spark data lineage

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Spark writes Dataframe data to the Hive partition table __spark

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

160728. Spark streaming Kafka Several ways to achieve data 0 loss

, 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

Spark SQL data loading and saving instance explanation _mssql

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

Spark Data Partitioning

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

Spark solves the problem of data skew by breaking hot key __spark

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

Spark Order (desc ("col")) Partial data sort failed

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

Spark SQL External Data Sources JDBC Official implementation write test

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

The JSON data processing of spark

--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 ":

Big Data high Salary training video tutorial Hadoop HBase Hive Storm Spark Sqoop Flume ZooKeeper Kafka Redis Cloud Computing

Training Big Data Architecture development!from zero-based to advanced, one-to-one training! [Technical qq:2937765541]--------------------------------------------------------------------------------------------------------------- ----------------------------Course System:get video material and training answer technical support addressCourse Presentation ( Big Data technology is very wide, has been online f

Big Data Architecture Development mining analysis Hadoop Hive HBase Storm Spark Flume ZooKeeper Kafka Redis MongoDB Java cloud computing machine learning video tutorial, flumekafkastorm

Big Data Architecture Development mining analysis Hadoop Hive HBase Storm Spark Flume ZooKeeper Kafka Redis MongoDB Java cloud computing machine learning video tutorial, flumekafkastorm Training big data architecture development, mining and analysis! From basic to advanced, one-on-one training! Full technical guidance! [Technical QQ: 2937765541] Get the big

Getting started with Big Data day 22nd--spark (iii) custom partitioning, sorting, and finding

(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

Big Data Architecture Development Mining Analytics Hadoop HBase Hive Storm Spark Sqoop Flume ZooKeeper Kafka Redis MongoDB machine Learning cloud computing

Label:Training Big Data architecture development, mining and analysis! From zero-based to advanced, one-to-one training! [Technical qq:2937765541] --------------------------------------------------------------------------------------------------------------- ---------------------------- Course System: get video material and training answer technical support address Course Presentation ( Big Data technology

Liaoliang daily Big Data quotes Spark 0018 (2015.11.7 in Nanning)

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

Spark-cassandra-connector Inserting data Functions Savetocassandra

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

Spark reads data from HBase

("-----------------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.

Machine learning with Spark learning notes (training on 100,000 movie data, using recommended models)

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)

Machine learning with Spark learning notes (training on 100,000 movie data, using recommended models)

) / (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)

Spark architecture development Big Data Video Tutorials SQL streaming Scala Akka Hadoop

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Storm big data video tutorial install Spark Kafka Hadoop distributed real-time computing, kafkahadoop

Storm big data video tutorial install Spark Kafka Hadoop distributed real-time computing, kafkahadoop The video materials are checked one by one, clear and high-quality, and contain various documents, software installation packages and source code! Permanent free update! The technical team permanently answers various technical questions for free: Hadoop, Redis, Memcached, MongoDB,

Storm Big Data Video tutorial installs Spark Kafka Hadoop distributed real-time computing

Video materials are checked one by one, clear high quality, and contains a variety of documents, software installation packages and source code! Perpetual FREE Updates!Technical teams are permanently free to answer technical questions: Hadoop, Redis, Memcached, MongoDB, Spark, Storm, cloud computing, R language, machine learning, Nginx, Linux, MySQL, Java EE,. NET, PHP, Save your time!Get video materials and technical support addresses----------------

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