Sunday, March 11, 2018

Find In Cache By Key ADF BC API Method Usage

What if you need to verify - if row with given key exists in fetched rowset? This could be useful while implementing validation logic. ADF BC API method findByKey - will trigger SQL call and fetch row from DB, if row with given key doesn't exist in fetched rowset. Luckily there is ADF BC API method called findInCacheByKey, this method only checks for row in fetched rowset, without going to DB - very convenient in certain situations, when you actually don't want to bring record from DB, if it wasn't fetched.

Imagine table with pagination feature. First ten rows are fetched and exist in the cache:

Now if we call custom method, where findInCacheByKey is invoked twice - you will see different results. First call is using key from fetched rowset - this call will find a row. Second call is using key, which doesn't belong to the fetched rowset - row is not in cache and call will return zero rows:

Download sample app from my GitHub repository.

Tuesday, March 6, 2018

REST Paging Support by Oracle Offline Persistence in JET

Oracle Offline Persistence query handler - Oracle Rest Query Handler supports pagination for Oracle ADF BC REST service out of the box. Check my previous post to see how querying works through offline persistence toolkit for ADF BC REST service - Shredding and Querying with Oracle Offline Persistence in JET.

Pagination is a must for large REST resources, its great that Oracle offline persistence toolkit supports it. Let's see it in action.

I navigate through the data with left/right arrows, this triggers REST call with pagination parameters - limit and offset. These are standard parameters supported by ADF BC REST. Requests are executed online:

All pages of data are cached by offline toolkit, if while offline we try to access previously cached page by executing REST request with paging parameters - we will get data from offline toolkit. Now I switch offline and try to navigate to the one of cached pages - data is retrieved from cache automatically:

If I navigate to the page, which was not cached (meaning - not accessed while online) - no results returned. In such situation I can navigate back (paging parameters will be updated) and cached data will be displayed for the page which was cached:

Paging navigation control buttons are calling JS functions to update startIndex:

Sample application is using JET Collection API to execute fetch requests. Collection is extended with getURL function which sets limit and offset parameters to execute paging request:

Once again, make sure to use Oracle Rest Query Handler in offline persistence toolkit configuration:

Fetch function is called through JET Collection API. Start index value is calculated dynamically - this allows to execute paging requests. Same function works online and offline, no need to worry about connection status, all online/offline logic is handled by persistence toolkit:

Sample application for this post is available on GitHub.

Saturday, March 3, 2018

Classification - Machine Learning Chatbot with TensorFlow

Visual conversation flow is a first thing to create, when you want to build chatbot. Such flow will help to define proper set of intents along with dialog path. Otherwise it is very easy to get lost in conversation transitions and this will lead to chatbot implementation failure. Our chatbot for medical system doesn't make any decisions, instead it helps user to work with enterprise system. It gets user input and during conversation leads to certain API call - which at the end triggers enterprise system to execute one or another action. If user is looking for patient blood pressure results, chatbot will open blood pressure module with patient ID. If user wants to edit or review blood pressure results in general, chatbot will load blood pressure results module without parameters. This kind of chatbot is very helpful in large and complex enterprise systems, this helps to onboard new users much quicker without extra training for system usage. Example of visual conversation flow for chatbot:

Conversation intents can be logged in JSON file. Where you should list conversation patterns mapped with tags, responses and contextual information. Chatbot is not only about machine learning and user input processing, very important is to handle conversation contextual flow and usually this is done outside of machine learning area in another module. We will look into it later. Machine learning with neural network is responsible to allow chatbot to calculate tag probability based on user input. In other words - machine learning helps to bring the best matching tag for current sentence, based on predefined intents patterns. As long as we get probability for the intent tag - we know what user wants, we can set conversation context and in the next user request - react based on current context:

TensorFlow runs neural network, which trains on supplied list of intents. Each training run may produce different learning results, you should check total loss value - lower value, better learning result. Probably you will run training multiple times to get optimal learning model:

TensorFlow can save learned model to be reusable by classification API. REST interface which calls classification API is developed as separate TensorFlow module. REST is handled by Flask library installed into TensorFlow runtime:

Classification function gets user input from REST call and runs it through TensorFlow model. Results with higher probability than defined by threshold are collected into ordered array and returned back. We have classification function without REST annotation for local tests within TensorFlow runtime:

Let's see how classification works, result of classification will drive next action for the chatbot. Each classification request returns matched tag and probability. User input is not identical to the patterns defined in intents, thats why matching probability may differ - this is core part of machine learning. Neural network constructed with TensorFlow, based on learned model, assumes the best tag for current user input.

User input "Checking blood pressure results for patient". This input can be related to both tags blood_pressure_search and blood_pressure, but classification decides higher probability for the first option, and this is correct. Similar for user input "Any recommendations for adverse drugs?":

Through REST endpoint we can call classification function outside of TensorFlow environment. This will allows us to maintain conversation context outside TensorFlow:

Useful resources:

- TensorFlow notebooks and intents JSON are available on GitHub repository.
- Excellent article about Contextual Chatbots with TensorFlow
- My previous post about Red Samurai chatbot

Saturday, February 24, 2018

Microservices with Oracle ADF Remote Regions

ADF remote regions - functionality available in the latest ADF 12c versions. ADF remote region runs on different server and content is delivered to consuming module through ADF remote region servlet. This allows to decouple large enterprise system into separate modules, each running independently on separate servers. Gained advantage - system becomes more scalable and reliable, even if several modules will be down, system will continue to be functional.

Concept of ADF remote regions, reminds me closely microservices architecture. Microservices - also known as the microservice architecture - is an architectural style that structures an application as a collection of loosely coupled services, which implement business capabilities. The microservice architecture enables the continuous delivery/deployment of large, complex applications. It also enables an organization to evolve its technology stack (as describe here).

Let's see how ADF remote regions are configured and consumed. Sample application (available for download from GitHub repository) is based on Employees and Jobs modules. Each module is deployed on different servers, Employees module is consumed in Jobs. Microservice here - Employees table. This table comes from loosely coupled service and is consumed within Jobs module:

Employees module runs on ADF server in Docker container (read more: Essential WebLogic Tuning to Run on Docker and Avoid OOM):

I will stop this module:

Jobs module still works, even Employees module is not available anymore - it is stopped. Otherwise if both modules would be deployed as single application - if application is down, system will be completely unavailable. But now users can access part of the functionality:

Will dive into technical part. ADF remote region is not different from the way how regular ADF region is consumed. You still must use ADF region tag, to define region:

Key difference is in region bindings - there is one extra property called Remote Connection. This connection defines source, from where remote region content is transferred. All other properties are the same, we can pass parameters too:

Consuming module must define ADF remote region connection. If connection details are correct, you should see ADF task flows with remote access listed:

Remote region connection wizard. You should use module context root and point to ADF remote region servlets rtfquery and rr:

This wizard can be opened by right clicking on Connections folder and going to New Connections section:

Consumer module should be enabled with remote region consumer support:

Producer module should be enabled with remote region producer support:

Producer module is configured with required servlets automatically, as soon as you enable remote region producer support:

Read more about ADF remote regions in ADF developer guide - 26.13 Creating Remote Regions in a Fusion Web Application.

Saturday, February 17, 2018

Shredding and Querying with Oracle Offline Persistence in JET

I think offline functionality topic should become a trend in the future. Its great that Oracle already provides solution for offline - Oracle Offline Persistence toolkit. This is my second post related to offline support, read previous post - Oracle Offline Persistence Toolkit - Simple GET Response Example with JET. I have tested and explained with sample app how it works to handle simple GET response offline. While today I would like to go one step further and check how to filter offline data - shredding and querying offline.

Sample app is fetching a list of employees - Get Employees button. It shows online/offline status - see icon in top right corner. We are online and GET response was cached by persistence toolkit:

We can test offline behaviour easily - this can be done through Chrome Developer Tools - turn on Offline mode. Btw, take a look into Initiator field for GET request - it comes from Oracle Offline Persistence toolkit. As I mention it in my previous post - once persistence toolkit is enabled, all REST calls are going through toolkit, this is how it is able to cache response data:

While offline, click on Get Employees button - you should see data returned from cache. Did you noticed - icon in the top right corner was changed to indicate we are offline:

Ok, now we will see how shredding mechanism works (more about it read on GitHub). While offline, we can search for subset of cached data. Search By Name does that, it gets from cache entry for Lex:

Switch online and call same action again, but with different name - REST call will be invoked against back-end server as expected. Again it is transparent to JET developer, no need to worry if app state is online/offline, same REST request is done in both cases:

Let's take a quick look into implementation part (complete example is available on my GitHub repository).

Online/offline status icon is controlled by observable variable:

It is very simple to determine online/offline state. We need to add event listener for online/offline and reset observable variable accordingly:

Persistence toolkit supports Simple and Oracle shredder/query handlers. I'm using ADF BC REST for backend and so my choice is oracleRestJsonShredding and oracleRestQueryHandler. Oracle shredder understands REST structure returned by ADF BC REST. Oracle query handler support filtering parameters for ADF BC REST for offline filtering - this allows to use same query format for both online and offline. I was happy to read that Oracle query handler explicitly supports ADF BC REST - queryHandlers:

Same REST call with filtering is executed online and offline:

Wednesday, February 7, 2018

Oracle JET Composite Components - Manual for JET Coder

JET Composite Components - are useful not only to build UI widgets, but also to group and simplify JET code. In this post, I will show how to wrap JET table into composite component and use all essential features, such as properties, methods, events and slots.

Sample app code is available on GitHub. JET table is wrapped into composite component, it comes with slot for toolbar buttons:

What is the benefit to wrap such components as JET table into your own composite? To name a few:

1. Code encapsulation. Complex functionality, which requires multiple lines of HTML and JS code resides in the composite component
2. Maintenance and migration. It is easier to fix JET specific changes in single place
3. Faster development. There is less steps to repeat and less code to write for developer, when using shorter definition of the wrapper composite component

Sample application implements table-redsam component, for the table UI you can see above. Here is component usage example, very short and clean:

All the properties specific to given table are initialised in the module. Developer should provide REST endpoint, key values, pagination size and column structure. The rest is happening in the composite component and is hidden from the developer, who wants to implement a table:


We should take a look into array type property. Such property allows to pass array into component. This can be useful either to pass array of data to be displayed or array of metadata to help with component rendering. In our case we pass array of metadata, which helps to render table columns. Array type property is based on two attributes - Header Text and Field. Properties are defined in composite component JSON file:

Properties are retrieved from variable inside component and are assigned to local variables:

This is table implementation inside component, columns are initialised from component property:


Slot defines a placeholder, where developer who is using composite component can add additional elements. Slot is defined in component JSON definition file:

To define slot, JET slot component should be defined inside composite. You can control layout and location where slot will be rendered:

In our case, we use slot for table toolbar buttons. These buttons are added later, when developer is using composite. To place button into slot, put button inside composite component tag and assign defined slot name for the button. This will allow to render button in the slot:


Method defined in composite component, can be called from outside. In example below, I call JS function from toolbar slot button:

Function gets composite by ID and calls exposed method:

Method should be defined in composite JSON definition:

Method is implemented inside composite JS module:


Events allows to implement external listeners. Basically this allows to override composite logic in external functions. Event is declared in composite JSON definition:

Composite tag contains event property mapped with external JS function, which will be called when event happens inside composite:

Function code in the module, it prints current row selection key:

Table is defined with listener property inside composite:

Listener inside composite initiates event, which will be distributed outside and handled by method defined in composite tag on-handle-selection property:

Let's see how it works. Call Method button invokes method inside composite:

Table row selection first triggers listener inside composite, then it initiates event and external listener is invoked too:

I think this lists pretty much all of the essential functionality given by JET composite components. I hope you will find it useful in your development.