gaurav-narula commented on code in PR #15836:
URL: https://github.com/apache/kafka/pull/15836#discussion_r1590274476
##########
core/src/main/scala/kafka/server/FetchSession.scala:
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@@ -787,9 +803,37 @@ class FetchSessionCache(private val maxEntries: Int,
}
}
}
+object FetchSessionCache {
+ private[server] val metricsGroup = new
KafkaMetricsGroup(classOf[FetchSessionCache])
+ private val counter = new AtomicLong(0)
+}
+
+class FetchSessionCache(private val cacheShards: Seq[FetchSessionCacheShard]) {
+ // Set up metrics.
+
FetchSessionCache.metricsGroup.newGauge(FetchSession.NUM_INCREMENTAL_FETCH_SESSIONS,
() => cacheShards.map(_.size).sum)
+
FetchSessionCache.metricsGroup.newGauge(FetchSession.NUM_INCREMENTAL_FETCH_PARTITIONS_CACHED,
() => cacheShards.map(_.totalPartitions).sum)
+
+ def getCacheShard(sessionId: Int): FetchSessionCacheShard = {
+ val shard = sessionId / cacheShards.head.sessionIdRange
+ cacheShards(shard)
+ }
+
+ // Returns the shard in round-robin
+ def getNextCacheShard: FetchSessionCacheShard = {
+ val shardNum = (FetchSessionCache.counter.getAndIncrement() % size).toInt
Review Comment:
I used `AtomicLong` to practically rule out an overflow but found
`Utils.toPositive` which is used by `RoundRobinPartitioner` :) Updated to use
an `AtomicInteger` and also added some test to ensure round-robin allocations.
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