kafka consumer performance tuning

Have always wanted to write a little about Kafka consumer, especially about the new version of the consumer Chinese information is very few. Recently, the Kafka Community Mail group has been discussing whether to formally use the new version consumer replace the old version,

Kafka the number of partitions is not the more the better? Advantages of multiple partitionsKafka uses partitioning to break topic messages to multiple partition distributions on different brokers, enabling high throughput of producer and consumer message processing. Kafka's producer and consumer can operate in parallel in multiple threads, and each thread is pro

output, the script also provides CSV Reporter, which stores the results as a CSV file for easy use in other analysis tools $KAFKA_HOME/bin/kafka-consumer-perf-test.shThe script is used to test the performance of the Kafka consumer, and the test metrics are the same as t

), sending a total of messages, and sending messages per second (Records/second). In addition to outputting test results to standard output, the script also provides CSV Reporter, which stores the results as a CSV file for easy use in other analysis tools $KAFKA_HOME/bin/kafka-consumer-perf-test.shThe script is used to test the performance of the

the consumer pull to the message, then remove the thread from the thread pool processing data, one of the biggest problems, is how to ensure that messages are processed sequentially, for example, if there are 2 messages in a partition, and when consumer poll to the message, it commits to 2 threads, which does not guarantee sequential processing and requires an additional thread synchronization mechanism. A

Lesson (Program optimization--selecting treemap sorting to implement red-black trees to improve performance)20th Lesson (Program optimization-commonly used to improve performance skills 01)21st Lesson (Program optimization-commonly used to improve performance skills 02)22nd Lesson (Program optimization-commonly used to improve

coordinator to get the commit offset (committed offset, the previous consumer's last commit offset) and update the local pull offset (fetch position). When the consumer submits the offset, there are 2 strategies to choose from, Auto commit (Auto commits) and manual commit (manually commit)Auto Commit:Typically, in order to improve performance, automatic submission is used, and the auto-commit interval (aut

Secrets of Kafka performance parameters and stress tests The previous article Kafka high throughput performance secrets introduces how Kafka is designed to ensure high timeliness and high throughput. The main content is focused on the underlying principle and architecture, b

I. Some concepts and understandings about Kafka Kafka is a distributed data flow platform that provides high-performance messaging system functionality based on a unique log file format. It can also be used for large data stream pipelines. Kafka maintains a directory-based message feed, called Topic. The project call

Kafka is a distributed, high-throughput, information-fragmented storage, message-synchronous, open-source messaging service that provides the functionality of the messaging system, but with a unique design.Originally developed by LinkedIn, Kafka is used in the Scala language as the activity stream data and operational data processing tool for LinkedIn, where activity flow data refers to the amount of page v

Librdkafka is a Apachekafka high-performance client for the C language implementation, providing efficient and reliable clients for production and use of Kafka, and provides C + + interfacePerformance:Librdkafka is a high-performance library designed for modern hardware use, which attempts to keep memory replication to a minimum, allowing users to decide whether

to the server, rather than to the delivery of a strip; The server appends the message set to the log file one time, which reduces trivial I/O operations. Consumer can also request a message set at once.Another performance optimization is in the byte copy aspect. This is not a problem in low-load situations, but it is still very large in the case of high loads. To avoid this problem,

=9092Host. Name=hadoop165. Kuaiyong. inNum. Network. Threads=3Num. IO. Threads=8Socket. Send. Buffer. Bytes=102400Socket. Receive. Buffer. Bytes=102400Socket. Request. Max. Bytes=104857600Log. dirs=/download/kafka-logsnum. Partitions=1Num. Recovery. Threads. Per. Data. Dir=1Log. Retention. Hours=168Log. Segment. Bytes=1073741824Log. Retention. Check. Interval. Ms=300000Log. Cleaner. Enable=falsezookeeper. Connect=hadoop165. XXX. in:2181, hadoop166. XX

Original link: Spark Streaming performance tuning　The Spark streaming provides an efficient and convenient streaming mode, but in some scenarios the default configuration is not optimal, and even the external data cannot be processed in real time, and we need to make relevant modifications to the default configuration. Because of the reality of the scene and the amount of data is not the same, so we can not