AndrewJSchofield commented on code in PR #20953:
URL: https://github.com/apache/kafka/pull/20953#discussion_r2551080131
##########
clients/clients-integration-tests/src/test/java/org/apache/kafka/clients/consumer/ShareConsumerTest.java:
##########
@@ -3435,6 +3435,315 @@ public void
testSharePartitionLagOnShareCoordinatorMovement() {
}
}
+ @ClusterTest
+ public void testFetchWithThrottledDelivery() {
+ alterShareAutoOffsetReset("group1", "earliest");
+ try (Producer<byte[], byte[]> producer = createProducer();
+ ShareConsumer<byte[], byte[]> shareConsumer = createShareConsumer(
+ "group1",
+ Map.of(ConsumerConfig.SHARE_ACKNOWLEDGEMENT_MODE_CONFIG,
EXPLICIT))
+ ) {
+ // Produce a batch of 100 messages
+ for (int i = 0; i < 100; i++) {
+ ProducerRecord<byte[], byte[]> record = new
ProducerRecord<>(tp.topic(), tp.partition(), null, "key".getBytes(), ("Message
" + i).getBytes());
+ producer.send(record);
+ }
+ producer.flush();
+
+ shareConsumer.subscribe(List.of(tp.topic()));
+ // Fetch records in 5 iterations, each time acknowledging with
RELEASE. 5 is the default
+ // delivery limit hence we should see throttling from
Math.ceil(5/2) = 3 fetches.
+ int throttleDeliveryLimit = 3;
+ for (int i = 0; i < 5; i++) {
+ // Adjust expected fetch count based on throttling. If i <
throttleDeliveryLimit, we get full batch of 100.
+ // If i == 4 i.e. the last delivery, then we get 1 record.
+ // Otherwise, we get half the previous fetch count due to
throttling. In this case, 100 >> (i - throttleDeliveryLimit + 1) it is 50 for
i=3.
+ int expectedFetchCount = (i < throttleDeliveryLimit) ? 100 :
((i == 4) ? 1 : 50);
+ ConsumerRecords<byte[], byte[]> records =
waitedPoll(shareConsumer, 2500L, expectedFetchCount);
+ assertEquals(expectedFetchCount, records.count());
+
+ records.forEach(record -> shareConsumer.acknowledge(record,
AcknowledgeType.RELEASE));
+ Map<TopicIdPartition, Optional<KafkaException>> result =
shareConsumer.commitSync();
+ assertEquals(1, result.size());
+ assertEquals(Optional.empty(),
+ result.get(new TopicIdPartition(tpId, tp.partition(),
tp.topic())));
+ }
+
+ // Offset 0 has already reached the delivery limit hence shall be
archived.
+ // Offset 1 to 49 shall be in last deliver attempt and hence 1
record per poll.
Review Comment:
nit: delivery attempt, not deliver attempt
##########
clients/clients-integration-tests/src/test/java/org/apache/kafka/clients/consumer/ShareConsumerTest.java:
##########
@@ -3435,6 +3435,315 @@ public void
testSharePartitionLagOnShareCoordinatorMovement() {
}
}
+ @ClusterTest
+ public void testFetchWithThrottledDelivery() {
+ alterShareAutoOffsetReset("group1", "earliest");
+ try (Producer<byte[], byte[]> producer = createProducer();
+ ShareConsumer<byte[], byte[]> shareConsumer = createShareConsumer(
+ "group1",
+ Map.of(ConsumerConfig.SHARE_ACKNOWLEDGEMENT_MODE_CONFIG,
EXPLICIT))
+ ) {
+ // Produce a batch of 100 messages
+ for (int i = 0; i < 100; i++) {
+ ProducerRecord<byte[], byte[]> record = new
ProducerRecord<>(tp.topic(), tp.partition(), null, "key".getBytes(), ("Message
" + i).getBytes());
+ producer.send(record);
+ }
+ producer.flush();
+
+ shareConsumer.subscribe(List.of(tp.topic()));
+ // Fetch records in 5 iterations, each time acknowledging with
RELEASE. 5 is the default
+ // delivery limit hence we should see throttling from
Math.ceil(5/2) = 3 fetches.
+ int throttleDeliveryLimit = 3;
+ for (int i = 0; i < 5; i++) {
+ // Adjust expected fetch count based on throttling. If i <
throttleDeliveryLimit, we get full batch of 100.
+ // If i == 4 i.e. the last delivery, then we get 1 record.
+ // Otherwise, we get half the previous fetch count due to
throttling. In this case, 100 >> (i - throttleDeliveryLimit + 1) it is 50 for
i=3.
+ int expectedFetchCount = (i < throttleDeliveryLimit) ? 100 :
((i == 4) ? 1 : 50);
+ ConsumerRecords<byte[], byte[]> records =
waitedPoll(shareConsumer, 2500L, expectedFetchCount);
+ assertEquals(expectedFetchCount, records.count());
+
+ records.forEach(record -> shareConsumer.acknowledge(record,
AcknowledgeType.RELEASE));
+ Map<TopicIdPartition, Optional<KafkaException>> result =
shareConsumer.commitSync();
+ assertEquals(1, result.size());
+ assertEquals(Optional.empty(),
+ result.get(new TopicIdPartition(tpId, tp.partition(),
tp.topic())));
+ }
+
+ // Offset 0 has already reached the delivery limit hence shall be
archived.
+ // Offset 1 to 49 shall be in last deliver attempt and hence 1
record per poll.
+ validateExpectedRecordsInEachPollAndRelease(shareConsumer, 1, 50,
1);
+ // Delivery limit 4.
+ validateExpectedRecordsInEachPollAndRelease(shareConsumer, 50,
100, 50);
+ // Delivery limit 5.
+ validateExpectedRecordsInEachPollAndRelease(shareConsumer, 50,
100, 1);
+ // Next poll should not have any records as all records have
reached delivery limit.
+ ConsumerRecords<byte[], byte[]> records =
shareConsumer.poll(Duration.ofMillis(2500L));
+ assertTrue(records.isEmpty(), "Records should be empty as all
records have reached delivery limit. But received: " + records.count());
+ }
+ }
+
+ @ClusterTest(
+ serverProperties = {
+ @ClusterConfigProperty(key = "group.share.delivery.count.limit",
value = "10"),
+ }
+ )
+ public void
testFetchWithThrottledDeliveryBatchesWithIncreasedDeliveryLimit() {
+ alterShareAutoOffsetReset("group1", "earliest");
+ try (Producer<byte[], byte[]> producer = createProducer();
+ ShareConsumer<byte[], byte[]> shareConsumer = createShareConsumer(
+ "group1",
+ Map.of(ConsumerConfig.SHARE_ACKNOWLEDGEMENT_MODE_CONFIG,
EXPLICIT))
+ ) {
+ // Produce records in complete power of 2 to fully test the
throttling behavior.
+ int producedMessageCount = 512;
+ // Produce a batch of 512 messages
Review Comment:
I think it would be prudent to keep an eye out for flakiness here. I think
we are depending on a single batch being written which will be true if the
records are all sent within the linger delay.
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