I've noticed that a lot of the sample flux projects follow the convention of a single file per action rather than grouping them together. What is the reason for that, is that a strong convetion?
My preference is to group related actions together into single files, e.g. wishlistActions.js would include, create, addProduct, removeProduct, and delete.
Does that go against the grain? If so, why?
I don't think that goes against the grain and I don't know if Flux prescribes any singular way to do that.
In fact, Ryan Florence (one of the authors of react-router) has a demo repo where he has a Constants.js file listing all his actions. And then he has individual actionCreator classes.
https://github.com/FrontendMasters/2015-02-13-React/tree/master/excercises/5-flux/app
Related
I have a situation as described in the ExtbaseFluid book:
I would like to store information in the intermediate table which is not recommended at all.
Here is a cite from the warning box of the above linked book chapter:
Do not store data in the Intermediate Table that concern the Domain. Though TYPO3 supports this (especially in combination with Inline Relational Record Editing (IRRE) but this is always a sign that further improvements can be made to your Domain Model. Intermediate Tables are and should always be tools for storing relationships and nothing else.
Let’s say you want to store a CD with its containing music tracks: CD -- m:n (Intermediate Table) -- Song. The track number may be stored in a field of the Intermediate Table. However, the track should be stored as a separate domain object, and the connection be realized as CD -- 1:n -- Track -- n:1 -- Song.
So I want not to do what is not recommended. But thinking about the workflow for the editor that results of the recommended solution rises a few question for me.
To stay with this example I would need the following tables:
tx_extname_domain_model_cd
tx_extname_domain_model_cd_track_mm
tx_extname_domain_model_track (which holds the track number)
tx_extname_domain_model_track_song_mm
tx_extname_domain_model_song
From what I know this would end in the situation that the editor would need to create following records:
one record for the cd
one record for the song
now the editor can create one record for the track.
There the track number is added.
Furthermore the cd record needs to be assigned as well as the song.
So here are my questions:
I guess this workflow cannot be improved with some (to me unknown) TCA setup?
An editor cannot directly reach the song when the cd record is opened?
Instead first she / he has to open the track record and can from there navigate to the song?
Is it really that bad to store data in the intermediate table? The TYPO3 table sys_file_reference does the same!? But I wonder how those data could be shown (because IRRE is not possible because it shall only be used for 1:n relations (source).
The question you have to ask yourself is: Do I want to do coding by the book, or do I want to create a pragmatic approach to solve a customer's problem?
In this specific case the additional problem is, that the people who originally invented Extbase had a quite sophisticated and academic approach, but when it comes to a pragmatic use and performance, they were blocked by their own rules and stuck with coding by the book.
Especially this example and the warning message shows a way of thinking that was one of the reasons, why I never actually used Extbase but went for Core-API methods to create performant and pragmatic queries to get the desired result sets. Now that we've got Doctrine under the hood, this works like a charm even with quite exotic DB flavors.
Of course intermediate tables are a good idea and of course those intermediate tables can and should be enriched with additional data fields, that do not require a 3rd, 4th or nth table to store i.e. a simple set of dropdown options, since this can easily be handled with attributes configured in TCA, as it is shown here: https://docs.typo3.org/m/typo3/reference-tca/master/en-us/ColumnsConfig/Type/Inline/Examples.html
sys_file_reference is the most prominent example since it provides exactly that kind of additional information that should not be pumped into additional tables - and guess what, the TYPO3 core does not make use of a single line of Extbase code to deal with that data or almost any other data of the core tables.
To answer your last question: Take a look at the good old IRRE Tutorial to get a clue how to do m:n connections with intermediate inline tables.
https://docs.typo3.org/typo3cms/extensions/irre_tutorial/0.4.0/Manual/Index.html#intermediate-tables-for-m-n-relations
Depends on the issue, sometimes the intermediate table is an entity, sometimes not. In this example the intermediate table is the track, which would contain: [uid, cd, song, track_no, ... (whatever else needed to define the track)]
Be carefull when you define your data, that you do not make it too advanced.
How to I create an #Intent which looks something like this:
How much is a #ProductType?
Whereas the #ProductType is an simple Entity which consists of:
Soft Drinks: Coke, Pepsi, Sprite, Fanta
Fruits: Apple, Banana, Watermelon
I tried adding an Intent with above settings, but it doesn't seem to work. Is such ability natively supported in IBM Watson? Or otherwise, do I need to manually handle in the Dialog, using Conditions and stuffs? Please kindly advise.
The training is based on regular language and typical sentences or phrases. So #ProductType is not what you want in the phrase, but any of the fruits or drinks.
By defining the entities, Watson Assistant later learns the connection and to identify the entities and intents.
To get started, you define the intents and entities. Both can be imported from lists. Then you add the dialog which references the different types.
This blog should give insight to all the ways to train an entity and how it is used within intents.
https://medium.com/ibm-watson/all-about-entities-dictionaries-and-patterns-with-watson-assistant-part-1-5ef7254df76b
There are a number of possible pipelines you can choose from.
1. Indirect references: this is the preferred method.
Use natural language in your intent training data. "I want to buy a pear"
Watson will automatically see the other values you have related to pear and use those as intent training as well. This will be the fastest and simplest way to manage your data
2. Direct references: this should only be used if absolutely necessary
Directly reference the entity in your intent data. "I want to buy an #pear"
Nothing is done in the UI to tell you this works, but it does. This tells Watson the entity is a very important term and will increase the weight, as well as reference all synonyms with high weight. This is more effort for you to go through your entire workspace and relabel everything this way, hence why it is not recommended unless absolutely necessary. By doing this, you also tell watson that when the system sees various fruits without the # symbol, to ignore them as entities which is not ideal
3. Contextual entities. This is highlighting them like in your screenshot.
Note the UI has been updated so there is no an annotation mode instead of just highlighting. This builds a model around the entity, and is good for things like names or locations, but not necessary for a small list of items like crayons in a box, or fruit in a store. This will ignore all of the dictionary values youve created and only look at the model. It should be used according to the blog above when the use case is ideal.
What #data_henrik answered was partially correct. But it doesn't seem like Watson Assistant "automatically" learns the preferred #Entity just by simply inputting the pure (plain-text) Examples into the #Intent. In fact, that step was required. But we still need to do one more step.
After keying in the good plain-text Examples into the #Intent, we then still need to "right click" on the text-string of the possible #Entity entry, and then choose (teach Watson) the correct #Entity name from the dropdown list appeared.
Only then Watson starts to understand such; this #Intent uses that #Entity, I suppose.
Thank you #data_henrik, and appreciate your hint.
I'm very much at the beginning of using / understanding EventStore or get-event-store as it may be known here.
I've consumed the documentation regarding clients, projections and subscriptions and feel ready to start using on some internal projects.
One thing I can't quite get past - is there a guide / set of recommendations to describe the difference between event metadata and data ? I'm aware of the notional differences; Event data is 'Core' to the domain, Meta data for describing, but it is becoming quite philisophical.
I wonder if there are hard rules regarding implementation (querying etc).
Any guidance at all gratefully received!
Shamelessly copying (and paraphrasing) parts from Szymon Kulec's blog post "Enriching your events with important metadata" (emphases mine):
But what information can be useful to store in the metadata, which info is worth to store despite the fact that it was not captured in
the creation of the model?
1. Audit data
who? – simply store the user id of the action invoker
when? – the timestamp of the action and the event(s)
why? – the serialized intent/action of the actor
2. Event versioning
The event sourcing deals with the effect of the actions. An action
executed on a state results in an action according to the current
implementation. Wait. The current implementation? Yes, the
implementation of your aggregate can change and it will either because
of bug fixing or introducing new features. Wouldn’t it be nice if
the version, like a commit id (SHA1 for gitters) or a semantic version
could be stored with the event as well? Imagine that you published a
broken version and your business sold 100 tickets before fixing a bug.
It’d be nice to be able which events were created on the basis of the
broken implementation. Having this knowledge you can easily compensate
transactions performed by the broken implementation.
3. Document implementation details
It’s quite common to introduce canary releases, feature toggling and
A/B tests for users. With automated deployment and small code
enhancement all of the mentioned approaches are feasible to have on a
project board. If you consider the toggles or different implementation
coexisting in the very same moment, storing the version only may be
not enough. How about adding information which features were applied
for the action? Just create a simple set of features enabled, or map
feature-status and add it to the event as well. Having this and the
command, it’s easy to repeat the process. Additionally, it’s easy to
result in your A/B experiments. Just run the scan for events with A
enabled and another for the B ones.
4. Optimized combination of 2. and 3.
If you think that this is too much, create a lookup for sets of
versions x features. It’s not that big and is repeatable across many
users, hence you can easily optimize storing the set elsewhere, under
a reference key. You can serialize this map and calculate SHA1, put
the values in a map (a table will do as well) and use identifiers to
put them in the event. There’s plenty of options to shift the load
either to the query (lookups) or to the storage (store everything as
named metadata).
Summing up
If you create an event sourced architecture, consider adding the
temporal dimension (version) and a bit of configuration to the
metadata. Once you have it, it’s much easier to reason about the
sources of your events and introduce tooling like compensation.
There’s no such thing like too much data, is there?
I will share my experiences with you which may help. I have been playing with akka-persistence, akka-persistence-eventstore and eventstore. akka-persistence stores it's event wrapper, a PersistentRepr, in binary format. I wanted this data in JSON so that I could:
use projections
make these events easily available to any other technologies
You can implement your own serialization for akka-persistence-eventstore to do this, but it still ended up just storing the wrapper which had my event embedded in a payload attribute. The other attributes were all akka-persistence specific. The author of akka-persistence-eventstore gave me some good advice, get the serializer to store the payload as the Data, and the rest as MetaData. That way my event is now just the business data, and the metadata aids the technology that put it there in the first place. My projections now don't need to parse out the metadata to get at the payload.
In most examples I see, they tend to have one GitHub/cvs repository per bounded context, this does seem to be the best thing to do.
My question pertains specifically to user interfaces do they live in a separate repository which holds just ui's or is each interface included within the repository of the bc itself ?
What about interfaces which compose data from multiple bc's ?
Just to make it explicit here I am trying to gather how to physically organise code in a ddd project
Considering a tag would apply to the full Git repo, it is best to have two sets of files (like an UI and a BC) in two separate repos if:
you can make evolutions (and apply new tags) to one without touching the other
the number of files involved is important enough (if the UI is just one or two file, that might not be worth the trouble to create a dedicate repo for it)
As the OP Sudarshan summarizes below in the comments:
If a UI was dedicated to a BC, then it could live within the same repo as the BC itself or a separate one, depending on whether it will evolves on it own or not.
However for UI's that span across BC's it is better to spawn them in a repo of their own and use submodules to reference the right BC repo's
Looking at all the examples of Operational Transformation Frameworks out there, they all seem to resolve around the transformation of changes to plain text documents. How would an OT framework be used for more complex objects?
I'm wanting to dev a real-time sticky notes style app, where people can co-create sticky notes, change their positon and text value. Would I be right in assuming that the position values wouldn't be transformed? (I mean, how would they, you can't merge them right?). However, I would want to use an OT framework to resolve conflicts with the posit-its value, correct?
I do not see any problem to use Operational Transformation to work with Complex Objects, what you need is to define what operations your OT system support and how concurrency is solved for them
For instance, if you receive two Sticky notes "coordinates move operation" from two different users from same 'client state', you need to make both states to converge, probably cancelling out second operation.
This is exactly the same behaviour with text when two users generate two updates to delete a text range that overlaps completely, (or maybe partially), the second update processed must be transformed against the previous and the resultant operation will only effectively delete a portion of the original one, (or completely cancelled with a 'no-op')
You can take a look on this nice explanation about how Google Wave Operational Transformation works and guess from this point how it should work your own implementation
See the following paper for an approach to using OT with trees if you want to go down that route:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.74
However, in your particular case, I would use a separate plain text OT document for each stickynote and use an existing library, eg: etherPad, to do the heavy lifting. The positions of the notes could then be broadcast on a last-committer-wins basis.
Operation Transformation is a general technique, it works for any data type. The point is you need to define your transformation functions. Also, there are some atomic attributes that you cannot merge automatically like (position and background color) those will be mostly "last-update wins" or the user solves them manually when there is a conflict.
there are some nice libs and frameworks that provide OT for complex data already out there:
ShareJS : library for Node which provides all operations on JSON objects
DerbyJS: framework for NodeJS, it uses ShareJS for OT stuff.
Open Coweb framework : Dojo foundation project for cooperative web applications using OT