RxLceModel

A reactive data loading for Android based on RxJava. The library follows the guidelines recommended in official Android guide to app architecture to load data and report an operation state (Loading/Content/Error).

A compiled javadoc is available at Github Pages

Dart/Flutter port of the library is available: DartLceModel

Features

• Widely used design with Loading/Content/Error states
• Uses cache as a 'source of truth' with CacheThenNetLceModel.
• Checks data is valid (up-to-date or whatever).
• Falls back to invalid cache data if failed to refresh which allows offline application use.
• Supports data refresh or update to implement reload or server data update operations.
• Cache may be invalidated separately from loading to allow lazy data updates and complex data linking.
• Extendable architecture on every level.
• Thoroughly tested.

Sample application

Included as a sample module - a classic notes application that illustrates RxLceModel use along with:

• Dagger2
• Android architecture components:
  • LiveData
  • ViewModel
  • Navigation components
  • WorkManager

If you have questions on a general use of these tools in Android projects please refer to the following excellent articles by James Shvarts:

• Implementing MVVM using LiveData, RxJava, Dagger Android
• Navigation Architecture Component for the Rest of Us

Table of Contents

• v5.x
• Setting up the dependency
• Enabling desugaring
• LceState
• LceModel
• CacheThenNetLceModel
• Getting and caching data
• Choosing EntityValidator
• Displaying 'invalid' data and cache fall-back
• Cache invalidation and data updates
• On-demand cache refetch
• Cache service implementation and DiskLruCache
• A complete example of model setup
• Kotlin serialization
• Cache key normalization
• ProGuard configuration
• Updating data on server
• Getting data-only stream
• LCE ViewModel

v5.x

Originally written for RxJava, the version 5.x introduces the experimental coroutines port. To upgrade the version you need to change the dependency as described in the migration guide. Different artifacts for the main use-cases are to be required depending on which library you choose.

Setting up the dependency

Rx Usecase module :

dependencies {
    // Base model components
    implementation "com.motorro.rxlcemodel:rx:x.x.x"
}

Coroutines Usecase module :

dependencies {
    // Base model components
    implementation "com.motorro.rxlcemodel:coroutines:x.x.x"
}

Optional: Jake Wharton's DiskLruCache cache delegate for RxLceModel :

dependencies {
    // Jake Wharton's DiskLruCache delegate for cache implementation
    implementation "com.motorro.rxlcemodel:disklrucache:x.x.x"
}

Optional: Kotlin serialization serializer for DiskLruCache delegate :

dependencies {
    // Data serializer for DiskLruCache using Kotlin serialization
    implementation "com.motorro.rxlcemodel:kserializer:x.x.x"
}

Optional: LCE ViewModel and base view ready to accept LCE use-case for your view :

dependencies {
    // Data serializer for DiskLruCache using Kotlin serialization
    implementation "com.motorro.rxlcemodel:viewmodel:x.x.x"
}

Optional: Compose LCE view :

dependencies {
    // Data serializer for DiskLruCache using Kotlin serialization
    implementation "com.motorro.rxlcemodel:composeview:x.x.x"
}

Enabling Java8 desugaring for Android

IMPORTANT Version 3.x takes advantage of AGP 4.x Java8 desugaring. Follow instructions to enable desugaring.

LceState

A modern approach to architecting the reactive application suggests packing the combined state of the application into a flow of immutable state-objects. Each of them should contain the whole set of data required to process, transform, and display according to the business requirement. The most commonly used information besides the data itself is a state of data-loading pipeline.

Each LceState<DATA, PARAMS> subclass represents a data-loading phase and contains the following data:

• data: DATA? - Loaded data
• dataIsValid: Boolean - The validity of data at the time of emission. May be used by caching services to indicate the need of data refresh. More about it in CacheThenNetLceModel section.

States being emitted are:

• Loading - data is being loaded or updated. May contain some data. The exact state is defined by type property:

/**
 * Loading type
 */
enum class Type {
    /**
     * Just loads. May be initial load operation
     */
    LOADING,
    /**
     * Loading more items for paginated view
     */
    LOADING_MORE,
    /**
     * Refreshing content
     */
    REFRESHING,
    /**
     * Updating data on server
     */
    UPDATING,
}        

• Content - data is loaded.
• Error - some error while loading or updating data. May also contain some data.
• Terminated - a special state to indicate that resource identified by params is not available anymore. The sample application demonstrates the use of Terminated to indicate that the note is deleted and view should be destroyed:

override fun processTermination() {
    findNavController().popBackStack()
}

The sample fragment that demonstrates the use of LCE state to update it's view may be found here.

LceModel

LceState<DATA, PARAMS> in this library is being produced by a simple model interface:

interface LceModel<DATA: Any, PARAMS: Any> {
    val state: Observable<LceState<DATA, PARAMS>>
    val refresh: Completable
    val params: PARAMS
}

The model contains the following properties:

• state - the Observable that emits LceState
• refresh - the Completable to perform data refresh
• params - arbitrary parameters that identify the data being loaded

As you may see, parameters for model is a property - thus making the model immutable itself. This approach makes things a bit easier in many cases like:

• When you share your model and it's emission
• You don't need to supply an Observable for PARAMS which complicates design a lot If you need dynamic params - just flat-map your params with creating a new model for each parameter value like this:

val params = Observable.just("peach", "banana", "apple")
val state = params.switchMap { createNewModelWith(it).state }

CacheThenNetLceModel

As you may guess from it's name this kind of model tries to get cached data first and than loads data from network if nothing found. It goes along with current Android guide to app architecture. This type of model is called CacheThenNetLceModel. Here is the sequence diagram of data loading using this type of model:

The model creates a data observable for given PARAMS in a cache-service and transmits it to subscriber. If cache does not contain any data or data is not valid (more on validation later) the model subscribes a net-service to download data from network and saves it to cache for a later use.

It is worth nothing that cache and net here is just a common use-case of data-sources: locally stored data (cache) and some data that is maybe not that easy to get (net). You may easily adopt data sources of your choice to that approach. Say you have a resource-consuming computation result which may be cached and consumed later. The computation itself than becomes a net-service while the result is being stashed to a cache-service of your choice for later reuse.

To create new CacheThenNet model call a factory function:

protected open fun createLceModel() = LceModel.cacheThenNet(
    params = "user_123",
    net = netService, // Gets original data
    cache = cacheService, // Caches it to local storage
    ioScheduler = Schedulers.io(), // Optional scheduler to use for internal IO operations
    logger = Logger { level, message -> }, // Optional adapter to your debug-logger
)

Getting and caching data

As already mentioned above caching model uses two services to get data from network and to store it locally.

• NetService - loads data from network.
• CacheService - saves data locally.

Caching data always brings up a problem of cache updates and invalidation. Be it a caching policy of your backend team or some internal logic of your application the data validity evaluation may be easily implemented:

The NetService retrieved data and packages it to Entity wrapper - effectively the data itself and some EntityValidator to provide information when data expires. Validator is a simple interface with only three essential methods:

• isValid(): Boolean - being used by loading pipeline to determine if data is still valid
• serialize(): String - being called by CacheService to save data validation parameters along with data
• createSnapshot(): EntityValidator - creates a 'snapshot' of isVAlid() value at the moment of creation (more about it later).

The resulting Entity is then saved using CacheService preserving the data itself and the way to tell when data expires.

To convert Any data to Entity within your services use the following function:

fun createValidator() {
    // Create a validator
}

val data = "Some data"
val entity = data.toEntity(createValidator())

Choosing EntityValidator

There are some validators available already:

• EntityValidator.Simple - just a data-class that is initialized with boolean validity status.
• EntityValidator.Never - never valid.
• EntityValidator.Always - always valid.
• EntityValidator.Lifespan - A validator that is initialized with Time-To-Live value and becomes invalid after it expires.

While the first three of above-listed validators are easy to use and intuitive the last one needs a to be explained. Lifespan when created gets a reference to a system clock and evaluates it's validity against it every time it is being asked of it. Thus Lifespan is not an immutable and is an object with a self-changing internal state. A valid Entity with Lifespan validator that just seats in memory will expire eventually and become non-valid. That may be a desired behavior however in most cases the most useful way to deal with validity is to take a shapshot of data state at the time data is being emitted from the CacheService. To be able to do this both EntityValidator and Entity wrappers both have createSnapshot() methods that fix the validation status at the time of function is called.

To create a LifeSpan validator, there is a helper-factory that takes a single parameter of TTL in a constructor:

// Creates validators that are valid for 5 seconds
val validatorFactory = LifespanValidatorFactory(5000L)

The LifespanValidatorFactory is an implementation of EntityValidatorFactory that you may implement in case you need your own custom validator.

Displaying 'invalid' data and cache fall-back

Having a cache of required data besides eliminating extra network calls gives us the ability to fall-back to cached data in case network is not available and to keep working. This is an easy way to create an offline-capable mobile app when complex state synchronization between the app and server is not required. With 'cache-then-net' model you get the cache fall-back already implemented. Here is what you get when network connection is not available:

When there is no cached data available you just get null for data property in emitted LceState.Error.

Cache invalidation and data updates

A common task in complex applications may be the need to refresh some data in a part of application whenever something happens in another part. Reloading a list of messages in a chat application when push arrives may be a simple example. There are different ways of doing this - event-buses, Rx-subjects, you name it. With reactive cache-service the library provides such an invalidation is made in a simple and clean way:

If the push-message brings a pyload that is enough to display data change you could simply save the new data to cash with save method or delete it with delete method of CacheService interface:

The sample application demonstrates cache invalidation with notes CRUD. Here is how the delete use-case invalidates notes list and removes a note from cache if successfully deleted on server:

/**
 * Deletes note
 */
fun delete(noteId: Int): Completable = connectionChecker.connectionCheck
    .andThen(netRepository.deleteNote(noteId))
    .andThen(noteCache.delete(noteId))
    .andThen(listCache.invalidateAll)

On-demand cache refetch

Consider a cache service with complex internal structure that is updated by some internal logic. For example a database that saves entities and something that updates records directly. In case of Room you may observe a query and get updates if something changes underneath. But sometimes you have a complex entity with relations that are not so easy to fetch as they need conditional processing in synchronous way. In this case you may write an SQL delegate for sync-delegate service (see below) to implement reactive cache. When you get/put the whole entity the solution works. But as soon as you start to update entity parts you need some way to notify subscribers of data change.

CacheService has two methods that when called makes it to refetch data and update its active clients:

• refetch(params: P): Completable - makes cache service to refetch data for params and update corresponding clients
• refetchAll: Completable - makes cache service to refetch data for all subscribers

Cache service implementation and DiskLruCache

While you can implement any cache-service you like the library comes with a simple SyncDelegateCacheService which uses the following delegate for data IO:

The interface is self-explanatory and does all the IO for CacheService in a synchronous way. The library comes with a delegate that uses famous Jake Wharton's DiskLruCache cache delegate to store cache in files. The delegate is to be plugged-in as a separate dependency as described in Setting up the dependency.

The delegate requires a CacheDelegateSerializerDeserializer to save/restore data from file streams. The implemented serializer that comes with the library uses ObjectStream and requires cached data to be Serializable.

The delegate is designed to use the same directory for several delegates each maintaining the cache of specific type or data kind. It might come in handy if you need to destroy all cached data at once - say when user logs out. Thus delegate constructor has a prefix parameter that is used to distinguish it's cache files from others.

The DiskLruCacheSyncDelegate requires PARAMS to be String as it becomes a part of a file name.

The effort to configure delegate may seem overwhelming but thanks to Kotlin reified generics there is a couple of factory functions that make things easier in everyday life. Follow the example below and the CacheModule in sample application.

A complete example of model setup

Here is a complete setup of LceModel with services using DiskLruCacheDelegate:

/**
 * Data.
 * If using common setup mind the Serializable
 */
data class Data(val a: Int, val b: String) : Serializable {
    companion object {
        private const val serialVersionUID: Long = 1
    }
}

val data = mapOf( 1 to Data(1, "One"))

/**
 * Cache is valid for 5 seconds
 */
val validatorFactory = LifespanValidatorFactory(5000L)

/**
 * Net service - loads data from network
 */
val netService = object : NetService<Data, Int> {
    override fun get(params: Int): Single<Entity<Data>> = Single.just(
        data[params]?.toEntity(validatorFactory.create()) ?: throw IllegalArgumentException("Not found")
    )
}

/**
 * DiskLru cache setup
 */
val diskCache = DiskLruCacheSyncDelegate.DiskLruCacheProvider(
    directory = context.cacheDir,
    appVersion = BuildConfig.VERSION_CODE,
    maxSize = 20 * 1024 * 1024
)

/**
 * Cache service
 */
val cacheService: CacheService<Data, Int> = CacheService.withSyncDelegate(
    diskCache.withObjectStream(validatorFactory) { toString() }
)

/**
 * A model to load [Data] identified by [1]
 */
val model = LceModel.cacheThenNet(
    params = 1,
    net = netService,
    cache = cacheService
)

Kotlin serialization

Instead of using Java serialization in cache delegate you may add kserializer library and use @Serializable data classes. Here is an updated above configuration using kotlin serialization:

/**
 * Data.
 */
@Serializable
data class Data(val a: Int, val b: String)

/**
 * Cache service
 */
val cacheService: CacheService<Data, Int> = CacheService.withSyncDelegate(
    diskCache.withKotlin(validatorFactory, Data.serializer()) { toString() }
)

Cache key normalization

Sometimes the data that form your key is too long for your cache key. For example the latest published version of DiskLruCache has the following pattern to validate key: [a-z0-9_-]{1,64}. Also the total length of cash key in DiskLruCache delegate includes the length of prefix used to construct delegate so stringifying params should result in even shorter string to fit into 64 symbols. Thus if your parameters exceed the maximum length or has unsupported symbols you'll want to make use of some hashing function to generate cache key which may (depending on the has algorithm used) lead to key collisions and wrong results from cache. To make things easier there are several utility classes and functions you may use to automate hasing and validation:

/**
 * Cache service with Serializable
 */
val cacheService: CacheService<Data, Int> = CacheService.withObjectStreamNormalized(
    diskCache.withObjectStream(validatorFactory) { toString() }
)

/**
 * Cache service with kotlin
 */
val cacheService: CacheService<Data, Int> = CacheService.withSyncDelegateNormalized (
    diskCache.withKotlinNormalized(validatorFactory, Data.serializer()) { toString() }
)

These functions create a wrapping CacheFriendDelegate which stores data along with original key to be able to compare it to the one used when reading data. If original keys do not match - null is returned. Besides that the key is normalized before going to DiskLruCache - if it is considered invalid then it is being hashed.

To make delegate do less transformations and to try to avoid hashing your PARAMS class may implement the following interface:

/**
 * Generates a cache-friendly key value for parameters
 */
interface CacheFriend {
    /**
     * A cache key
     */
    val cacheKey: String get() = toString()
}

All delegate factories have overloads to accept parameters of that kind.

ProGuard configuration

Nothing special is required for library itself. If you use DiskLruCache delegate than you may want to add general rules for Serializable classes as described in the official guide.

Updating data on server

With LceModel you can update data easily with update operation state being transmitted through a state property just as with data loading. The updates are made with descendants of UpdateWrapper. The idea behind is to wrap an existing LceModel and to mix the update status to the existing state stream. The selection of ready-to use wrappers is:

• UpdatingLceModelWrapper<DATA, UPDATE, PARAMS> - the wrapper that updates the whole DATA using the UPDATE class. The existing model may be extended with the factory function:

val serviceSet = object: UpdatingServiceSet<Data, Data, Int> {
    override val net: UpdatingNetService<Data, Data, Int> = net
    override val cache: CacheService<Data, Int> = cache
}

val read = LceModel.cacheThenNet(
    params = 1,
    serviceSet = serviceSet
)

val write = read.withUpdates(serviceSet)

// Update data
write.update(Data(10, "Ten")).subscribe()

• StrategyUpdateWrapper<DATA, PARAMS> - may perform any update (patch) using a strategy passed to class instance:

// A repository interface
interface DataRepository {
    // Patching `a` property of `Data`
    fun updateA(id: Int, value: Int): Single<Data>
}

val read = LceModel.cacheThenNet(
    params = 1,
    net = netService, // Gets original data
    cache = cacheService // Caches it to local storage
)

val write = StrategyUpdateWrapper(
    upstream = read,
    cache = cacheService // Caches new data to local storage
)

// Patch `Data`
write.update { id -> 
    dataRepository
        .updateA(id, 10) // Patch data
        .map { it.toEntity(validatorFactory.create()) } // Create new entity
}.subscribe()

• A subclass of UpdateWrapper - implement any logic you like calling doUpdate to perform update. An example may be found in sample application.

Getting data-only stream

You may transform the state property Observable to strip state information and to get only the data. The library ships with some of the functions already implemented like:

• dataStopOnErrors - emits data stopping on any error
• dataStopOnEmptyErrors - emits data and terminates on error only if there is no data in original emission (LceState.Error with null for data property)
• dataNoErrors - emits data and ignores errors
• validData - emits data only if it is valid More information about them may be found in documentation or in a source code.

LCE ViewModel

You may also take a look at basic Android ViewModels provided as a separate package. Use them as-is or as a delegate in your own ViewModel system. There are three main classes:

• BaseLceModel - a common frame for LCE view-model having a signature for all common tasks to:
  • Load data
  • Dismiss error
  • Refresh data
• BaseLceModel.Impl - a common implementation to subclass if you need advanced logic
• BaseLceModel.WithUpdates - for those models that run some update operations on loaded data. This model will mix loading and error states from an operation to main data state. See usage example in a sample application.

To create a model from an LceUseCase call BaseLceModel.create methods and pass your state use-cases or LCE observables to ViewModel

You may also want to use basic views to switch your display according to LCE state:

• LceStateView - for Android view system.
• LceStateView - for Android Compose view system.