kafka offset

model is based on push, which is where the broker pushes the message to the consumer side. However, in Kafka, the Pull method is used, that is, after the consumer has established a connection with the broker, it is actively pulling (or fetch) messages This model has some advantages, the first consumer can be based on their own consumption ability to fetch messages and processing timely, and can control the progress of the message consumption (

same category are sent to the same topic and then consumed by topic consumer. Topic is a logical concept, and the physical realization is partition. (3) Partition: partition. Partitioning is a physical concept, each topic contains one or more partition, and each partition is an ordered queue . Messages sent to topic are partitioned (customizable) to determine which partition the message is stored in. Each piece of data will be assigned an ordered id:offset. Note:

or more replicas of a partition, the role of which is to improve the availability of partitions.Offset (offset): Offsets are similar to the database self-increment int Id, which is identified by a unique offset as the offset in the Kafka partition continues to increase as the data is continuously written. The effect o

can start the Kafka with the zookeeper or start a separate installation of the Kafka, the following take Kafka to take the example) Cd/usr/local/kafka Bin/zookeeper-server-start.sh config/zookeeper.properties Step Three Start Kafka Cd/usr/local/

through binary search, if the specific location of the data file is not found in index file, the relative location of the data file continues to be read and searched until it is found. 2.7 structure of the Kafka File System-general directory structure Figure 7 The same topic has different partitions. Each partition is divided into multiple (segment) files, with only one current file being written and other files being read-only. When a file

Kafka.log.OffsetIndex.resize (offsetindex.scala:276)At Kafka.log.Log.loadSegments (log.scala:179)At Kafka.log.log.Atkafka.log.logmanager$ $anonfun $loadlogs$2$ $anonfun $3$ $anonfun $apply$7$ $anonfun $apply$1.apply$mcv$sp ( logmanager.scala:142)At kafka.utils.utils$ $anon $1.run (utils.scala:54)At Java.util.concurrent.executors$runnableadapter.call (executors.java:439)At Java.util.concurrent.futuretask$sync.innerrun (futuretask.java:303)At Java.util.concurrent.FutureTask.run (futuretask.java:1

servers (called brokers) in the Kafka cluster. Each replica maintains a log on the disk. The order in which the producer publishes messages is appended to the log, and each message in the log is identified by a monotonically incrementing offset.Offset is a logical concept within a partition that, given an offset, can identify the same message in each copy of the partition. When a consumer subscribes to a t

provider, a Topic is a directory or message name, messages will be sent to Topic, for each topic,kafka maintain a shard log (partitioned log), as shown in a Topic anatomy diagram:Each shard log is an ordered, immutable message series that is constantly added to the end of the Shard log, in which each message is assigned a sequential ID of Howl offset, which uniquely defines a message.Within a configured ti

Kafka installation and use of Kafka-PHP extension, kafkakafka-php Extension If it is used, it will be a little output, or you will forget it after a while, so here we will record the installation process of the Kafka trial and the php extension trial. To be honest, if it is used in the queue, it is better than PHP, or Redis. It's easy to use, but Redis cannot hav

Reference Site:https://github.com/yahoo/kafka-managerFirst, the function Managing multiple Kafka clusters Convenient check Kafka cluster status (topics,brokers, backup distribution, partition distribution) Select the copy you want to run Based on the current partition status You can choose Topic Configuration and Create topic (different c

message processing results:L can be done in the same transaction, eliminate the distributed consistency problem, keep the message index and message state in syncThe user can use intentional fallback (rewind) to the previous offset, again using the previously used data.2) Agent broker divides the data stream into a set of separate partitions. The semantics of these partitions are defined by the producer, which specifies which partition each message b

batch flush. Flush interval can be configured via Log.flush.interval.messages and log.flush.interval.ms but in version 0.8.0, the data is guaranteed to be not lost through the replica mechanism. The price is to need more resources, especially disk resources, Kafka currently supports gzip and snappy compression to mitigate whether the problem using replica (replicas) depends on the balance (balance) replica between reliability and resource cost (Dunge

zookeeper to ensure data consistency. 4.2TopicAfter each message is delivered to the Kafka cluster, the message is made up of a type, which is called a topic, and different topic messages are stored separately. As shown in the following illustration: A topic is categorized as a message, each topic can be split into multiple partition, and in each message its position in the file is called offset, which mar

The previous introduction of how to use thrift source production data, today describes how to use Kafka sink consumption data.In fact, in the Flume configuration file has been set up with Kafka sink consumption dataAgent1.sinks.kafkaSink.type =Org.apache.flume.sink.kafka.KafkaSinkagent1.sinks.kafkaSink.topic=TRAFFIC_LOGagent1.sinks.kafkaSink.brokerList=10.208.129.3:9092,10.208.129.4:9092,10.208.129.5:9092ag