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aeon_receiver
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完善redis消费

jwy_csdj
review512jwy@163.com 2 days ago
parent
1f7338678d
commit
f1c6fe4aac
1 changed files with 150 additions and 82 deletions
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  1. 202
      src/main/java/com/techsor/datacenter/receiver/listener/RedisStreamConsumer.java

202
src/main/java/com/techsor/datacenter/receiver/listener/RedisStreamConsumer.java
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@ -11,12 +11,13 @@ import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.domain.Range;
import org.springframework.data.redis.connection.stream.*;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.core.RedisCallback;
import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.scheduling.concurrent.CustomizableThreadFactory;
import org.springframework.stereotype.Component;
import java.net.InetAddress;
import java.nio.charset.StandardCharsets;
import java.time.Duration;
import java.util.List;
import java.util.Map;
@ -37,6 +38,19 @@ public class RedisStreamConsumer {
private static final Gson gson = new Gson();
/**
* 普通业务 Stream 最大保留消息数。
* Redis Stream 中，即使消息已经 ACK，也不会自动删除，如果不进行 Trim，Stream 会一直增长，最终占满 Redis 内存。
*/
private static final long STREAM_MAX_LEN = 100_000L; //100_000L;
/**
* 死信队列（DLQ）最大保留消息数。
* DLQ 中保存的是处理失败的消息，一般数量远少于正常 Stream，因此可以设置得更小。
*/
private static final long DLQ_MAX_LEN = 10_000L;
private static final int PARTITIONS = 16;
private static final String STREAM_PREFIX = "aeon_tcp_stream_";
private static final String GROUP = "aeon_tcp_stream_consumer_group";
@ -48,7 +62,7 @@ public class RedisStreamConsumer {
/**
* 读取 Pending 消息和 reclaim 时 批量处理消息数
*/
private static final int BATCH_SIZE = 50;
private static final int BATCH_SIZE = 200;
private volatile boolean running = true;
private String consumerName;
@ -56,7 +70,7 @@ public class RedisStreamConsumer {
/**
* 接收消息总数
*/
private final LongAdder receiveCounter = new LongAdder();
private final LongAdder processSubmitCounter = new LongAdder();
private final ExecutorService consumerExecutor =
Executors.newFixedThreadPool(PARTITIONS, new CustomizableThreadFactory("redis-stream-consumer-"));
@ -90,6 +104,18 @@ public class RedisStreamConsumer {
new CustomizableThreadFactory("redis-stats-")
);
/**
* 定时执行 Stream Trim 的线程池。
* 单线程即可，因为：
* 1、Trim 操作频率很低（例如每 30 分钟一次）
* 2、每次只是循环处理 16 个 Stream + 1 个 DLQ
* 3、不需要并发执行
*/
private final ScheduledExecutorService trimScheduler =
Executors.newSingleThreadScheduledExecutor(
new CustomizableThreadFactory("redis-trim-")
);
@PostConstruct
public void start() throws Exception {
consumerName = buildConsumerName();
@ -104,8 +130,6 @@ public class RedisStreamConsumer {
int partition = i;
consumerExecutor.submit(() -> {
// 启动时处理未 ack 消息
recoverPending(stream);
// 主消费循环
consumeLoop(stream, partition);
});
@ -121,9 +145,16 @@ public class RedisStreamConsumer {
// 定时统计接收次数
statsScheduler.scheduleAtFixedRate(() -> {
long count = receiveCounter.sum();
long count = processSubmitCounter.sum();
log.info("[F10] RedisStreamConsumer receive count={}", count);
}, 10, 10, TimeUnit.SECONDS);
trimScheduler.scheduleWithFixedDelay(
this::trimStreams,
10,
30,
TimeUnit.MINUTES
);
}
private String buildConsumerName() throws Exception {
@ -163,8 +194,20 @@ public class RedisStreamConsumer {
private void createGroup(String stream) {
try {
redisTemplate.opsForStream()
.createGroup(stream, ReadOffset.from("0-0"), GROUP);
// redisTemplate.opsForStream()
// .createGroup(stream, ReadOffset.from("0-0"), GROUP);
redisTemplate.execute((RedisCallback<Object>) connection -> {
connection.execute(
"XGROUP",
"CREATE".getBytes(StandardCharsets.UTF_8),
stream.getBytes(StandardCharsets.UTF_8),
GROUP.getBytes(StandardCharsets.UTF_8),
"0-0".getBytes(StandardCharsets.UTF_8),
"MKSTREAM".getBytes(StandardCharsets.UTF_8)
);
return null;
});
log.info("create group success stream={}", stream);
} catch (Exception e) {
Throwable cause = e.getCause();
@ -177,48 +220,6 @@ public class RedisStreamConsumer {
}
}
/**
* 启动时恢复未 ack 消息
*/
private void recoverPending(String stream) {
try {
// 分批次处理 Pending
while (true) {
PendingMessages pending = redisTemplate.opsForStream()
.pending(stream, GROUP, Range.unbounded(), BATCH_SIZE);
if (pending == null || pending.isEmpty()) break;
for (PendingMessage msg : pending) {
// claim 未 ack 消息
List<MapRecord<String, Object, Object>> claimed =
redisTemplate.opsForStream().claim(
stream,
GROUP,
consumerName,
IDLE_TIMEOUT,
msg.getId()
);
for (MapRecord<String, Object, Object> record : claimed) {
receiveCounter.increment();
// workerExecutor.submit(() -> process(record, stream, msg.getTotalDeliveryCount()));
submitTask(() ->
process(record, stream, msg.getTotalDeliveryCount())
);
}
}
// 如果少于 BATCH_SIZE，说明已处理完
if (pending.size() < BATCH_SIZE) break;
}
log.info("recoverPending stream={}", stream);
} catch (Exception e) {
log.error("recoverPending error stream={}", stream, e);
}
}
private void consumeLoop(String stream, int partition) {
while (running) {
try {
@ -233,7 +234,7 @@ public class RedisStreamConsumer {
if (messages == null || messages.isEmpty()) continue;
receiveCounter.add(messages.size());
processSubmitCounter.add(messages.size());
for (MapRecord<String, Object, Object> message : messages) {
// workerExecutor.submit(() -> process(message, stream, 1));
@ -323,49 +324,108 @@ public class RedisStreamConsumer {
*/
private void reclaimIdleMessages() {
if (workerExecutor.getQueue().size() > WORK_QUEUE_LIMIT) {
log.warn("skip reclaim, worker queue busy");
log.warn("skip reclaim, worker queue busy, queueSize={}", workerExecutor.getQueue().size());
return;
}
for (int i = 1; i <= PARTITIONS; i++) {
String stream = STREAM_PREFIX + i;
try {
while (true) {
PendingMessages pending =
redisTemplate.opsForStream().pending(stream, GROUP, Range.unbounded(), BATCH_SIZE);
if (pending == null || pending.isEmpty()) break;
if (pending == null || pending.isEmpty()) {
continue;
}
for (PendingMessage p : pending) {
if (p.getElapsedTimeSinceLastDelivery().compareTo(IDLE_TIMEOUT) < 0) continue;
if (p.getElapsedTimeSinceLastDelivery().compareTo(IDLE_TIMEOUT) < 0) {
continue;
}
List<MapRecord<String,Object,Object>> claimed =
redisTemplate.opsForStream().claim(
redisTemplate.opsForStream()
.claim(
stream,
GROUP,
consumerName,
IDLE_TIMEOUT,
p.getId()
);
if (claimed == null || claimed.isEmpty()) {
continue;
}
for(MapRecord<String, Object, Object> r : claimed){
receiveCounter.increment();
processSubmitCounter.increment();
// workerExecutor.submit(() -> process(r, stream, p.getTotalDeliveryCount()));
submitTask(() ->
process(r, stream, p.getTotalDeliveryCount())
);
}
}
} catch(Exception e){
log.error("reclaim error stream={}",stream,e
);
}
}
}
if (pending.size() < BATCH_SIZE) break;
/**
* 定时裁剪所有 Stream。
* 当前系统包括：
*
* 业务 Stream：
* aeon_tcp_stream_1
* aeon_tcp_stream_2
* ...
* aeon_tcp_stream_16
*
* 死信队列：
* aeon_tcp_stream_dlq
*
* 主 Stream 和 DLQ 分开裁剪，
* 可以使用不同的最大保留数量。
*/
private void trimStreams() {
// 裁剪所有业务 Stream
for (int i = 1; i <= PARTITIONS; i++) {
trimStream(STREAM_PREFIX + i, STREAM_MAX_LEN);
}
} catch (Exception e) {
log.error("[F10] reclaim error stream={}", stream, e);
// 裁剪死信队列
trimStream(DLQ_STREAM, DLQ_MAX_LEN);
}
/**
* 裁剪指定 Stream。
* Redis 命令：XTRIM stream MAXLEN ~ maxLen
* 例如：XTRIM aeon_tcp_stream_1 MAXLEN ~ 100000 表示：当 Stream 超过约10万条时，Redis 自动删除最旧的数据。
*
* "~" 表示 Approximate Trim（近似裁剪）。
*
* 为什么不用精确裁剪？
* 精确裁剪：MAXLEN 1000000，Redis 每次都会精确删除到1000000条，CPU 消耗较高。
* 近似裁剪：MAXLEN ~ 1000000，Redis 会在内部合适的时机进行裁剪，性能远高于精确裁剪。
*
* @param streamName Stream 名称
* @param maxLen 最大保留消息数量
*/
private void trimStream(String streamName, long maxLen) {
try {
redisTemplate.execute((RedisCallback<Void>) connection -> {
connection.streamCommands().xTrim(
streamName.getBytes(StandardCharsets.UTF_8),
maxLen,
true
);
return null;
});
log.info("Trim stream success, stream={}, maxLen={}", streamName, maxLen);
} catch (Exception e) {
log.error("Trim stream failed, stream={}", streamName, e);
}
}
public long getReceiveCount() {
return receiveCounter.sum();
return processSubmitCounter.sum();
}
private void sleep(long ms) {
@ -385,11 +445,13 @@ public class RedisStreamConsumer {
workerExecutor.shutdown();
reclaimScheduler.shutdown();
statsScheduler.shutdown();
trimScheduler.shutdown();
boolean consumerTerminated = consumerExecutor.awaitTermination(10, TimeUnit.SECONDS);
boolean workerTerminated = workerExecutor.awaitTermination(10, TimeUnit.SECONDS);
boolean reclaimTerminated = reclaimScheduler.awaitTermination(10, TimeUnit.SECONDS);
boolean statsTerminated = statsScheduler.awaitTermination(10, TimeUnit.SECONDS);
boolean trimTerminated = trimScheduler.awaitTermination(10, TimeUnit.SECONDS );
if (!consumerTerminated) {
consumerExecutor.shutdownNow();
@ -403,11 +465,17 @@ public class RedisStreamConsumer {
if (!statsTerminated) {
statsScheduler.shutdownNow();
}
if (!trimTerminated) {
trimScheduler.shutdownNow();
}
log.info("RedisStreamConsumer shutdown done, consumerTerminated={}, workerTerminated={}, reclaimTerminated={}, statsTerminated={}",
log.info(
"RedisStreamConsumer shutdown done, consumerTerminated={}, workerTerminated={}, reclaimTerminated={}, statsTerminated={}, trimTerminated={}",
consumerTerminated,
workerTerminated,
reclaimTerminated,
statsTerminated);
statsTerminated,
trimTerminated
);
}
}
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