spark streaming tutorial

In the previous section, we explained the operational mechanism of the spark streaming job in general. In this section we elaborate on how the job is generated, see:650) this.width=650; "src=" Http://s4.51cto.com/wyfs02/M01/80/0C/wKiom1c1bjDw-ZyRAAE2Njc7QYE577.png "title=" Untitled. png "alt=" Wkiom1c1bjdw-zyraae2njc7qye577.png "/>In spark

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

In spark streaming, for Receiverinputdstream, it's a real receiver, used to receive data. Receiver can have many and run on a different worker node. These receiver are managed by Receivertracker.In the Start method of Receivertracker, a message communication body Receivertrackerendpoint is created:/** Start The endpoint and receiver execution thread. */def start (): Unit = synchronized {if (istrackerstarted

Contents of this issue:1, Jobscheduler Insider realization2, Jobscheduler deep thinkingJobscheduler is the dispatch core of spark streaming, and it is important to be the Dag Scheduler of the dispatch center on Spark Core!Jobgenerator Every batch duration time will be dynamically generated Jobset submitted to Jobscheduler,jobscheduler received Jobset, how to deal

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

Contents of this issue: Updatestatebykey decryption Mapwithstate decryption 　　　　Spark Streaming is a state-management factor:01, Spark streaming is in accordance with the entire Bachduration division job, each bachduration will produce a job, in order to meet the needs of business operations,need to c

Contents of this issue: The way receiver starts is conceived Receiver Start source thorough analysis 　　Multiple input source input started, receiver failed to start, as long as our cluster exists in the hope that receiver boot success, running process based on each Teark boot may fail to run.Starting a different receiver for an application that uses a different RDD partion to represent different receiver, and then starts when different partion execution planes are different tea

spark-streaming window application, Spark streaming provides support for sliding window operations, allowing us to perform calculations on the data in a sliding window. Each time the data of the RDD that is dropped in the window is aggregated to perform the calculation operation, and the resulting rdd is used as an rdd

Nonsense not to say, first, an example, a perceptual knowledge to introduce.This example comes from the example of Spark's own, and the basic steps are as follows:(1) Use the following command to enter a stream message: $ nc-lk 9999 (2) Run Networkwordcount in a new terminal to count the number of words and output: $ bin/run-example streaming.networkwordcount localhost 9999 (3) Type some content into the input process created in the first step and see the results in t

In the spark streaming documentation, there's this:def Sendpartition (ITER): # ConnectionPool is a static, lazily initialized pool of connections Connection = connectionpool.getconnection () for in iter: connection.send ( Record) # return to the pool for future reuse Connectionpool.returnconnection (Connection) Dstream.foreachrdd (Lambda rdd:rdd.foreachPartition ( Sendpartition))Bu

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 offset failed, then after the driver failure recov

Recently, when upgrading a framework, it was found that the GC overhead limit exceeded error occurred at some point in time for a streaming computation program. This problem is certainly not enough memory, but the initial set of memory is enough ah, so a variety of memory optimization, such as the definition of the variable in the loop outside the body control, but found that only the interval of time to push back a bit. Still did not find the c

, and the following isAddblock'sSource:Here actually called the Addblock method of Receivedblocktracker, Receivedblocktracker is REceivedblocktracker object, it is in theReceivertracker is created when instantiated:Here's a look at Receivedblocktracker'sAddblock Method:Can seeReceivedblocktracker'sThe Addblock method adds the meta information of the block to a queue of queues, which is eventually added to astreamidtounallocatedblockqueuesHashMap, where key is Streamid and the value is the corres

When implementing the Spark streaming demo, the code:1 Public Static voidMain (string[] args) {2 3 4sparkconf conf =NewSparkconf (). Setappname ("spark_streaming"). Setmaster ("local");5Javasparkcontext sc =Newjavasparkcontext (conf); 6Javastreamingcontext JSSC =NewJavastreamingcontext (SC,NewDuration (2)); 7Javasqlcontext Sqlctx =NewJavasqlcontext (SC); 8 9 TenString[] Filters =NewString[] {"SOC"}; One A

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

Spark Streaming Application Simple example Package Com.orc.stream Import org.apache.spark.{ sparkconf, Sparkcontext} import org.apache.spark.streaming.{ Seconds, StreamingContext} /** * Created by Dengni on 2016/9/15. Today also are mid-Autumn Festival * Scala 2.10.4 ; 2.11.X not Works * Use method: * Start this program in this window * 192.168.184.188 Start command nc-l 7777 input valu

( Args:array[string]): unit = { sparkstreaming.printwebsites () //initiate spark val sc = new Sparkcontext (conf) Read file from local disc val rdd = Sc.textfile ("F:\\code\\scala2.10.6_spark1.6_hadoop2.8\\test.log") } } Where Sparkstreaming.scala is: /** *notes:to Test Spark streaming * date:2017.12.21 * Author:gendlee/pa

maximum ingestion rate */def sendrateupdate (Streamuid:int, newrate:long): Unit = synchronized { if (istrackerstarted) {endpoint.send (Updatereceiverratelimit (Streamuid, Newrate))}}Case Updatereceiverratelimit (Streamuid, newrate) + = (Info The rate at which the data flow is controlled is finally adjusted by Blockgenerator to adjust the rate at which the message is sent to Receiver,receiver.Case Updateratelimit (EPS) = Loginfo (S "Received a new rate limit: $eps.") Registeredblockgenerators.fo