kafka offset

Reference article kafka.common.OffsetOutOfRangeException problem handlingSome summaries of the low-order API Createdirectstream that toss Spark's Kafka these days. Problem Description National day before the start of Spark streaming task to consume the Kafka, later for other reasons stopped, after the holiday back, restart Spark task, Newspaper Kafka.common.OffsetOutOfRangeException, the beginning of the pe

Kafka provides a number of configuration parameters for Broker,producer and consumer. Understanding and understanding these configuration parameters is very important for us to use Kafka.This article lists some of the important configuration parameters.The Official document configuration is older, many parameters are changed, and some names have been altered. I also made corrections based on 0.8.2 's code in the process of tidying up.Boker Configurati

where the message is written is active. The concept of the activity of a replica is discussed in the next section of the document. Now assume that the broker is not down.If a network error occurs when producer publishes a message, but it is not certain that the actual commit occurred before or after the commit, although this is not common, it must be considered, and now the Kafka version has not resolved the issue, and future versions are trying to r

); ......}Before Kafka 0.9, the consumer group was maintained by ZK, but because of the "herd" and "split brain" problems, after redesign, in the new version by the broker cluster to select a node as coordinator, To resolve synchronization of individual consumer in group, such as Rebalance,failover,partition Assignment,offset CommitRefer to Kafka consumer desig

partition (partition) To begin with, there are some deep concepts: each topic will be divided into multiple partition (zones), and a message will be stored in a partition according to the algorithm specified by producer, which means storing the data separately . Avoid large amounts of data stored on a single Kafka instance. In addition, the more partitions means that more consumer can be accommodated, effectively increasing the capacity of concurrent

, causing the problem to be re-issued. When consumer consumption is very slow, it may not be completed in a session cycle, causing heartbeat mechanism detection report problems. Underlying root cause: data has been consumed, but offset has not been submitted. Configuration issue: Offset auto-commit set Problem Scenario:1. Set offset to auto-commit, consuming data

The role of consumer is to load log information onto a central storage system. Kafka provides two consumer interfaces, one of low levels, that maintains a connection to a broker, and the connection is stateless, that is, the offset of the broker data is told each time the data is pull from broker. The other is the high-level interface, which hides the details of the broker, allowing the consumer to push da

Description Operating system: CentOS 6.x 64-bit Kafka version: kafka_2.11-0.8.2.1 To achieve the purpose: Stand-alone installation Configuration Kafka Specific actions: First, close SELinux, open firewall 9092 port 1. Close SELinux Vi/etc/selinux/config #SELINUX =enforcing #注释掉 #SELINUXTYPE

in the current local queue.Hint: see EXAMPLES/RDKAFKA_PERFORMANCE.C to obtain consumer's use. The server.properties file configuration (parameter log.dirs=/data2/logs/kafka/) in Kafka Broker causes the topic in the message queue to be stored in the form of the partition under that directory. Each partition partition is composed of segment file, and segment file includes 2 parts: The index file and data fil

consumer crashes trigger rebalancing, and when the new consumer tries to update the offset information, it will cause the missing message, as shown in:Pull and consume of messagesConsumer if a message exists during the last message pull, it is returned directly, otherwise the pull cancellation request is resent from the previously updated pull offset location.Pull cancellation request to the consumer consu

Personal opinion: Big data we all know about Hadoop, but not all of it. How do we build a large database project. For offline processing, Hadoop is still more appropriate, but for real-time, relatively strong, the amount of data is large, we can use storm, then storm and what technology collocation, to be able to do a suitable project. We can refer to the following.You can read this article with the following questions:1. What are the characteristics of a good project architecture?2. How does th

Http://www.aboutyun.com/thread-6855-1-1.htmlPersonal opinion: Big data we all know about Hadoop, but not all of it. How do we build a large database project. For offline processing, Hadoop is still more appropriate, but for real-time, relatively strong, the amount of data is large, we can use storm, then storm and what technology collocation, to be able to do a suitable project. We can refer to the following.You can read this article with the following questions:1. What are the characteristics o

In jquery, there are two ways to get the position of an element offset () and position (), what are the similarities and differences between the two methods? What problems should be paid attention to when using? When do I use offset () and when do I use position ()?Let's look at the definitions of these two methods first. Offset (): Gets the relative

http://blog.csdn.net/weijonathan/article/details/18301321Always want to contact storm real-time computing this piece of things, recently in the group to see a brother in Shanghai Luobao wrote Flume+kafka+storm real-time log flow system building documents, oneself also followed the whole, before Luobao some of the articles in some to note not mentioned, some of the wrong points later, In this way I will do the amendment, the content should say that mos

of various data senders in the log system and collects data, while Flume provides simple processing of data and writes to various data recipients (customizable) capabilities. typical architecture for flume:flume data source and output mode:Flume provides 2 modes from console (console), RPC (THRIFT-RPC), text (file), tail (UNIX tail), syslog (syslog log system, TCP and UDP support), EXEC (command execution) The ability to collect data on a data source is currently used by exec in our system for

are responsible for the same partition, what is the problem? We know that Kafka is designed to ensure the order of messages in a partition, that is, the order of messages in a partition, so what order does the consumer see during consumption? To achieve this, we must first ensure that the message is actively pulled by the consumer (pull ), second, ensure that only one consumer is responsible for a partition. If the two consumers are responsible for t

message middleware. Kafka is a highly throughput, high performance messaging middleware that works in sequential writes with a single partition, and supports random reads at offset offsets, making it ideal for implementations of the topic Release subscription model. There are multiple Kafka in the diagram, because the cluster feature is supported, and the Flume

To demonstrate the effect of the cluster, a virtual machine (window 7) is prepared, and a single IP multi-node zookeeper cluster is built in the virtual machine (the same is true for multiple IP nodes), and Kafka is installed in both native (Win 7) and virtual machines.Pre-preparation instructions:1. Three zookeeper servers, the local installation of one as Server1, virtual machine installation two (single IP)2. Three

Yesterday in writing a Java consumption Kafka data instance, clearly set auto.offset.reset for earliest, but still do not start from scratch, the meaning of the official website is too abstract.earliest: Automatically reset the offset to the earliest offset, automatically resets the offsets to the earliest. is not the beginning of the partitions in the topic. The

Yesterday in writing a Java consumption Kafka data example, clearly set auto.offset.reset for earliest, but still do not start from the beginning of consumption, the meaning of the official website is too abstract.earliest: Automatically reset the offset to the earliest offset, automatically offsets the first. is not the beginning of each partition in the topic.