rdd usa

-heap memory using memory outside the JVM heap, not being recycled by GC, reducing the frequency of full GC, so in spark programs, Long stay. The large memory objects in the Spark program can use out-of-heap memory storage. There are two ways to use out-of-heap memory, one is to pass in the parameter storagelevel.off_heap when the RDD calls persist, which needs to be used in conjunction with Tachyon. The other is to use the spark.memory.offHeap.enabl

Transferred from: http://www.cnblogs.com/hseagle/p/3664933.htmlWedgeSource reading is a very easy thing, but also a very difficult thing. The easy is that the code is there, and you can see it as soon as you open it. The hard part is to understand the reason why the author should have designed this in the first place, and what is the main problem to solve at the beginning of the design.It's a good idea to read the spark paper from Matei Zaharia, before you take a concrete look at Spark's source

One of the simplest examples of Spark's own is mentioned earlier, as well as the section on Sparkcontext, which describes the transformation in the rest of the content.Object SPARKPI { def main (args:array[string]) { val conf = new sparkconf (). Setappname ("Spark Pi") val spark = New Sparkcontext (conf) val slices = if (args.length > 0) args (0). ToInt Else 2 val n = math.min (100000L * Slice S, int.maxvalue). ToInt//Avoid overflow val count = spark.parallelize (1 until n, slice

Hadoop version and the spark version when compiling spark_hadoop_version=2.4.1 spark_version=1.2.0./install-dev.sh At this point, the standalone version of the SPARKR has been installed.1.3.3. Deployment configuration for Distributed Sparkr 1) After the successful compilation, will generate a Lib folder, into the Lib folder, packaging Sparkr for SparkR.tar.gz, which is the key to distributed SPARKR deployment. 2) Install SPARKR on each cluster node by the packaged SparkR.tar.gz R CMD INSTALL Sp

Transfer from http://www.cnblogs.com/hseagle/p/3664933.htmlVersion: UnknownWedgeSource reading is a very easy thing, but also a very difficult thing. The easy is that the code is there, and you can see it as soon as you open it. The hard part is to understand the reason why the author should have designed this in the first place, and what is the main problem to solve at the beginning of the design.It's a good idea to read the spark paper from Matei Zaharia, before you take a concrete look at Spa

times higher than it was before. Correspondingly, the performance (speed of execution) can also be increased several times ~ dozens of times times. Increase the amount of memory per executor. Increase the amount of memory, the performance of the increase, there are two points: 1, if you need to cache the RDD, then more RAM, you can cache more data, write less data to disk, or even write to disk. Reduced disk IO. 2, for shuffle operation, the reduce s

logical level of the data quantitative standards, with time slices as the basis for splitting data;4. Window Length: The length of time the stream data is overwritten by a window. For example, every 5 minutes to count the past 30 minutes of data, window length is 6, because 30 minutes is the batch interval 6 times times;5. Sliding time interval: for example, every 5 minutes to count the past 30 minutes of data, window time interval of 5 minutes;6. Input DStream: A inputdstream is a special DStr

Reprinted from: http://www.cnblogs.com/hseagle/p/3664933.htmlBasic concept (Basic concepts)Rdd-resillient distributed DataSet Elastic distributed data setOperation-the various operations that act on the Rdd are divided into transformation and actionJob-Jobs, one job containing multiple RDD and various operation acting on the corresponding RDDStage-a job is divide

. Spark GraphX: Figure calculation Framework. Pyspark (SPARKR): Python and R framework above spark. From off-line calculation of RDD to streaming real-time computing. From the support of Dataframe and SQL to the Mllib machine learning Framework, from the GRAPHX graph to the support of statisticians ' favorite R, you can see that spark is building its own full-stack data ecosystem. From the current academic and industrial feedback, Spark h

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 this step,ReceiverInputDStreamImplementation, includingReceiverTo receive data and convert it

Introduction: Spork is the highly experimental version of Pig on Spark, and the dependent version is also relatively long. As mentioned in the previous article, I have maintained Spork on my github: flare-spork. This article analyzes the implementation method and specific content of Spork.Spark Launcher writes a Spark initiator in the path of the hadoop executionengine package. Similar to MapReduceLauncher, Spark launchPig translates the input physical execution plan. MR starters translate MR op

Basic Data Source 1. File Flow Reading data from a file lines= Ssc.textfilestream ("File:///usr/local/spark/mycode/streaming/logfile") 2. Socket Stream Spark streaming can listen and receive data through the socket port and then handle it accordingly. Javareceiverinputdstream 3.RDD Queue Flow When debugging spark streaming applications, we can use Streamingcontext.queuestream (QUEUEOFRDD) to create RDD base