'm keen to hear what people's experience has taught them.
After a few iterations, the approach we took was roughly like this:
One file per table and per stored procedure. Also separate files for other things like setting up database users, populating look-up tables with their data.
The file for a table starts with the CREATE command and a succession of ALTER commands added as the schema evolves. Each of these commands is bracketed in tests for whether the table or column already exists. This means each script can be run in an up-to-date database and won't change anything. It also means that for any old database, the script updates it to the latest schema. And for an empty database the CREATE script creates the table and the ALTER scripts are all skipped.
We also have a program (written in Python) that scans the directory full of scripts and assembles them in to one big script. It parses the SQL just enough to deduce dependencies between tables (based on foreign-key references) and order them appropriately. The result is a monster SQL script that gets the database up to spec in one go. The script-assembling program also calculates the MD5 hash of the input files, and uses that to update a version number that is written in to a special table in the last script in the list.
Barring accidents, the result is that the database script for a give version of the source code creates the schema this code was designed to interoperate with. It also means that there is a single (somewhat large) SQL script to give to the customer to build new databases or update existing ones. (This was important in this case because there would be many instances of the database, one for each of their customers.)
There is an interesting article at this link:
https://blog.codinghorror.com/get-your-database-under-version-control/
It advocates a baseline 'create' script followed by checking in 'alter' scripts and keeping a version table in the database.
The upgrade script option
Store each change in the database as a separate sql script. Store each group of changes in a numbered folder. Use a script to apply changes a folder at a time and record in the database which folders have been applied.
Pros:
Fully automated, testable upgrade path
Cons:
Hard to see full history of each individual element
Have to build a new database from scratch, going through all the versions
I tend to check in the initial create script. I then have a DbVersion table in my database and my code uses that to upgrade the database on initial connection if necessary. For example, if my database is at version 1 and my code is at version 3, my code will apply the ALTER statements to bring it to version 2, then to version 3. I use a simple fallthrough switch statement for this.
This has the advantage that when you deploy a new version of your application, it will automatically upgrade old databases and you never have to worry about the database being out of sync with the software. It also maintains a very visible change history.
This isn't a good idea for all software, but variations can be applied.
You could get some hints by reading how this is done with Ruby On Rails' migrations.
The best way to understand this is probably to just try it out yourself, and then inspecting the database manually.
Answers to each of your factors:
Store CREATE scripts. If you want to checkout version x.y.z then it'd be nice to simply run your create script to setup the database immediately. You could add ALTER scripts as well to go from the previous version to the next (e.g., you commit version 3 which contains a version 3 CREATE script and a version 2 → 3 alter script).
See the Rails migration solution. Basically they keep the table version number in the database, so you always know.
Use CREATE scripts.
Using version numbers would probably be the most generic solution — script names and paths can change over time.
My two cents!
We create a branch in Subversion and all of the database changes for the next release are scripted out and checked in. All scripts are repeatable so you can run them multiple times without error.
We also link the change scripts to issue items or bug ids so we can hold back a change set if needed. We then have an automated build process that looks at the issue items we are releasing and pulls the change scripts from Subversion and creates a single SQL script file with all of the changes sorted appropriately.
This single file is then used to promote the changes to the Test, QA and Production environments. The automated build process also creates database entries documenting the version (branch plus build id.) We think this is the best approach with enterprise developers. More details on how we do this can be found HERE
The create script option:
Use create scripts that will build you the latest version of the database from scratch, which is empty except the default lookup data.
Use standard version control techniques to store,branch,tag versions and view histories of your objects.
When upgrading a live database (where you don't want to loose data), create a blank second copy of the database at the new version and use a tool like red-gate's link text
Pros:
Changes to files are tracked in a standard source-code like manner
Cons:
Reliance on manual use of a 3rd party tool to do actual upgrades (no/little automation)
Our company checks them in simply because someone decided to put it in some SOX document that we do. It makes no sense to me at all, except possible as a reference document. I can't see a time we'd pull them out and try and use them again, and if we did we'd have to know which one ran first and which one to run after which. Backing up the database is much more important then keeping the Alter scripts.
for every release we need to give one update.sql file which contains all the new table scripts, alter statements, new/modified packages,roles,etc. This file is used to upgrade the database from 1 version to 2.
What ever we include in update.sql file above one all this statements need to go to individual respective files. like alter statement has to go to table as a new column (table script has to be modifed not Alter statement is added after create table script in the file) in the same way new tables, roles etc.
So whenever if user wants to upgrade he will use the first update.sql file to upgrade.
If he want to build from scrach then he will use the build.sql which already having all the above statements, it makes the database in sync.
sriRamulu
Sriramis4u#yahoo.com
In my case, I build a SH script for this work: https://github.com/reduardo7/db-version-updater
How is an open question
In my case I am trying to create something simple that is easy to use for developers and I do it under the following scheme
Things I tested:
File-based script handling in git using GitlabCI
It does not work, collisions are created and the Administration part has to be done by hand in case of disaster and the development part is too complicated
Use of permissions and access via mysql clients
There is no traceability on changes to the database and the transition to production is manual
Use of programs mentioned here
They require uploading the structures and many adaptations and usually you end up with change control just like the word
Repository usage
Could not control the DRP part
I could not properly control the backups
I don't think it is a good idea to have the backups on the same server and you generate high lasgs for the process
This was what worked best
Manage permissions per user and generate traceability of everything that is sent to the database
Multi platform
Use of development-Production-QA database
Always support before each modification
Manage an open repository for change control
Multi-server
Deactivate / Activate access to the web page or App through Endpoints
the initial project is in:
In case the comment manager reads this part, I understand the self-promotion but please just remove this part and leave the rest since I think it complies with the answer to the question reacted in the post ...
https://hub.docker.com/r/arelis/gitdb
I hope this reaches you since I see that several
There is an interesting article with new URL at: https://blog.codinghorror.com/get-your-database-under-version-control/
It a bit old but the concepts are still there. Good Read!