pyspark basics

dataframe container, Datafram is equivalent to a table, row format is often used;Others can go online to understand the following: Dataframe/rdd the difference between the contact, the current mlib are mostly written with Rdd;Here is an pyspark to write:# # #first TableFrom Pyspark.sql import Sqlcontext,rowCcdata=sc.textfile ("/home/srtest/spark/spark-1.3.1/examples/src/main/resources/cc.txt")Ccpart = Ccdata.map (Lambda le:le.split (",")) # #我的表是以逗号做

through the basic data processingThe main purpose of the next release is to build a model of the data prediction through these known relationships, train with training data, test with test data, and then modify the parameters to get the best model# # Fifth Major modified version# # # Date 20160901The serious problem this morning is that there is not enough memory, because I have cached the rdd of the computational process, especially the initial data, which is so large that it is not enough.The

Python Pyspark Introductory articleI. Introduction to the Environment:1. Install JDK 7 or more2.python 2.7.113.IDE Pycharm4.package:spark-1.6.0-bin-hadoop2.6.tar.gzTwo. Setup1. Unzip spark-1.6.0-bin-hadoop2.6.tar.gz to directory D:\spark-1.6.0-bin-hadoop2.62. Configure the environment variable path, add D:\spark-1.6.0-bin-hadoop2.6\bin, after which you can enter Pyspark on the CMD side and return to the fol

2016 in Tsinghua research----launch the python version of Spark Direct input Pyspark-"Help Pyspark--help---" Execute python instance spark-submit/usr/local/spark-1.5.2-bin-hadoop2.6/examples/src/main/ python/pi.py-"Data parallelization, creating a parallelized collection input Pyspark >>>data=[1,2,3,4,5] >>>disdata=sc.parallelize (data) > >>disdata.reduce (Lambda

Spark mllib is a library dedicated to processing machine learning tasks in Spark, but in the latest Spark 2.0, most machine learning-related tasks have been transferred to the Spark ML package. The difference is that Mllib is based on RDD source data, and ML is a more abstract concept based on dataframe that can create a range of machine learning tasks, from data cleaning to feature engineering to model training. Therefore, the future in the use of spark processing machine learning tasks, will b

Prerequisites :1. Spark is already installed. Mine is spark2.2.0.2. There is already a Python environment, and my side uses python3.6.First, install the py4jUsing PIP, run the following command:　　Install py4jUsing Conda, run the following command:Install py4jSecond, create a project using Pycharm.Select the python environment during the creation process. After entering, click run--"Edit configurations--" environment variables.Add Pythonpath and Spark_home, where Pythonpath is the Python director

Installing the Redis moduleand pack the Redis module Pip install Redis mkdir redis mv .../site-packages/redis redis import shutil dir_name = "Redis" output_filename = "./redis" shutil.make_archive (output_filename, ' zip ', dir_name) Redis.zip

Basic operations: Get the Spark version number (in Spark 2.0.0 for example) at run time: SPARKSN = SparkSession.builder.appName ("Pythonsql"). Getorcreate () Print sparksn.version Create and CONVERT formats: The dataframe of

Catalogue1. Connect Spark 2. Create Dataframe2.1. Create 2.2 from the variable. Create 2.3 from a variable. Read JSON 2.4. Read CSV 2.5. Read MySQL 2.6. Created from Pandas.dataframe 2.7. Reads 2.8 from the parquet stored in the column. Read 3 from

From pyspark.sql import sparksession spark= sparksession\ . Builder \. appName ("DataFrame") \ . Getorcreate () #1生成JSON数据 Stringjsonrdd = spark.sparkContext.parallelize ((' ' ' {' id ': ' 123 ',

First, local CSV file read: The easiest way: Import pandas as PD lines = pd.read_csv (file) lines_df = Sqlcontest.createdataframe (lines) Or use spark to read directly as Rdd and then in the conversion lines = sc.textfile (' file ')If your CSV

Reference Address: 1, http://spark.apache.org/docs/latest/ml-guide.html 2, https://github.com/apache/spark/tree/v2.2.0 3, http://spark.apache.org/docs/latest/ml-collaborative-filtering.html From pyspark.ml.evaluation import Regressionevaluator to

When viewing dataframe information, you can view the data in Dataframe by Collect (), show (), or take (), which contains the option to limit the number of rows returned. 1. View the number of rows You can use the count () method to view the number

Python programming basics-hardware basics of computer principles and python programming Basics I. registers: registers are some small storage areas used inside the CPU to store data, and are used to temporarily store data and calculation results involved in calculation. 1. register features: 1) registers are located inside the CPU. The number is very small. There

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Python BASICS (12)-module, python basics-Module URL: http://www.cnblogs.com/archimedes/p/python-modules.html.Module Introduction If you exit the Python interpreter and re-enter, all previously created definitions (variables and functions) will be lost. Therefore, if you want to write a program that has been saved for a long time, you 'd better use a text editor to write the program and input the saved file

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