kafka客户端执行流程和源码分析

先看下客户端的整体架构：

整个生产者客户端由2个线程协调运行，就是主线程和sender线程。在主线程中由Kafka Producer创建消息，然后通过拦截器、序列化器和分区器的作用后，缓存到消息累加器（也称为消息收集器），Sender负责从消息累加器获取消息并将其发送到Kafka中。

消息累加器主要用来缓存消息以便于sender线程可以批量发送，进而减少网络传输的资源消耗以提升性能。缓存大小是配置的，默认为32MB，如果生产速度过快，被阻塞最多60秒（配置的），就会抛异常。

主线程发送的消息会追加到RecoreAccumulator的双端队列中，在RecoreAccumulator对每个分区维护一个双端队列，内容就是ProducerBatch，Sender从队头获取消息，处理完后清除消息。

为了节省byteBuffer的创建和释放，在RecoreAccumulator内部有一个BufferPool，用来支持复用，不过只支持特定大小，默认为batch.size=16KB（可配置），其他大小的不会缓存到BufferPool。ProducerBatch的大小和batch.size有联系，如果创建消息没有大于batch.size，就会继续放入ProducerBatch中，否则就创建新的。

请求在Sender发往Kafka之前还会保存到InFlightRequests中，它的主要作用就是缓存了已经发送出去但是没有收到响应的请求，并且InFlightRequests提供许多管理方法，其中关键的就是限制最多缓存的请求数量--max.in.flight.requests.per.connection，默认为5.这个参数时每个连接最多只能缓存5个未响应的请求，超过该值就不能再向这个连接发送更多请求。

demo:

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("acks", "all");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
Producer producer = new KafkaProducer<>(props);
for (int i = 0; i < 100; i++)
     producer.send(new ProducerRecord("my-topic", Integer.toString(i), Integer.toString(i)));
producer.close();

接下来就看源码：

public Future send(ProducerRecord record, Callback callback) {
    // intercept the record, which can be potentially modified; this method does not throw exceptions
    ProducerRecord interceptedRecord = this.interceptors.onSend(record);
    return doSend(interceptedRecord, callback);
}

  private Future doSend(ProducerRecord record, Callback callback) {
        TopicPartition tp = null;
        try {
            throwIfProducerClosed();
            // first make sure the metadata for the topic is available
            long nowMs = time.milliseconds();
            ClusterAndWaitTime clusterAndWaitTime;
            try {
                clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), nowMs, maxBlockTimeMs);
            } catch (KafkaException e) {
                if (metadata.isClosed())
                    throw new KafkaException("Producer closed while send in progress", e);
                throw e;
            }
            nowMs += clusterAndWaitTime.waitedOnMetadataMs;
            long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);
            Cluster cluster = clusterAndWaitTime.cluster;
            byte[] serializedKey;
            try {
                serializedKey = keySerializer.serialize(record.topic(), record.headers(), record.key());
            } catch (ClassCastException cce) {
                throw new SerializationException("Can't convert key of class " + record.key().getClass().getName() +
                        " to class " + producerConfig.getClass(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG).getName() +
                        " specified in key.serializer", cce);
            }
            byte[] serializedValue;
            try {
                serializedValue = valueSerializer.serialize(record.topic(), record.headers(), record.value());
            } catch (ClassCastException cce) {
                throw new SerializationException("Can't convert value of class " + record.value().getClass().getName() +
                        " to class " + producerConfig.getClass(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG).getName() +
                        " specified in value.serializer", cce);
            }
            int partition = partition(record, serializedKey, serializedValue, cluster);
            tp = new TopicPartition(record.topic(), partition);

            setReadOnly(record.headers());
            Header[] headers = record.headers().toArray();

            int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),
                    compressionType, serializedKey, serializedValue, headers);
            ensureValidRecordSize(serializedSize);
            long timestamp = record.timestamp() == null ? nowMs : record.timestamp();
            if (log.isTraceEnabled()) {
                log.trace("Attempting to append record {} with callback {} to topic {} partition {}", record, callback, record.topic(), partition);
            }
            // producer callback will make sure to call both 'callback' and interceptor callback
            Callback interceptCallback = new InterceptorCallback<>(callback, this.interceptors, tp);

            if (transactionManager != null && transactionManager.isTransactional()) {
                transactionManager.failIfNotReadyForSend();
            }
            RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
                    serializedValue, headers, interceptCallback, remainingWaitMs, true, nowMs);

            if (result.abortForNewBatch) {
                int prevPartition = partition;
                partitioner.onNewBatch(record.topic(), cluster, prevPartition);
                partition = partition(record, serializedKey, serializedValue, cluster);
                tp = new TopicPartition(record.topic(), partition);
                if (log.isTraceEnabled()) {
                    log.trace("Retrying append due to new batch creation for topic {} partition {}. The old partition was {}", record.topic(), partition, prevPartition);
                }
                // producer callback will make sure to call both 'callback' and interceptor callback
                interceptCallback = new InterceptorCallback<>(callback, this.interceptors, tp);

                result = accumulator.append(tp, timestamp, serializedKey,
                    serializedValue, headers, interceptCallback, remainingWaitMs, false, nowMs);
            }

            if (transactionManager != null && transactionManager.isTransactional())
                transactionManager.maybeAddPartitionToTransaction(tp);

            if (result.batchIsFull || result.newBatchCreated) {
                log.trace("Waking up the sender since topic {} partition {} is either full or getting a new batch", record.topic(), partition);
                this.sender.wakeup();
            }
            return result.future;
            // handling exceptions and record the errors;
            // for API exceptions return them in the future,
            // for other exceptions throw directly
        } catch (ApiException e) {
            log.debug("Exception occurred during message send:", e);
            if (callback != null)
                callback.onCompletion(null, e);
            this.errors.record();
            this.interceptors.onSendError(record, tp, e);
            return new FutureFailure(e);
        } catch (InterruptedException e) {
            this.errors.record();
            this.interceptors.onSendError(record, tp, e);
            throw new InterruptException(e);
        } catch (KafkaException e) {
            this.errors.record();
            this.interceptors.onSendError(record, tp, e);
            throw e;
        } catch (Exception e) {
            // we notify interceptor about all exceptions, since onSend is called before anything else in this method
            this.interceptors.onSendError(record, tp, e);
            throw e;
        }
    }

这就是主线程部分，再看sender部分，先看sender初始化和启动部分

KafkaProducer(Map configs,
                  Serializer keySerializer,
                  Serializer valueSerializer,
                  ProducerMetadata metadata,
                  KafkaClient kafkaClient,
                  ProducerInterceptors interceptors,
                  Time time) {
        ProducerConfig config = new ProducerConfig(ProducerConfig.addSerializerToConfig(configs, keySerializer,
                valueSerializer));
        try {
            Map userProvidedConfigs = config.originals();
            this.producerConfig = config;
            this.time = time;

            String transactionalId = userProvidedConfigs.containsKey(ProducerConfig.TRANSACTIONAL_ID_CONFIG) ?
                    (String) userProvidedConfigs.get(ProducerConfig.TRANSACTIONAL_ID_CONFIG) : null;

            this.clientId = config.getString(ProducerConfig.CLIENT_ID_CONFIG);

            LogContext logContext;
            if (transactionalId == null)
                logContext = new LogContext(String.format("[Producer clientId=%s] ", clientId));
            else
                logContext = new LogContext(String.format("[Producer clientId=%s, transactionalId=%s] ", clientId, transactionalId));
            log = logContext.logger(KafkaProducer.class);
            log.trace("Starting the Kafka producer");

            Map metricTags = Collections.singletonMap("client-id", clientId);
            MetricConfig metricConfig = new MetricConfig().samples(config.getInt(ProducerConfig.METRICS_NUM_SAMPLES_CONFIG))
                    .timeWindow(config.getLong(ProducerConfig.METRICS_SAMPLE_WINDOW_MS_CONFIG), TimeUnit.MILLISECONDS)
                    .recordLevel(Sensor.RecordingLevel.forName(config.getString(ProducerConfig.METRICS_RECORDING_LEVEL_CONFIG)))
                    .tags(metricTags);
            List reporters = config.getConfiguredInstances(ProducerConfig.METRIC_REPORTER_CLASSES_CONFIG,
                    MetricsReporter.class,
                    Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId));
            JmxReporter jmxReporter = new JmxReporter();
            jmxReporter.configure(userProvidedConfigs);
            reporters.add(jmxReporter);
            MetricsContext metricsContext = new KafkaMetricsContext(JMX_PREFIX,
                    config.originalsWithPrefix(CommonClientConfigs.METRICS_CONTEXT_PREFIX));
            this.metrics = new Metrics(metricConfig, reporters, time, metricsContext);
            this.partitioner = config.getConfiguredInstance(ProducerConfig.PARTITIONER_CLASS_CONFIG, Partitioner.class);
            long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);
            if (keySerializer == null) {
                this.keySerializer = config.getConfiguredInstance(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
                                                                                         Serializer.class);
                this.keySerializer.configure(config.originals(), true);
            } else {
                config.ignore(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG);
                this.keySerializer = keySerializer;
            }
            if (valueSerializer == null) {
                this.valueSerializer = config.getConfiguredInstance(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
                                                                                           Serializer.class);
                this.valueSerializer.configure(config.originals(), false);
            } else {
                config.ignore(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG);
                this.valueSerializer = valueSerializer;
            }

            // load interceptors and make sure they get clientId
            userProvidedConfigs.put(ProducerConfig.CLIENT_ID_CONFIG, clientId);
            ProducerConfig configWithClientId = new ProducerConfig(userProvidedConfigs, false);
            List> interceptorList = (List) configWithClientId.getConfiguredInstances(
                    ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, ProducerInterceptor.class);
            if (interceptors != null)
                this.interceptors = interceptors;
            else
                this.interceptors = new ProducerInterceptors<>(interceptorList);
            ClusterResourceListeners clusterResourceListeners = configureClusterResourceListeners(keySerializer,
                    valueSerializer, interceptorList, reporters);
            this.maxRequestSize = config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG);
            this.totalMemorySize = config.getLong(ProducerConfig.BUFFER_MEMORY_CONFIG);
            this.compressionType = CompressionType.forName(config.getString(ProducerConfig.COMPRESSION_TYPE_CONFIG));

            this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
            int deliveryTimeoutMs = configureDeliveryTimeout(config, log);

            this.apiVersions = new ApiVersions();
            this.transactionManager = configureTransactionState(config, logContext);
            this.accumulator = new RecordAccumulator(logContext,
                    config.getInt(ProducerConfig.BATCH_SIZE_CONFIG),
                    this.compressionType,
                    lingerMs(config),
                    retryBackoffMs,
                    deliveryTimeoutMs,
                    metrics,
                    PRODUCER_METRIC_GROUP_NAME,
                    time,
                    apiVersions,
                    transactionManager,
                    new BufferPool(this.totalMemorySize, config.getInt(ProducerConfig.BATCH_SIZE_CONFIG), metrics, time, PRODUCER_METRIC_GROUP_NAME));

            List addresses = ClientUtils.parseAndValidateAddresses(
                    config.getList(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG),
                    config.getString(ProducerConfig.CLIENT_DNS_LOOKUP_CONFIG));
            if (metadata != null) {
                this.metadata = metadata;
            } else {
                this.metadata = new ProducerMetadata(retryBackoffMs,
                        config.getLong(ProducerConfig.METADATA_MAX_AGE_CONFIG),
                        config.getLong(ProducerConfig.METADATA_MAX_IDLE_CONFIG),
                        logContext,
                        clusterResourceListeners,
                        Time.SYSTEM);
                this.metadata.bootstrap(addresses);
            }
            this.errors = this.metrics.sensor("errors");
            
            
            this.sender = newSender(logContext, kafkaClient, this.metadata);
            String ioThreadName = NETWORK_THREAD_PREFIX + " | " + clientId;
            this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
            this.ioThread.start();
            
            
            
            config.logUnused();
            AppInfoParser.registerAppInfo(JMX_PREFIX, clientId, metrics, time.milliseconds());
            log.debug("Kafka producer started");
        } catch (Throwable t) {
            // call close methods if internal objects are already constructed this is to prevent resource leak. see KAFKA-2121
            close(Duration.ofMillis(0), true);
            // now propagate the exception
            throw new KafkaException("Failed to construct kafka producer", t);
        }
    }
    
.......

Sender newSender(LogContext logContext, KafkaClient kafkaClient, ProducerMetadata metadata) {
        int maxInflightRequests = configureInflightRequests(producerConfig);
        int requestTimeoutMs = producerConfig.getInt(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
        ChannelBuilder channelBuilder = ClientUtils.createChannelBuilder(producerConfig, time, logContext);
        ProducerMetrics metricsRegistry = new ProducerMetrics(this.metrics);
        Sensor throttleTimeSensor = Sender.throttleTimeSensor(metricsRegistry.senderMetrics);
        KafkaClient client = kafkaClient != null ? kafkaClient : new NetworkClient(
                new Selector(producerConfig.getLong(ProducerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG),
                        this.metrics, time, "producer", channelBuilder, logContext),
                metadata,
                clientId,
                maxInflightRequests,
                producerConfig.getLong(ProducerConfig.RECONNECT_BACKOFF_MS_CONFIG),
                producerConfig.getLong(ProducerConfig.RECONNECT_BACKOFF_MAX_MS_CONFIG),
                producerConfig.getInt(ProducerConfig.SEND_BUFFER_CONFIG),
                producerConfig.getInt(ProducerConfig.RECEIVE_BUFFER_CONFIG),
                requestTimeoutMs,
                ClientDnsLookup.forConfig(producerConfig.getString(ProducerConfig.CLIENT_DNS_LOOKUP_CONFIG)),
                time,
                true,
                apiVersions,
                throttleTimeSensor,
                logContext);
        short acks = configureAcks(producerConfig, log);
        return new Sender(logContext,
                client,
                metadata,
                this.accumulator,
                maxInflightRequests == 1,
                producerConfig.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),
                acks,
                producerConfig.getInt(ProducerConfig.RETRIES_CONFIG),
                metricsRegistry.senderMetrics,
                time,
                requestTimeoutMs,
                producerConfig.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG),
                this.transactionManager,
                apiVersions);
    }

在看下Sender的run方法

public void run() {
        log.debug("Starting Kafka producer I/O thread.");

        // main loop, runs until close is called
        while (running) {
            try {
                runOnce();
            } catch (Exception e) {
                log.error("Uncaught error in kafka producer I/O thread: ", e);
            }
        }

        log.debug("Beginning shutdown of Kafka producer I/O thread, sending remaining records.");

        // okay we stopped accepting requests but there may still be
        // requests in the transaction manager, accumulator or waiting for acknowledgment,
        // wait until these are completed.
        while (!forceClose && ((this.accumulator.hasUndrained() || this.client.inFlightRequestCount() > 0) || hasPendingTransactionalRequests())) {
            try {
                runOnce();
            } catch (Exception e) {
                log.error("Uncaught error in kafka producer I/O thread: ", e);
            }
        }

        // Abort the transaction if any commit or abort didn't go through the transaction manager's queue
        while (!forceClose && transactionManager != null && transactionManager.hasOngoingTransaction()) {
            if (!transactionManager.isCompleting()) {
                log.info("Aborting incomplete transaction due to shutdown");
                transactionManager.beginAbort();
            }
            try {
                runOnce();
            } catch (Exception e) {
                log.error("Uncaught error in kafka producer I/O thread: ", e);
            }
        }

        if (forceClose) {
            // We need to fail all the incomplete transactional requests and batches and wake up the threads waiting on
            // the futures.
            if (transactionManager != null) {
                log.debug("Aborting incomplete transactional requests due to forced shutdown");
                transactionManager.close();
            }
            log.debug("Aborting incomplete batches due to forced shutdown");
            this.accumulator.abortIncompleteBatches();
        }
        try {
            this.client.close();
        } catch (Exception e) {
            log.error("Failed to close network client", e);
        }

        log.debug("Shutdown of Kafka producer I/O thread has completed.");
    }

void runOnce() {
        if (transactionManager != null) {
            try {
                transactionManager.maybeResolveSequences();

                // do not continue sending if the transaction manager is in a failed state
                if (transactionManager.hasFatalError()) {
                    RuntimeException lastError = transactionManager.lastError();
                    if (lastError != null)
                        maybeAbortBatches(lastError);
                    client.poll(retryBackoffMs, time.milliseconds());
                    return;
                }

                // Check whether we need a new producerId. If so, we will enqueue an InitProducerId
                // request which will be sent below
                transactionManager.bumpIdempotentEpochAndResetIdIfNeeded();

                if (maybeSendAndPollTransactionalRequest()) {
                    return;
                }
            } catch (AuthenticationException e) {
                // This is already logged as error, but propagated here to perform any clean ups.
                log.trace("Authentication exception while processing transactional request", e);
                transactionManager.authenticationFailed(e);
            }
        }

        long currentTimeMs = time.milliseconds();
        long pollTimeout = sendProducerData(currentTimeMs);
        client.poll(pollTimeout, currentTimeMs);
    }

接下来看下sendProducerData：

private long sendProducerData(long now) {
        Cluster cluster = metadata.fetch();
        // get the list of partitions with data ready to send
        RecordAccumulator.ReadyCheckResult result = this.accumulator.ready(cluster, now);

        // if there are any partitions whose leaders are not known yet, force metadata update
        if (!result.unknownLeaderTopics.isEmpty()) {
            // The set of topics with unknown leader contains topics with leader election pending as well as
            // topics which may have expired. Add the topic again to metadata to ensure it is included
            // and request metadata update, since there are messages to send to the topic.
            for (String topic : result.unknownLeaderTopics)
                this.metadata.add(topic, now);

            log.debug("Requesting metadata update due to unknown leader topics from the batched records: {}",
                result.unknownLeaderTopics);
            this.metadata.requestUpdate();
        }

        // remove any nodes we aren't ready to send to
        Iterator iter = result.readyNodes.iterator();
        long notReadyTimeout = Long.MAX_VALUE;
        while (iter.hasNext()) {
            Node node = iter.next();
            if (!this.client.ready(node, now)) {
                iter.remove();
                notReadyTimeout = Math.min(notReadyTimeout, this.client.pollDelayMs(node, now));
            }
        }

        // create produce requests
        Map> batches = this.accumulator.drain(cluster, result.readyNodes, this.maxRequestSize, now);
        addToInflightBatches(batches);
        if (guaranteeMessageOrder) {
            // Mute all the partitions drained
            for (List batchList : batches.values()) {
                for (ProducerBatch batch : batchList)
                    this.accumulator.mutePartition(batch.topicPartition);
            }
        }

        accumulator.resetNextBatchExpiryTime();
        List expiredInflightBatches = getExpiredInflightBatches(now);
        List expiredBatches = this.accumulator.expiredBatches(now);
        expiredBatches.addAll(expiredInflightBatches);

        // Reset the producer id if an expired batch has previously been sent to the broker. Also update the metrics
        // for expired batches. see the documentation of @TransactionState.resetIdempotentProducerId to understand why
        // we need to reset the producer id here.
        if (!expiredBatches.isEmpty())
            log.trace("Expired {} batches in accumulator", expiredBatches.size());
        for (ProducerBatch expiredBatch : expiredBatches) {
            String errorMessage = "Expiring " + expiredBatch.recordCount + " record(s) for " + expiredBatch.topicPartition
                + ":" + (now - expiredBatch.createdMs) + " ms has passed since batch creation";
            failBatch(expiredBatch, -1, NO_TIMESTAMP, new TimeoutException(errorMessage), false);
            if (transactionManager != null && expiredBatch.inRetry()) {
                // This ensures that no new batches are drained until the current in flight batches are fully resolved.
                transactionManager.markSequenceUnresolved(expiredBatch);
            }
        }
        sensors.updateProduceRequestMetrics(batches);

        // If we have any nodes that are ready to send + have sendable data, poll with 0 timeout so this can immediately
        // loop and try sending more data. Otherwise, the timeout will be the smaller value between next batch expiry
        // time, and the delay time for checking data availability. Note that the nodes may have data that isn't yet
        // sendable due to lingering, backing off, etc. This specifically does not include nodes with sendable data
        // that aren't ready to send since they would cause busy looping.
        long pollTimeout = Math.min(result.nextReadyCheckDelayMs, notReadyTimeout);
        pollTimeout = Math.min(pollTimeout, this.accumulator.nextExpiryTimeMs() - now);
        pollTimeout = Math.max(pollTimeout, 0);
        if (!result.readyNodes.isEmpty()) {
            log.trace("Nodes with data ready to send: {}", result.readyNodes);
            // if some partitions are already ready to be sent, the select time would be 0;
            // otherwise if some partition already has some data accumulated but not ready yet,
            // the select time will be the time difference between now and its linger expiry time;
            // otherwise the select time will be the time difference between now and the metadata expiry time;
            pollTimeout = 0;
        }
        sendProduceRequests(batches, now);
        return pollTimeout;
    }

再看ready方法：

public ReadyCheckResult ready(Cluster cluster, long nowMs) {
        Set readyNodes = new HashSet<>();
        long nextReadyCheckDelayMs = Long.MAX_VALUE;
        Set unknownLeaderTopics = new HashSet<>();

        boolean exhausted = this.free.queued() > 0;
        for (Map.Entry> entry : this.batches.entrySet()) {
            Deque deque = entry.getValue();
            synchronized (deque) {
                // When producing to a large number of partitions, this path is hot and deques are often empty.
                // We check whether a batch exists first to avoid the more expensive checks whenever possible.
                ProducerBatch batch = deque.peekFirst();
                if (batch != null) {
                    TopicPartition part = entry.getKey();
                    Node leader = cluster.leaderFor(part);
                    if (leader == null) {
                        // This is a partition for which leader is not known, but messages are available to send.
                        // Note that entries are currently not removed from batches when deque is empty.
                        unknownLeaderTopics.add(part.topic());
                    } else if (!readyNodes.contains(leader) && !isMuted(part)) {
                        long waitedTimeMs = batch.waitedTimeMs(nowMs);
                        boolean backingOff = batch.attempts() > 0 && waitedTimeMs < retryBackoffMs;
                        long timeToWaitMs = backingOff ? retryBackoffMs : lingerMs;
                        boolean full = deque.size() > 1 || batch.isFull();
                        boolean expired = waitedTimeMs >= timeToWaitMs;
                        boolean sendable = full || expired || exhausted || closed || flushInProgress();
                        if (sendable && !backingOff) {
                            readyNodes.add(leader);
                        } else {
                            long timeLeftMs = Math.max(timeToWaitMs - waitedTimeMs, 0);
                            // Note that this results in a conservative estimate since an un-sendable partition may have
                            // a leader that will later be found to have sendable data. However, this is good enough
                            // since we'll just wake up and then sleep again for the remaining time.
                            nextReadyCheckDelayMs = Math.min(timeLeftMs, nextReadyCheckDelayMs);
                        }
                    }
                }
            }
        }
        return new ReadyCheckResult(readyNodes, nextReadyCheckDelayMs, unknownLeaderTopics);
    }

基本上上面主流程就都过了一边，如果再看其他细节点，就自行了解吧！