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高级软件工程
云计算和大数据
主讲:刘 驰
2014年12月15日
Content
Cloud Computing
 Hadoop
 HDFS
 MapReduce
 HBase

2
为什么需要云计算?
案例一:
写文件
电脑硬盘坏了,文件丢失
存储在云中的文件不会丢失
3
为什么需要云计算?
案例二:
QQ聊天 --- 下载、安装、使用
使用C++---下载、安装、使用
……
从云中获取服务
4
为什么需要云计算?
案例三:
华盛顿邮报突然需要大量计算资源进行文件格式
转化
 报社现有计算能力每页需要30分钟
 新闻时效性不允许

使用Amazon EC2的计算资源
5
5
云计算产生的原动力
芯片与硬
件技术
Web2.0
技术
资源虚拟
化
互联网技
术
面向服务
构架
软件即服
务
6
云计算的分类

按服务类型
基础设施即服务IaaS
• 基础设施云
平台即服务PaaS
• 平台云
软件即服务SaaS
• 应用云
7
云计算分类

服务类型分类
分类
服务类型
运用灵活性
运用难易程度
IaaS
接近原始的计算存储能力
高
难
PaaS
应用的托管环境
中
中
SaaS
特定功能
低
易
8
云计算分类

不同类型云的案例
9
云计算分类

按服务方式分
10
云构架层次
共有云
(通过Internet提
供公共服务)
混合云
(通过Internet和
Intranet提供公共
和私有服务)
私有云
(通过Intranet提
供私有服务)
应用层
软件及服务(SaaS)
平台层
平台即服务(PaaS)
基础设施层
基础设施即服务(Iaas)
11
IaaS基本功能
资源抽象
计费管理
安全管理
资源部署
资源监控
复杂管理
数据管理
12
Example: Amazon EC2

底层采用Xen虚拟化技术,以Xen虚拟机的形式向
用户动态提供计算资源
按照用户使用资源的数量和时间计费
 http://aws.amazon.com/ec2/

13
PaaS

开发测试环境


运行时环境


应用模型、API代码库、开发测试环境
验证、配置、部署、激活
运营环境

升级、监控、淘汰、计费
14
Example: Google App Engine
在Google的基础架构上运行自己的网络应用程序
 提供网址抓取、邮件、memcache、图像操作、计
划任务等服务
 目前支持Java和Python

15
15
SaaS

可以通过浏览器访问,具有开放的API

在使用的过程中根据实际使用情况付费

较强的云应用之间的整合能力
16
Example: Google Docs & Docs for Facebook
• 在线文档编辑
• 多人协作编辑
17
An Ecosystem for Cloud Computing
18
Problem

Batch (offline) processing of huge data set using
commodity hardware is not enough for real-time
applications

Strong desire for linear scalability

Need infrastructure to handle all mechanics

allow developers to focus on the processing
logic/algorithms
19
Explosive Data! – Storage
 New
York Stock Exchange: 1 TB data per day
 Facebook: 100
billion photos, 1 PB (1000 TB)
 Internet Archive: 2
month
 Can’t
PB data, growing by 20 TB per
put data on a SINGLE node
 Strong
needs for distributed file systems
Java/Python/C interfaces
21
Commercial Hardware
典型的2层构架
– 节点是普通的商业PC机
– 30-40 节点/Rack
– 顶层到Rack 带宽3-4Gbps
– Rack到节点带宽1Gbps
23
Who is (was) Using Hadoop?
24
Example: Facebook的Hadoop集群

产品集群
 4800个内核,600个机器,每个机器16GB—2009年4月
 8000个内核,1000个机器,每个机器32GB—2009年7月
 每个机器拥有4个1TB大小的SATA硬盘
 两层网络结构,每个Rack有40个机器
 整个集群大小为2PB,未来还会不断增加

测试集群
• 800 个内核, 每个16GB
25
A Distributed File System
Single-Node Architecture
CPU
Machine Learning, Statistics
Memory
“Classical” Data Mining
Disk
27
Commodity Clusters




Web data sets can be very large
 Tens to hundreds of TB
Cannot mine on a single server
Standard architecture emerging:
 Cluster of commodity Linux nodes
 Gigabit Ethernet interconnect
How to organize computations on this
architecture?
 Mask issues such as hardware failure
28
Cluster Architecture
2-10 Gbps backbone between racks
1 Gbps between
any pair of nodes
in a rack
Switch
Switch
CPU
Mem
Disk
…
Switch
CPU
CPU
Mem
Mem
Disk
Disk
CPU
…
Mem
Disk
Each rack contains 16-64 nodes
29
Stable storage



First order problem: if nodes can fail, how can we
store data persistently?
Answer: Distributed File System
 Provides global file namespace
 Google GFS; Hadoop HDFS; Kosmix KFS
Typical usage pattern
 Huge files (100s of GB to TB)
 Data is rarely updated in place
 Reads and appends are common
30
31
32
Namenode and Datanodes
 Master/slave architecture
1
Namenode, a master server that manages the file
system namespace and regulates access to files by
clients.
 many DataNodes usually one per node in a cluster.
 manage storage
 serves read, write requests, performs block creation,
deletion, and replication upon instruction from
Namenode.
 HDFS exposes a file system namespace and allows user
data to be stored in files.
 A file is split into one or more blocks and set of blocks
are stored in DataNodes.
33
2015/4/13
Namespace
 Hierarchical
file system with directories and files
 Create, remove, move, rename etc.
 Namenode maintains the file system
 Any meta information changes to the file system
recorded by the Namenode.
 An application can specify the number of replicas of the
file needed: replication factor of the file. This
information is stored in the Namenode.
34
4/13/2015
Data Replication
 Store very
large files across machines in a large
cluster.
 Each file is a sequence of blocks of same size.
 Blocks are replicated 2-3 times.
 Block size and replicas are configurable per file.
 Namenode receives a Heartbeat and a
BlockReport from each DataNode in the cluster.
 BlockReport contains all the blocks on a Datanode.
35
4/13/2015
Replica Placement
 Rack-aware:
 Goal: improve reliability,
availability and
network bandwidth utilization
 Research topic
 Namenode determines the rack id for each
DataNode.
 Replicas are placed:
1 in a local rack, 1 on a different node in the local rack
and 1 on a node in a different rack.
 1/3 of the replica on a node, 2/3 on a rack and 1/3
distributed evenly across remaining racks.

36
4/13/2015
HDFS: Data Node Distance
37
Replication Pipelining
 When the client receives response from
Namenode, it
flushes its block in small pieces (4K) to the first replica,
that in turn copies it to the next replica and so on.
 Thus data is pipelined from Datanode to the next.
38
4/13/2015
Replica Selection
 Replica selection for
READ operation: HDFS tries to
minimize the bandwidth consumption and latency.
 If there is a replica on the Reader node then that is
preferred.
 HDFS cluster may span multiple data centers: replica in
the local data center is preferred over the remote one.
39
4/13/2015
Datanode
A
Datanode stores data in files in its local file system.
 Datanode has no knowledge about HDFS filesystem
 It stores each block of HDFS data in a separate file.
 Datanode does not create all files in the same directory.
 It uses heuristics to determine optimal number of files per
directory and creates directories appropriately:

Research issue?
 When
the filesystem starts up it generates a list of all HDFS
blocks and send this report to Namenode: Blockreport.
40
4/13/2015
HDFS: File Read
41
HDFS: File Write
42
Communication Protocol
 All
protocols are layered on top of the TCP/IP protocol
 A client establishes a connection to a configurable TCP port
on the Namenode machine. It talks ClientProtocol with the
Namenode.
 Datanodes talk to the Namenode using Datanode protocol.
 RPC abstraction wraps both ClientProtocol and Datanode
protocol.
 Namenode is simply a server and never initiates a request;
it only responds to RPC requests issued by DataNodes or
clients.
43
4/13/2015
DataNode Failure and Heartbeat
 Datanodes lose connectivity with Namenode.
 Namenode detects this condition by
the absence of a
Heartbeat message.
 Namenode marks Datanodes without Hearbeat and
does not send any IO requests to them.
 Any data registered to the failed Datanode is not
available to the HDFS.
44
4/13/2015
Cluster Rebalancing
 HDFS
architecture is compatible with data rebalancing
schemes.
 A scheme might move data from one Datanode to
another if the free space on a Datanode falls below a
certain threshold.
 In the event of a sudden high demand for a particular file,
a scheme might dynamically create additional replicas
and rebalance other data in the cluster.
 These types of data rebalancing are not yet implemented:
research issue.
45
4/13/2015
APIs
 HDFS
provides Java API for application to use.
 Python access is also used in many applications.
 A C language wrapper for Java API is also available.
 A HTTP browser can be used to browse the files of a
HDFS instance.
46
4/13/2015
A Distributed Computation
Framework for Batch Processing
What is Map/Reduce?

A Programming Model

Decompose a processing job into Map and Reduce
stages

Developer need to
 provide codes for Map and Reduce functions
 configure the job
 let Hadoop handle the rest
49
MapReduce Model
50
Architecture Overview
Master Node
user
Job tracker
Slave node 1
Task tracker
Slave node 2
Task tracker
Slave node N
Task tracker
51
Workers
Workers
Workers
How It Works?
52
Distributed Execution Overview
User
Program
fork
assign
map
Input Data
Split 0 read
Split 1
Split 2
fork
Master
fork
assign
reduce
Worker
Worker
Worker
local
write
Worker
Worker
remote
read,
sort
write
Output
File 0
Output
File 1
53
MapReduce


Input: a set of key/value pairs
User supplies two functions:




map(k,v)  list(k1,v1)
reduce(k1, list(v1))  v2
(k1,v1) is an intermediate key/value pair
Output is the set of (k1,v2) pairs
54
What is MAP?

Map each data entry into a pair


<key, value>
Examples


Map each log file entry into <URL,1>
Map day stock trading record into <STOCK, Price>
55
What is Shuffle/Merge phase?

Hadoop merges(shuffles) output of the MAP stage
into:
 <key, valulue1, value2, value3>
 Examples
 <URL,
1 ,1 ,1 ,1 ,1 1>
 <STOCK, Price On day 1, Price On day 2,…...>
56
What is Reduce?

Reduce entries produces by Hadoop merging
processing into <key, value> pair

Examples
 Map <URL, 1,1,1> into <URL, 3>
 Map <Stock, 3,2,10> into <Stock, 10>
57
Example: Word Count



We have a large file of words, one word to a line
Count the number of appearances for each distinct
word
Sample application: analyze web server logs to find
popular URLs
58
Pseudo-Code: Word Count
map(key, value):
// key: document name; value: text of document
for each word w in value:
emit(w, 1)
reduce(key, values):
// key: a word; values: an iterator over counts
result = 0
for each count v in values:
result += v
emit(key,result)
59
Word Count
map(key=url, val=contents):
For each word w in contents, emit (w, “1”)
reduce(key=word, values=uniq_counts):
Sum all “1”s in values list
Emit result “(word, sum)”
see bob run
see spot throw
see
bob
run
see
spot
throw
1
1
1
1
1
1
bob
run
see
spot
throw
1
1
2
1
1
60
Data Flow



Input, final output are stored on HDFS
 Scheduler tries to schedule map tasks “close”
to physical storage location of input data
Intermediate results are stored on local FS of
map and reduce workers
Output is often input to another map reduce
task
63
Coordination


Master data structures
 Task status: (idle, in-progress, completed)
 Idle tasks get scheduled as workers become
available
 When a map task completes, it sends the
master the location and sizes of its R
intermediate files, one for each reducer
 Master pushes this info to reducers
Master pings workers periodically to detect
failures
64
Failures

Map worker failure



Reduce worker failure


Map tasks completed or in-progress at worker are reset
to idle
Reduce workers are notified when task is rescheduled on
another worker
Only in-progress tasks are reset to idle
Master failure

MapReduce task is aborted and client is notified
65
Execution
66
Parallel Execution
67
How Many Map and Reduce Jobs?



M map tasks, R reduce tasks
Rule of thumb:
 M, R >> (# of nodes) in cluster
 One DFS chunk per map is common
 Improves dynamic load balancing and speeds
recovery from worker failure
Usually R is smaller than M, because output is spread
across R files
68
Combiners


Often a map task will produce many pairs of the
form (k,v1), (k,v2), … for the same key k
 e.g., popular words in Word Count
Can save network time by pre-aggregating at
mapper
 combine(k1, list(v1))  v2
 same as reduce function
69
Partition Function




Inputs to map tasks are created by contiguous
splits of input file
For reduce, we need to ensure that records with
the same intermediate key end up at the same
worker
System can use a default partition function e.g.,
hash(key) mod R
Sometimes useful to override
 e.g., hash(hostname(URL)) mod R ensures
URLs from a host end up in the same output
file
70
Execution Summary


How is this distributed?
1. Partition input key/value pairs into chunks,
run map() tasks in parallel
2. After all map()s are complete, consolidate all
emitted values for each unique emitted key
3. Now partition space of output map keys, and
run reduce() in parallel
If map() or reduce() fails, re-execute!
71
Example: Trading Data Processing

Input:
 Historical Stock Data
 Records are CSV (comma separated values) text file
 Each line : stock_symbol, low_price, high_price
 1987-2009 data for all stocks one record per stock per
day

Output:
 Maximum interday delta for each stock
72
Map Function: Part I
73
Map Function: Part II
74
Reduce Function
75
Running the Job : Part I
76
Running the Job: Part II
77
A Distributed Storage System
What is HBase?

Distributed Column-Oriented database on top of HDFS

Modeled after Google’s BigTable data store

Random Reads/Writes on sequential stream-oriented
HDFS

Billions of Rows * Millions of Columns * Thousands of
Versions
79
Where is HBase?
HBase is built on top of HDFS
HBase files are
internally
stored in HDFS
80
Logical View
Row Key
Time
Stamp
Column
Contents
Column Family Anchor
(Referred by/to)
“com.cnn.w T9
ww”
cnnsi.com
cnn.com/1
T8
my.look.ca
cnn.com/2
T6
“<html>..
“
T5
“<html>..
“
t3
“<html>..
“
Column
“mime”
Text/html
81
Physical View
Row Key
Time Stamp
Column: Contents
Com.cnn.www
T6
“<html>..”
T5
“<html>..”
T3
“<html>..”
Row Key
Time Stamp
Column Family: Anchor
Com.cnn.www
T9
cnnsi.com
cnn.com/1
T5
my.look.ca
cnn.com/2
Row Key
Time Stamp
Column: mime
Com.cnn.www
T6
text/html
82
Region Servers
 Tables are split into horizontal regions
 Each region comprises a
subset of rows
 HDFS
 Namenode, dataNode
 MapReduce
 JobTracker, TaskTracker
 HBASE
 Master Server,
Region Server
83
HBASE Architecture
84
HBase vs. RDMS
 HBase
tables are similar to RDBS tables
 Differences:
 Rows are sorted with a Row Key
 Columns can be added on-the-fly by client as
long as the column family they belong to preexists
85
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