I started using django-simple-history in order to keep the history but when I delete an object (from admin page at least) I notice that it is gone for good.
I suppose I could create tags and "hide" objects instead of deleting in my views but would be nice if there is an easier way with django-simple-history, which would also cover admin operations.
When objects are deleted, that deletion is also recorded in history. The object does not exist anymore, but its history is safe.
If you browse your database, you should find a table named:
[app_name]_history[model_name]
It contains a line with the last state of the object. That line also contains additional columns: history_id, history_change_reason, history_date, history_type. For a deletion, history_type will be set to "-" (minus sign).
Knowing that, it is possible to revert a deletion programmatically, but not through the Django Admin. Have a look at django-simple-history documentation for details on how to do it programmatically.
Hope that helps!
Related
I want to achieve that if I delete a record of an entity, the records of entities that are related to it are also deleted. How could I do this? For example, I have a meeting system where reports are generated about the meeting. What I want is to delete the record of that meeting and that with it the report is also deleted.
There are two ways to do this. One is automatic and another one is manual. I strongly recommend the automatic way except that you have any special needs.
The automatic way is by using the cascade options. Basically, in the relationship field, in the section Type Rules there is a sub-section called Cascade Updates. Here you will find the option Delete Policy, which should be set to Delete Record. For your case, I guess the report is pointing to the meeting, so in that field, you should set the setting mentioned before. This way, when a meeting is deleted, the report will be deleted as well.
The second option is to put a listener when a record of the meetings is deleted and manually find and delete all the reports associated to it. I guess something like this:
sys.data.delete('reports', {meeting: record.id()});
I hope my question makes sense, I'll try to give as much info as possible.I should probably start off by saying this is the first access database (any database) I have ever done and my knowledge comes from trial and error as well as youtube and the occasional google search...NOOB
So I'm attempting to build a database using microsoft access (2007) for the first time (Student Records in my department). I have pulled in all the data I had available (names, major, graduate, advisor etc.) and made several appended tables for additional data using an append query (usually just pulling over name and ID# and major, and then adding the information that is related to the particular table).
Now I am going through the paper files (which we would like to get rid of) to update any missing data or add new students that we didn't have stored anywhere electronically.
I have created a form in which I can add new records or edit/add already available data that I need.
The problem that I have is that it pretty much pulls up everything I need except the occasional record (which I do a search in the search field on the bottom using the ID#) so I figure hey I must not have this student and add it, when I hit save it basically tells me this record can't be added as there already is a conflicted value. And when I check my table sure enough the record is there. In the form query where I check what tables the field's information is pulled from I have no criteria in there to filter any information out, the relationships overall are just based on the ID# (which is my primary key in all tables). When I check the data everything seems to be correct (not a wrong major, etc.) so I can't quite figure out why some records are not being pulled up.
My question is why and what can I do to fix it...
I hope my explanation is not to confusing. Thank you in advance.
Is there a way to detect that a record being inserted is the result of a clone operation in a trigger?
As part of a managed package, I'd like to clear out some of the custom fields when Opportunity and OpportunityLineItem records are cloned.
Or is a trigger not the correct place to prevent certain fields being cloned?
I had considered creating dedicated code to invoke sObject.Clone() and excluding the fields that aren't required. This doesn't seem like an ideal solution for a managed package as it would also exclude any other custom fields on Opportunity.
In the Winter '16 release, Apex has two new methods that let you detect if a record is being cloned and from what source record id. You can use this in your triggers.
isClone() - Returns true if an entity is cloned from something, even if the entity hasn’t been saved.
getCloneSourceId() - Returns the ID of the entity from which an object was cloned.
https://developer.salesforce.com/docs/atlas.en-us.apexcode.meta/apexcode/apex_methods_system_sobject.htm#apex_System_SObject_getCloneSourceId
https://developer.salesforce.com/docs/atlas.en-us.apexcode.meta/apexcode/apex_methods_system_sobject.htm#apex_System_SObject_getCloneSourceId
One approach, albeit kind of kludgy, would be to create a new field, say original_id__c, which gets populated by a workflow (or trigger, depending on your preference for the order of execution) when blank with the salesforce id of the record. For new records this field will match the standard salesforce id, for cloned records they won't. There are a number of variations on when and how and what to populate the field with, but the key is to give yourself your own hook to differentiate new and cloned records.
If you're only looking to control the experience for the end user (as opposed to a developer extending your managed package) you can override the standard clone button with a custom page that clears the values for a subset of fields using url hacking. There are some caveats, namely that the field is editable and visible on the page layout for the user who clicked the clone button. As of this writing I don't believe you can package standard button overrides, but the list of what's possible changes with ever release.
You cannot detect clone operation inside the trigger. It is treated as "Insert" operation.
You can still use dedicated code to invoke sObject.Clone() and exclude the fields that aren't required. You can ensure that you include all fields by using the sObject describe information to get hold of all fields for that object, and then exclude the fields that are not required.
Hope this makes sense!
Anup
We've got a set of forms in our web application that is managed by multiple staff members. The forms are common for all staff members. Right now, we've implemented a locking mechanism. But the issue is that there's no reliable way of knowing when a user has logged out of the system, so the form needs to be unlocked. I was wondering if there was a better way to manage concurrent users editing the same data.
You can use optimistic concurrency which is how the .Net data libraries are designed. Effectively you assume that usually no one will edit a row concurrently. When it occurs, you can either throw away the changes made, or try and create some nicer retry logic when you have two users edit the same row.
If you keep a copy of what was in the row when you started editing it and then write your update as:
Update Table set column = changedvalue
where column1 = column1prev
AND column2 = column2prev...
If this updates zero rows, then you know that the row changed during the edit and you can then deal with it, or simply throw an error and tell the user to try again.
You could also create some retry logic? Re-read the row from the database and check whether the change made by your user and the change made in the database are able to be safely combined, then do so automatically. Or you could present a choice to the user as to whether they still wish to make their change based on the values now in the database.
Do something similar to what is done in many version control systems. Allow anyone to edit the data. When the user submits the form, the database is checked for changes. If the record has not been changed prior to this submission, allow it as usual. If both changes are the same, ignore the incoming (now redundant) change.
If the second change is different from the first, the record is now in conflict. The user is presented with a new form, which indicates which fields were changed by the conflicting update. It is then the user's responsibility to resolve the conflict (by updating both sets of changes), or to allow the existing update to stand.
As Spence suggested, what you need is optimistic concurrency. A standard website that does no accounting for whether the data has changed uses what I call "last write wins". Simply put, whichever connection saves to the database last, that version of the data is the one that sticks. In optimistic concurrency, you use a "first write wins" logic such that if two connections try to save the same row at the same time, the first one that commits wins and the second is rejected.
There are two pieces to this mechanism:
The rules by which you fail the second commit
How the system or the user handles the rejected commit.
Determining whether to reject the commit
Two approaches:
Comparison column that changes each time a commit happens
Compare the data with its committed version in the database.
The first one entails using something like SQL Server's rowversion data type which is guaranteed to change each time the row changes. The upside is that it makes it simple to roll your own logic to determine if something has changed. When you get the data, you pull the rowversion column's value and when you commit, you compare that value with what is currently in the database. If they are different, the data has changed since you last retrieved it and you should reject the commit otherwise proceed to save the data.
The second one entails comparing the columns you pulled with their existing committed values in the database. As Spence suggested, if you attempt the update and no rows were updated, then clearly one of the criteria failed. This logic can get tricky when some of the values are null. Many object relational mappers and even .NET's DataTable and DataAdapter technology can help you handle this.
Handling the rejected commit
If you do not leave it up to the user, then the form would throw some message stating that the data has changed since they last edited and you would simply re-retrieve the data overwriting their changes. As you can imagine, users aren't particularly fond of this solution especially in a high volume system where it might happen frequently.
A more sophisticated (and also more complicated) approach is to show the user what has changed allow them to choose which items to try to re-commit, Behind the scenes you would retrieve the data again, overwrite the values picked by the user with their entries and try to commit again. In high volume system, this will still be problematic because by the time the user has tried to re-commit, the data may have changed yet again.
The checkout concept is effectively pessimistic concurrency where users "lock" rows. As you have discovered, it is difficult to implement in a stateless environment. Users are notorious for simply closing their browser while they have something checked out or using the Back button to return a set that was checked out and try to recommit it. IMO, it is more trouble than it is worth to try go this route in a web-based solution. Assuming you write the user name that last changed a given row, with optimistic concurrency, you can inform the user whose changes are rejected who saved the data before them.
I have seen this done two ways. The first is to have a "checked out" column in your database table associated with that data. Your service would have to look for this flag to see if it is being edited. You can have this expire after a time threshold is met (with a trigger) if the user doesn't commit changes. The second way is having a dedicated "checked out" table that stores id's and object names (probably the table name). It would work the same way and you would have less lookup time, theoretically. I see concurrency issues using the second method, however.
Why do you need to look for session timeout? Just synchronize access to your data (forms or whatever) and that's it.
UPDATE: If you mean you have "long transactions" where form is locked as soon as user opens editor (or whatever) and remains locked until user commits changes, then:
either use optimistic locking, implement it by versioning of forms data table
optimistic locking can cause loss of work, if user have been away for a long time, then tried to commit his changes and discovered that someone else already updated a form. In this case you may want to implement explicit "locking" of form, where user "locks" form as soon as he starts work on it. Other user will notice that form is "locked" and either communicate with lock owner to resolve issue, or he can "relock" form for himself, loosing all updates of first user in process.
We put in a very simple optimistic locking scheme that works like this:
every table has a last_update_date
field in it
when the form is created
the last_update_date for the record
is stored in a hidden input field
when the form is POSTED the server
checks the last_update_date in the
database against the date in the
hidden input field.
If they match,
then no one else has changed the
record since the form was created so
the system updates the data.
If they don't match, then someone else has
changed the record since the form was
created. The system sends the user back to the form edit page and tells the user that someone else edited the record and they must reapply their changes.
It is very simple and works well enough.
You can use "timestamp" column on your table. Refer: What is the mysterious 'timestamp' datatype in Sybase?
I understand that you want to avoid overwriting existing data with consecutively updates.
If so, when the user opens a screen you have to get last "timestamp" column to the client.
After changing data just before update, you should check the "timestamp" columns(yours and db) to make sure if anyone has changed tha data while he is editing.
If its changed you will alert an error and he has to startover. If it is not, update the data. Timestamp columns updated automatically.
The simplest method is to format your update statement to include the datetime when the record was last updated. For example:
UPDATE my_table SET my_column = new_val WHERE last_updated = <datetime when record was pulled from the db>
This way the update only succeeds if no one else has changed the record since the last read.
You can message to the user on conflict by checking if the update suceeded via a SELECT after the UPDATE.
I keep seeing questions floating through that make reference to a column in a database table named something like DateLastUpdated. I don't get it.
The only companion field I've ever seen is LastUpdateUserId or such. There's never an indicator about why the update took place; or even what the update was.
On top of that, this field is sometimes written from within a trigger, where even less context is available.
It certainly doesn't even come close to being an audit trail; so that can't be the justification. And if there is and audit trail somewhere in a log or whatever, this field would be redundant.
What am I missing? Why is this pattern so popular?
Such a field can be used to detect whether there are conflicting edits made by different processes. When you retrieve a record from the database, you get the previous DateLastUpdated field. After making changes to other fields, you submit the record back to the database layer. The database layer checks that the DateLastUpdated you submit matches the one still in the database. If it matches, then the update is performed (and DateLastUpdated is updated to the current time). However, if it does not match, then some other process has changed the record in the meantime and the current update can be aborted.
It depends on the exact circumstance, but a timestamp like that can be very useful for autogenerated data - you can figure out if something needs to be recalculated if a depedency has changed later on (this is how build systems calculate which files need to be recompiled).
Also, many websites will have data marking "Last changed" on a page, particularly news sites that may edit content. The exact reason isn't necessary (and there likely exist backups in case an audit trail is really necessary), but this data needs to be visible to the end user.
These sorts of things are typically used for business applications where user action is required to initiate the update. Typically, there will be some kind of business app (eg a CRM desktop application) and for most updates there tends to be only one way of making the update.
If you're looking at address data, that was done through the "Maintain Address" screen, etc.
Such database auditing is there to augment business-level auditing, not to replace it. Call centres will sometimes (or always in the case of financial services providers in Australia, as one example) record phone calls. That's part of the audit trail too but doesn't tend to be part of the IT solution as far as the desktop application (and related infrastructure) goes, although that is by no means a hard and fast rule.
Call centre staff will also typically have some sort of "Notes" or "Log" functionality where they can type freeform text as to why the customer called and what action was taken so the next operator can pick up where they left off when the customer rings back.
Triggers will often be used to record exactly what was changed (eg writing the old record to an audit table). The purpose of all this is that with all the information (the notes, recorded call, database audit trail and logs) the previous state of the data can be reconstructed as can the resulting action. This may be to find/resolve bugs in the system or simply as a conflict resolution process with the customer.
It is certainly popular - rails for example has a shorthand for it, as well as a creation timestamp (:timestamps).
At the application level it's very useful, as the same pattern is very common in views - look at the questions here for example (answered 56 secs ago, etc).
It can also be used retrospectively in reporting to generate stats (e.g. what is the growth curve of the number of records in the DB).
there are a couple of scenarios
Let's say you have an address table for your customers
you have your CRM app, the customer calls that his address has changed a month ago, with the LastUpdate column you can see that this row for this customer hasn't been touched in 4 months
usually you use triggers to populate a history table so that you can see all the other history, if you see that the creationdate and updated date are the same there is no point hitting the history table since you won't find anything
you calculate indexes (stock market), you can easily see that it was recalculated just by looking at this column
there are 2 DB servers, by comparing the date column you can find out if all the changes have been replicated or not etc etc ect
This is also very useful if you have to send feeds out to clients that are delta feeds, that is only the records that have been changed or inserted since the data of the last feed are sent.