Is there a way of knowing that a table's data has changed (insert/update/delete) without using a trigger on that table? Perhaps a global trigger to indicate changes on a table?
If you want notification of changes, you will need to add a trigger yourself. Firebird 3 added a new feature to simplify identifying changed rows, the pseudo-column RDB$RECORD_VERSION. This pseudo-column contains the transaction that created the current version of a row.
Alternatively, you could try and use the trace facility to monitor for changes, but that is not an out of the box solution, as you will need to write the necessary logic to parse the trace output (and take things like transaction commit/rollback into account).
Related
I have following idea: user can edit database and when he will press exit from database he will be asked if he want to save edited database or if he edited something and then turned it back he wont be asked.
I think i should compare created database and database after editing when user press exit, but don't know how.
This is my code for creating database
model = new QSqlRelationalTableModel(this, *db);
model->setTable("cv");
model->setFilter("cv_id = "+currentCV+"");
model->removeColumns(0,1);
model->select();
ui->tableView->show();
ui->tableView->setModel(model);
You have 2 options.
Create event triggers in the database. Event triggers work when users change database table structures or create tables or change columns. Thus, these triggers work when the user executes any DDL commands. (change table, add column, create index, drop table, etc.) You can insert these commands into your log tables using event triggers.
Your database structures (all tables, columns, indexes, etc.) are stored in information_shema. You can select the data of these tables and save it somewhere and then compare it with the data that has changed.
Take in mind that no change to database is performed before you're submitting the user changes. By default, sumbit is performed when changes occur, and this does not help you. But, you can set
model.setEditStrategy(QSqlTableModel.EditStrategy.OnManualSubmit)
At this point, changes are persisted only when the method model.submitAll() is called.
All you have to do, at this point, is exploiting dataChanged signal of your model and use a flag variable to check if changes have been operated:
data_changed = False
def on_data_changed()
data_changed = True
model.dataChanged.connect(on_data_changed)
Now you can adjust the logic of the code to serve your specific purpose
I have a requirement to trigger Azure Data Factory pipeline whenever there is a new record in a table. Is there any way to achieve this.
No, Event-based trigger only support Azure Blob Storage by now.
You can vote here to progress this feature in Azure Data Factory.
Currently it is not possible.
There is a similar discussion going on in the below thread :
https://learn.microsoft.com/en-us/answers/questions/197465/how-to-trigger-an-adf-based-on-any-data-changes-wi.html
While this is not currently supported, here's an idea on how to fake it. (Just sharing an idea, not sure whether this is feasible.)
Create a Trigger on INSERT
Trigger executes a Stored Procedure
Stored Procedure uses Polybase to create text file in Blob Storage with the relevant information (like new row ID).
Create a BlobCreated event trigger over that Storage location in ADF or Logic App.
Doing this should end up with an Event Trigger that fires whenever a new row is inserted.
We can use logic app to trigger a pipeline based on a query that finds data inserted in past x minutes/seconds
There's no direct way to do this in ADF (yet). As others have pointed out, you can vote for the feature to be added and Microsoft might add it. But there's still a way to do this:
You can set up a Logic App. There's a really nice video tutorial on this here: https://www.youtube.com/watch?v=z0sMIN4xMSY
You can set up a logic app to use a SQL database as the trigger, and then you can decide if you want to have it trigger based on something new created in a specific table or something modified in a specific table. You can then action the logic app to trigger the run of an Azure data factory pipeline. You can set the Logic App to check the table as often as you like, for example, every minute, 3 minutes or longer etc.
If you want an ADF pipeline to run every time a certain table is modified or row inserted, but the table in question is very large, you can reduce compute by creating a track changes table. Basically have another, very small table that logs changes and then use this table for the one the logic app monitors. This would make it faster and more efficient in such a case.
Give the video a watch.
Afaik, although I'm pretty new to Postgres, Before-triggers are less expensive then After-triggers.
After all, if you want to change the current record (using NEW), you can change the record before it is written. In contrast, with After-triggers you need two writes: 1 verbatim write and 1 as a result of the after-trigger.
At the same time, all functionality that is available in after-triggers seems to be available in before-triggers. If I'm not mistaken.
So why would you ever use After-triggers to begin with?
If you're changing the record upon which the trigger is acting use a BEFORE trigger. If you're doing some complex logic that may prevent the record from being changed, use a BEFORE trigger.
Almost anything else, use an AFTER trigger. An example might be where you're inserting child records which rely upon the primary key of a record being inserted. For example, if you're adding an entry to a history table for a newly inserted row. The parent row won't exist in the BEFORE trigger, so would fail foreign key checks.
In our production org, we have a system of uploading sales data into Salesforce using command line data loader. This data is loaded into a temporary object Temp. We have created a formula field (which combines three fields) to form a unique key. The purpose of the object is to reduce user efforts for creating the key manually.
There is an after insert trigger on Temp which calls an asynchronous method which upserts the data to another object SalesData using the key. The insert/update trigger on SalesData checks the various fields and creates/updates the records in another object SalesRecords. After the insertion/updation is complete, all the records in temp object Temp are deleted. The SalesRecords object does not have any trigger on it and is a child of another object Sales. The Sales object has some rollup fields which are summing up fields from SalesRecords object.
Lately, we are getting the below error for some of the records which are updated.
UNABLE_TO_LOCK_ROW, unable to obtain exclusive access to this record
Please provide some pointers to resolve the issue
this could either be caused by conflicting DML operations in the various trigger execution or some recursive trigger execution. i would assume that the async executions cause multiple subsequent updates on the same records, probably on the SalesRecords object. I would recommend to try to simplify the process to avoid too many related trigger executions.
I'm a little surprised you were able to get this to work in the first place. After triggers should be used with caution and only when before triggers can't be. One reason for this is that you don't need to perform additional DML to make changes to records, since in before triggers you simply change the values and the insert/update commit happens automatically. But recursive trigger firings is the main problem with after triggers.
One quick way to avoid trigger re-entry is to use a public static Boolean in a class that states whether you're already in this trigger from the same thread of execution.
Something like:
public static Boolean isExecuting = false;
Once set to true, any trigger code that is a re-fire can be avoided with:
if(Class.isExecuting == false)
{
Class.isExecuting = true;
// Perform trigger logic
// ...
}
Additionally, since the order of trigger execution cannot be determined up front, you might be seeing an issue with deletions or other data changes that depend on other parts of your flow to finish first.
Also, without knowing the details of your custom unique 3-part key, I'd wonder if there's a problem there too such as whether it's truly unique or not. Case insensitivity is a common mistake and it's the reason there are 15 AND 18 character Ids in Salesforce. For example, when people export to Excel (a case-insensitive environment) and do VLOOKUPs, they would occasionally find the wrong record. The 3-digit calculated suffix was added to disambiguate for case-insensitive environments.
Googling for this same error lead me to this post:
http://boards.developerforce.com/t5/General-Development/Unable-to-obtain-exclusive-access-to-this-record/td-p/345319
Which points out some common causes for this to happen:
Sharing Rules are being calculated.
A picklist value has been replaced and replacement is in progress.
A custom index creation/removal is in progress.
Most unlikely one - someone else is already editing the same record that you are trying to access at the same time.
Posting here in case somebody else needs it.
I got this error multiple times today. Turned out one of our vendors was updating their installed package during that time in the same org. All kinds of things were going wrong also - some object validation exceptions were being thrown on DMLs, without any error message content.
Resolution
The error is shown when a field update such as a roll-up summary field is being attempted on a parent object that already had a field update to cause the roll-up summary field to calculate. This could also occur if a trigger or another apex job running on the master object and it also attempting to do an update.
You can either reduce the batch size and try again or create separate smaller files to be imported if this issue occurs.
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.