Introducing Compass: Effective Paging with Realm and Jetpack Paging 3

I like Realm mobile database. I first started using Realm at a time when there were limited options for a reactive database - a feature common today with tools like Room and SqlDelight (remember SqlBrite?). With reactivity, Realm was pushing for persistence as single source of truth much earlier than the pattern caught on if I recall correctly. Realm’s reactivity is fine grained as well i.e can emit added, removed or modified changes on a collection without tools like DiffUtil and with correct usage it can integrate directly with RecyclerView. Apart from being reactive, it is an object oriented database, so relations can be directly expressed as Java/Kotlin objects and has a capable query system with support for aggregations and backlinks.

However not all of these were smooth sailing in my opinion, particularly paging, lifecycle and threading. Realm’s live object model i.e at any point in time, we interact with the live view of the underlying database and objects are brought into memory only when they are read like Object.getPropery() (lazy by default). For paging, Realm initially recommended that it can be fast enough to read objects inside a RecyclerView.Adapter.getItem() call (this pattern is no longer recommended). I disagree with it since storage performance is highly variable (a background Play Store update can bring storage performance to its knees) and chances of ANR are high.

Threading is not straight forward and had few rules that need to be followed.

  • Realm instances and the results RealmResults should not cross thread boundaries and each Realm instance carried a lifecycle with it should call close() when done.
  • Realm objects can be observed only on a thread that has Android’s Looper prepared on them.

Because of these rules, writing Realm database code carried a bit of ceremony around it especially considering Android’s already extensive lifecyle expectations.


Having discovered couple of patterns that can make working with Realm easier especially around threading, lifecycle and paging, I took the opportunity to use Kotlin’s capable type system to provide safe defaults with limited options for developer error when using Realm. In the remainder of the article, I would like to talk about paging in particular with Jetpack Paging 3 and how to integrate Realm with it by using Compass.

compass is available on mavenCentral:

dependencies {
    implementation "dev.arunkumar.compass:compass:1.0.0"
    // Paging integration
    implementation "dev.arunkumar.compass:compass-paging:1.0.0"


Compass has two main components that provide the core compass module with extensions for threading and lifecycle and compass-paging for integration with Jetpack Paging 3. To observe a RealmQuery with compass we can use the asPagingItems extension function as shown below.

val pagingFlow: Flow<PagingData<Person>> = RealmQuery { where<Person>().sort(Person.NAME) }

Since asPagingItems() returns Flow<PagingData<T>> it integrates well with

The extension function internally manages threading, lifecycle of Realms and confirms to typical expectations of a Flow<T>:

  1. Returned objects can be safely passed around threads.
  2. Automatic closing of any internal resources when Flow collection is stopped.

Let’s unpack one by one on how compass achieves this.



Consider a typical Realm code with the following

val realm = Realm.getDefaultInstance() // 1

val persons = realm.where<Person>().findAll() // 2

persons.addChangeListener{ } // 3

realm.close() // 4

This looks straight-forward but as soon as threading is introduced, couple of challenges arise. Namely, 1,2,3,4 should be on the same thread. Since 2 is a database operation, our instinct should be to move this entire operation to background thread, and we would be correct. However if we want to observe any changes in 3 it would crash since the background thread most likely would not have Looper by default. The official solution to this problem is to explicitly use *Async APIs like findAllAsync() etc. This works for most cases but still has room for developer errors due to lack of checks especially in changes observation due to ties to Android Looper.

Deferred Execution

This problem can be solved by moving the construction of the query and observation both to usage site (background thread). Compass provides RealmQuery {} construction function that can be used to construct RealmQueryBuilder<T> which is a typealias for Realm.() -> RealmQuery<T>. The design is inspired by lazy APIs like Kotlin sequences/flows where construction happens lazily and only invoked when certain terminal operators are called.

In this case, the terminal operator can enforce threading rules as needed without any ceremony needed inside the construction function. For example:

val personQuery = RealmQuery { where<Person>() }

personQuery.getAll() // Acquire an instance of `Realm`, run the query, return results and close Realm.
personQuery.asFlow() // Observable variant


compass provides dedicated constructs like RealmDispatcher that automatically handles basic threading expectations of Realm. The requirement is to ensure Looper is available and it is stopped when work is done. This is done via Android OS’s HandlerThread which is the official way to manage the Looper correctly. Converting HandlerThread to a dedicated Executor instance will allow us to use variety of extensions that Kotlin offers like asCoroutineDispatcher(). HandlerExecutor:

public class HandlerExecutor(private val tag: String? = null) : CloseableExecutor {

  private val handlerThread by lazy {
      tag ?: + hashCode(),
    ).apply { start() }

  private val handler by lazy { Handler(handlerThread.looper) }

  override fun execute(command: Runnable) {
    if (Looper.myLooper() == handler.looper) {
    } else {

  override fun close() {

Using RealmDispatcher, the following is valid:

withContext(RealmDispatcher()) {
  Realm { realm -> // Acquire default Realm with `Realm {}`
    val persons = realm.where<Person>().findAll()    

    val realmChangeListener = RealmChangeListener<RealmResults<Person>> {
      println("Change liseneter called")

    persons.addChangeListener(realmChangeListener) // Safe to add    
    // Make a transaction
    realm.transact { // this: Realm
    delay(500)  // Wait till change listener is triggered
  } // Acquired Realm automatically closed

Note that RealmDispatcher should be closed when no longer used to release resources. For automatic lifecycle handling via Flow, see below.

Streams via Flow

compass builds on top of the lazy query API to provide observable functions like asFlow() which confirms to basic threading expectations of a Flow.

  • Returned objects can be passed to different threads.
  • Handles Realm lifecycle until Flow collection is stopped.
val personsFlow = RealmQuery { where<Person>() }.asFlow()

Internally asFlow creates a dedicated RealmDispatcher to run the queries and observe changes. The created dispatcher is automatically closed and recreated when collection stops/restarted all established through use of callbackFlow and awaitClose.

For ensuring Realm objects can be passed around threads, compass copies the object to memory using Realm.copyFromRealm(). Copying objects detaches it from realm and is no longer a live updating object.

Reading subset of data

Copying large objects from Realm can be expensive in terms of memory, to read only subset of results to memory we can use asFlow() overload that takes a transform function.

data class PersonName(val name: String)

val personNames = RealmQuery { where<Person>() }.asFlow { PersonName( }

In the above example, since Realm is lazy by default, only name is brought into memory from eash Person instance. Conceptually in SQL terms, this is like querying only one column from a table.


So far we have explored how compass handles common patterns around threading and lifecycle. All those concepts come together in compass’s paging integration.

RealmResults is like a live cursor

compass takes advantage of Realms unique lazy loading feature to implement paging. Jetpack Paging 1 and 2 provided APIs to implement different types of paging sources, this can be key based, position based and even dependant key based. Realm’s paging support can be implemented in variety of ways, compass implements it using PositionalDataSource. It works under the premise that RealmResults<T> returned by RealmQuery is a live cursor. For example,

val persons = realm.where<Person>().findAll()

Here persons can contain 1 million entries but not all are brought into memory, they are read only when persons.get(index) is called, if one were to bind this to a RecyclerView only items in the view port would be read (nifty!). This feature alone would satisfy PositionalDataSource expectations that the implementation should be able to read data at any arbitary position.

Since paging can involve complicated threading, compass’s default implementation detaches the object from Realm using Realm.copyFromRealm, this makes it easy to apply any further transformations as needed without concern on threading.

As mentioned earlier, copyFromRealm is expensive for large nested objects and it is advised to take advantage of Realm’s lazy loading to bring only needed data to memory, this is done via RealmModelTransform API.

public typealias RealmModelTransform<T, R> = Realm.(realmModel: T) -> R

compass provides ReamlCopyTransform which does simply copying as shown below.

public fun <T : RealmModel, R> RealmCopyTransform(): RealmModelTransform<T, R> {
  return { model -> copyFromRealm(model) as R }

For customized reading of data, we can use the asPagingItems overload which takes a transform function.

val pagedPersonNames = RealmQuery { where<Person>() }.asPagingItems { }
Observability and threading

For threading, compass uses RealmDispatcher to run queries and manage observability in a predictable way. Paging 3 expects that whenever there is data change, the corresponding DataSource implementation should invalidate() itself and new instance should be created. With Realm this can be easily accomplished by using change listeners:

// this: RealmTiledDataSource
private val realmChangeListener = { _: RealmResults<T> -> invalidate() }

private val realmResults by lazy {
    realmQuery.findAll().apply {

init {
  addInvalidatedCallback {
    if (realmResults.isValid) {

See RealmTiledDataSource

Note: PositionalDataSource is technincally deprecated, however similar to Room, compass relies on Paging 2 to 3 migration support specifically asPagingSourceFactory to support Paging 3. Although technically it should be possible to implement Paging 3’s PagingSource with the same patterns described in this article.


Jetpack ViewModel integration is straight-forward as shown below:

class MyViewModel: ViewModel() {

    val results = RealmQuery { where<Task>() }.asPagingItems().cachedIn(viewModelScope)

The Flow returned by asPagingItems() can be safely used for transformations, seperators and caching. Although supported, for converting to UI model prefer using asPagingItems { /* convert */ } as it is more efficient.


A basic paging implementation with Jetpack Compose with options to sort shown below:


For simpler APIs, compass makes tradeoffs in managing Realm objects. It prefers to open short lived Realm instances using Realm.getDefaultInstance() instead of scoping Realms to UI controllers.

The official guide points that

If the realm is already open on a different thread within the same process, opening the realm is less expensive, but still nontrivial.

So far the pattern has worked well, but if your use case with different Realm schema has any troubles, I would love to hear in the comments.

Comparison to official APIs

Problems outlined in this article around observability has in part been addressed officially by the Realm team, namely with the introduction of Freezing and toFlow / toFlowable APIs.


Freezing done by calling realm.freeze(), creates immutable views that do not have threading limitations.

val frogs = realm.where<Frog>().findAll().freeze()

Although freezing ticks the most boxes of concern, namely being able to move objects across thread:

Freezing creates an immutable view of a specific object, collection, or realm that still exists on disk and does not need to be deeply copied when passed around to other threads. You can freely share a frozen object across threads without concern for thread issues.

The returned frozen objects still has ties to Realm and carries lifecycle risk with it:

Frozen objects remain valid for as long as the realm that spawned them stays open. Avoid closing realms that contain frozen objects until all threads are done working with those frozen objects.

compasss asFlow APIs produce completely detached objects + the option of reading subset of data from each RealmObject.

Streams via toFlow

Official recommendation around streams API is to use toFlow which internally uses Freeze to address threading concerns. However still lifecycle needs to be managed on realm seperately as shown below.

val realm = Realm.getDefaultInstance()

  .flatMapLatest { frozenTasks ->
  }.map { }

// realm?

In comparison, compass’s API RealmQuery { where<Task>() }.asFlow { } automatically manages internal Realm instances and runs whole construction and execution in a background thread.


compass provides simpler APIs and types to make working with Realm easier. Abstractions around threading, lifecycle and paging with the use of Kotlin’s capable type system helps in avoiding common pitfalls.

Possible improvements

compass’s paging, currently does not take advantage of fine-grained notifications from Realm and still relies on DiffUtil / Composition Keys to perform updates. Through some carefull structuring it should be possible to implement changes without needing to manually calculate changeset.

Compass is available here:

Any feedback greatly appreciated!

– Arun