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Udi Bauman
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Non-Relational Databases for Web Scale
1.
Non-relational Databases A
new kind of Databases for handling Web Scale
2.
3.
The solution
4.
Benefits
5.
Cost
6.
Example: Cassandra
7.
8.
Data (peta-bytes
generated daily)
9.
Processing (all
this data needs processing)
10.
Exponential growth
(surging unpredictable demands)
11.
12.
MS SQL
13.
Sybase
14.
MySQL
15.
16.
17.
Existing RDBMS clustering
solutions require scale-up, which is limited & not really scalable when dealing with exponential growth
18.
Machines have upper
limits on capacity, & sharding the data & processing across machines is very complex & app-specific
19.
20.
Basically, you end
up denormalizing everything & loosing all benefits of relational databases
21.
22.
Yahoo!
23.
Amazon
24.
Facebook
25.
Twitter
26.
Linked-In
27.
& many more
28.
29.
Web search result
(Google /Yahoo!)
30.
Item added to
cart (Amazon)
31.
32.
33.
A vailability
34.
35.
36.
This means, that
when writing a record, all replica's must be updated too
37.
38.
Don't lock the
replicas => be less c onsistent
39.
40.
Drop availability (CP)
41.
42.
43.
Hbase (developed
at Yahoo!)
44.
Dynamo (developed
at Amazon)
45.
Cassandra (developed
at FaceBook)
46.
Voldemort (developed
at LinkedIn)
47.
48.
49.
Extremely fast
50.
Highly available, decentralized
& fault tolerant (no single-point-of-failure)
51.
Transparent sharding (consistent
hashing)
52.
Elasticity
53.
Parallel processing
54.
Dynamic schema
55.
Automatic conflict resolution
56.
Consistent hashing
57.
Replication
58.
Replication – node
joining
59.
Replication – node
leaving
60.
61.
Runs on a
large number of cheap commodity machines
62.
Replication
63.
Gossip protocol
64.
Transparently handles adding/removing
nodes
65.
66.
Read your writes
consistency
67.
Session consistency
68.
Monotonic read consistency
69.
70.
Quorum
71.
72.
Vector clock conflict
resolution
73.
74.
75.
Record can have
several “supercolumns”
76.
77.
Brings the workers
to the data – excellent fit for non-relational databases
78.
Minimizes the programming
to 2 simple functions (map & reduce)
79.
Example: count appearances
of a word in a giant table of large texts
80.
Map/Reduce (contd.)
81.
Storage
82.
83.
Non-standard new API
model
84.
Non-standard new Schema
model
85.
New knowledge required
to tune/optimize
86.
Less mature
87.
88.
Put(key, value)
89.
Delete(key)
90.
91.
a record (list
of “columns”: <name, value, timestamp>)
92.
93.
No schema
94.
95.
96.
Columns / column
families
97.
Slicing with predicates
(queries)
98.
PartitionOrderer
99.
100.
101.
102.
Use any node
(closest to you)
103.
Cassandra API
104.
Cassandra API (contd.)
105.
106.
Simple client
107.
108.
Digg
109.
Twitter
110.
111.
112.
http://prettyprint.me/2010/01/20/introduction-to-nosql-and-cassandra-part-2/
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