kafka and spark streaming example

Forwarded from the Mad BlogHttp://www.cnblogs.com/lxf20061900/p/3866252.htmlSpark Streaming is a new real-time computing tool, and it's fast growing. It converts the input stream into a dstream into an rdd, which can be handled using spark. It directly supports a variety of data sources: Kafka, Flume, Twitter, ZeroMQ, TCP sockets, etc., there are functions that c

)//Here The Updatefunc is passed into Val statedstream = Worddstream.updatestatebykey (updatefunc) statedstream.Print() Streaming.start () streaming.awaittermination ()}There is also a window concept in spark streaming, which is the sliding formis an explanation given in the official documentation:Use the sliding form to set two specified parameters:1. Form length2. Sliding timeFor

Thanks for the original link: https://www.jianshu.com/p/a1526fbb2be4 Before reading this article, please step into the spark streaming data generation and import-related memory analysis, the article is focused on from the Kafka consumption to the data into the Blockmanager of this line analysis. This content is a personal experience, we use the time or suggest a

DStream, the next line is the windowing DStream.Common window operationOfficial Document code exampleJoin (Otherstream, [numtasks])Connecting data streamsOfficial Document code Example 1Official Document code Example 2Output operationCaching and Persistence:Each RDD in DStream is stored in memory by persist ().Window operations is automatically persisted in memory without the need to show call persist ().W

RDD (transformations) and by recording the lineage (descent) of each rdd;　4. Transaction processing for exactly once:　　　　01, Data 0 lost: Must have a reliable data source and reliable receiver, and the entire application metadata must be checkpoint, and through the Wal to ensure data security;02, Spark streaming 1.3 time in order to avoid Wal performance loss and implementation exactly once and provide

The main content of this section:I. Data acceptance architecture and design patternsSecond, the acceptance of the data source interpretationSpark streaming continuously receives data, with receiver's spark application in mind.Receiver and driver in different processes, receiver to receive data after the continuous reporting to deriver.Because driver is responsible for scheduling, receiver received data if n

consume this data, this is zookeeper guarantee, there is a data duplication consumption problem, is the consumption is finished but have not had time to zookeeper synchronization, may be repeated.2, Direct mode: directly to operate Kafka, and is the management of the offset, Kafka itself has offset, this way can ensure that there is and once the operation of processing, this need to checkpoint operation, m

Spark StreamingSpark streaming uses the spark API for streaming calculations, which means that streaming and batching are done on spark. So you can reuse batch code, build powerful interactive applications using

Spark streaming can receive streaming data from any arbitrary data source beyond the one's for which it has in-built support (that is, beyond flume, Kafka, files, sockets, etc .). this requires the developer to implementCyclerThat is customized for processing data from the concerned data source. This Guide walks throug

data will be lost a bit, because the Wal this write data is also batch write, (real-time write data can be very performance) so the data may be lost a few2. Data re-read situationWhen receiver receives the data and saves it to a persistence engine such as HDFS but does not have time to updateoffsets, the receiver crashes and restarts the data again by managing the metadata in the Kafka zookeeper. But at this time sparkstreaming think is successful, b

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