[Spark][Python]Spark 访问 mysql , 生成 dataframe 的例子:
[Spark][Python]Spark 访问 mysql , 生成 dataframe 的例子:
mydf001=sqlContext.read.format("jdbc").option("url","jdbc:mysql://localhost/loudacre")\
.option("dbtable","accounts").option("user","training").option("password","training").load()
In [10]: mydf001=sqlContext.read.format("jdbc").option("url","jdbc:mysql://localhost/loudacre")\
....: .option("dbtable","accounts").option("user","training").option("password","training").load()
17/10/03 05:59:53 INFO hive.HiveContext: default warehouse location is /user/hive/warehouse
17/10/03 05:59:53 INFO hive.HiveContext: Initializing metastore client version 1.1.0 using Spark classes.
17/10/03 05:59:53 INFO client.ClientWrapper: Inspected Hadoop version: 2.6.0-cdh5.7.0
17/10/03 05:59:53 INFO client.ClientWrapper: Loaded org.apache.hadoop.hive.shims.Hadoop23Shims for Hadoop version 2.6.0-cdh5.7.0
17/10/03 05:59:56 INFO hive.metastore: Trying to connect to metastore with URI thrift://localhost.localdomain:9083
17/10/03 05:59:56 INFO hive.metastore: Opened a connection to metastore, current connections: 1
17/10/03 05:59:56 INFO hive.metastore: Connected to metastore.
17/10/03 05:59:56 INFO session.SessionState: Created local directory: /tmp/c2d22d09-7425-4bb3-94c3-39cb32267c7d_resources
17/10/03 05:59:56 INFO session.SessionState: Created HDFS directory: /tmp/hive/training/c2d22d09-7425-4bb3-94c3-39cb32267c7d
17/10/03 05:59:56 INFO session.SessionState: Created local directory: /tmp/training/c2d22d09-7425-4bb3-94c3-39cb32267c7d
17/10/03 05:59:56 INFO session.SessionState: Created HDFS directory: /tmp/hive/training/c2d22d09-7425-4bb3-94c3-39cb32267c7d/_tmp_space.db
17/10/03 05:59:56 INFO session.SessionState: No Tez session required at this point. hive.execution.engine=mr.
In [11]:
In [11]: type(mydf001)
Out[11]: pyspark.sql.dataframe.DataFrame
In [12]: mydf001.count()
17/10/03 06:00:29 INFO spark.SparkContext: Starting job: count at NativeMethodAccessorImpl.java:-2
17/10/03 06:00:29 INFO scheduler.DAGScheduler: Registering RDD 2 (count at NativeMethodAccessorImpl.java:-2)
17/10/03 06:00:29 INFO scheduler.DAGScheduler: Got job 0 (count at NativeMethodAccessorImpl.java:-2) with 1 output partitions
17/10/03 06:00:29 INFO scheduler.DAGScheduler: Final stage: ResultStage 1 (count at NativeMethodAccessorImpl.java:-2)
17/10/03 06:00:29 INFO scheduler.DAGScheduler: Parents of final stage: List(ShuffleMapStage 0)
17/10/03 06:00:29 INFO scheduler.DAGScheduler: Missing parents: List(ShuffleMapStage 0)
17/10/03 06:00:29 INFO scheduler.DAGScheduler: Submitting ShuffleMapStage 0 (MapPartitionsRDD[2] at count at NativeMethodAccessorImpl.java:-2), which has no missing parents
17/10/03 06:00:30 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 11.0 KB, free 11.0 KB)
17/10/03 06:00:31 INFO storage.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 5.2 KB, free 16.1 KB)
17/10/03 06:00:31 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:36793 (size: 5.2 KB, free: 208.8 MB)
17/10/03 06:00:31 INFO spark.SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1006
17/10/03 06:00:31 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD[2] at count at NativeMethodAccessorImpl.java:-2)
17/10/03 06:00:31 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
17/10/03 06:00:31 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, partition 0,PROCESS_LOCAL, 1911 bytes)
17/10/03 06:00:31 INFO executor.Executor: Running task 0.0 in stage 0.0 (TID 0)
17/10/03 06:00:32 INFO codegen.GenerateMutableProjection: Code generated in 425.82589 ms
17/10/03 06:00:32 INFO codegen.GenerateUnsafeProjection: Code generated in 78.278589 ms
17/10/03 06:00:33 INFO codegen.GenerateMutableProjection: Code generated in 84.676206 ms
17/10/03 06:00:33 INFO codegen.GenerateUnsafeRowJoiner: Code generated in 60.144399 ms
17/10/03 06:00:33 INFO codegen.GenerateUnsafeProjection: Code generated in 95.977074 ms
17/10/03 06:00:34 INFO jdbc.JDBCRDD: closed connection
17/10/03 06:00:34 INFO executor.Executor: Finished task 0.0 in stage 0.0 (TID 0). 1334 bytes result sent to driver
17/10/03 06:00:34 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 3081 ms on localhost (1/1)
17/10/03 06:00:34 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
17/10/03 06:00:34 INFO scheduler.DAGScheduler: ShuffleMapStage 0 (count at NativeMethodAccessorImpl.java:-2) finished in 3.163 s
17/10/03 06:00:34 INFO scheduler.DAGScheduler: looking for newly runnable stages
17/10/03 06:00:34 INFO scheduler.DAGScheduler: running: Set()
17/10/03 06:00:34 INFO scheduler.DAGScheduler: waiting: Set(ResultStage 1)
17/10/03 06:00:34 INFO scheduler.DAGScheduler: failed: Set()
17/10/03 06:00:34 INFO scheduler.DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[5] at count at NativeMethodAccessorImpl.java:-2), which has no missing parents
17/10/03 06:00:34 INFO storage.MemoryStore: Block broadcast_1 stored as values in memory (estimated size 12.1 KB, free 28.3 KB)
17/10/03 06:00:34 INFO storage.MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 5.6 KB, free 33.9 KB)
17/10/03 06:00:34 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on localhost:36793 (size: 5.6 KB, free: 208.8 MB)
17/10/03 06:00:34 INFO spark.SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1006
17/10/03 06:00:34 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD[5] at count at NativeMethodAccessorImpl.java:-2)
17/10/03 06:00:34 INFO scheduler.TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
17/10/03 06:00:34 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 1.0 (TID 1, localhost, partition 0,NODE_LOCAL, 1999 bytes)
17/10/03 06:00:34 INFO executor.Executor: Running task 0.0 in stage 1.0 (TID 1)
17/10/03 06:00:34 INFO storage.ShuffleBlockFetcherIterator: Getting 1 non-empty blocks out of 1 blocks
17/10/03 06:00:34 INFO storage.ShuffleBlockFetcherIterator: Started 0 remote fetches in 32 ms
17/10/03 06:00:35 INFO codegen.GenerateMutableProjection: Code generated in 52.636353 ms
17/10/03 06:00:35 INFO codegen.GenerateMutableProjection: Code generated in 49.757505 ms
17/10/03 06:00:35 INFO executor.Executor: Finished task 0.0 in stage 1.0 (TID 1). 1666 bytes result sent to driver
17/10/03 06:00:35 INFO scheduler.DAGScheduler: ResultStage 1 (count at NativeMethodAccessorImpl.java:-2) finished in 0.795 s
17/10/03 06:00:35 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 1.0 (TID 1) in 789 ms on localhost (1/1)
17/10/03 06:00:35 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool
17/10/03 06:00:35 INFO scheduler.DAGScheduler: Job 0 finished: count at NativeMethodAccessorImpl.java:-2, took 6.451521 s
Out[12]: 129761
In [13]:
[Spark][Python]Spark 访问 mysql , 生成 dataframe 的例子:的更多相关文章
- [Spark][Python]spark 从 avro 文件获取 Dataframe 的例子
[Spark][Python]spark 从 avro 文件获取 Dataframe 的例子 从如下地址获取文件: https://github.com/databricks/spark-avro/r ...
- Spark(Python) 从内存中建立 RDD 的例子
Spark(Python) 从内存中建立 RDD 的例子: myData = ["Alice","Carlos","Frank"," ...
- [Spark][Python]Spark Python 索引页
Spark Python 索引页 为了查找方便,建立此页 === RDD 基本操作: [Spark][Python]groupByKey例子
- [spark][python]Spark map 处理
map 就是对一个RDD的各个元素都施加处理,得到一个新的RDD 的过程 [training@localhost ~]$ cat names.txtYear,First Name,County,Sex ...
- crontab定时运行python脚本访问MySQL遇到问题
最近写了一个python脚本来定时备份MySQL数据库.具体实现如下: 1)python脚本中使用os.system("mysqldump -h127.0.0.1 -uroot -ppass ...
- python+pymysql访问mysql数据库
今天跟大家分享两种场景的python连接MySQL方法: 场景一:连接远程MySQL 首先,安装pymysql:在命令行执行pip install pymysql指令. 然后,导入pymysql: i ...
- [Spark][Python]Spark Join 小例子
[training@localhost ~]$ hdfs dfs -cat people.json {"name":"Alice","pcode&qu ...
- 今天看到可以用sqlalchemy在python上访问Mysql
from sqlalchemy import create_engine, MetaData, and_ 具体的还没有多看.
- 基础 ADO.NET 访问MYSQL 与 MSSQL 数据库例子
虽然实际开发时都是用 Entity 了,但是基础还是要掌握和复习的 ^^ //set connection string, server,database,username,password MySq ...
随机推荐
- 《数据库系统概念》11-扩展的E-R特性
虽然基本的E-R特性可以应对大部分数据库建模,但为了使用方便,也提供扩展的E-R特性 一.特化Specialization有时一个实体集会保护若干子集,这些子集各自具有不同的属性.比如person可 ...
- 听说你还不会用Dagger2?Dagger2 For Android最佳实践教程
前言 Dagger2是现在非常火的一个依赖注入框架,目前由Google维护,在Github上面已经有12K star了.Dagger2的入门门槛其实是比较高的,据了解,目前有很多Android工程师对 ...
- debian图形界面安装安装GNOME中文桌面环境_刀光剑影_新浪博客 - Google Chrome
debian图形界面安装安装GNOME中文桌面环境 (2012-06-12 16:47:41) 转载▼ 标签: 杂谈 分类: linux 安装GNOME中文桌面环境 安装基本的X系统 # apt-g ...
- excel中如何隐藏列和取消隐藏列
https://jingyan.baidu.com/article/148a192191dc9a4d71c3b11c.html excel如何隐藏列 1 先看下原表格是怎么样的. 2 隐藏列方法一:首 ...
- python第二十天
logging模块 re正则表达式
- Vlc支持IE 360 低版本的Google浏览器
VLC 插件代码: <object type='application/x-vlc-plugin' pluginspage="http://www.videolan.org/" ...
- Python基础知识:类
初级篇 面向过程:根据业务逻辑从上到下写垒代码 函数式:将某功能代码封装到函数中,日后便无需重复编写,仅调用函数即可 面向对象:对函数进行分类和封装,让开发“更快更好更强...” 1.面向对象三大特性 ...
- 1.2 Spyder的基本使用
[TOC] 1.0 Spyder的基本使用 1.Spyder的主题与文字修改: 2.Spyder的使用技巧: (一)安装Anaconda: 官网下载:https://www.anaconda.com/ ...
- 第一条:了解Objective-C语言的起源
第一条:了解Objective-C语言的起源 Objective-C使用的消息结构而非函数调用. Objective-C的重要工作都由"运行组件(runtime component)&quo ...
- 17秋 软件工程 团队第五次作业 Alpha Scrum2
17秋 软件工程 团队第五次作业 Alpha Scrum2 今日完成的任务 杰麟:Java后端的学习: 世强:登录和注册接口编写: 港晨:完成数据库表的设计: 树民.陈翔:完成超级管理员后端框架. 其 ...