spark streaming kafka tutorial

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Spark Streaming flow calculation optimization record (2)-Join for different time slice data streams

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

Big Data Spark Enterprise Project combat (stream data processing applications for real-sparksql and Kafka) download

Link: http://pan.baidu.com/s/1dFqbD4l Password: treq1. Curriculum development EnvironmentProject source code is based on spark1.5.2,jdk8,scala2.10.5.Development tools: SCALA IDE eclipse;Other tools: Shell scripts2. Introduction to the ContentThis tutorial starts with the most basic spark introduction, introduces the various deployment modes of spark and hands-on

Spark Streaming Integrated Kafak The problem of the RAN out of messages

) The exception here is because the Kafka is reading the specified offset log (here is 264245135 to 264251742), because the log is too large, causing the total size of the log to exceed Fetch.message.max.bytesThe Set value (default is 1024*1024), which causes this error. The workaround is to increase the value of fetch.message.max.bytes in the parameters of the Kafka client.For example://

Analysis of Spark Streaming principles

Analysis of Spark Streaming principlesReceive Execution Process Data StreamingContextDuring instantiation, You need to inputSparkContextAnd then specifyspark matser urlTo connectspark engineTo obtain executor. After instantiation, you must first specify a method for receiving data, as shown in figure val lines = ssc.socketTextStream(localhost, 9999) In this way, text data is received from the socket. In thi

12th lesson: Spark Streaming Source interpretation of executor fault-tolerant security

=newcountingiterator (iterator) valputresult=blockmanager.putiterator (blockId, countiterator,storagelevel,Nbsp;tellmaster=true) numRecords= countiterator.countputresultcase Bytebufferblock (Bytebuffer) =>blockmanager.putbytes (blockId, Bytebuffer,storagelevel,tellmaster=true) caseo=> thrownewsparkexception ( s "couldnotstore $blockId toblockmanager,unexpected Blocktype${o.getclass.getname} ") }if (!putresult.map{_._1 }.contains (Blockid)) {thrownewsparkexception ( s "couldnotstore $blockId tobl

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