Pavlo Baron. Big Data and CDN

Transcription

1 Pavlo Baron Big Data and CDN

2 Pavlo Baron

3

4

5 What is Big Data

6 Big Data describes datasets that grow so large that they become awkward to work with using on-hand database management tools (Wikipedia)

7 Huh?

8 Somewhere a mosquito coughs

9 and somewhere else a data center gets flooded with data

10 Huh???

11 More than 30 billion pieces of content (web links, news stories, blog posts, notes, photo albums, etc.) get shared each month on Facebook

12 Aha

13 Twitter users are, in total, tweeting an average of 55 million tweets a day, also including links etc.

14 OMG!

15 But there is even much more: cameras, sensors, RFID, logs, geolocation, GIS and so on

16 kk

17 There are several perspectives at Big Data

18 Data storage and archiving

19 Data preparation

20 Live data provisioning

21 Data analysis / analytics

22 Real-time event and stream processing

23 Data visualization

24 Where does Big Data come from

25 Uncontrolled human activities in the world wide web, or Web 2.0 if you like

26 Huh?

27 Das Bild kann nicht angezeigt werden. Dieser Computer verfügt möglicherweise über zu wenig Arbeitsspeicher, um das Bild zu öffnen, oder das Bild ist beschädigt. Starten Sie den Computer neu, und öffnen Sie dann erneut die Datei. Wenn weiterhin das rote x angezeigt wird, müssen Sie das Bild möglicherweise löschen und dann erneut einfügen. Every human leaves a vast number of data marks on the web every day: intentionally, accidentally and unknowingly

28 Huh???

29 Intentionally: we blog, tweet, upload, flatter, link, etc

30 And: the web has become an industry of its own. With us in the thick of it

31 Accidentally: we are humans and we make mistakes

32 Unknowingly: we get tricked, misled, controlled, logged etc

33 The vast number of data marks we leave on the web every day gets copied, duplicated. Data explodes.

34 Panic!

35 Wait! There s even more!

36 Huh?

37 Data flowing on streams at a very high rate from many actors

38 Huh??

39 The amount of data flying over the air has become enormous, and it s growing unpredictably

40 Aha

41 It s not only nuclear reactors anymore having hi-tech sensors and generating tons of data

42 Aha

43 And our physically huge globe

44 has become a tiny electronic ball. It s completely wired. Data needs just seconds to circumnavigate the world

45 OMG!

46 But there s even more!

47 Huh?

48 Laws and regulations force us to store and archive all sorts of data, and it s getting more and more

49 Human knowledge grows extremely fast. It s far too gigantic for one single brain

50 Oh no

51 And there s still more!

52 Huh?

53 Big Brother Big Data. We get observed, filmed, recorded, logged, geolocated etc.

54 Panic!

55 Don t panic. Get over it. Brace yourself for the battle.

56 First of all, some major changes have happened

57 Instead of huge expensive cabinets

58 we can use lots of cheap commodity hardware

59 Physics hit the wall

60 and we need to think parallel

61 Our physically huge globe

62 s has become a tiny electronic ball. It s completely wired

63 Spontaneous requirements

64 can be covered by the fog (aka cloud)

65 And what are my weapons

66 Cut your data in smaller pieces

67 Make those pieces bitesize (manageable)

68 Bring the data closer to those who need it

69 Bring the data closer to where it s physically accessed

70 Give up relations where you don t need them

71 Give up actuality where you don t need it

72 Find optimal and effective replication mechanisms

73 Consider latency an adjustment screw if you can

74 Consider availability an adjustment screw if you can

75 Be prepared to deal with unlimited amount of data depending on the perspective

76 Know your data

77 Know your volumes

78 Know your scenarios

79 Consider it what it is: a science

80 Right tool for the job

81 kk

82 And how does this technically work

83 Live data provisioning

84 What s the problem

85 Your users are widely spread, maybe all over the world

86 And you own Big Data, which has many facets geographic, financial etc.

87 And your classic silo architecture could break under the weight of such data

88 And why would I need that

89 You start and want to be one of those. Aha, ok

90 You simply grew up to a level

91 Now you need to segment your users and thus to be faster and more reliable at locations,

92 to keep your servers free of load and thus to avoid bottle necks,

93 to cut your big data in smaller, better manageable chunks

94 What are my weapons

95 If your content is static in web terms, you are already well prepared

96 In many cases, you can make your dynamic data static (precompute content)

97 Huh?

98 Let s take a look at an online bookstore

99 Hey, the online bookstore is completely dynamic (except images) it s a shop system!

100 Really?

101 Book description page: even when you modify the prices and offer Web 2.0 features such as rating you still can pre-compute the page at some time you don t need to compute the content while the page is getting accessed

102 Browser mode: this is a classic use case for static content precomputation. There is often simply no need to navigate through dynamically built paths

103 Book search: even this ultimately dynamic sounding feature can be (partially) dedynamized. Consider the index as static content, not necessarily the data itself

104 You see: many parts of an online bookstore seem dynamic, but can be actually pre-computed and delivered as static content in web terms. It s all about the frequency of change and the big data pain

105 Owning big data doesn t necessarily mean owning 100% dynamic data in terms of web

106 Aha

107 And now distribute it with CDN content delivery network

108 Huh?

109 Akamai web traffic dominance

110 Akamai web traffic monitoring

111 Akamai EdgePlatform

112 73,000 servers 70 countries 1,000 networks 30% of world s web traffic (OMG, is the rest Google?)

113 There are several CDN providers offering (world wide) such infrastructures

114 And now let s get a little insane

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117

118 Huh???

119 Yeah, something s going on behind the scenes

120 How does this technically work

121 CDN is like a deputy. You make a contract, and it takes over parts of your platform. From here, it delivers to your users the content you tell it to deliver, but being much closer to them and much more intelligent than you when it comes to managing the load

122 Huh?

123 CDN has its infrastructure including actual nodes directly at the backbones, offering web caching, server load balancing, request routing and, based upon these techniques, content delivery services

124 Aha

125 What you have seen earlier: based on the IP address of the machine (origin) which made the DNS A query, the DNS server of the CDN has each time decided to return a different IP address e.g. one from the same geographic region

126 Aha

127 What you now can expect is that the returned IP address leads you to a load balancer your gate to a whole subinfrastructure of the CDN which balances between web caches or web servers or similar

128 Aha

129 CDN uses different algorithms to decide where it routes user requests to: based upon current load, cost, location etc.

130 Aha

131 But in the end, your content gets delivered to the user. If it expires, CDN refreshes it from your servers in the background

132 Often, you have to offer the last mile the very last database access, e.g. the last item view or similar. Here, the user hits your server

133 Huh?

134 cache access A query inter-cache updates caches cache refresh caches your servers

135 kk

136 How can I benefit from this having big data

137 When you have e.g. images as your big data, you can consider this data as static and thus push down-/uploads to CDN. So, you segment your users and keep your own servers free of load. What you might lose, is consistency between segments

138 Or you pre-compute static content out of your dynamic big data a sort of snapshots, and push them to CDN. So, you keep you database servers free of load and scale only through the web servers. Complexity comes with the snapshot management

139 Or you can even push some functional parts of your platform to CDN such as searches etc. You win a lot dealing with big data, but you are more dependent from the CDN provider, and your overall architecture is weaker

140 Or if you really want to experiment, you can even try to push whole executed database queries to CDN like you would do it with memory caches. That s really cool, but even much more complex and unreliable than a clusterdistributed memory cache

141 If you use CDN to collect your new data, you might need some complex replication mechanisms

142 Anyway, with all that in mind: you can have a lot of your big data out there with CDN

143 Thank you

144

145 Most images were licensed from istockphoto.com Several images were taken from corresponding Wikipedia articles, product pages and open sources