Spark 分布式内存计算框架 百度网盘(80.04G)

Spark 分布式内存计算框架资源大小共:80.04G,该课程资源共1164个文件,其中包含18个高清视频,54相关文档,详见下面资源目录。

Spark 分布式内存计算框架 百度网盘(80.04G)

Spark 分布式内存计算框架 百度网盘(80.04G)

资源目录:

Spark 分布式内存计算框架 [80.04G]

视频-Spark分布式内存计算框架[12.04G]

01_Spark框架中流式处理模块和四大流式计算框架.mp4[21.89M]

01_大数据技术框架总述.mp4[31.45M]

01_上次课程内容回顾.mp4[47.21M]

01_昨日课程内容回顾(一).mp4[24.39M]

01_昨日课程内容回顾.mp4[33.24M]

01_昨日课程内容回顾:SparkStreaming窗口和偏移量管理.mp4[26.30M]

01_昨日课程内容回顾:核心要点.mp4[15.72M]

01_昨日课程内容回顾:入门案例和DStream.mp4[26.70M]

02_RDD共享变量:含义及案例需求说明.wmv[69.80M]

02_Spark开发词频统计程序.wmv[42.05M]

02_Spark课程说明.wmv[46.22M]

02_今日课程内容提纲.wmv[35.39M]

02_实时应用数据源Kafka中数据来源.wmv[25.79M]

02_昨日课程内容回顾(二).wmv[38.97M]

02_昨日课程内容回顾:StructuredStreaming基础入门.wmv[62.50M]

02_昨日课程内容回顾:集成Kafka.wmv[67.52M]

02_昨日课程内容回顾:思维导图.wmv[60.56M]

03_Straming概述:流式应用场景.wmv[26.85M]

03_分布式SQL引擎:spark-sql交互式命令行使用.wmv[47.56M]

03_共享变量:编程实现非单词过滤.wmv[67.64M]

03_广告投放的地域分布(五).wmv[85.99M]

03_今日课程内容提纲.wmv[31.95M]

03_实时存储HBase:业务实现概述.wmv[26.43M]

03_物联网数据实时分析:需求概述及数据准备.wmv[55.84M]

03_昨日课程内容回顾:无状态和有状态计算.wmv[45.14M]

04_Spark应用提交:spark-submit命令参数.wmv[55.57M]

04_Spark框架中各个模块的数据结构抽象.wmv[36.87M]

04_Spark是什么.wmv[54.33M]

04_Straming概述:Lambda架构.wmv[30.16M]

04_分布式SQL引擎:启动ThriftServer服务和beeline连接.wmv[25.41M]

04_分区操作函数mapPartitions和foreachPartition.wmv[39.38M]

04_广告投放其他维度分析.wmv[45.69M]

04_实时存储HBase:编写应用主类整体结构.wmv[23.73M]

04_物联网数据实时分析:基于DSL实现.wmv[69.30M]

04_应用案例:实时窗口统计window.wmv[75.98M]

05_Spark四大特点.wmv[41.84M]

05_Spark应用提交:提交local和Standalone模式运行.wmv[67.96M]

05_Straming概述:流式数据计算模式.wmv[40.46M]

05_分布式SQL引擎:JDBCClient使用.wmv[31.04M]

05_今日课程内容提纲.wmv[30.99M]

05_实时存储HBase:编写streamingProcess方法整体结构.wmv[40.07M]

05_物联网数据实时分析:基于SQL实现.wmv[43.17M]

05_应用案例:实时窗口统计reduceByKeyAndWindow.wmv[66.32M]

05_应用提交:应用开发测试概述.wmv[8.94M]

05_重分区函数repartition和coalesce.wmv[30.21M]

06_Scala集合中聚合函数reduce和fold.wmv[21.29M]

06_SparkonYARN:参数配置和服务启动.wmv[68.42M]

06_Spark框架模块.wmv[53.33M]

06_SparkSession应用入口.wmv[46.11M]

06_Straming概述:SparkStreaming计算思想.wmv[32.76M]

06_离线数据分析流程(五步).wmv[22.34M]

06_偏移量管理:概述及Checkpoint原理.wmv[46.99M]

06_实时存储HBase:编写HBaseDao数据层.wmv[73.00M]

06_数据去重及案例演示.wmv[49.13M]

06_应用提交:应用打包.wmv[51.93M]

07_ContinuousProcessing连续处理原理及演示.wmv[119.07M]

07_RDD中聚合函数reduce和fold.wmv[40.44M]

07_SparkonYARN:提交运行PI和WordCount.wmv[39.40M]

07_Spark运行模式.wmv[19.43M]

07_词频统计WordCount:基于DSL编程.wmv[50.18M]

07_官方案例运行:每批次词频统计WordCount.wmv[29.51M]

07_今日课程内容提纲.wmv[44.23M]

07_偏移量管理:重构代码.wmv[47.68M]

07_实时存储HBase:保存偏移量至Zookeeper.wmv[66.68M]

07_应用提交:删除分区数据和报表数据.wmv[11.77M]

08_DeployMode部署模式:client和cluster区别(理论).wmv[17.87M]

08_RDD中聚合函数aggregate.wmv[45.93M]

08_Spark课程环境准备(虚拟机).wmv[69.79M]

08_词频统计WordCount:基于SQL编程.wmv[56.59M]

08_集群提交运行(本地模式).wmv[61.93M]

08_流式处理:时间概念.wmv[27.40M]

08_偏移量管理:Checkpoint编码实现.wmv[144.96M]

08_入门案例:Streaming编程模块.wmv[28.05M]

08_实时存储HBase:从Zookeeper加载偏移量.wmv[56.44M]

08_综合实战业务背景和需求概述.wmv[72.76M]

09_DeployMode部署模式:client和cluster演示(理Standalone集群).wmv[35.11M]

09_PairRDDFunctions中聚合函数.wmv[51.79M]

09_Spark本地模式配置.wmv[53.15M]

09_SparkSQL前世今生.wmv[69.15M]

09_集群提交运行(集群模式).wmv[69.31M]

09_偏移量管理:手动管理偏移量和状态思路_.wmv[58.46M]

09_入门案例:代码实现及测试运行.wmv[38.87M]

09_实时存储HBase:测试运行SparkStreaming实现应用.wmv[66.56M]

09_数据调研和业务需求.wmv[62.38M]

09ent-time窗口分析:原理剖析.wmv[24.11M]

10_Oozie集成Spark2安装配置.wmv[46.81M]

10_RDD关联函数.wmv[44.33M]

10_SparkonYARN不同DeployMode区别(画图演示).wmv[54.21M]

10_SparkSQL官方定义和特性.wmv[28.50M]

10_本地模式运行spark-shell及测试.wmv[59.74M]

10_环境搭建:大数据环境.wmv[35.02M]

10_偏移量管理:MySQL存储偏移量(一).wmv[46.31M]

10_入门案例:Streaming应用监控.wmv[28.17M]

10_实时存储HBase:基于StructuredStreaming实现.wmv[78.33M]

10ent-time窗口分析:编程测试.wmv[56.77M]

11_DataFrame是什么.wmv[44.61M]

11_Oozie调度框架回顾.wmv[49.14M]

11_RDD函数练习.wmv[10.44M]

11_SparkStreaming运行工作原理.wmv[68.90M]

11_环境搭建:应用开发环境.wmv[25.72M]

11_偏移量管理:MySQL存储偏移量(二).wmv[119.84M]

11_上午课程内容回顾.wmv[38.40M]

11_上午内容复习回顾.wmv[32.67M]

11_实时订单报表:整体业务概述.wmv[19.49M]

11ent-time窗口生成.wmv[51.15M]

12_BatchInterval和BlockInterval.wmv[24.78M]

12_DataFrame中Schema.wmv[7.68M]

12_Oozie调度Spark2应用.wmv[139.45M]

12_RDD持久化.wmv[49.08M]

12_spark-shell运行词频统计WordCount(一).wmv[36.12M]

12_YARNClient模式运行流程及演示.wmv[51.47M]

12_偏移量管理:Kafka自身管理(异步提交偏移量).wmv[56.82M]

12_实时订单报表:Spark-Redis库使用.wmv[73.60M]

12_水位Watermark引入及延迟数据.wmv[37.53M]

12_项目初始化:属性文件和工具类.wmv[18.97M]

13_DataFrame中每行数据Row.wmv[25.90M]

13_spark-shell运行词频统计WordCount(二).wmv[49.82M]

13_StructuredStreaming结构化流模块综合概述.wmv[29.41M]

13_YARNCluster模式运行流程及演示.wmv[55.47M]

13_上午课程内容回顾.wmv[46.83M]

13_深入剖析Oozie组件及运行本质和配置.wmv[104.45M]

13_实时订单报表:报表业务主类结构编写.wmv[60.18M]

13_水位Watermark计算及案例讲解.wmv[35.80M]

13_项目初始化:加载属性文件.wmv[34.86M]

14_DStream是什么.wmv[28.75M]

14_Hue创建Oozie工作流.wmv[69.94M]

14_RDDCheckpoint.wmv[48.29M]

14_SparkApplication运行MAIN函数代码执行.wmv[30.98M]

14_StructuredStreaming课程内容提纲.wmv[35.03M]

14_报表开发:总销售额实现和测试.wmv[61.66M]

14_监控页面及圆周率PI运行.wmv[57.89M]

14_上午课程内容回顾.wmv[40.26M]

14_实时综合案例:背景概述.wmv[32.20M]

14_项目初始化:SparkSession工具类.wmv[65.72M]

15_DStreamOperations函数概述.wmv[29.00M]

15_DStream中针对RDD操作函数说明.wmv[5.83M]

15_Oozie调度应用:调度【ETL应用】.wmv[56.01M]

15_RDD转换DataFrame:综合概述.wmv[25.25M]

15_SogouQ日志分析:数据调研和业务分析.wmv[42.18M]

15_SparkStandalone集群架构.wmv[35.78M]

15_SparkStreaming不足及Structured诞生.wmv[70.62M]

15_报表开发:省份销售额实现.wmv[42.55M]

15_实时综合案例:内容提纲.wmv[38.47M]

15_项目初始化:记录日志和配置log4j.properties文件.wmv[32.45M]

15_总述Spark应用运行.wmv[15.62M]

16_DStream中transform函数使用.wmv[23.85M]

16_Oozie调度应用:调度【报表应用】.wmv[36.78M]

16_RDD概念:核心抽象及RDD论文.wmv[82.98M]

16_RDD转换DataFrame:反射类型推断.wmv[32.31M]

16_SogouQ日志分析:HanLP中文分词.wmv[42.94M]

16_SparkStandalone集群部署测试(一).wmv[84.86M]

16_StructuredStreaming概述:模块介绍和核心思想.wmv[29.50M]

16_报表开发:城市销售额实现.wmv[54.97M]

16_广告数据ETL:IP地址解析.wmv[48.63M]

16_实时综合案例:业务需求概述.wmv[48.43M]

17_DStream中foreachRDD函数使用.wmv[34.27M]

17_Hue创建OozieWorkFlow:ETL应用.wmv[34.60M]

17_RDD概念:RDD定义.wmv[31.86M]

17_RDD转换DataFrame:自定义Schema.wmv[26.55M]

17_SogouQ日志分析:读取数据封装SogouRecord.wmv[42.70M]

17_SparkStandalone集群部署测试(二).wmv[23.64M]

17_StructuredStreaming概述:编程模型.wmv[31.05M]

17_报表开发:Double精度丢失处理.wmv[59.91M]

17_广告数据ETL:IP工具类.wmv[21.23M]

17_环境搭建说明:大数据环境.wmv[23.33M]

18_Hue创建OozieWorkFlow:报表应用.wmv[27.78M]

18_RDD概念:RDD特性.wmv[43.72M]

18_SogouQ日志分析:搜索关键词统计.wmv[34.93M]

18_SparkApplicaiton应用组成.wmv[34.50M]

18_StructuredStreaming概述:编程模型(二).wmv[20.07M]

18_toDF函数指定列名称转换为DataFrame.wmv[31.98M]

18_报表开发:每日实时统计需求说明.wmv[17.02M]

18_广告数据ETL:Hive表创建.wmv[66.44M]

18_环境搭建说明:应用开发环境.wmv[19.93M]

18_上午课程内容回顾.wmv[36.59M]

19_Hue创建OozieCoordinator:ETL应用.wmv[38.83M]

19_RDD概念:WordCount中RDD.wmv[44.11M]

19_SogouQ日志分析:用户搜索点击统计.wmv[27.97M]

19_Spark应用WEBUI监控.wmv[57.66M]

19_SparkStreaming流式应用三种状态(一).wmv[16.50M]

19_报表开发:每日实时统计(一).wmv[82.58M]

19_广告数据ETL:日期获取.wmv[26.42M]

19_基于DSL分析(函数说明)和SQL分析.wmv[37.45M]

19_入门案例WordCount:功能演示.wmv[45.57M]

19_项目初始化:加载属性文件.wmv[47.13M]

20_Hue创建OozieCoordinator:报表应用.wmv[22.82M]

20_RDD创建:两种方式和并行化集合.wmv[26.17M]

20_SogouQ日志分析:搜索时间段统计.wmv[25.56M]

20_SparkStandaloneHA高可用配置测试.wmv[43.42M]

20_SparkStreaming流式应用三种状态(二).wmv[50.92M]

20_报表开发:每日实时统计(二).wmv[98.58M]

20_电影评分数据分析:需求说明.wmv[21.26M]

20_广告数据ETL:加载JSON数据.wmv[29.57M]

20_入门案例WordCount:Socket数据源和Console接收器.wmv[15.83M]

20_项目初始化:工具类SparkUtils.wmv[34.56M]

21_RDD创建:外部存储系统和读取小文件.wmv[46.99M]

21_Spark内核调度:引例WordCount.wmv[19.93M]

21_Spark应用开发:创建Maven工程及模块.wmv[30.07M]

21_报表开发:每日实时统计(三).wmv[54.96M]

21_电影评分数据分析:数据ETL.wmv[18.50M]

21_广告数据ETL:数据ETL(一).wmv[54.01M]

21_流式应用技术栈及Kafka面试题.wmv[21.32M]

21_入门案例WordCount:编程实现.wmv[75.48M]

21_实时综合案例:模拟交易订单数据.wmv[57.54M]

21_外部数据源:综合概述.wmv[17.05M]

22_InputSources输入源概述及File数据源.wmv[39.17M]

22_RDD分区数目.wmv[17.43M]

22_Spark内核调度:RDD依赖.wmv[29.34M]

22_Spark应用开发:创建SparkContext对象.wmv[31.96M]

22_SparkStreaming集成Kafka两种方式(Old和NewConsumerAPI).wmv[52.76M]

22_电影评分数据分析:SQL分析.wmv[23.06M]

22_广告数据ETL:数据ETL(二).wmv[18.86M]

22_流式应用调优综合概述(三个方面).wmv[41.45M]

22_实时综合案例:数据实时ETL(一).wmv[38.07M]

22_外部数据源:Spark与HBase交互概述.wmv[14.92M]

23_Ratesource数据源.wmv[19.34M]

23_RDD函数分类.mp4[16.06M]

23_Spark内核调度:DAG和Stage.wmv[21.26M]

23_Spark应用开发:词频统计Wordc编写测试.wmv[20.89M]

23_电影评分数据分析:DSL分析.wmv[42.36M]

23_广告数据ETL:数据ETL(三).wmv[33.23M]

23_集成KafkaOldConsumerAPI两种区别.wmv[12.76M]

23_实时综合案例:数据实时ETL(二).wmv[82.67M]

23_外部数据源:HBaseSink.wmv[68.77M]

23_应用打包提交运行本地模式(一).wmv[66.39M]

24_OldConsumerAPI中Direct方式集成:编程实现.wmv[79.18M]

24_Spark内核调度:SparkShuffle.wmv[24.08M]

24_Spark应用开发:词频统计TopKey(一).wmv[14.67M]

24_StreamingQueries基本设置(名称、触发、检查点及输出模式).wmv[50.92M]

24_电影评分数据分析:保存结果至MySQL和CSV文件.wmv[39.13M]

24_广告数据ETL:数据ETL(四).wmv[76.48M]

24_实时综合案例:数据实时ETL(三).wmv[159.96M]

24_外部数据源:HBaseSource.wmv[92.76M]

24_应用打包提交运行本地模式(二).wmv[42.56M]

25_OldConsumerAPI中Direct方式集成:底层原理及最大数据量.wmv[31.09M]

25_Spark内核调度:Job调度流程.wmv[42.06M]

25_Spark应用开发:词频统计TopKey(二).mp4[8.86M]

25_电影评分数据分析:SparkSQL中Shuffle分区数.wmv[19.63M]

25_广告数据ETL:Spark分布式缓存.wmv[27.63M]

25_实时应用停止:思路分析.wmv[40.69M]

25_输出终端Sink概述.wmv[17.44M]

25_外部数据源:MySQLSink.mp4[26.51M]

25_应用性能调优:数据本地性.wmv[37.79M]

26_Dataset是什么.wmv[23.62M]

26_NewConsumerAPI方式集成编程.wmv[56.60M]

26_Spark内核调度:Spark基本概念.wmv[24.28M]

26_广告数据ETL:SparkonHive与HiveonSpark区别.wmv[17.49M]

26_实时应用停止:编程实现及测试.wmv[51.67M]

26_输出函数foreach使用.wmv[58.01M]

26_应用性能调优:SparkStreaming反压机制.wmv[24.11M]

27_RDD、DS和DF之间转换.wmv[48.84M]

27_Spark并行度(一).wmv[10.79M]

27_集成Kafka时获取消费偏移量信息.wmv[72.60M]

27_实时增量存储:概述(HBase及Elasticsearch).wmv[10.55M]

27_输出函数foreachBatch使用.wmv[33.30M]

27_业务报表分析:业务需求.wmv[12.53M]

27_应用性能调优:动态资源分配.wmv[24.66M]

28_Spark并行度(一).mp4[14.87M]

28_StructuredStreaming如何保证容错语义.wmv[26.36M]

28_存储Elasticsearch:集成Elasticsearch.wmv[25.69M]

28_面试题:如何理解RDD、DF和DS.wmv[12.84M]

28_业务报表分析:报表运行主类.wmv[44.81M]

28_应用案例:业务场景和需求说明.wmv[25.56M]

28_应用性能调优:日志管理.mp4[14.58M]

29_存储Elasticsearch:StructuredStreaming实现.mp4[40.40M]

29_集成Kafka概述及Kafka消费数据.wmv[47.02M]

29_外部数据源:加载load和保存save数据.wmv[41.78M]

29_业务报表分析:各地域数量分布(一).wmv[18.79M]

29_应用案例:初始化环境.wmv[107.90M]

30_集成Kafka:Kafka数据源.wmv[40.91M]

30_外部数据源:案例演示.(parquet、json、csv和jdbc).wmv[66.95M]

30_业务报表分析:各地域数量分布(二).wmv[49.37M]

30_应用案例:StreamingContextUtils工具类.wmv[24.12M]

31_集成Kafka:实时数据ETL架构.wmv[18.92M]

31_外部数据源:集成Hive概述.wmv[17.46M]

31_业务报表分析:各地域数量分布(三).wmv[59.29M]

31_应用案例:实时数据ETL存储.wmv[99.94M]

32_集成Kafka:基站数据准备.wmv[25.91M]

32_外部数据源:集成Hive(spark-shell).wmv[32.62M]

32_业务报表分析:各地域数量分布(四).wmv[64.78M]

32_应用案例:updateStateByKey函数.wmv[112.20M]

33_广告投放的地域分布(一).wmv[21.30M]

33_集成Kafka:KafkaSink.mp4[19.15M]

33_外部数据源:集成Hive(IDEA开发).wmv[24.29M]

33_应用案例:mapWithState函数.mp4[25.56M]

34_广告投放的地域分布(二).wmv[35.44M]

34_自定义UDF函数在SQL和DSL中使用.mp4[16.94M]

35_广告投放的地域分布(三).wmv[97.13M]

36_广告投放的地域分布(四).mp4[21.38M]

资料-Spark分布式内存计算框架[68.00G]

01_讲义[52.00M]

01_第一部分【Spark基础环境】讲义_V1.2.pdf[2.35M]

01_第一部分【Spark基础环境】教案_V1.2.pdf[4.91M]

02_第二部分【SparkCore】讲义_V1.2.pdf[1.86M]

02_第二部分【SparkCore】教案_V1.2.pdf[4.87M]

03_第三部分【SparkSQL】讲义_V1.2.pdf[1.96M]

03_第三部分【SparkSQL】教案_V1.2.pdf[4.29M]

04_第四部分【离线综合实战】讲义_V1.2.pdf[534.74K]

04_第四部分【离线综合实战】教案_V1.2.pdf[2.80M]

05_第五部分【SparkStreaming】讲义_V1.0.pdf[1.48M]

05_第五部分【SparkStreaming】讲义_V1.0.pptx[3.86M]

05_第五部分【SparkStreaming】教案_V1.2.pdf[5.58M]

06_第六部分【StructuredStreaming】讲义_V1.0.pdf[1.47M]

06_第六部分【StructuredStreaming】教案_V1.2.pdf[4.14M]

07_第七部分【实时综合实战】讲义_V1.0.pdf[570.46K]

07_第七部分【实时综合实战】讲义_V1.2.pdf[570.46K]

07_第七部分【实时综合实战】教案_V1.0.pdf[2.95M]

07_第七部分【实时综合实战】教案_V1.2.pdf[2.93M]

07_第七部分【实时综合实战】提纲_V1.0.xmind[611.11K]

大数据技术框架.xmind[62.61K]

第四部分【离线综合实战】教案_V1.0.pdf[4.25M]

03_笔记[11.57M]

img[10.46M]

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rm-ha-overview.png[30.01K]

yarn_architecture.gif[32.26K]

yarn_architecture-1599884674805.gif[32.26K]

03_笔记.zip[53.86K]

A01-RDD.png[40.53K]

A01-RDD#reduceByKey函数.png[24.62K]

A01-SparkStreaming状态统计.png[24.53K]

A01-SparkStreaming工作原理(BatchInterval和BlockInterval).png[24.97K]

A01-窗口生成规则.png[44.93K]

A01-分区操作函数.png[21.56K]

A01-数据本地性.png[30.39K]

A02-反压机制.png[21.95K]

A03-动态资源管理.png[46.58K]

A04-日志管理.png[42.14K]

B01-RDD共享变量.png[39.67K]

B01-RDD#reduce函数.png[32.37K]

B01-Standalone中cluster模式.png[29.62K]

B01-窗口含义.png[22.92K]

B01-窗口统计:滚动窗口.png[17.44K]

B02-RDD广播变量.png[137.60K]

B02-RDD#aggregate.png[45.78K]

B02-SparkStreaming滑动窗口.png[19.31K]

B02-窗口统计:滑动窗口.png[18.45K]

C01-Kafka消费者API.png[39.48K]

C01-SparkSQL外部数据源.png[36.16K]

C01-词频统计中RDD.png[31.18K]

C01-结构化编程模型.png[46.69K]

C02-集成Kafka时OldConsumer两种方式区别.png[62.96K]

C03-Direct方式消费数据.png[42.83K]

SparkDay06:SparkStreaming.md[11.35K]

Spark_Day01:Spark基础环境.md[14.20K]

Spark_Day02:数据结构RDD.md[14.04K]

Spark_Day03:SparkCore.md[12.94K]

Spark_Day04:SparkSQL.md[21.08K]

Spark_Day05:离线综合实战.md[17.44K]

Spark_Day07:SparkStreaming.md[12.39K]

Spark_Day08:StructuredStreaming.md[9.46K]

Spark_Day09:实时综合案例.md[16.49K]

Spark_Day10:实时综合案例.md[9.11K]

04_代码[220.37K]

参考代码[49.12K]

ckpt[10.57K]

StreamingStateCkpt.scala[5.70K]

StreamingTemplate.scala[4.86K]

hbase[12.42K]

HBaseDao.scala[3.87K]

RealTimeOrder2HBase.scala[5.03K]

ZkOffsetsUtils.scala[3.52K]

mock[5.68K]

MockOrderProducer.scala[5.08K]

OrderRecord.scala[0.59K]

offset[10.08K]

OffsetsUtils.scala[4.25K]

StreamingManagerOffsets.scala[5.83K]

resources[4.21K]

config.properties[2.13K]

log4j.properties[2.08K]

utils[6.18K]

JedisUtils.scala[1.00K]

SparkUtils.scala[2.37K]

StreamingUtils.scala[2.82K]

bigdata-spark.zip[15.87K]

spark-day03.zip[26.21K]

spark-day04.zip[22.72K]

spark-day05(原版).zip[8.70K]

spark-day05.zip[21.41K]

spark-day07.zip[35.60K]

spark-day08.zip[21.93K]

spark-day09.zip[18.82K]

05_数据[275.56M]

datas[166.21M]

filter[0.21K]

datas.input[0.21K]

sogou[154.39M]

SogouQ.reduced[153.48M]

SogouQ.sample[926.08K]

wordcount[0.17K]

wordcount.data[0.17K]

dataset[11.83M]

ip2region.db[6.74M]

pmt.json[5.08M]

hive[1.26K]

dept.txt[0.08K]

emp.txt[0.64K]

EMP-DEPT表.sql[0.55K]

iot[2.28K]

DeviceData.scala[0.48K]

MockIotDatas.scala[1.80K]

json[4.49M]

2015-03-01-11.json.gz[4.49M]

ml-100k[4.03M]

README[6.59K]

u.dat[1.89M]

u.data[1.89M]

u.item[230.83K]

u.user[22.10K]

ml-1m[23.75M]

movies.dat[167.29K]

ratings.dat[23.45M]

README[5.45K]

users.dat[131.22K]

mock[2.87K]

MockStationLog.scala[2.16K]

StationLog.scala[0.71K]

ratings100[77.05M]

part-00000[789.07K]

part-00001[789.15K]

part-00002[789.11K]

part-00003[789.10K]

part-00004[789.16K]

part-00005[789.15K]

part-00006[789.13K]

part-00007[789.09K]

part-00008[789.15K]

part-00009[789.14K]

part-00010[789.00K]

part-00011[788.98K]

part-00012[789.01K]

part-00013[789.00K]

part-00014[788.97K]

part-00015[788.98K]

part-00016[788.94K]

part-00017[789.10K]

part-00018[789.08K]

part-00019[788.99K]

part-00020[789.08K]

part-00021[789.01K]

part-00022[789.01K]

part-00023[788.99K]

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part-00025[788.99K]

part-00026[788.93K]

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part-00030[788.95K]

part-00031[788.92K]

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part-00033[788.89K]

part-00034[788.91K]

part-00035[788.92K]

part-00036[788.85K]

part-00037[788.89K]

part-00038[788.90K]

part-00039[788.80K]

part-00040[788.90K]

part-00041[788.91K]

part-00042[788.94K]

part-00043[789.08K]

part-00044[789.01K]

part-00045[789.07K]

part-00046[788.98K]

part-00047[789.03K]

part-00048[789.03K]

part-00049[789.01K]

part-00050[788.98K]

part-00051[789.05K]

part-00052[789.11K]

part-00053[789.23K]

part-00054[789.19K]

part-00055[789.08K]

part-00056[789.00K]

part-00057[789.06K]

part-00058[789.09K]

part-00059[789.15K]

part-00060[789.06K]

part-00061[789.12K]

part-00062[789.13K]

part-00063[789.08K]

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part-00065[788.93K]

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part-00085[788.85K]

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part-00090[788.63K]

part-00091[788.77K]

part-00092[788.88K]

part-00093[788.87K]

part-00094[788.81K]

part-00095[788.95K]

part-00096[788.89K]

part-00097[788.96K]

part-00098[788.90K]

part-00099[789.00K]

resources[7.25K]

employees.json[0.13K]

full_user.avsc[0.23K]

kv1.txt[5.68K]

people.json[0.07K]

people.txt[0.03K]

user.avsc[0.18K]

users.avro[0.33K]

users.parquet[0.60K]

wordcount[0.17K]

wordcount.data[0.17K]

资源文件和工具类[12.49K]

config[1.34K]

ApplicationConfig.scala[1.34K]

resources[1.93K]

config.properties[0.63K]

log4j.properties[1.30K]

utils[1.80K]

SparkUtils.scala[1.80K]

ReportSQLConstant.scala[7.42K]

06_软件[398.20M]

BuildSpark.lnk.重命名[0.92K]

kafkatool_64bit.zip[58.88M]

scala-2.11.12.tgz[27.77M]

scala-2.11.12.zip[27.82M]

spark-2.4.5.tgz[14.93M]

spark-2.4.5-bin-cdh5.16.2-2.11.tgz[268.81M]

07_资料[88.05M]

jedis[1.02K]

JedisUtils.scala[1.02K]

spark-redis-master[372.48K]

build[9.21K]

sbt[4.12K]

sbt-launch-lib.bash[5.09K]

dev[1.55K]

change-scala-version.sh[1.55K]

doc[41.06K]

cluster.md[0.73K]

configuration.md[0.83K]

dataframe.md[11.83K]

dev.md[0.94K]

getting-started.md[2.58K]

java.md[3.16K]

python.md[1.40K]

rdd.md[7.90K]

streaming.md[6.01K]

structured-streaming.md[5.68K]

project[0.29K]

build.properties[0.08K]

plugins.sbt[0.21K]

src[290.63K]

main[105.32K]

resources[0.05K]

META-INF[0.05K]

services[0.05K]

org.apache.spark.sql.sources.DataSourceRegister[0.05K]

scala[105.27K]

com[71.54K]

redislabs[71.54K]

provider[71.54K]

redis[71.54K]

partitioner[0.33K]

RedisPartition.scala[0.28K]

RedisPartitioner.scala[0.05K]

rdd[16.70K]

RedisRDD.scala[16.70K]

streaming[11.08K]

package.scala[0.10K]

RedisInputDStream.scala[2.33K]

redisStreamingFunctions.scala[2.47K]

RedisStreamReceiver.scala[6.18K]

util[10.32K]

CollectionUtils.scala[0.70K]

ConnectionUtils.scala[2.22K]

JsonUtils.scala[0.33K]

Logging.scala[0.89K]

ParseUtils.scala[1.38K]

PipelineUtils.scala[3.92K]

StreamUtils.scala[0.89K]

ConnectionPool.scala[1.44K]

package.scala[0.20K]

RedisConfig.scala[9.93K]

redisFunctions.scala[21.53K]

org[33.72K]

apache[33.72K]

spark[33.72K]

sql[33.72K]

redis[33.72K]

stream[14.26K]

RedisSource.scala[5.90K]

RedisSourceConfig.scala[1.49K]

RedisSourceOffset.scala[1.52K]

RedisSourceRdd.scala[1.16K]

RedisSourceTypes.scala[0.40K]

RedisStreamProvider.scala[1.20K]

RedisStreamReader.scala[2.60K]

BinaryRedisPersistence.scala[1.29K]

DefaultSource.scala[1.76K]

HashRedisPersistence.scala[1.65K]

redis.scala[1.28K]

RedisPersistence.scala[1.43K]

RedisSourceRelation.scala[12.05K]

test[185.30K]

resources[102.13K]

blog[10.06K]

log4j.properties[0.73K]

test.csv[91.34K]

scala[83.17K]

com[69.81K]

redislabs[69.81K]

provider[69.81K]

redis[69.81K]

df[45.24K]

benchmark[7.30K]

cluster[2.26K]

BinaryModelManyValueClusterBenchmarkSuite.scala[0.56K]

BinaryModelSingleValueClusterBenchmarkSuite.scala[0.56K]

HashModelManyValueClusterBenchmarkSuite.scala[0.57K]

HashModelSingleValueClusterBenchmarkSuite.scala[0.57K]

DataframeBenchmarkSuite.scala[3.67K]

ManyValueBenchmarkSuite.scala[0.88K]

SingleValueBenchmarkSuite.scala[0.50K]

cluster[2.48K]

BinaryDataframeClusterSuite.scala[0.77K]

CsvDataframeClusterSuite.scala[0.24K]

DataframeClusterSuite.scala[0.23K]

FilteredDataframeClusterSuite.scala[0.31K]

HashDataframeClusterSuite.scala[0.68K]

SparkSqlClusterSuite.scala[0.26K]

standalone[2.44K]

BinaryDataframeStandaloneSuite.scala[0.73K]

CsvDataframeStandaloneSuite.scala[0.25K]

DataframeStandaloneSuite.scala[0.24K]

FilteredDataframeStandaloneSuite.scala[0.32K]

HashDataframeStandaloneSuite.scala[0.64K]

SparkSqlStandaloneSuite.scala[0.27K]

BinaryDataframeSuite.scala[4.11K]

CsvDataframeSuite.scala[1.08K]

DataframeSuite.scala[9.63K]

FilteredDataframeSuite.scala[1.33K]

HashDataframeSuite.scala[11.01K]

RedisDataframeSuite.scala[2.11K]

SparkSqlSuite.scala[3.75K]

env[1.75K]

Env.scala[0.46K]

RedisClusterEnv.scala[0.63K]

RedisStandaloneEnv.scala[0.66K]

rdd[8.34K]

cluster[0.69K]

RedisKeysClusterSuite.scala[0.23K]

RedisRDDClusterSuite.scala[0.23K]

RedisRddExtraClusterSuite.scala[0.24K]

standalone[0.73K]

RedisKeysStandaloneSuite.scala[0.24K]

RedisRddExtraStandaloneSuite.scala[0.25K]

RedisRDDStandaloneSuite.scala[0.24K]

RedisKeysSuite.scala[1.22K]

RedisRddExtraSuite.scala[1.32K]

RedisRddSuite.scala[4.38K]

stream[6.64K]

cluster[0.24K]

RedisXStreamClusterSuite.scala[0.24K]

standalone[0.25K]

RedisXStreamStandaloneSuite.scala[0.25K]

RedisXStreamSuite.scala[6.15K]

util[4.53K]

BenchmarkTest.java[0.38K]

CollectionUtilsTest.scala[0.62K]

ConnectionUtilsTest.scala[0.78K]

EntityId.scala[0.29K]

JsonUtilsTest.scala[0.29K]

Person.scala[1.18K]

TestUtils.scala[0.99K]

RedisBenchmarks.scala[0.68K]

RedisConfigSuite.scala[1.07K]

SparkRedisSuite.scala[0.73K]

SparkStreamingRedisSuite.scala[0.83K]

org[13.37K]

apache[13.37K]

spark[13.37K]

sql[13.37K]

redis[13.37K]

stream[12.91K]

cluster[0.25K]

RedisStreamSourceClusterSuite.scala[0.25K]

standalone[0.27K]

RedisStreamSourceStandaloneSuite.scala[0.27K]

RedisConsumerOffsetTest.scala[0.58K]

RedisSourceConfigSuite.scala[1.54K]

RedisSourceTest.scala[0.75K]

RedisStreamSourceSuite.scala[9.52K]

RedisSourceRelationTest.scala[0.46K]

.gitignore[0.41K]

.travis.yml[0.54K]

LICENSE[1.48K]

Makefile[3.39K]

pom.xml[11.93K]

README.md[3.84K]

scalastyle-config.xml[8.16K]

Spark框架论文[1.83M]

EECS-2011-82.pdf[0.98M]

nsdi_spark.pdf[865.54K]

参考代码[19.46K]

mock[4.52K]

MockSearchLogs.scala[3.99K]

SearchLog.scala[0.53K]

offset[10.08K]

OffsetsUtils.scala[4.25K]

StreamingManagerOffsets.scala[5.83K]

StreamingTemplate.scala[4.86K]

流式计算引擎论文[3.09M]

ApacheFlink:StreamandBatchProcessinginaSingleEngine.pdf[388.04K]

DiscretizedStreams:Fault-TolerantStreamingComputationatScale.pdf[739.02K]

Storm@Twitter.pdf[1.99M]

流式系统[26.26M]

StreamingSystem第二章【TheWhat-Where-When-andHowofDataProcessing】.pdf[15.88M]

StreamingSystem第一章【Streaming101】.pdf[10.38M]

an-introduction-to-higher-order-functions-in-spark-sql.pdf[398.20K]

ApacheSpark2.4内置的Avro数据源介绍.mhtml[2.29M]

ApacheSpark2.4新增内置函数和高阶函数使用介绍.mhtml[2.91M]

bk_spark-component-guide.pdf[1.67M]

Google-Bigtable中文版_1.0.pdf[837.70K]

Google-File-System中文版_1.0.pdf[1.18M]

Google-MapReduce中文版_1.0.pdf[653.85K]

ip2region解析库概述.png[548.92K]

Job提交过程.png[540.08K]

KafkaConsumer-Zookeper.png[365.56K]

mysql练习题.md[20.15K]

RDDOperationFunctions.xmind[254.57K]

SparkSQL,Built-inFunctions.mhtml[576.96K]

spark-redis.png[22.58K]

spark-redis-master.zip[144.38K]

Spark-Shuffle前世今生.xmind[113.33K]

StreamingSystemsTheWhat,Where,When,andHowofLarge-ScaleDataProcessing.epub[31.56M]

StructuredStreaming编程向导.mhtml[826.08K]

wordcount-jobs-job.png[74.81K]

淘宝技术这十年.pdf[11.01M]

运行Spark-shell,解决Unabletoloadnative-hadooplibraryforyourplatform.mhtml[636.44K]

08_提交[42.98M]

ads_etl[270.63K]

lib[268.77K]

config-1.2.1.jar[214.41K]

ip2region-1.7.2.jar[16.34K]

spark-ads_2.11-1.0.0.jar[38.02K]

job.properties[0.67K]

workflow.xml[1.18K]

ads_report[3.85M]

lib[3.85M]

config-1.2.1.jar[214.41K]

mysql-connector-java-8.0.19.jar[2.25M]

protobuf-java-3.6.1.jar[1.36M]

spark-ads_2.11-1.0.0.jar[38.02K]

job.properties[0.89K]

workflow.xml[1.18K]

cron_ads_etl[2.26K]

coordinator.xml[0.32K]

job.properties[0.77K]

workflow.xml[1.18K]

cron_ads_report[2.48K]

coordinator.xml[0.32K]

job.properties[0.98K]

workflow.xml[1.18K]

jars[3.83M]

config-1.2.1.jar[214.41K]

ip2region-1.7.2.jar[16.34K]

mysql-connector-java-8.0.19.jar[2.25M]

protobuf-java-3.6.1.jar[1.36M]

submit[33.06M]

order-es[3.24M]

config-1.2.1.jar[214.41K]

elasticsearch-spark-20_2.11-6.0.0.jar[735.73K]

kafka-clients-2.0.0.jar[1.81M]

spark-sql-kafka-0-10_2.11-2.4.5.jar[522.38K]

submit-es.sh[0.99K]

order-etl[2.54M]

config-1.2.1.jar[214.41K]

ip2region-1.7.2.jar[16.34K]

kafka-clients-2.0.0.jar[1.81M]

spark-sql-kafka-0-10_2.11-2.4.5.jar[522.38K]

submit-etl.sh[1.13K]

order-hbase[17.17M]

config-1.2.1.jar[214.41K]

fastjson-1.2.47.jar[533.76K]

hbase-client-1.2.0-cdh5.16.2.jar[1.27M]

hbase-common-1.2.0-cdh5.16.2.jar[573.30K]

hbase-hadoop2-compat-1.2.0-cdh5.16.2.jar[99.08K]

hbase-hadoop-compat-1.2.0-cdh5.16.2.jar[40.74K]

hbase-prefix-tree-1.2.0-cdh5.16.2.jar[99.60K]

hbase-protocol-1.2.0-cdh5.16.2.jar[4.48M]

hbase-server-1.2.0-cdh5.16.2.jar[4.19M]

high-scale-lib-1.1.1.jar[93.73K]

htrace-core-3.2.0-incubating.jar[1.42M]

kafka_2.11-0.8.2.1.jar[3.77M]

metrics-core-2.2.0.jar[80.20K]

spark-streaming-kafka-0-8_2.11-2.4.5.jar[295.90K]

submit-hbase.sh[1.55K]

zkclient-0.3.jar[62.51K]

order-report[3.25M]

commons-pool2-2.0.jar[104.55K]

config-1.2.1.jar[214.41K]

jedis-3.2.0.jar[640.19K]

kafka-clients-2.0.0.jar[1.81M]

spark-sql-kafka-0-10_2.11-2.4.5.jar[522.38K]

submit-report.sh[1.06K]

config.properties[2.13K]

ip2region.db[6.74M]

orders-app-1.0.0.jar[102.43K]

wf_spark_pi[1.93M]

lib[1.92M]

spark-examples_2.11-2.4.5.jar[1.92M]

job.properties[0.64K]

workflow.xml[1.18K]

config.properties[0.63K]

oozie-spark2.sh[1.76K]

spark-ads_2.11-1.0.0.jar[38.02K]

spark-oozie-wf.xml[1.23K]

submit-app.sh[3.84K]

spark_day00_虚拟机[44.20G]

SparkNode01[39.23G]

NewCentOS-cl1.vmdk[0.85K]

NewCentOS-cl1-000001.vmdk[0.68K]

NewCentOS-cl1-000001-s001.vmdk[1.48G]

NewCentOS-cl1-000001-s002.vmdk[2.55G]

NewCentOS-cl1-000001-s003.vmdk[1.59G]

NewCentOS-cl1-000001-s004.vmdk[2.24G]

NewCentOS-cl1-000001-s005.vmdk[1.88G]

NewCentOS-cl1-000001-s006.vmdk[75.13M]

NewCentOS-cl1-000001-s007.vmdk[1.38G]

NewCentOS-cl1-000001-s008.vmdk[320.00K]

NewCentOS-cl1-000002.vmdk[0.67K]

NewCentOS-cl1-000002-s001.vmdk[263.19M]

NewCentOS-cl1-000002-s002.vmdk[1.42G]

NewCentOS-cl1-000002-s003.vmdk[1.59G]

NewCentOS-cl1-000002-s004.vmdk[2.14G]

NewCentOS-cl1-000002-s005.vmdk[100.38M]

NewCentOS-cl1-000002-s006.vmdk[81.06M]

NewCentOS-cl1-000002-s007.vmdk[529.06M]

NewCentOS-cl1-000002-s008.vmdk[320.00K]

NewCentOS-cl1-000003.vmdk[0.68K]

NewCentOS-cl1-000003-s001.vmdk[9.44M]

NewCentOS-cl1-000003-s002.vmdk[9.63M]

NewCentOS-cl1-000003-s003.vmdk[8.06M]

NewCentOS-cl1-000003-s004.vmdk[576.00K]

NewCentOS-cl1-000003-s005.vmdk[10.81M]

NewCentOS-cl1-000003-s006.vmdk[7.13M]

NewCentOS-cl1-000003-s007.vmdk[15.81M]

NewCentOS-cl1-000003-s008.vmdk[320.00K]

NewCentOS-cl1-000004.vmdk[0.68K]

NewCentOS-cl1-000004-s001.vmdk[21.00M]

NewCentOS-cl1-000004-s002.vmdk[11.06M]

NewCentOS-cl1-000004-s003.vmdk[17.19M]

NewCentOS-cl1-000004-s004.vmdk[512.00K]

NewCentOS-cl1-000004-s005.vmdk[23.56M]

NewCentOS-cl1-000004-s006.vmdk[15.06M]

NewCentOS-cl1-000004-s007.vmdk[6.25M]

NewCentOS-cl1-000004-s008.vmdk[320.00K]

NewCentOS-cl1-000005.vmdk[0.68K]

NewCentOS-cl1-000005-s001.vmdk[402.19M]

NewCentOS-cl1-000005-s002.vmdk[138.50M]

NewCentOS-cl1-000005-s003.vmdk[348.13M]

NewCentOS-cl1-000005-s004.vmdk[1.19M]

NewCentOS-cl1-000005-s005.vmdk[113.19M]

NewCentOS-cl1-000005-s006.vmdk[23.69M]

NewCentOS-cl1-000005-s007.vmdk[56.38M]

NewCentOS-cl1-000005-s008.vmdk[320.00K]

NewCentOS-cl1-000006.vmdk[0.68K]

NewCentOS-cl1-000006-s001.vmdk[1.22G]

NewCentOS-cl1-000006-s002.vmdk[855.38M]

NewCentOS-cl1-000006-s003.vmdk[656.94M]

NewCentOS-cl1-000006-s004.vmdk[2.56M]

NewCentOS-cl1-000006-s005.vmdk[724.31M]

NewCentOS-cl1-000006-s006.vmdk[58.75M]

NewCentOS-cl1-000006-s007.vmdk[225.94M]

NewCentOS-cl1-000006-s008.vmdk[320.00K]

NewCentOS-cl1-000007.vmdk[0.68K]

NewCentOS-cl1-000007-s001.vmdk[7.38M]

NewCentOS-cl1-000007-s002.vmdk[300.63M]

NewCentOS-cl1-000007-s003.vmdk[19.06M]

NewCentOS-cl1-000007-s004.vmdk[640.00K]

NewCentOS-cl1-000007-s005.vmdk[16.94M]

NewCentOS-cl1-000007-s006.vmdk[5.94M]

NewCentOS-cl1-000007-s007.vmdk[16.44M]

NewCentOS-cl1-000007-s008.vmdk[320.00K]

NewCentOS-cl1-000008.vmdk[0.68K]

NewCentOS-cl1-000008-s001.vmdk[184.13M]

NewCentOS-cl1-000008-s002.vmdk[369.13M]

NewCentOS-cl1-000008-s003.vmdk[155.25M]

NewCentOS-cl1-000008-s004.vmdk[576.00K]

NewCentOS-cl1-000008-s005.vmdk[33.88M]

NewCentOS-cl1-000008-s006.vmdk[62.13M]

NewCentOS-cl1-000008-s007.vmdk[476.81M]

NewCentOS-cl1-000008-s008.vmdk[320.00K]

NewCentOS-cl1-000009.vmdk[0.68K]

NewCentOS-cl1-000009-s001.vmdk[1.92G]

NewCentOS-cl1-000009-s002.vmdk[1.79G]

NewCentOS-cl1-000009-s003.vmdk[1.25G]

NewCentOS-cl1-000009-s004.vmdk[960.00K]

NewCentOS-cl1-000009-s005.vmdk[2.72G]

NewCentOS-cl1-000009-s006.vmdk[75.38M]

NewCentOS-cl1-000009-s007.vmdk[581.75M]

NewCentOS-cl1-000009-s008.vmdk[320.00K]

NewCentOS-cl1-000011.vmdk[0.68K]

NewCentOS-cl1-000011-s001.vmdk[721.25M]

NewCentOS-cl1-000011-s002.vmdk[646.69M]

NewCentOS-cl1-000011-s003.vmdk[174.69M]

NewCentOS-cl1-000011-s004.vmdk[576.00K]

NewCentOS-cl1-000011-s005.vmdk[602.69M]

NewCentOS-cl1-000011-s006.vmdk[71.31M]

NewCentOS-cl1-000011-s007.vmdk[223.88M]

NewCentOS-cl1-000011-s008.vmdk[320.00K]

NewCentOS-cl1-000012.vmdk[0.63K]

NewCentOS-cl1-000012-s001.vmdk[512.00K]

NewCentOS-cl1-000012-s002.vmdk[512.00K]

NewCentOS-cl1-000012-s003.vmdk[512.00K]

NewCentOS-cl1-000012-s004.vmdk[512.00K]

NewCentOS-cl1-000012-s005.vmdk[512.00K]

NewCentOS-cl1-000012-s006.vmdk[512.00K]

NewCentOS-cl1-000012-s007.vmdk[512.00K]

NewCentOS-cl1-000012-s008.vmdk[320.00K]

NewCentOS-cl1-s001.vmdk[1.70G]

NewCentOS-cl1-s002.vmdk[512.00K]

NewCentOS-cl1-s003.vmdk[1.24G]

NewCentOS-cl1-s004.vmdk[512.00K]

NewCentOS-cl1-s005.vmdk[881.25M]

NewCentOS-cl1-s006.vmdk[771.88M]

NewCentOS-cl1-s007.vmdk[161.69M]

NewCentOS-cl1-s008.vmdk[320.00K]

spark-node01.nvram[8.48K]

spark-node01.vmsd[3.60K]

spark-node01.vmx[2.72K]

spark-node01.vmxf[0.26K]

spark-node01-Snapshot18.vmsn[27.56K]

spark-node01-Snapshot19.vmsn[27.56K]

spark-node01-Snapshot2.vmsn[27.50K]

spark-node01-Snapshot21.vmsn[27.56K]

spark-node01-Snapshot22.vmsn[27.58K]

spark-node01-Snapshot23.vmsn[27.58K]

spark-node01-Snapshot25.vmsn[27.56K]

spark-node01-Snapshot7.vmsn[27.57K]

vmware.log[257.34K]

vmware-0.log[259.86K]

vmware-1.log[254.14K]

vmware-2.log[476.81K]

SparkNode02[2.82G]

NewCentOS-cl2.vmdk[0.66K]

NewCentOS-cl2-000001.vmdk[0.62K]

NewCentOS-cl2-000001-s001.vmdk[512.00K]

NewCentOS-cl2-000001-s002.vmdk[512.00K]

NewCentOS-cl2-000001-s003.vmdk[512.00K]

NewCentOS-cl2-000001-s004.vmdk[512.00K]

NewCentOS-cl2-000001-s005.vmdk[512.00K]

NewCentOS-cl2-000001-s006.vmdk[512.00K]

NewCentOS-cl2-000001-s007.vmdk[512.00K]

NewCentOS-cl2-000001-s008.vmdk[320.00K]

NewCentOS-cl2-000003.vmdk[0.67K]

NewCentOS-cl2-000003-s001.vmdk[17.19M]

NewCentOS-cl2-000003-s002.vmdk[176.81M]

NewCentOS-cl2-000003-s003.vmdk[470.44M]

NewCentOS-cl2-000003-s004.vmdk[512.00K]

NewCentOS-cl2-000003-s005.vmdk[145.50M]

NewCentOS-cl2-000003-s006.vmdk[7.38M]

NewCentOS-cl2-000003-s007.vmdk[422.81M]

NewCentOS-cl2-000003-s008.vmdk[320.00K]

NewCentOS-cl2-s001.vmdk[269.06M]

NewCentOS-cl2-s002.vmdk[51.44M]

NewCentOS-cl2-s003.vmdk[421.69M]

NewCentOS-cl2-s004.vmdk[512.00K]

NewCentOS-cl2-s005.vmdk[618.63M]

NewCentOS-cl2-s006.vmdk[76.13M]

NewCentOS-cl2-s007.vmdk[202.81M]

NewCentOS-cl2-s008.vmdk[320.00K]

spark-node02.nvram[8.48K]

spark-node02.vmsd[1.14K]

spark-node02.vmx[2.79K]

spark-node02.vmxf[0.26K]

spark-node02-Snapshot4.vmsn[27.62K]

spark-node02-Snapshot5.vmsn[27.62K]

vmware.log[260.15K]

vmware-0.log[312.77K]

vmware-1.log[258.90K]

vmware-2.log[247.02K]

SparkNode03[2.15G]

NewCentOS-cl2.vmdk[0.66K]

NewCentOS-cl2-000001.vmdk[0.62K]

NewCentOS-cl2-000001-s001.vmdk[512.00K]

NewCentOS-cl2-000001-s002.vmdk[512.00K]

NewCentOS-cl2-000001-s003.vmdk[512.00K]

NewCentOS-cl2-000001-s004.vmdk[512.00K]

NewCentOS-cl2-000001-s005.vmdk[512.00K]

NewCentOS-cl2-000001-s006.vmdk[512.00K]

NewCentOS-cl2-000001-s007.vmdk[512.00K]

NewCentOS-cl2-000001-s008.vmdk[320.00K]

NewCentOS-cl2-000003.vmdk[0.67K]

NewCentOS-cl2-000003-s001.vmdk[29.63M]

NewCentOS-cl2-000003-s002.vmdk[465.63M]

NewCentOS-cl2-000003-s003.vmdk[13.88M]

NewCentOS-cl2-000003-s004.vmdk[512.00K]

NewCentOS-cl2-000003-s005.vmdk[148.13M]

NewCentOS-cl2-000003-s006.vmdk[7.94M]

NewCentOS-cl2-000003-s007.vmdk[299.50M]

NewCentOS-cl2-000003-s008.vmdk[320.00K]

NewCentOS-cl2-s001.vmdk[258.06M]

NewCentOS-cl2-s002.vmdk[4.94M]

NewCentOS-cl2-s003.vmdk[394.81M]

NewCentOS-cl2-s004.vmdk[512.00K]

NewCentOS-cl2-s005.vmdk[221.50M]

NewCentOS-cl2-s006.vmdk[74.19M]

NewCentOS-cl2-s007.vmdk[275.75M]

NewCentOS-cl2-s008.vmdk[320.00K]

spark-node03.nvram[8.48K]

spark-node03.vmsd[1.15K]

spark-node03.vmx[2.78K]

spark-node03.vmxf[0.26K]

spark-node03-Snapshot4.vmsn[27.62K]

spark-node03-Snapshot5.vmsn[27.62K]

vmware.log[255.59K]

vmware-0.log[313.39K]

vmware-1.log[261.47K]

vmware-2.log[248.22K]

Spark基础环境补充资料[99.53M]

第二部分SparkCore[8.17M]

02-第二部分【SparkCore】_V1.0.docx[8.17M]

第一部分Spark基础环境[13.79M]

01-V8.0:第一部分【Spark基础环境】_V1.0.xmind[423.22K]

02-V8.0:第一部分【Spark基础环境】_V1.0.docx[9.21M]

03-V8.0:第一部分【Spark基础环境】_V1.0.pptx[4.17M]

01_第一部分【Spark基础环境】教案_V1.2.pdf[4.89M]

01-第三部分【SparkSQL】_V1.0.xmind[538.70K]

01-第四部分【离线综合实战】_V1.0.xmind[547.04K]

02_第二部分【SparkCore】教案_V1.2.pdf[4.82M]

02-第三部分【SparkSQL】_V1.0.docx[6.98M]

02-第四部分【离线综合实战】_V1.0.docx[4.77M]

03_第三部分【SparkSQL】教案_V1.2.pdf[4.34M]

03-第三部分【SparkSQL】_V1.0.pptx[4.82M]

03-第四部分【离线综合实战】_V1.0.pptx[1.75M]

04_第四部分【离线综合实战】教案_V1.2.pdf[2.80M]

05_第五部分【SparkStreaming】教案_V1.2.pdf[5.61M]

0501-第五部分【SparkStreaming】.xmind[580.58K]

0502-第五部分【SparkStreaming】_V1.0.docx[8.73M]

0503-第五部分【SparkStreaming】_V1.0.pptx[3.94M]

06_第六部分【StructuredStreaming】教案_V1.2.pdf[4.14M]

0601-第六部分【StructuredStreaming】_V1.0.xmind[624.76K]

0602-第六部分【StructuredStreaming】_V1.0.docx[5.90M]

0603-第六部分【StructuredStreaming】_V1.0.pptx[3.42M]

07_第七部分【实时综合实战】教案_V1.2.pdf[2.95M]

0701-第七部分【实时综合实战】_V1.0.xmind[648.71K]

0702-第七部分【实时综合案例】_V1.0.docx[3.06M]

0703-第四部分【实时综合实战】_V1.0.pptx[1.77M]

软件包[22.85G]

DataGrip资料[321.59M]

jetbrains-agent[2.47M]

lib[2.33M]

ACTIVATION_CODE.txt[3.59K]

important.txt[0.28K]

jetbrains-agent.jar[2.33M]

sha1sum.txt[0.06K]

resetal[1.50K]

reset_jetbrainsal_mac_linux.sh[0.50K]

reset_jetbrainsal_windows.vbs[1.00K]

ChangeLogs.txt[0.93K]

LICENSE[21.84K]

README.pdf[111.39K]

README.txt[4.46K]

datagrip-2019.1.4.exe[301.49M]

DataGrip激活码.txt[3.04K]

JetbrainsCrack.jar[837.10K]

resources_cn.jar[16.82M]

finalshell[69.93M]

finalshell_install.exe[69.93M]

Mysql8.0[512.37M]

4_安装Mysql8.0.docx[87.43K]

mysql-8.0.13-1.el7.x86_64.rpm-bundle.tar[507.27M]

mysqldump.exe[5.02M]

Superset[516.80M]

Anaconda3-2019.07-Linux-x86_64.sh[516.80M]

虚拟机资料[19.40G]

Centos_iso[4.21G]

CentOS-7-x86_64-DVD-1708.iso[4.21G]

VMware[405.55M]

01_安装VMware虚拟机.doc[642.00K]

VMware所有版本永久许可证激活秘钥.txt[1.17K]

VMware-workstation-full-12.5.6-5528349.exe[404.92M]

已搭建环境虚拟机[14.79G]

Centos7.4[14.79G]

node1.nvram[8.48K]

node1.vmdk[0.87K]

node1.vmsd[0.09K]

node1.vmx[2.74K]

node1.vmxf[0.25K]

node1-s001.vmdk[167.88M]

node1-s002.vmdk[3.05G]

node1-s003.vmdk[0.98G]

node1-s004.vmdk[2.22G]

node1-s005.vmdk[2.70G]

node1-s006.vmdk[1.38G]

node1-s007.vmdk[1.00G]

node1-s008.vmdk[547.19M]

node1-s009.vmdk[2.76G]

node1-s010.vmdk[512.00K]

node1-s011.vmdk[64.00K]

vmware.log[291.05K]

vmware-0.log[282.72K]

vmware-1.log[258.73K]

vmware-2.log[252.89K]

kettle.zip[2.06G]

07_第七部分【实时综合实战】提纲_V1.0.xmind[631.99K]

08_提交.lnk.重命名[1.07K]

Spark应用运行架构原理图.png[246.81K]

Spark_Day01.xmind[75.07K]

Spark_Day02.xmind[127.16K]

Spark_Day03.xmind[55.73K]

Spark_Day04.xmind[72.93K]

Spark_Day05.xmind[32.88K]

Spark_Day06.xmind[32.52K]

Spark_Day07.xmind[65.32K]

Spark_Day08.xmind[32.52K]

wordcount-jobs-job.png[74.81K]

第四部分【离线综合实战】.xmind[534.49K]

离线数据分析流程.png[43.87K]

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