I have a email folder in Outlook that contains 100s of emails which record my discussions with a developer of some bespoke software. I want to import these into SQL to create a knowledge base of information that can be searched upon to extract all the decisions that we have made during the course of the 2 year project.
Having sreached the net, I found that it is very easy to dump the contents of an email folder into Access using the import data functionality. In fact I have linked the table and so believe (never used Access before!!) that I now have an Access table that is connected in 'real-time' to the Outlook folder. This is eactly what I want BUT in SLQ as this is something that I am very familiar with using.
So I have tried to import the Access database into SQL (which also appears to be relatively easy) but keep getting the message that 'The source database ...contains no visible tables or views'. Checking SQL pemissions, I am owner of this new databse.
Two questions please. First, cant believe that going through Access is the simplest way to do this and presume that I will loose the 'real-time' link - am I right? Second, given that I can see my Access database has a visible table, why am I getting the error?
The easiest and quickest way is to create a VBA macro where you can populate your SQL database from Outlook emails. You can build the table structure according to your needs and extract the required information from Outlook using VBA. I'd suggest processing emails in chunks using the Find/FindNext or Restrict methods of the Items class, so you will not reach the reference counter limit. The MailItem properties you may find described in MSDN.
BTW The internal store (if you use the cached mode) in Outlook acts like a database. So, why do you need to introduce yet a new database?
I'm trying to migrate specific objects from one database to another using sqlpackage.exe /action:Extract and sqlpackage.exe /action:Script. Currently I'm creating the script and filtering the unneeded objects manually, I would like to be able to exclude them all together and to automate the process. So far I didn't find in the documentation any option that does it.Thanks.
There is no way to remove single objects with native functionality. Natively you can remove only specific object types.
You can write your own deployment contributor and then skip whatever objects you need. Here is an example here.
Check Ed Elliot's ready to use contributor with bunch of configuration options (I haven't used it for a while and do not know how does it work with the new versions of SQL Server).
Additionally, in Ed Elliot's blog you can find a lot of useful information.
I have a database which includes many cached/generated tables. I'd like to exclude these (permanently) when I do a schema compare. Is there any way to configure the schema compare to ignore a specific schema or, even better, a specific object name? E.g. I'd like to ignore all tables within the "cache" schema whose name begins with "XYZ".
I realize that if I ignore an object once, its "ignore status" is persisted. However, I will still see it in the comparison results (figure 1,000 ignored tables, for example). I'm looking for a way to permanently hide, or simply not show ignored objects.
Another example would be if I wanted to split a large database into multiple, reusable projects. I may want to exclude certain schemas from each project when running a schema compare.
You can't use this with the schema compare ui but if you deploy using sqlpackage.exe or publish in visual studio you can write a deployment contributor to allow exactly this i.e. remove certain steps from the deployment.
You need to write in c# (or vb.net) or there is a generic one you can use:
http://agilesqlclub.codeplex.com/
If you get stuck with it, give me a shout!
We are building a webapp which is shipped to several client as a debian package. Each client runs his own server. But the update and support is done by us.
We make regular releases of the product, with a clean version number. Most of the users get an automatic update (by Puppet), some others don't.
We want to keep a trace of the version of the application (in order to allow the user to check the version in an "about" section, and for our support to help the user more accurately).
We plan to store the version of the code and the version of the base in our database, and to keep the info up to date automatically.
Is that a good idea ?
The other alternative we see is a file.
EDIT : The code and database schema are updated together. ( if we update to version x.y.z , both code and database go to x.y.z )
Using a table to track every change to a schema as described in this post is a good practice that I'd definitely suggest to follow.
For the application, if it is shipped independently of the database (which is not clear to me), I'd embed a file in the package (and thus not use the database to store the version of the web application).
If not and thus if both the application and the database versions are maintained in sync, then I'd just use the information stored in the database.
As a general rule, I would have both, DB version and application version. The problem here is how "private" is the database. If the database is "private" to the application, and user never modifies the schema then your initial solution is fine. In my experience, databases which accumulate several years of data stop being private, it means that users add a table or two and access data using some reporting tool; from that point on the database is not exclusively used by the application any more.
UPDATE
One more thing to consider is users (application) not being able to connect to the DB and calling for support. For this case it would be better to have version, etc.. stored on file system.
Assuming there are no compelling reasons to go with one approach or the other, I think I'd go with keeping them in the database.
I'd put them in both places. Then when running your about function you quickly check that they are both the same, and if they aren't you can display extra information about the version mismatch. If they're the same then you will only need to display one of them.
I've generally found users can do "clever" things like revert databases back to old versions by manually copying directories around "because they can" so defensively dealing with it is always a good idea.
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!