This small and simple utility library is a pure Java 8 port of Facebook DataLoader.
It can serve as integral part of your application's data layer to provide a consistent API over various back-ends and reduce message communication overhead through batching and caching.
An important use case for java-dataloader is improving the efficiency of GraphQL query execution. Graphql fields
are resolved independently and, with a true graph of objects, you may be fetching the same object many times.
A naive implementation of graphql data fetchers can easily lead to the dreaded "n+1" fetch problem.
Most of the code is ported directly from Facebook's reference implementation, with one IMPORTANT adaptation to make it work for Java 8. (more on this below).
Before reading on, be sure to take a short dive into the original documentation provided by Lee Byron (@leebyron) and Nicholas Schrock (@schrockn) from Facebook, the creators of the original data loader.
- Features
- Getting started!
- Examples
- Other information sources
- Contributing
- Acknowledgements
- Licensing
java-dataloader is a feature-complete port of the Facebook reference implementation with one major difference. These features are:
- Simple, intuitive API, using generics and fluent coding
- Define batch load function with lambda expression
- Schedule a load request in queue for batching
- Add load requests from anywhere in code
- Request returns a
CompleteableFuture<V>of the requested value - Can create multiple requests at once
- Caches load requests, so data is only fetched once
- Can clear individual cache keys, so data is re-fetched on next batch queue dispatch
- Can prime the cache with key/values, to avoid data being fetched needlessly
- Can configure cache key function with lambda expression to extract cache key from complex data loader key types
- Individual batch futures complete / resolve as batch is processed
- Results are ordered according to insertion order of load requests
- Deals with partial errors when a batch future fails
- Can disable batching and/or caching in configuration
- Can supply your own
CacheMap<K, V>implementations - Can supply your own
ValueCache<K, V>implementations - Has very high test coverage
Gradle users configure the java-dataloader dependency in build.gradle:
repositories {
jcenter()
}
dependencies {
compile 'com.graphql-java:java-dataloader: 3.1.0'
}
To build from source use the Gradle wrapper:
./gradlew clean build
A DataLoader object requires a BatchLoader function that is responsible for loading a promise of values given
a list of keys
BatchLoader<Long, User> userBatchLoader = new BatchLoader<Long, User>() {
@Override
public CompletionStage<List<User>> load(List<Long> userIds) {
return CompletableFuture.supplyAsync(() -> {
return userManager.loadUsersById(userIds);
});
}
};
DataLoader<Long, User> userLoader = DataLoaderFactory.newDataLoader(userBatchLoader);You can then use it to load values which will be CompleteableFuture promises to values
CompletableFuture<User> load1 = userLoader.load(1L);or you can use it to compose future computations as follows. The key requirement is that you call
dataloader.dispatch() or its variant dataloader.dispatchAndJoin() at some point in order to make the underlying calls happen to the batch loader.
In this version of data loader, this does not happen automatically. More on this in Manual dispatching .
userLoader.load(1L)
.thenAccept(user -> {
System.out.println("user = " + user);
userLoader.load(user.getInvitedByID())
.thenAccept(invitedBy -> {
System.out.println("invitedBy = " + invitedBy);
});
});
userLoader.load(2L)
.thenAccept(user -> {
System.out.println("user = " + user);
userLoader.load(user.getInvitedByID())
.thenAccept(invitedBy -> {
System.out.println("invitedBy = " + invitedBy);
});
});
userLoader.dispatchAndJoin();As stated on the original Facebook project :
A naive application may have issued four round-trips to a backend for the required information, but with DataLoader this application will make at most two.
DataLoader allows you to decouple unrelated parts of your application without sacrificing the performance of batch data-loading. While the loader presents an API that loads individual values, all concurrent requests will be coalesced and presented to your batch loading function. This allows your application to safely distribute data fetching requirements throughout your application and maintain minimal outgoing data requests.
In the example above, the first call to dispatch will cause the batched user keys (1 and 2) to be fired at the BatchLoader function to load 2 users.
Since each thenAccept callback made more calls to userLoader to get the "user they have invited", another 2 user keys are given at the BatchLoader
function for them.
In this case the userLoader.dispatchAndJoin() is used to make a dispatch call, wait for it (aka join it), see if the data loader has more batched entries, (which is does)
and then it repeats this until the data loader internal queue of keys is empty. At this point we have made 2 batched calls instead of the naive 4 calls we might have made if
we did not "batch" the calls to load data.
You will notice in our BatchLoader example that the backing service had the ability to get a list of users given a list of user ids in one call.
public CompletionStage<List<User>> load(List<Long> userIds) {
return CompletableFuture.supplyAsync(() -> {
return userManager.loadUsersById(userIds);
});
}This is important consideration. By using dataloader you have batched up the requests for N keys in a list of keys that can be
retrieved at one time.
If you don't have batched backing services, then you can't be as efficient as possible as you will have to make N calls for each key.
BatchLoader<Long, User> lessEfficientUserBatchLoader = new BatchLoader<Long, User>() {
@Override
public CompletionStage<List<User>> load(List<Long> userIds) {
return CompletableFuture.supplyAsync(() -> {
//
// notice how it makes N calls to load by single user id out of the batch of N keys
//
return userIds.stream()
.map(id -> userManager.loadUserById(id))
.collect(Collectors.toList());
});
}
};That said, with key caching turn on (the default), it will still be more efficient using dataloader than without it.
Often there is a need to call the batch loader function with some sort of call context environment, such as the calling users security credentials or the database connection parameters.
You can do this by implementing a org.dataloader.BatchLoaderContextProvider and using one of
the batch loading interfaces such as org.dataloader.BatchLoaderWithContext.
It will be given a org.dataloader.BatchLoaderEnvironment parameter and it can then ask it
for the context object.
DataLoaderOptions options = DataLoaderOptions.newOptions()
.setBatchLoaderContextProvider(() -> SecurityCtx.getCallingUserCtx());
BatchLoaderWithContext<String, String> batchLoader = new BatchLoaderWithContext<String, String>() {
@Override
public CompletionStage<List<String>> load(List<String> keys, BatchLoaderEnvironment environment) {
SecurityCtx callCtx = environment.getContext();
return callDatabaseForResults(callCtx, keys);
}
};
DataLoader<String, String> loader = DataLoaderFactory.newDataLoader(batchLoader, options);The batch loading code will now receive this environment object and it can be used to get context perhaps allowing it to connect to other systems.
You can also pass in context objects per load call. This will be captured and passed to the batch loader function.
You can gain access to them as a map by key or as the original list of context objects.
DataLoaderOptions options = DataLoaderOptions.newOptions()
.setBatchLoaderContextProvider(() -> SecurityCtx.getCallingUserCtx());
BatchLoaderWithContext<String, String> batchLoader = new BatchLoaderWithContext<String, String>() {
@Override
public CompletionStage<List<String>> load(List<String> keys, BatchLoaderEnvironment environment) {
SecurityCtx callCtx = environment.getContext();
//
// this is the load context objects in map form by key
// in this case [ keyA : contextForA, keyB : contextForB ]
//
Map<Object, Object> keyContexts = environment.getKeyContexts();
//
// this is load context in list form
//
// in this case [ contextForA, contextForB ]
return callDatabaseForResults(callCtx, keys);
}
};
DataLoader<String, String> loader = DataLoaderFactory.newDataLoader(batchLoader, options);
loader.load("keyA", "contextForA");
loader.load("keyB", "contextForB");Often there is not a 1:1 mapping of your batch loaded keys to the values returned.
For example, let's assume you want to load users from a database, you could probably use a query that looks like this:
SELECT * FROM User WHERE id IN (keys)Given say 10 user id keys you might only get 7 results back. This can be more naturally represented in a map than in an ordered list of values from the batch loader function.
You can use org.dataloader.MappedBatchLoader for this purpose.
When the map is processed by the DataLoader code, any keys that are missing in the map
will be replaced with null values. The semantic that the number of DataLoader.load requests
are matched with an equal number of values is kept.
The keys provided MUST be first class keys since they will be used to examine the returned map and create the list of results, with nulls filling in for missing values.
MappedBatchLoaderWithContext<Long, User> mapBatchLoader = new MappedBatchLoaderWithContext<Long, User>() {
@Override
public CompletionStage<Map<Long, User>> load(Set<Long> userIds, BatchLoaderEnvironment environment) {
SecurityCtx callCtx = environment.getContext();
return CompletableFuture.supplyAsync(() -> userManager.loadMapOfUsersByIds(callCtx, userIds));
}
};
DataLoader<Long, User> userLoader = DataLoaderFactory.newMappedDataLoader(mapBatchLoader);
// ...It may be that your batch loader function can use a Reactive Streams Publisher, where values are emitted as an asynchronous stream.
For example, let's say you wanted to load many users from a service without forcing the service to load all users into its memory (which may exert considerable pressure on it).
A org.dataloader.BatchPublisher may be used to load this data:
BatchPublisher<Long, User> batchPublisher = new BatchPublisher<Long, User>() {
@Override
public void load(List<Long> userIds, Subscriber<User> userSubscriber) {
Publisher<User> userResults = userManager.streamUsersById(userIds);
userResults.subscribe(userSubscriber);
}
};
DataLoader<Long, User> userLoader = DataLoaderFactory.newPublisherDataLoader(batchPublisher);
// ...Rather than waiting for all user values to be returned on one batch, this DataLoader will complete
the CompletableFuture<User> returned by Dataloader#load(Long) as each value is
published.
This pattern means that data loader values can (in theory) be satisfied more quickly than if we wait for all results in the batch to be retrieved and hence the overall result may finish more quickly.
If an exception is thrown, the remaining futures yet to be completed are completed exceptionally.
You MUST ensure that the values are streamed in the same order as the keys provided, with the same cardinality (i.e. the number of values must match the number of keys).
Failing to do so will result in incorrect data being returned from DataLoader#load.
BatchPublisher is the reactive version of BatchLoader.
Your publisher may not necessarily return values in the same order in which it processes keys and it may not be able to find a value for each key presented.
For example, let's say your batch publisher function loads user data which is spread across shards, with some shards responding more quickly than others.
In instances like these, org.dataloader.MappedBatchPublisher can be used.
MappedBatchPublisher<Long, User> mappedBatchPublisher = new MappedBatchPublisher<Long, User>() {
@Override
public void load(Set<Long> userIds, Subscriber<Map.Entry<Long, User>> userEntrySubscriber) {
Publisher<Map.Entry<Long, User>> userEntries = userManager.streamUsersById(userIds);
userEntries.subscribe(userEntrySubscriber);
}
};
DataLoader<Long, User> userLoader = DataLoaderFactory.newMappedPublisherDataLoader(mappedBatchPublisher);
// ...Like the BatchPublisher, if an exception is thrown, the remaining futures yet to be completed are completed
exceptionally.
Unlike the BatchPublisher, however, it is not necessary to return values in the same order as the provided keys,
or even the same number of values.
MappedBatchPublisher is the reactive version of MappedBatchLoader.
In the reference JS implementation if the batch loader returns an Error object back from the load() promise is rejected
with that error. This allows fine grain (per object in the list) sets of error. If I ask for keys A,B,C and B errors out the promise
for B can contain a specific error.
This is not quite as loose in a Java implementation as Java is a type safe language.
A batch loader function is defined as BatchLoader<K, V> meaning for a key of type K it returns a value of type V.
It can't just return some Exception as an object of type V. Type safety matters.
However, you can use the Try data type which can encapsulate a computation that succeeded or returned an exception.
Try<String> tryS = Try.tryCall(() -> {
if (rollDice()) {
return "OK";
} else {
throw new RuntimeException("Bang");
}
});
if (tryS.isSuccess()) {
System.out.println("It work " + tryS.get());
} else {
System.out.println("It failed with exception : " + tryS.getThrowable());
}DataLoader supports this type, and you can use this form to create a batch loader that returns a list of Try objects, some of which may have succeeded,
and some of which may have failed. From that data loader can infer the right behavior in terms of the load(x) promise.
DataLoader<String, User> dataLoader = DataLoaderFactory.newDataLoaderWithTry(new BatchLoader<String, Try<User>>() {
@Override
public CompletionStage<List<Try<User>>> load(List<String> keys) {
return CompletableFuture.supplyAsync(() -> {
List<Try<User>> users = new ArrayList<>();
for (String key : keys) {
Try<User> userTry = loadUser(key);
users.add(userTry);
}
return users;
});
}
});On the above example if one of the Try objects represents a failure, then its load() promise will complete exceptionally and you can
react to that, in a type safe manner.
DataLoader has a two tiered caching system in place.
The first cache is represented by the interface org.dataloader.CacheMap. It will cache CompletableFutures by key and hence future load(key) calls
will be given the same future and hence the same value.
This cache can only work local to the JVM, since its caches CompletableFutures which cannot be serialised across a network say.
The second level cache is a value cache represented by the interface org.dataloader.ValueCache. By default, this is not enabled and is a no-op.
The value cache uses an async API pattern to encapsulate the idea that the value cache could be in a remote place such as REDIS or Memcached.
The default future cache behind DataLoader is an in memory HashMap. There is no expiry on this, and it lives for as long as the data loader
lives.
However, you can create your own custom future cache and supply it to the data loader on construction via the org.dataloader.CacheMap interface.
MyCustomCache customCache = new MyCustomCache();
DataLoaderOptions options = DataLoaderOptions.newOptions().setCacheMap(customCache);
DataLoaderFactory.newDataLoader(userBatchLoader, options);You could choose to use one of the fancy cache implementations from Guava or Caffeine and wrap it in a CacheMap wrapper ready
for data loader. They can do fancy things like time eviction and efficient LRU caching.
As stated above, a custom org.dataloader.CacheMap is a local cache of CompleteFutures to values, not values per se.
If you want to externally cache values then you need to use the org.dataloader.ValueCache interface.
The org.dataloader.ValueCache allows you to use an external cache.
The API of ValueCache has been designed to be asynchronous because it is expected that the value cache could be outside
your JVM. It uses CompleteableFutures to get and set values into cache, which may involve a network call and hence exceptional failures to get
or set values.
The ValueCache API is batch oriented, if you have a backing cache that can do batch cache fetches (such a REDIS) then you can use the ValueCache.getValues*(
call directly. However, if you don't have such a backing cache, then the default implementation will break apart the batch of cache value into individual requests
to ValueCache.getValue() for you.
This library does not ship with any implementations of ValueCache because it does not want to have
production dependencies on external cache libraries, but you can easily write your own.
The tests have an example based on Caffeine.
In certain uncommon cases, a DataLoader which does not cache may be desirable.
DataLoaderFactory.newDataLoader(userBatchLoader, DataLoaderOptions.newOptions().setCachingEnabled(false));Calling the above will ensure that every call to .load() will produce a new promise, and requested keys will not be saved in memory.
However, when the memoization cache is disabled, your batch function will receive an array of keys which may contain duplicates! Each key will
be associated with each call to .load(). Your batch loader MUST provide a value for each instance of the requested key as per the contract
userDataLoader.load("A");
userDataLoader.load("B");
userDataLoader.load("A");
userDataLoader.dispatch();
// will result in keys to the batch loader with [ "A", "B", "A" ]More complex cache behavior can be achieved by calling .clear() or .clearAll() rather than disabling the cache completely.
If a batch load fails (that is, a batch function returns a rejected CompletionStage), then the requested values will not be cached.
However, if a batch function returns a Try or Throwable instance for an individual value, then that will be cached to avoid frequently loading
the same problem object.
In some circumstances you may wish to clear the cache for these individual problems:
userDataLoader.load("r2d2").whenComplete((user, throwable) -> {
if (throwable != null) {
userDataLoader.clear("r2dr");
throwable.printStackTrace();
} else {
processUser(user);
}
});DataLoader keeps statistics on what is happening. It can tell you the number of objects asked for, the cache hit number, the number of objects
asked for via batching and so on.
Knowing what the behaviour of your data is important for you to understand how efficient you are in serving the data via this pattern.
Statistics statistics = userDataLoader.getStatistics();
System.out.println(format("load : %d", statistics.getLoadCount()));
System.out.println(format("batch load: %d", statistics.getBatchLoadCount()));
System.out.println(format("cache hit: %d", statistics.getCacheHitCount()));
System.out.println(format("cache hit ratio: %d", statistics.getCacheHitRatio()));DataLoaderRegistry can also roll up the statistics for all data loaders inside it.
You can configure the statistics collector used when you build the data loader
DataLoaderOptions options = DataLoaderOptions.newOptions().setStatisticsCollector(() -> new ThreadLocalStatisticsCollector());
DataLoader<String,User> userDataLoader = DataLoaderFactory.newDataLoader(userBatchLoader,options);Which collector you use is up to you. It ships with the following: SimpleStatisticsCollector, ThreadLocalStatisticsCollector, DelegatingStatisticsCollector
and NoOpStatisticsCollector.
If you are serving web requests then the data can be specific to the user requesting it. If you have user specific data then you will not want to cache data meant for user A to then later give it user B in a subsequent request.
The scope of your DataLoader instances is important. You will want to create them per web request to ensure data is only cached within that
web request and no more.
If your data can be shared across web requests then use a custom org.dataloader.ValueCache to keep values in a common place.
Data loaders are stateful components that contain promises (with context) that are likely share the same affinity as the request.
The original Facebook DataLoader was written in Javascript for NodeJS.
NodeJS is single-threaded in nature, but simulates asynchronous logic by invoking functions on separate threads in an event loop, as explained in this post on StackOverflow.
NodeJS generates so-call 'ticks' in which queued functions are dispatched for execution, and Facebook DataLoader uses
the nextTick() function in NodeJS to automatically dequeue load requests and send them to the batch execution function
for processing.
Here there is an IMPORTANT DIFFERENCE compared to how java-dataloader operates!!
In NodeJS the batch preparation will not affect the asynchronous processing behaviour in any way. It will just prepare batches in 'spare time' as it were.
This is different in Java as you will actually delay the execution of your load requests, until the moment where you make a
call to dataLoader.dispatch().
Does this make Java DataLoader any less useful than the reference implementation? We would argue this is not the case,
and there are also gains to this different mode of operation:
- In contrast to the NodeJS implementation you as developer are in full control of when batches are dispatched
- You can attach any logic that determines when a dispatch takes place
- You still retain all other features, full caching support and batching (e.g. to optimize message bus traffic, GraphQL query execution time, etc.)
However, with batch execution control comes responsibility! If you forget to make the call to dispatch() then the futures
in the load request queue will never be batched, and thus will never complete! So be careful when crafting your loader designs.
By default, when dataLoader.dispatch() is called, the BatchLoader / MappedBatchLoader function will be invoked
immediately.
However, you can provide your own BatchLoaderScheduler that allows this call to be done some time into
the future.
You will be passed a callback (ScheduledBatchLoaderCall / ScheduledMapBatchLoaderCall) and you are expected
to eventually call this callback method to make the batch loading happen.
The following is a BatchLoaderScheduler that waits 10 milliseconds before invoking the batch loading functions.
new BatchLoaderScheduler() {
@Override
public <K, V> CompletionStage<List<V>> scheduleBatchLoader(ScheduledBatchLoaderCall<V> scheduledCall, List<K> keys, BatchLoaderEnvironment environment) {
return CompletableFuture.supplyAsync(() -> {
snooze(10);
return scheduledCall.invoke();
}).thenCompose(Function.identity());
}
@Override
public <K, V> CompletionStage<Map<K, V>> scheduleMappedBatchLoader(ScheduledMappedBatchLoaderCall<K, V> scheduledCall, List<K> keys, BatchLoaderEnvironment environment) {
return CompletableFuture.supplyAsync(() -> {
snooze(10);
return scheduledCall.invoke();
}).thenCompose(Function.identity());
}
@Override
public <K> void scheduleBatchPublisher(ScheduledBatchPublisherCall scheduledCall, List<K> keys, BatchLoaderEnvironment environment) {
snooze(10);
scheduledCall.invoke();
}
};You are given the keys to be loaded and an optional BatchLoaderEnvironment for informative purposes. You can't change the list of
keys that will be loaded via this mechanism say.
Also note, because there is a max batch size, it is possible for this scheduling to happen N times for a given dispatch()
call. The total set of keys will be sliced into batches themselves and then the BatchLoaderScheduler will be called for
each batch of keys.
Do not assume that a single call to dispatch() results in a single call to BatchLoaderScheduler.
This code is inspired from the scheduling code in the reference JS implementation
ScheduledDataLoaderRegistry is a registry that allows for dispatching to be done on a schedule. It contains a
predicate that is evaluated (per data loader contained within) when dispatchAll is invoked.
If that predicate is true, it will make a dispatch call on the data loader, otherwise is will schedule a task to
perform that check again. Once a predicate evaluated to true, it will not reschedule and another call to
dispatchAll is required to be made.
This allows you to do things like "dispatch ONLY if the queue depth is > 10 deep or more than 200 millis have passed since it was last dispatched".
DispatchPredicate depthOrTimePredicate = DispatchPredicate
.dispatchIfDepthGreaterThan(10)
.or(DispatchPredicate.dispatchIfLongerThan(Duration.ofMillis(200)));
ScheduledDataLoaderRegistry registry = ScheduledDataLoaderRegistry.newScheduledRegistry()
.dispatchPredicate(depthOrTimePredicate)
.schedule(Duration.ofMillis(10))
.register("users",userDataLoader)
.build();The above acts as a kind of minimum batch depth, with a time overload. It won't dispatch if the loader depth is less than or equal to 10 but if 200ms pass it will dispatch.
It's natural to want to have chained DataLoader calls.
CompletableFuture<Object> chainedCalls = dataLoaderA.load("user1")
.thenCompose(userAsKey -> dataLoaderB.load(userAsKey));However, the challenge here is how to be efficient in batching terms.
This is discussed in detail in the #54 issue.
Since CompletableFuture's are async and can complete at some time in the future, when is the best time to call
dispatch again when a load call has completed to maximize batching?
The most naive approach is to immediately dispatch the second chained call as follows :
CompletableFuture<Object> chainedWithImmediateDispatch = dataLoaderA.load("user1")
.thenCompose(userAsKey -> {
CompletableFuture<Object> loadB = dataLoaderB.load(userAsKey);
dataLoaderB.dispatch();
return loadB;
});The above will work however the window of batching together multiple calls to dataLoaderB will be very small and since
it will likely result in batch sizes of 1.
This is a very difficult problem to solve because you have to balance two competing design ideals which is to maximize the batching window of secondary calls in a small window of time so you customer requests don't take longer than necessary.
-
If the batching window is wide you will maximize the number of keys presented to a
BatchLoaderbut your request latency will increase. -
If the batching window is narrow you will reduce your request latency, but also you will reduce the number of keys presented to a
BatchLoader.
The ScheduledDataLoaderRegistry offers one solution to this called "ticker mode" where it will continually reschedule secondary
DataLoader calls after the initial dispatch() call is made.
The batch window of time is controlled by the schedule duration setup at when the ScheduledDataLoaderRegistry is created.
ScheduledExecutorService executorService = Executors.newSingleThreadScheduledExecutor();
ScheduledDataLoaderRegistry registry = ScheduledDataLoaderRegistry.newScheduledRegistry()
.register("a", dataLoaderA)
.register("b", dataLoaderB)
.scheduledExecutorService(executorService)
.schedule(Duration.ofMillis(10))
.tickerMode(true) // ticker mode is on
.build();
CompletableFuture<Object> chainedCalls = dataLoaderA.load("user1")
.thenCompose(userAsKey -> dataLoaderB.load(userAsKey));When ticker mode is on the chained dataloader calls will complete but the batching window size will depend on how quickly
the first level of DataLoader calls returned compared to the schedule of the ScheduledDataLoaderRegistry.
If you use ticker mode, then you MUST registry.close() on the ScheduledDataLoaderRegistry at the end of the request (say) otherwise
it will continue to reschedule tasks to the ScheduledExecutorService associated with the registry.
You will want to look at sharing the ScheduledExecutorService in some way between requests when creating the ScheduledDataLoaderRegistry
otherwise you will be creating a thread per ScheduledDataLoaderRegistry instance created and with enough concurrent requests
you may create too many threads.
When ticker mode is false the ScheduledDataLoaderRegistry algorithm is as follows :
- Nothing starts scheduled - some code must call
registry.dispatchAll()a first time - Then for every
DataLoaderin the registry- The
DispatchPredicateis called to test if the data loader should be dispatched- if it returns false then a task is scheduled to re-evaluate this specific dataloader in the near future
- If it returns true, then
dataLoader.dispatch()is called and the dataloader is not rescheduled again
- The
- The re-evaluation tasks are run periodically according to the
registry.getScheduleDuration()
When ticker mode is true the ScheduledDataLoaderRegistry algorithm is as follows:
- Nothing starts scheduled - some code must call
registry.dispatchAll()a first time - Then for every
DataLoaderin the registry- The
DispatchPredicateis called to test if the data loader should be dispatched- if it returns false then a task is scheduled to re-evaluate this specific dataloader in the near future
- If it returns true, then
dataLoader.dispatch()is called and a task is scheduled to re-evaluate this specific dataloader in the near future
- The
- The re-evaluation tasks are run periodically according to the
registry.getScheduleDuration()
- Facebook DataLoader Github repo
- Facebook DataLoader code walkthrough on YouTube
- Using DataLoader and GraphQL to batch requests
All your feedback and help to improve this project is very welcome. Please create issues for your bugs, ideas and enhancement requests, or better yet, contribute directly by creating a PR.
When reporting an issue, please add a detailed instruction, and if possible a code snippet or test that can be used as a reproducer of your problem.
When creating a pull request, please adhere to the current coding style where possible, and create tests with your code so it keeps providing an excellent test coverage level. PR's without tests may not be accepted unless they only deal with minor changes.
This library was originally written for use within a VertX world and it used the vertx-core Future classes to implement
itself. All the heavy lifting has been done by this project : vertx-dataloader
including the extensive testing (which itself came from Facebook).
This particular port was done to reduce the dependency on Vertx and to write a pure Java 8 implementation with no dependencies and also to use the more normative Java CompletableFuture.
vertx-core is not a lightweight library by any means so having a pure Java 8 implementation is very desirable.
This library is entirely inspired by the great works of Lee Byron and Nicholas Schrock from Facebook whom we would like to thank, and especially @leebyron for taking the time and effort to provide 100% coverage on the codebase. The original set of tests were also ported.
This project is licensed under the Apache Commons v2.0 license.
Copyright © 2016 Arnold Schrijver, 2017 Brad Baker and others contributors