In my KafkaStreams app I have a registered local store (simple counters) that is updated in the transform method.
In the punctuate method I basically loop over the KV-store and push all the data to the output topic (so even if the value hasn't been updated).
One idea is to store the update timestamp for every key and forward only records updated since the last punctuate call.
But I think there should be a more convenient solution for that.
How to make this more performant and forward updated entries only?
As indicated in the comments from Matthias, keeping track of updated the records is not supported at the moment.
Your approach of updating a timestamp in the value (or creating a "Value Wrapper" object that contains a timestamp you can modify) and checking if an update has occurred since the last punctuate call is a valid approach.
-Bill
I am using breezejs in a offline first manner, executing query’s initially against a server and stashing the entities in local storage where I query the entity manager cache.
When data changes on the server (by means of another app changing it using breeze) the client app synchronizes by just getting a new copy of the entities from the server.
This works great but I am wondering if there is a way that I can get only the changes from the server, I was thinking maybe set a revision GUID or timestamp on each record and then checking the metadata if it needs updating but I really have no idea on how to proceed.
So my question is can breeze be tweaked to allow for such a use case?
And is there maybe a beter way that I am overlooking?
I think your direction is correct .If you had a column with a TimeDate in every table e.g "LastModified" and that column would get updated on every record update. then you could add a filter to every breeze query after the first that says that that date must be later than the last time you did this "rebase" query or the initial loading. so It's not supported out of the box, but you can get it to work yourself. the guid per version will not really be a good idea, as you will have to send all these guids on every request, and then check all of them. time stamp makes more sense.
I have a collection with a bunch of documents representing various items. Once in a while, I need to update item properties, but the update takes some time. When properties are updated, the item gets a new timestamp for when it was modified. If I run updates one at a time, then there is no problem. However, if I want to run multiple update processes simultaneously, it's possible that one process starts updating the item, but the next process still sees the item as needing an update and starts updating it as well.
One solution is to mark the item as soon as it is retrieved for update (findAndModify), but it seems wasteful to add a whole extra field to every document just to keep track of items currently being updated.
This should be a very common issue. Maybe there are some built-in functions that exist to address it? If not, is there a standard established method to deal with it?
I apologize if this has been addressed before, but I am having a hard time finding this information. I may just be using the wrong terms.
You could use db.currentOp() to check if an update is already in flight.
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.