Scenario: Read from one database using an ActorPublisher, write to another
database using a subscriber.
I expect the reads to be much faster than the writes, so we need to slow
down the reads at some threshold. Growing an unbounded queue of data, will
simply OOM. The below works for small datasets. With large datasets, the
gap between read-write becomes enormous and so OOM.
My ActorPublisher:
class ScrollPublisher(clientFrom: ElasticClient, config: Config) extends
ActorPublisher[SearchHits] {
val logger = Logger(LoggerFactory.getLogger(this.getClass))
var readCount = 0
var processing = false
import akka.stream.actor.ActorPublisherMessage._
@volatile var executeQuery = () => clientFrom.execute {
search in config.indexFrom / config.mapping scroll "30m" limit
config.scrollSize
}
def nextHits(): Unit = {
if (!processing) {
processing = true
val future = executeQuery()
future.foreach {
response =>
processing = false
if (response.getHits.hits.nonEmpty) {
logger.info("Fetched: \t" + response.getHits.getHits.length + "
documents in\t" + response.getTookInMillis + "ms.")
readCount += response.getHits.getHits.length
logger.info("Total Fetched:\t" + readCount)
if (isActive && totalDemand > 0) {
executeQuery = () => clientFrom.execute {
searchScroll(response.getScrollId).keepAlive("30m")
}
nextHits()
onNext(response.getHits) // sends elements to the stream
}
} else {
onComplete()
}
}
future.onFailure {
case t =>
processing = false
throw t
}
}
}
def receive = {
case Request(cnt) =>
logger.info("ActorPublisher Received: \t" + cnt)
if (isActive && totalDemand > 0) {
nextHits()
}
case Cancel =>
context.stop(self)
case _ =>
}
}
Enter code here...
Source declaration:
// SearchHits Akka Stream Source
val documentSource = Source.actorPublisher[SearchHits](Props(new
ScrollPublisher(clientFrom, config))).map {
case searchHits =>
searchHits.getHits
}
My Sink, which performs an asynch write to the new database:
documentSource.buffer(16, OverflowStrategy.backpressure).runWith(Sink.foreach {
searchHits =>
Thread.sleep(1000)
totalRec += searchHits.size
logger.info("\t\t\tRECEIVED: " + searchHits.size + " \t\t\t TOTAL RECEIVED:
"+ totalRec)
val bulkIndexes = searchHits.map(hit => (hit.`type`, hit.id,
hit.sourceAsString())).collect {
case (typ, _id, source) =>
index into config.indexTo / config.mapping id _id -> typ doc
JsonDocumentSource(source)
}
val future = clientTo.execute {
bulk(
bulkIndexes
)
}
The sleep is put in there to simulate lag for local development. I've tried
changing values for the buffer, and the max/initial values for the
materializer, and still it seems to ignore back pressure.
Is there a logic flaw in this code?
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