Spark Basics and architecture

Spark Basics and architecture

1. SparkIntroduction

Apache Spark is a open-source distributed计算framework, designed forlarge-scaledataprocessing而design. 它providing了一个统一 programmingmodel, 可以processing批processing, 流processing, 机器Learning, graphprocessingetc. many 种dataprocessingtask. Spark design理念 is " fast 速, 易用, common", 它throughmemory计算 and optimization 执行引擎, implementation了比传统Hadoop MapReduce更 fast dataprocessing速度.

1.1 Spark 发展history

Spark最初由加州 big 学伯克利分校 AMPLabDevelopment, 于2010年open-source, 2013年成 for Apache基金会 顶级project. 以 under is Spark 主要发展milestone:

• 2009年: Sparkproject in AMPLab启动, 旨 in 解决MapReduce performance瓶颈issues
• 2010年: Spark首次open-sourcerelease, supportScala API
• 2013年: 成 for Apache基金会顶级project, 开始获得广泛关注
• 2014年: releaseSpark 1.0, 增加Python and Java APIsupport
• 2015年: releaseSpark 1.3, 引入DataFrame API; releaseSpark 1.4, supportRlanguage
• 2016年: releaseSpark 2.0, 引入Dataset API and structure化流processing
• 2018年: releaseSpark 2.4, 增强了structure化流processing and 机器Learningfunctions
• 2020年: releaseSpark 3.0, 引入自适应query执行 and 动态partition裁剪etc.optimization
• 2023年: releaseSpark 3.5, 增强了Pythonsupport and 流processingfunctions

1.2 Spark core优势

• fast 速processing: adoptsmemory计算model, 比传统Hadoop MapReduce fast 100倍以 on
• 易用性: support many 种programminglanguage (Scala, Java, Python, R) , providing简洁 API
• common性: 统一 programmingmodelsupport批processing, 流processing, 机器Learning, graphprocessingetc.
• 可scale性: support from 单台serverscale to 数千台机器
• fault tolerance性: throughRDD血统mechanismimplementation high 效 fault toleranceprocessing
• and Hadoop集成: 兼容HDFS, YARN, Hiveetc.Hadoop生态component

2. Sparkcorecomponent

Sparkecosystem由 many 个corecomponent组成, 每个component负责不同class型 dataprocessingtask. 以 under is Spark 主要corecomponent:

2.1 Spark core

Spark core is Spark corecomponent, providing了Spark basicfunctions, including:

• RDD (弹性distributeddata集) : Spark basicdataabstraction, supportdistributedparallelprocessing
• taskscheduling: 负责将job分解 for task并scheduling to clusternode on 执行
• memorymanagement: managementSparkapplication程序 memory分配 and 释放
• fault tolerancemechanism: throughRDD血统mechanismimplementationtask失败时 自动restore
• storesystem集成: support and HDFS, S3, Cassandraetc.storesystem集成

2.2 Spark SQL

Spark SQL is Spark用于structure化dataprocessing component, providing了:

• DataFrame API: 用于processingstructure化 and 半structure化data advancedabstraction
• Dataset API: 结合了RDD 强class型features and DataFrame optimization执行
• SQLquerysupport: 允许using标准SQLquerydata
• datasources集成: support from many 种datasources读取 and 写入data, such asHive, JSON, Parquetetc.
• queryoptimization: 基于Catalystoptimization器 query计划optimization

2.3 Spark Streaming

Spark Streaming is Spark用于实时流processing component, support:

• DStream API: 基于微批processing 流processingAPI
• structure化流processing: support声明式流processing, providing and 批processing一致 API
• many 种datasources: supportKafka, Flume, Twitteretc. many 种流datasources
• statusmanagement: support has status 流processing, such as窗口operation, statusupdateetc.
• fault tolerance性: 基于Spark core fault tolerancemechanism, 确保流processing reliability

2.4 MLlib

MLlib is Spark 机器Learninglibrary, providing了:

• commonalgorithms: classification, 回归, 聚class, 协同filteretc.
• 特征工程: 特征提取, 转换, 选择etc.
• 管道API: 用于构建, assessment and deployment机器Learningpipeline
• model持久化: support将model保存 to disk并加载using
• distributed训练: supportlarge-scaledata集 distributedmodel训练

2.5 GraphX

GraphX is Spark graphprocessinglibrary, providing了:

• graphabstraction: 基于RDD graphdatastructure
• graphalgorithms: PageRank, 最 short path, 三角形计数etc.
• graphoperation: graph creation, 转换, filteretc.operation
• graph计算: supportPregel计算model
• and othercomponent集成: 可以 and Spark SQL, MLlibetc.component结合using

3. Sparkarchitecturedesign

Sparkadoptsmaster-slavearchitecturedesign, 主要由Driver and Executor组成:

3.1 architecture组成

• Driver: 驱动程序, 负责managementSparkapplication程序 执行
• Executor: 执行器, 负责 in clusternode on 执行task并storedata
• Cluster managementr: clustermanagement器, 负责resource分配 and management, such asYARN, Mesos, Standaloneetc.
• Spark Context: Sparkapplication程序 入口点, 负责 and clustermanagement器通信

3.2 workflow程

Sparkapplication程序 典型workflow程such as under :

1. usersubmittingSparkapplication程序 to clustermanagement器
2. clustermanagement器启动Driverprocess
3. Driverprocess初始化Spark Context
4. Driverprocess and clustermanagement器通信, requestresource
5. clustermanagement器 in 工作node on 启动Executorprocess
6. Driverprocess将job分解 for many 个Stage and Task
7. Driverprocess将Task分配给Executor执行
8. Executor执行Task并将结果返回给Driver
9. Driverprocess汇summarized果并返回给user
10. application程序执行completion, 释放resource

3.3 deployment模式

Sparksupport many 种deployment模式, 适应不同 clusterenvironment:

3.3.1 Standalone模式

Standalone模式 is Spark自带 clustermanagement模式, 适用于in small 型cluster. 它including一个Masternode and many 个Workernode, Master负责resourcemanagement and jobscheduling, Worker负责执行task.

3.3.2 YARN模式

YARN模式 is Spark in Hadoop YARNcluster on deployment模式, 适用于已 has Hadoopcluster environment. Sparkapplication程序serving asYARNjobrun, 由YARN负责resourcemanagement.

3.3.3 Mesos模式

Mesos模式 is Spark in Apache Mesoscluster on deployment模式, 适用于 big 型distributedsystem. Mesosproviding细粒度 resourcemanagement, support many 种framework共享clusterresource.

3.3.4 Kubernetes模式

Kubernetes模式 is Spark in Kubernetescluster on deployment模式, 适用于containerizationenvironment. Sparkapplication程序以Pod形式run, 由Kubernetes负责resourcemanagement and scheduling.

3.3.5 Local模式

Local模式 is Spark in 本地机器 on run模式, 适用于Development and test. 它using单个JVMprocessmockSparkcluster, 所 has component都 in 同一processinrun.

4. Sparkprogrammingmodel

Sparkproviding了统一 programmingmodel, support many 种dataprocessingtask. 以 under is Spark 主要programmingabstraction:

4.1 RDD (弹性distributeddata集)

RDD is Spark basicdataabstraction, is a distributed , 不可变 objectcollection. RDD具 has 以 under features:

• distributed: data分布 in many 个node on
• 不可变: 一旦creation, 就不能modify
• 弹性: support自动fault tolerance and datarestore
• 可partition: data可以划分 for many 个partitionparallelprocessing
• latency计算: 只 has in 需要结果时才执行计算

4.2 DataFrame

DataFrame is Spark SQLin advanceddataabstraction, is a distributed , 强class型 data集. DataFrame具 has 以 under features:

• structure化data: 具 has 命名列 and dataclass型
• optimization执行: 基于Catalystoptimization器 query计划optimization
• many 种datasources: support from many 种datasourcescreationDataFrame
• SQLsupport: 可以usingSQLqueryDataFrame
• Python, Scala, Java, Rsupport: support many 种programminglanguageAPI

4.3 Dataset

Dataset is Spark 2.0引入 new dataabstraction, 结合了RDD 强class型features and DataFrame optimization执行. Dataset具 has 以 under features:

• 强class型: 编译时class型check, reducingrun时error
• optimization执行: and DataFrame共享相同 optimization执行引擎
• function式programming: supportScala and Java function式programmingAPI
• and DataFrame互operation: 可以 and DataFrame相互转换

5. Sparkapplication场景

Spark适用于 many 种 big dataprocessing场景, 以 under is 一些典型 application场景:

5.1 批processing

Spark可以 high 效processinglarge-scale historydata, such as:

• loganalysis and processing
• data仓libraryETL流程
• large-scaledata清洗 and 转换
• 离线dataanalysis and report生成

5.2 流processing

Spark Streaming and structure化流processing可以processing实时data, such as:

• 实时loganalysis
• 实时monitor and 告警
• 实时推荐system
• 实时fraud detection

5.3 机器Learning

MLlib可以用于构建 and 训练large-scale机器Learningmodel, such as:

• classification and 回归model
• 聚classanalysis
• 协同filter推荐
• 自然languageprocessing
• graph像processing

5.4 graphprocessing

GraphX可以用于processinglarge-scalegraphdata, such as:

• 社交networkanalysis
• pathfind and 最 short path计算
• PageRankalgorithms
• community检测
• networktrafficanalysis

5.5 交互式query

Spark SQLsupport交互式query, such as:

• 即席query and dataanalysis
• data探索 and visualization
• BIreport and 仪表盘
• data科学 and data挖掘

6. summarized

本tutorial深入介绍了Spark basicconcepts, 发展history, corecomponent and architecturedesign. Sparkserving as一个 fast 速, 易用, common distributed计算framework, 已经成 for big dataecosystemin important 组成部分.

Spark core优势 in 于其memory计算model and 统一 programmingmodel, support批processing, 流processing, 机器Learning, graphprocessingetc. many 种dataprocessingtask. Spark architecturedesignadoptsmaster-slave模式, includingDriver, Executor and Cluster managementretc.component, support many 种deployment模式以适应不同 clusterenvironment.

Sparkproviding了 many 种programmingabstraction, includingRDD, DataFrame and Dataset, 每种abstraction都 has 其特点 and 适用场景. RDD适合底层operation and complex algorithms, DataFrame适合structure化dataprocessing, Dataset结合了两者 优点, providing了强class型 and optimization执行.

Spark适用于 many 种 big dataapplication场景, including批processing, 流processing, 机器Learning, graphprocessing and 交互式queryetc.. through合理选择Spark component and programmingabstraction, 可以 high 效地processing各种规模 dataprocessingtask.