spark streaming kafka

1. Background overview There is a certain demand in the business, in the hope of real-time to the data from the middleware in the already existing dimension table inner join, for the subsequent statistics. The dimension table is huge, with nearly 30 million records, about 3g data, and the cluster's resources are strained, so you want to squeeze the performance and throughput of spark streaming as much as po

Spark Streaming 1.2 provides a Wal based fault-tolerant mechanism (refer to the previous blog post http://blog.csdn.net/yangbutao/article/details/44975627), You can guarantee that the calculation of the data is executed at least once, However, it is not guaranteed to perform only once, for example, after Kafka receiver write data to Wal, to zookeeper write offse

. We must find a good balance between the two parameters, because we do not want the data block to be too large, and do not want to wait too long for localization. We want all tasks to be completed within several seconds. ?? Therefore, we changed the localization options from 3 s to 1 s, and we also changed the block interval to 1.5 s. --conf "spark.locality.wait=1s" --conf "spark.streaming.blockInterval=1500ms" \2.6 merge temporary files ?? Inext4In the file system, we recommend that you enable

includes Spark, Mesos, Akka, Cassandra, and Kafka, with the following features: Contains lightweight toolkits that are widely used in big data processing scenarios Powerful community support with open source software that is well-tested and widely used Ensures scalability and data backup at low latency. A unified cluster management platform to manage diverse, different load application

1 decrypting spark streaming operating mechanism Last lesson we talked about the technology industry's Dragon Quest. This is like Feng Shui in the past, each area has its own dragon vein, Spark is where the dragon vein, its dragon Cave or the key point is sparkstreaming. This is one of the conclusions we know very clearly in the last lesson. And in the last lesso

-snapshot.tar.gzcd/var/lib/tomcat7/webappscp/srv/jstorm/jstorm-ui-0.9.6.2.war./MV ROOT ROOT.oldln -sjstorm-ui-2.0.4-snapshot ROOT2.zookeeper-web-ui2.1. Download3.jstorm integration with Apache3.1Apache Load AJP ModuleApache2.2 above can use AJP way, simple and convenient;Execute the following command to view the modules that Apache has loaded:Apachectl-t-D Dump_modulesExecute the following command to load the PROXY_AJP module:A2enmod PROXY_AJPYou can use the View command to view the modules that

Schema background spark parameter optimization increase Executor-cores resize executor-memory num-executors set first deal decompression policy x Message Queuing bug bypass PHP end limit processing Action 1 processing speed increased from 1 to 10 peak Period non-peak status description increased from 10 to 50 peak off-peak status description use pipeline to elevate the QPS of the Redis 50 to a full-scale PM period Peak State Analysis Architecture back

, on the Hadoop monitor page, you can see it in the pending process, although I repeatedly delete-Create multiple6. After a while, we finally got a new environment and referenced the document (Https://kyligence.gitbooks.io/kap-manual/content/zh-cn/quickstart/quickstart_ cdh.cn.html), the yarn has been partially set as follows,Then submit the order, finally, finally see the expected results!!! (Cost 1 day)Command:Curl-x PUT--user admin:kylin-h "Content-type:application/json;charset=utf-8"-d ' {"S

Follow the spark and Kafka tutorials step-by-step, and when you run the Kafkawordcount example, there is always no expected output. If it's right, it's probably like this: ...... ------------------------------------------- time:1488156500000 Ms ------------------------------------- ------ (4,5) ( 8,12) (6,14) (0,19) (2,11) (7,20) (5,10) (9,9) (3,9 ) (1,11) ... In fact, only: ...... ----------------------

Feature Strom (Trident) Spark Streaming Description Parallel framework DAG-based task Parallel computing engine (task Parallel continuous computational engine Using DAG) Spark-based parallel computing engine (data Parallel general Purpose batch processing engine) Data processing mode (one at a time) to process

In order to better understand the processing mechanism of the spark streaming sub-framework, you have to figure out the most basic concepts yourself.1. Discrete stream (discretized stream,dstream): This is the spark streaming's abstract description of the internal continuous real-time data stream, a real-time data stream We're working on, in

1. Join for different time slice data streams After the first experience, I looked at Spark WebUi's log and found that because spark streaming needed to run every second to calculate the data in real time, the program had to read HDFs every second to get the data for the inner join. Sparkstreaming would have cached the data it was processing to reduce IO and incr

The contents of this lesson:1. Spark Streaming job architecture and operating mechanism2. Spark streaming job fault tolerant architecture and operating mechanismUnderstanding the entire architecture and operating mechanism of the spark s