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I am using HDP 3.0 with Hive LLAP. I have pasted the code and output below: scala> import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.HiveContext scala> val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc) warning: there was one deprecation warning; re-run with -...

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1、在服务器(虚拟机)spark-shell连接hive 1.1 将hive-site.xml拷贝到spark/conf里

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Sep 07, 2015 · All of the examples on this page use sample data included in R or the Spark distribution and can be run using the ./bin/sparkR shell. Starting Up: SparkContext, SQLContext. The entry point into SparkR is the SparkContext which connects your R program to a Spark cluster.

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Commonly used tools can assist you: / Scala shell spark-shell use :t scala> val rdd = sc.textFile() rdd: org.apache.spark.rdd.RDD[String] = README.md MapPartitionsRDD[] at textFile at <console>: scala> :t rdd org.apache.spark.rdd.RDD[String] "README.md" 1 24 InteliJ Idea Use + Alt Alt = Share Improve this answer Follow edited Jan 27 '18 at 10:41 answered Jan 24 '18 at 17:17 Alper t.

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为什么在spark-shell中导入SparkSession会因“对象SparkSession不是包org.apache.spark.sql的成员”而失败? object DataFrame不是包org.apache.spark.sql的成员 为什么sbt失败并且“对象SQLContext不是包org.apache.spark.sql的成员”?

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over21=sqlContext.sql("SELECT name, age FROM users WHERE age >21") ... bin/spark-shell. It will show the spark command line like the below picture spark scala library.

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2.分析数据进入spark目录执行 spark-shell 示例1//创建sqlContext实例 scala>val sqlContext = new org.apache.spark.sql.SQLContext(sc) ...

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Oct 17, 2019 · Note: The sqlContext object which was also a pre-built object in Spark Shell versions 1.* is now part of the API available through the spark object. The org.apache.spark.SparkContex t Scala object (aliased in earlier Spark Shells as sc) is available via the sparkContext() method of the org.apache.spark.sql.SparkSession class

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在spark 官网下载 spark的安装版:spark-1.6.3-bin-hadoop2.6.tgz,然后解压,配好环境变量,在window下CMD下运行spark-shell,启动spark失败(本地模式)。然后搜索了一下,发现中文基本找不到什么参考资料,不过在stackoverflow上找到了解决办法,遂记录下,供国内的同学参考下

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因为spark-shell工具实际就是运行的scala程序片段,为了方便,下面采用spark-shell进行演示。 首先来看SQLContext,因为是标准SQL,可以不依赖于Hive的metastore,比如下面的例子(没有启动hive metastore):

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The following import is necessary before sqlContext.read.couchbase. import com.couchbase.spark.sql._ It worked fine !

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public class SQLContext extends Object implements scala.Serializable The entry point for working with structured data (rows and columns) in Spark 1.x. As of Spark 2.0, this is replaced by SparkSession. However, we are keeping the class here for backward compatibility.

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Apache Spark puts the power of BigData into the hands of mere mortal developers to provide real-time data analytics. Spark SQL is an example of an easy-to-use but power API provided by Apache Spark. Display - Edit. Spark SQL. Spark SQL lets you run SQL and hiveQL queries easily.

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The spark-shell is an environment where we can run the spark scala code and see the output on the console for every execution of line of the code. It is more interactive environment. But, when we have more line of code, we prefer to write in a file and execute the file.

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Step 2 : Now start spark-shell Step 3 ; Select all the products name and quantity having quantity <= 2000 val results = sqlContext.sql(.....SELECT name, quantity FROM products WHERE quantity <= 2000.....) results.showQ Step 4 : Select all the products , which name starts with PENCIL Spark SQL is the newest component of Spark and provides a SQL like interface. Spark SQL is tightly integrated with the the various spark programming languages so we will start by launching the Spark shell from the root directory of the provided USB drive:

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SparkContext, SQLContext, ZeppelinContext SparkContext, SQLContext, ZeppelinContext are automatically created and exposed as variable names 'sc', 'sqlContext' and 'z', respectively, both in scala and python environments. Note that scala / python environment shares the same SparkContext, SQLContext, ZeppelinContext instance.

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May 29, 2018 · val sqlContext = new org.apache.spark.sql.SQLContext(sc) 3.Passing the jdbc connection values and creating the connection for the dataframe val df_jdbc_mysql = sqlContext.read.format("jdbc").option("url", "jdbc:mysql://**mysql_url**/**databasename**").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "tablename").option("user", "**username**").option("password", "password").load() Commonly used tools can assist you: / Scala shell spark-shell use :t scala> val rdd = sc.textFile() rdd: org.apache.spark.rdd.RDD[String] = README.md MapPartitionsRDD[] at textFile at <console>: scala> :t rdd org.apache.spark.rdd.RDD[String] "README.md" 1 24 InteliJ Idea Use + Alt Alt = Share Improve this answer Follow edited Jan 27 '18 at 10:41 answered Jan 24 '18 at 17:17 Alper t.

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D:\Spark\spark-1.6.1-bin-hadoop2.6\bin>spark-shell log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory). log4j:WARN Please initialize the log4j system properly.

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