I have use cases where the user can browse statistics.
The statistic should be generated automatically every 10 secs.
Whats the best way to model the dependency between view statistic and generate statistic?
So the user can change the interval or something else in the statistic generation.
Or should I remove generate statistic from the use case diagram?
___UPDATE
And what happens when I have one more use case for controlling the statistic generation? Would there be a stroke between generate statistic and control statistic generation or not?
The statistics a generated by a different actor (say Scheduler). So this needs to be the actor for this use case.
If something else controls creation of statistics you can go via Generalization:
DON'T remove Generate statistics. It is important part of functionality, a separate use case and removing it will confuse the actual functionality.
As #thomaskilian already provided answer how to handle Generate statistics it I'll not repeat it here.
Second important information - even though all mentioned use cases (View statistics, Generate statistics and Control statistics) are related to statistics as such, as behaviours they are separate and they are NOT related. So no relationship on diagrams.
Of course the statistics generation depends on current objects related to statistic generation while Control statistics changes those objects. Similarly Generate statistics generates object of type Statistics and View statistics gives possibility to view those objects, but those relations are only on data level. The behaviours (use cases) don't interact directly.
Related
I have a bunch of profiles about my application(200+ profiles per week), I want to analyze them for getting some performance information.
I don't know what information can be analyzed from these files, maybe
the slowest function or the hot-spot code in the different release versions?
I have no relevant experience in this field, has anyone had that?
Well, it depends on the kind of profiles you have, you can capture number of different kinds of profiles.
In general most people are interested in the CPU profile and heap profile since they allow you to see which functions/lines use the most amount of CPU and allocate the most memory.
There are a number of ways you can visualize the data and/or drill into them. You should take a look at help output of go tool pprof and the pprof blog article to get you going.
As for the amount of profiles. In general, people analyze them just one by one. If you want an average over multiple samples, you can merge multiple samples with pprof-merge.
You can substract one profile from another to see relative changes. By using the -diff_base and -base flags.
Yet another approach can be to extract percentages per function for each profile and see if they change over time. You can do this by parsing the output of go tool pprof -top {profile}. You can correlate this data with known events like software updates or high demand and see if one functions starts taking up more resources than others(it might not scale very well).
If you are ever that the point where you want to develop custom tools for analysis, you can use the pprof library to parse the profiles for you.
I want to sanity check myself on a view projection, in regards to if an intermediary concept can purely exist in the read model while providing a bridge between commands.
Let me use a contrived example to explain.
We place an order which raises an OrderPlaced event. The workflow then involves generating a picking slip, which is used to prepare a shipment.
A picking slip can be generated from an order (or group of orders) without any additional information being supplied from any external source or user. Is it acceptable then that the picking slip can be represented purely as a read model?
So:
PlaceOrderCommand -> OrderPlacedEvent
OrderPlacedEvent -> PickingSlipView
The warehouse manager can then view a picking slip, select the lines they would like to ship, and then perform a PrepareShipment command. A ShipmentPrepared event will then update the original order, and remove the relevant lines from the PickingSlipView.
I know it's a toy example, but I have a conceptually similar use case where a colleague believes the PickingSlip should be a domain entity/aggregate in its own right, as it's conceptually different to order. So you have PlaceOrder, GeneratePickingSlip, and PrepareShipment commands.
The GeneratePickingSlip command however simply takes an order number (identifier), transforms the order data into a picking slip entity, and persists the entity. You can't modify or remove a picking slip or perform any action on it, apart from using it to prepare a shipment.
This feels like introducing unnecessary overhead on the write model, for what is ultimately just a transformation of existing information to enable another command.
So (and without delving deeply into the problem space of warehouses and shipping)...
Is what I'm proposing a legitimate use case for a read model?
Acting as an intermediary between two commands, via transformation of some data into a different view. Or, as my colleague proposes, should every concept be represented in the write model in all cases?
I feel my approach is simpler, and avoiding unneeded complexity, but I'm new to CQRS and so perhaps missing something.
Edit - Alternative Example
Providing another example to explore:
We have a book of record for categories, where each record is information about products and their location. The book of record is populated by an external system, and contains SKU numbers, mapped to available locations:
Book of Record (Electronics)
SKU# Location1 Location2 Location3 ... Location 10
XXXX Introduce Remove Introduce ... N/A
YYYY N/A Introduce Introduce ... Remove
Each book of record is an entity, and each line is a value object.
The book of record is used to generate different Tasks (which are grouped in a TaskPlan to be assigned to a person). The plan may only cover a subset of locations.
There are different types of Tasks: One TaskPlan is for the individual who is on a location to add or remove stock from shelves. Call this an AllocateStock task. Another type of Task exists for a regional supervisor managing multiple locations, to check that shelving is properly following store guidelines, say CheckDisplay task. For allocating stock, we are interested in both introduced and removed SKUs. For checking the displays, we're only interested in newly Introduced SKUs, etc.
We are exploring two options:
Option 1
The person creating the tasks has a View (read model) that allows them to select Book of Records. Say they select Electronics and Fashion. They then select one or more locations. They could then submit a command like:
GenerateCheckDisplayTasks(TaskPlanId, List<BookOfRecordId>, List<Locations>)
The commands would then orchestrate going through the records, filtering out locations we don't need, processing only the 'Introduced' items, and creating the corresponding CheckDisplayTasks for each SKU in the TaskPlan.
Option 2
The other option is to shift the filtering to the read model before generating the tasks.
When a book of record is added a view model for each type of task is maintained. The data might be transposed, and would only include relevant info. ie. the CheckDisplayScopeView might project the book of record to:
Category SKU Location
Electronics (BookOfRecordId) XXXX Location1
Electronics (BookOfRecordId) XXXX Location3
Electronics (BookOfRecordId) YYYY Location2
Electronics (BookOfRecordId) YYYY Location3
Fashion (BookOfRecordId) ... ... etc
When generating tasks, the view enables the user to select the category and locations they want to generate the tasks for. Perhaps they select the Electronics category and Location 1 and 3.
The command is now:
GenerateCheckDisplayTasks(TaskPlanId, List<BookOfRecordId, SKU, Location>)
Where the command now no longer is responsible for the logic needed to filter out the locations, the Removed and N/A items, etc.
So the command for the first option just submits the ID of the entity that is being converted to tasks, along with the filter options, and does all the work internally, likely utilizing domain services.
The second option offloads the filtering aspect to the view model, and now the command submits values that will generate the tasks.
Note: In terms of the guidance that Aggregates shouldn't appear out of thin air, the Task Plan aggregate will create the Tasks.
I'm trying to determine if option 2 is pushing too much responsibility onto the read model, or whether this filtering behavior is more applicable there.
Sorry, I attempted to use the PickingSlip example as I thought it would be a more recognizable problem space, but realize now that there are connotations that go along with the concept that may have muddied the waters.
The answer to your question, in my opinion, very much depends on how you design your domain, not how you implement CQRS. The way you present it, it seems that all these operations and aggregates are in the same Bounded Context but at first glance, I would think that there are 3 (naming is difficult!):
Order Management or Sales, where orders are placed
Warehouse Operations, where goods are packaged to be shipped
Shipments, where packages are put in trucks and leave
When an Order is Placed in Order Management, Warehouse reacts and starts the Packaging workflow. At this point, Warehouse should have all the data required to perform its logic, without needing the Order anymore.
The warehouse manager can then view a picking slip, select the lines they would like to ship, and then perform a PrepareShipment command.
To me, this clearly indicates the need for an aggregate that will ensure the invariants are respected. You cannot select items not present in the picking slip, you cannot select more items than the quantities specified, you cannot select items that have already been packaged in a previous package and so on.
A ShipmentPrepared event will then update the original order, and remove the relevant lines from the PickingSlipView.
I don't understand why you would modify the original order. Also, removing lines from a view is not a safe operation per se. You want to guarantee that concurrency doesn't cause a single item to be placed in multiple packages, for example. You guarantee that using an aggregate that contains all the items, generates the packaging instructions, and marks the items of each package safely and transactionally.
Acting as an intermediary between two commands
Aggregates execute the commands, they are not in between.
Viewing it from another angle, an indication that you need that aggregate is that the PrepareShippingCommand needs to create an aggregate (Shipping), and according to Udi Dahan, you should not create aggregate roots (out of thin air). Instead, other aggregate roots create them. So, it seems fair to say that there needs to be some aggregate, which ensures that the policies to create shippings are applied.
As a final note, domain design is difficult and you need to know the domain very well, so it is very likely that my proposed solution is not correct, but I hope the considerations I made on each step are helpful to you to come up with the right solution.
UPDATE after question update
I read a couple of times the updated question and updated several times my answer, but ended up every time with answers very specific to your example again and I'm most likely missing a lot of details to actually be helpful (I'd be happy to discuss it on another channel though). Therefore, I want to go back to the first sentence of your question to add an important comment that I missed:
an intermediary concept can purely exist in the read model, while providing a bridge between commands.
In my opinion, read models are disposable. They are not a single source of truth. They are a representation of the data to easily fulfil the current query needs. When these query needs change, old read models are deleted and new ones are created based on the data from the write models.
So, only based on this, I would recommend to not prepare a read model to facilitate your commands operations.
I think that your solution is here:
When a book of record is added a view model for each type of task is maintained. The data might be transposed, and would only include relevant info.
If I understand it correctly, what you should do here is not create view model, but create an Aggregate (or multiple). Then this aggregate can receive the commands, apply the business rules and mutate the state. So, instead of having a domain service reading data from "clever" read models and putting it all together, you have an aggregate which encapsulates the data it needs and the business logic.
I hope it makes sense. It's a broad topic and we could talk about it for hours probably.
I'm currently working with a system that makes scheduling decisions based on a series of requests and the state of the system.
I would like to take the stream of real inputs, mock out some of the components, and run simulations against the rest. The idea is to use it for planning with respect to system capacity (i.e. when to scale certain components), tracking down certain failure modes, and analyzing the effects of changes to the codebase (i.e. simulations with version A compared to simulations with version B).
I can do everything related to this, except generate a suitable input stream. Replaying the exact input from production hasn't been very helpful because it's hard to get a long enough data stream to tease out some of the behavior that I'm trying to find. In other words, if production falls over at 300 days of input, I don't have enough data to find out until after it fell over. Repeating the same input set has been considered; but after a few initial tries, the developers all agree that the simulation seems to "need more random".
About this particular system:
The input is a series of irregularly spaced events (i.e. a stochastic process with discrete time and continuous state space).
Properties are not independent of each other.
Even the more independent of the properties are composites of other properties that will always be, by nature, invisible to me (leading to a multi-modal distribution).
Request interval is not independent of other properties (i.e. lots of requests for small amounts of resources come through in a batch, large requests don't).
There are feedback loops in it.
It's provably chaotic.
So:
Given a stream of input events with a certain distribution of various properties (including interval), how do I generate an infinite stream of events with the same distribution across a number of non-independent properties?
Having looked around, I think I need to do a Markov-Chain Monte-Carlo Simulation. My problem is figuring out how to build the Markov-Chain from the existing input data.
Maybe it is possible to model the input with a Copula. There are tools that help you doing so, e.g. see this paper. Apart from this, I would suggest to move the question to http://stats.stackexchange.com, as this is a statistical problem and will likely draw more attention over there.
I hope it belongs here.
Can anyone please tell me is there any method to compare different search applications working in the same domain with the same dataset?
The problem is they are quite different - one is a web application which looks up the database where items are grouped in categories, and another one is a rich client which makes search by keywords.
Is there any standard test giudes for that purpose?
There are testing methods. You may use e.g. Precision/Recall or the F beta method to estimate a rate which computes the "efficiency". However you need to make a reference set by yourself. That means you will somehow measure not the efficiency in the domain, more likely the efficiency compared to your own reasoning.
The more you need to make sure that your reference set is representative for the data you have.
In most cases common reasoning will give you also the result.
If you want to measure the performance in matters of speed you need to formulate a set of assumed queries against the search and query your search engine with these at a given rate. Thats doable with every common loadtesting tool.
For an ecommerce website how do you measure if a change to your site actually improved usability? What kind of measurements should you gather and how would you set up a framework for making this testing part of development?
Multivariate testing and reporting is a great way to actually measure these kind of things.
It allows you to test what combination of page elements has the greatest conversion rate, providing continual improvement on your site design and usability.
Google Web Optimiser has support for this.
Similar methods that you used to identify the usability problems to begin with-- usability testing. Typically you identify your use-cases and then have a lab study evaluating how users go about accomplishing certain goals. Lab testing is typically good with 8-10 people.
The more information methodology we have adopted to understand our users is to have anonymous data collection (you may need user permission, make your privacy policys clear, etc.) This is simply evaluating what buttons/navigation menus users click on, how users delete something (i.e. changing quantity - are more users entering 0 and updating quantity or hitting X)? This is a bit more complex to setup; you have to develop an infrastructure to hold this data (which is actually just counters, i.e. "Times clicked x: 138838383, Times entered 0: 390393") and allow data points to be created as needed to plug into the design.
To push the measurement of an improvement of a UI change up the stream from end-user (where the data gathering could take a while) to design or implementation, some simple heuristics can be used:
Is the number of actions it takes to perform a scenario less? (If yes, then it has improved). Measurement: # of steps reduced / added.
Does the change reduce the number of kinds of input devices to use (even if # of steps is the same)? By this, I mean if you take something that relied on both the mouse and keyboard and changed it to rely only on the mouse or only on the keyboard, then you have improved useability. Measurement: Change in # of devices used.
Does the change make different parts of the website consistent? E.g. If one part of the e-Commerce site loses changes made while you are not logged on and another part does not, this is inconsistent. Changing it so that they have the same behavior improves usability (preferably to the more fault tolerant please!). Measurement: Make a graph (flow chart really) mapping the ways a particular action could be done. Improvement is a reduction in the # of edges on the graph.
And so on... find some general UI tips, figure out some metrics like the above, and you can approximate usability improvement.
Once you have these design approximations of user improvement, and then gather longer term data, you can see if there is any predictive ability for the design-level usability improvements to the end-user reaction (like: Over the last 10 projects, we've seen an average of 1% quicker scenarios for each action removed, with a range of 0.25% and standard dev of 0.32%).
The first way can be fully subjective or partly quantified: user complaints and positive feedbacks. The problem with this is that you may have some strong biases when it comes to filter those feedbacks, so you better make as quantitative as possible. Having some ticketing system to file every report from the users and gathering statistics about each version of the interface might be useful. Just get your statistics right.
The second way is to measure the difference in a questionnaire taken about the interface by end-users. Answers to each question should be a set of discrete values and then again you can gather statistics for each version of the interface.
The latter way may be much harder to setup (designing a questionnaire and possibly the controlled environment for it as well as the guidelines to interpret the results is a craft by itself) but the former makes it unpleasantly easy to mess up with the measurements. For example, you have to consider the fact that the number of tickets you get for each version is dependent on the time it is used, and that all time ranges are not equal (e.g. a whole class of critical issues may never be discovered before the third or fourth week of usage, or users might tend not to file tickets the first days of use, even if they find issues, etc.).
Torial stole my answer. Although if there is a measure of how long it takes to do a certain task. If the time is reduced and the task is still completed, then that's a good thing.
Also, if there is a way to record the number of cancels, then that would work too.