63 celcius

A topic with three partitions that a consumer group with two consumers should read. Hence, one possible division is that the first consumer gets partitions one and two, and the second consumer only gets partition three.In the KIP-500 update, Kafka introduced a new consensus algorithm named KRaft. As we add consumers to a group or remove consumers from a group, KRaft rebalances the partitions between the remaining consumers proportionally. Thus, it guarantees that there’s no partition without a consumer assigned.5. Configure the ApplicationIn this section, we’ll create the classes to configure a topic, consumer, and producer service.5.1. Topic ConfigurationFirst, let’s create the configuration class for our topic:@Configurationpublic class KafkaTopicConfig { @Value(value = "${spring.kafka.bootstrap-servers}") private String bootstrapAddress; public KafkaAdmin kafkaAdmin() { Map configs = new HashMap(); configs.put(AdminClientConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapAddress); return new KafkaAdmin(configs); } public NewTopic celciusTopic() { return TopicBuilder.name("celcius-scale-topic") .partitions(2) .build(); }}The KafkaTopicConfig class injects two Spring beans. The KafkaAdmin bean initiates the Kafka cluster with the network address it should run, while the NewTopic bean creates a topic named celcius-scale-topic with one partition.5.2. Consumer and Producer ConfigurationWe need the necessary classes to inject the producer and consumer configurations for our topic.First, let’s create the producer configuration class:public class KafkaProducerConfig { @Value(value = "${spring.kafka.bootstrap-servers}") private String bootstrapAddress; @Bean public ProducerFactory producerFactory() { Map configProps = new HashMap(); configProps.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapAddress); configProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class); configProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, DoubleSerializer.class); return new DefaultKafkaProducerFactory(configProps); } @Bean public KafkaTemplate kafkaTemplate() { return new KafkaTemplate(producerFactory()); }}The KafkaProducerConfig injects two Spring beans. The ProducerFactory tells how Kafka is supposed to serialize events and which server the producer should listen to. The KafkaTemplate will be used in the consumer service class to create events.5.3. Kafka Producer ServiceFinally, after the initial configurations, we can create the driver application. Let’s first create the producer application:public class ThermostatService { private final KafkaTemplate kafkaTemplate; public ThermostatService(KafkaTemplate kafkaTemplate) { this.kafkaTemplate = kafkaTemplate; } public void measureCelsiusAndPublish(int numMeasurements) { new Random().doubles(25, 35) .limit(numMeasurements) .forEach(tmp -> { kafkaTemplate.send("celcius-scale-topic", tmp); }); }}The ThermostatService contains a single method named measureCelsiusAndPublish. This method produces random temperature measurements in the range [25, 35] and publishes to the celsius-scale-topic Kafka topic. To achieve this, we use

You are using an out of date browser. It may not display this or other websites correctly.You should upgrade or use an alternative browser. #1 Hello, so I bought a new cooler and under 100% load my CPU reached a maximum of 67 degrees celcius. Is this safe? When idle it can go down to 25 degrees and maximum 30-40 (don't remember well). When I play bf1 with fps cap to 60 fps (which makes me use 50% or so of my cpu) the temps are at about 50 or upper 40's. Are these temperatures safe?I used real temp GT to measure the hotest core and used prime95 to stress test my CPU. I ran the test for 25 minutes. I should also mention that most of the time, like 75% of the time, the hotest core is bellow 57 degrees. Only from time to time it reaches 60 and goes up to 63 then goes down more (I don't know how it red 67 degrees) CTurbo Jan 20, 2017 Hahahahahahaha yeah that's obviously supposed to be Celsius. Wow sorry. You're probably fine. As long as it's not in the high 60s for a prolonged period of time. Worst case scenario you'll have to allow the fan to run faster. Aug 27, 2013 22,421 14 84,965 #2 ~70F degrees is the max safe operating temp for that Xeon. 67F is a little high for continuous use, but I wouldn't worry about it if it was just a quick peak. What cooler are you using? Those temps are a little higher than they should be. Are you sure the heatsink is mounted correctly with thermal paste? #3 ~70F degrees is the max safe operating temp for that Xeon. 67F is a little high for continuous use, but I wouldn't worry about it if it was just a quick peak. What cooler are you using? Those temps are a little higher than they should be. Are you sure the heatsink is mounted correctly with thermal paste? I am quite sure my heatsink is properly mounted and I believe I have applied the thermal paste the right way because I have a huge improvement over the intel stock cooler and here's my cooler: also used an adapter for noise reduction (I think it's just a 3 pin fan resistor) from Fractal Design Silent Series R2 Black/White Silence-Optimized 120mm Case Fan. So the RPM

The doubles() method of the Random class to create a stream of random numbers. Then, we publish the event using the send() method of kafkaTemplate.6. Producing and Consuming EventsIn this section, we’ll see how to configure a Kafka consumer to read events from the topic using an embedded Kafka broker.6.1. Create the Consumer ServiceTo consume events, we need one or more consumer classes. Let’s create one consumer of the celcius-scale-topic:@Servicepublic class TemperatureConsumer { Map> consumedRecords = new ConcurrentHashMap(); @KafkaListener(topics = "celcius-scale-topic", groupId = "group-1") public void consumer1(ConsumerRecord consumerRecord) { trackConsumedPartitions("consumer-1", consumerRecord.partition()); } private void trackConsumedPartitions(String consumerName, int partitionNumber) { consumedRecords.computeIfAbsent(consumerName, k -> new HashSet()); consumedRecords.computeIfPresent(consumerName, (k, v) -> { v.add(String.valueOf(partitionNumber)); return v; }); }}Our consumer1() method uses the @KafkaListener annotation to initiate the consumer. The topics argument is a list of topics to consume, while the groupId argument identifies the consumer group to which the consumer belongs.To visualize the results later, we’ve used a ConcurrentHashMap to store the events consumed. The key corresponds to the consumer’s name, whereas the value contains the partitions that it consumed from.6.2. Create the Test ClassNow, let’s create our integration test class:@SpringBootTest(classes = ThermostatApplicationKafkaApp.class)@EmbeddedKafka(partitions = 2, brokerProperties = {"listeners=PLAINTEXT://localhost:9092", "port=9092"})public class KafkaTopicsAndPartitionsIntegrationTest { @ClassRule public static EmbeddedKafkaBroker embeddedKafka = new EmbeddedKafkaBroker(1, true, "multitype"); @Autowired private ThermostatService service; @Autowired private TemperatureConsumer consumer; @Test public void givenTopic_andConsumerGroup_whenConsumersListenToEvents_thenConsumeItCorrectly() throws Exception { service.measureCelsiusAndPublish(10000); Thread.sleep(1000); System.out.println(consumer.consumedRecords); }}We’re using an embedded Kafka broker to run the test with Kafka. The @EmbeddedKafka annotation uses the argument brokerProperties to configure the URL and port the broker will run on. Then, we start the embedded broker using a JUnit rule in the EmbeddedKafkaBroker field.Finally, in the test method, we call our thermostat service to produce 10,000 events.We’ll use Thread.sleep() to wait 1 second after the events are produced. This ensures the consumers are properly set up in the broker to start processing messages.Let’s see an example of the output we’ll get when we run the test:{consumer-1=[0, 1]}That means the same consumer processed all events in partitions 0 and 1 since we have only one consumer and one consumer group. This result may vary if