spark data lineage

The Schemardd from spark1.2 to Spark1.3,spark SQL has changed considerably from Dataframe,dataframe to Schemardd, while providing more useful and convenient APIs.When Dataframe writes data to hive, the default is hive default database, Insertinto does not specify the parameters of the database, this article uses the following method to write data to the hive tabl

, StringDecoder](ssc, kafkaParams, topicMap, StorageLevel.MEMORY_AND_DISK_SER).map(_._2)There are still data loss issues after opening WalEven if the Wal is officially set, there will still be data loss, why? Because the task is receiver also forced to terminate when interrupted, will cause data loss, prompted as follows:0: Stopped by driverWARN BlockGenerator: C

First, the knowledge of the prior detailedSpark SQL is important in that the operation Dataframe,dataframe itself provides save and load operations.Load: You can create Dataframe,Save: Saves the data in the Dataframe to a file, or to a specific format, indicating the type of file we want to read and what type of file we want to output with the specific format. Second, Spark SQL read and write

The Spark program can reduce network traffic overhead by partitioning. partitioning is not good for all scenarios: for example, if a given rdd is scanned only once, then there is absolutely no need for partitioning, and partitioning is helpful only if the data is multiple times in a key-based operation such as connecting. Suppose we have a constant large file UserData, and the small

1. Data skew for hot key In large data-related statistics and processing, the hot key caused by the data skew is very common and very annoying, often cause the job to run longer or cause job Oom finally cause the task to fail. For example, in the WordCount task, if a word is a hot word and there are a lot of occurrences, the last job's run time is determined by

start to write, the return is a Double type, but as a formatted result, I write String the return type String , the program can run, I ignore this thing, the result is wrong.That is, these appear to be numbers, but are actually strings, at this point the sort is sorted by string, the correct dimension, the first character is 1, and only 1 bits, so the correct sort is said, but the wrong dimension, 19 that although the two-digit, but the first character is 1, so came to the back. Only the UDF fu

The data of the RDD is written to the MySQL database via the spark SQL External-Data Sources JDBC implementation.Jdbc.scala Important API Description:/*** Save This RDD to a JDBC database at ' url ' under the Table name ' table '. * This would run a ' CREATE table ' and a BuNC H of ' INSERT into ' statements. * If you pass ' true ' for ' allowexisting ', it'll dr

--by default, the Sparkcontext object is initialized with Namesc when Spark-shell is started. Use the following command to create the SqlContext. Val SqlContext=New Org.apache.spark.sql.SQLContext (SC)--employee.json-Place this file in the same directory as the currentscala> pointer. {{"id": "1201"," name ":" Satish "," Age ":" -"} {"id": "1202"," name ":" Krishna "," Age ":" -"} {"id": "1203"," name ":" Amith "," Age ":" the"} {"id": "1204"," name ":

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(args:array[string]) {val conf=NewSparkconf (). Setappname ("Customsort"). Setmaster ("local[2]") Val SC=Newsparkcontext (conf) Val rdd1= Sc.parallelize (List ("Yuihatano", 1, 95, 22, 3, ("Angelababy", 2), ("Jujingyi",))) Importordercontext._ Val rdd2= Rdd1.sortby (x = Girl (x._2, X._3),false) println (Rdd2.collect (). Tobuffer) Sc.stop ()}}/*** First Way *@paramFacevalue *@paramAgecase class Girl (Val facevalue:int, Val age:int) extends Ordered[girl] with Serializable {override Def compare

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The shuffle process is triggered by the reducebykey operation of Spark, and before shuffle, there is a local aggregation process that produces mappartitionsrdd, and then shuffle is generated Shuffledrdd After doing the global aggregation build result MappartitionsrddThis article is from the "Liaoliang Big Data Quotes" blog, please be sure to keep this source http://wangjialin2dt.blog.51cto.com/10467465/1723

Save data to Cassandra in Spark-shell:vardata = Normalfill.map (line = Line.split ("\u0005")) Data.map ( line= = (Line (0), Line (1), Line (2)) . Savetocassandra ("Cui", "Oper_ios", Somecolumns ("User_no","cust_id","Oper_code","Oper_time"))Savetocassandra method when the field type is counter, the default behavior is countCREATE TABLE CUI.INCR (Name text,Count counter,PRIMARY KEY (name))scala> var rdd = Sc

("-----------------resultVal2:" + resultval2.length) Resultval2.map (f=>{println ("------------------------F:" +f)}) Val DataArray = resultval2.ma P (f = vectors.dense (f)) Val summary:multivariatestatisticalsummary = Statistics.colstats (Sc.parallelize (dataAr Ray)//println ("--------------------mean:" + Summary.mean + "--------------------") println ("----- ---------------Variance:"+ summary.variance +"--------------------") println ("--------------------mean apply 0: "+ summary.mean.toArray.

vectors:def cosineSimilarity(vec1: DoubleMatrix, vec2: DoubleMatrix): Double = { vec1.dot(vec2) / (vec1.norm2() * vec2.norm2()) }Now to check if it's right, pick a movie. See if it is 1 with its own similarity:val567val itemFactor = model.productFeatures.lookup(itemId).headvalnew DoubleMatrix(itemFactor)println(cosineSimilarity(itemVector, itemVector))Can see the result is 1!Next we calculate the similarity of other movies to it:valcase (id, factor) => valnew DoubleMatrix(factor)

) / (vec1.norm2() * vec2.norm2()) }Now to detect whether it is correct, choose a movie and see if it is 1 with its own similarity:val567val itemFactor = model.productFeatures.lookup(itemId).headvalnew DoubleMatrix(itemFactor)println(cosineSimilarity(itemVector, itemVector))You can see that the result is 1!Next we calculate the similarity of the other movies to it:valcase (id, factor) => valnew DoubleMatrix(factor) val sim = cosineSimilarity(factorVector, itemVector) (id,sim)

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