Introducing Gossip & Key-Value Store and basics of Cassandra. Directions for further learning and projects

1. Gossip Protocol & Key-Value Store
Theory and Practice
Dr. SAJEEV G P
April 16, 2016
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2. Contents
Outline
Background
Introduction to Gossip
Gossip Model
Key-Value Store
Cassandra
CAP Theorem
Further study
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3. Background
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4. Real World Applications
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5. Some Real World Applications
Facebook Search
Term Search
Amazon Search
Amazon Recommendations
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6. Cloud Platform
App Engine
Compute Engine
Cloud storage
Cloud BigTable
Google Dataow
Google Translate API
Google BigQuery
Cloud Prediction API
Gossip Key-Value Store
Gossip is for communication
Key-Value store is the database
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7. Gossip Protocol
Multicast
Multicast is group communication where information is addressed to a
group of destination computers simultaneously.
Types of Casting
Unicast
Multicast
Broadcast
Multicast in application level
Multicast in Network level
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8. Multicast Protocol: Centralized Tree Based
Centralized
Tree based
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9. Gossip Protocol: Epedemic multicast
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10. Gossip Analysis
Basics
Population of (n+1) individuals mixing homogeneously
Contact rate between any individual pair is β
At any time, each individual is either uninfected (numbering x) or
infected (numbering y)
Then, x0 = n , y0 = 1 and at all times x + y = n + 1
Infected-uninfected contact turns latter infected, and it stays infected
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11. Gossip Analysis
Gossip Properties
Lightweight in large groups
Spreads quickly
Fault-tolerant
Terms
n + 1 nodes
x: # of uninfected nodes
y: # of infected nodes
x + y = n + 1
Continuous time ..
dx
dt = −βxy
β contact rate = b
n
Solution:
x = n(n+1)
n+eβ(n+1)t
y = (n+1)
1+ne−β(n+1)t
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12. Gossip Analysis ..
No. of infected nodes ..
At t = c log (n),
y ≈ (n + 1) − 1
ncb−2
Low Latency
Set c, b to be
small numbers
Within
t = c log (n)
rounds:
all will receive
the multicast
except 1
ncb−2
Lightweight
Each node has
transmitted no
more than:
cb log (n)
gossip
messages.
Fault tolerance
With 50% of packet drop
b ← b
2 :
Takes twice as
many rounds
With 50% node failures
n ← n
2 :
Same as above
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13. Key-Value Stores
Simplest form of database
management systems.
They store pairs of keys and
values as well as retrieve
values when a key is known.
Examples
twitter.com: Tweet id ⇒
information about tweet
amazon.com: Item number ⇒
information about it
kayak.com: Flight number ⇒
information about ight, e.g.,
availability
yourbank.com: Ac number ⇒
information about it
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14. Key-Value Stores..
It's a dictionary data
structure.
NoSQL database
Insert, lookup, and delete
by key
E.g., hash table, binary tree
But distributed
Key-Value stores reuse many
techniques from DHTs
NoSQL Databases
Traditional RDBMS..
Schema-based, i.e., structured tables
Primary key that is unique within that table
Queried using SQL , Supports joins
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15. Todays Workload
Big Data Era ..
Data: Large and unstructured
Lots of random reads and writes
Sometimes write-heavy
Foreign keys rarely needed
Joins infrequent
Need of todays Workload
Speed
Avoid Single Point of Failure (SPOF)
Low TCO (Total cost of operation)
Fewer system administrators
Incremental scalability, Scale out, not up
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16. Key-Value or NoSQL
NoSQL systems often use column-oriented storage
RDBMSs store an entire row together (on disk or at a server)
NoSQL systems typically store a column together (or a group of
columns).
Entries within a column are indexed and easy to locate, given a key
(and vice-versa)
Why useful?
Range searches within a column are fast since you don't need to
fetch the entire database
E.g., get me all the blog-ids from the blog table that were updated
within the past month
Search in the the last-updated column, fetch corresponding blog-id
column
Don't need to fetch the other columns
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17. CASSANDRA
What is CASSANDRA
A distributed key-value store
Intended to run in a
data-center (and also across
DCs)
Originally designed at
Facebook
Open-sourced later, today an
Apache project
In use
Some of the companies that
use Cassandra in their
production clusters
IBM, Adobe, HP, eBay,
Ericsson, Symantec
Twitter, Spotify
PBS Kids
Netix: uses Cassandra to
keep track of your current
position in the video youâre
watching
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18. CASSANDRA
Objectives and Functions
Performance
Availability
Scalability
Fault-Tolerance
P2P Cluster
Decentralized design
Each node has the same role
No single point of failure
Avoids issues of master-slave DBMS's
No bottlenecking
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19. Cassandra Architecture
DHT Like
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20. Cassandra Architecture ..
Replication Strategy
Simple Strategy: uses the Partitioner, of which there are two kinds
RandomPartitioner: Chord-like hash partitioning
ByteOrderedPartitioner: Assigns ranges of keys to servers.
Easier for range queries (e.g., get me all twitter users starting with
[a-b])
NetworkTopologyStrategy NetworkTopologyStrategy: for multi-DC
deployments
Two replicas per DC
Three replicas per DC
Per DC
First replica placed according to Partitioner
Then go clockwise around ring until you hit a dierent rack
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21. Cassandra Architecture ..
At node
On receiving a write
Log it in disk commit log (for failure recovery)
Make changes to appropriate memtables
Memtable = In-memory representation of multiple key- value pairs
Later, when memtable is full or old, ush to disk
Data le: An SSTable (Sorted String Table) - list of key-value
pairs, sorted by key
Index le: An SSTable of (key, position in data sstable) pairs
And a Bloom lter (for ecient search)
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22. Cassandra Architecture ..
CAP
C consistency: all nodes see same data at any time, or reads
return latest written value by any client
A vailability: the system allows operations all the time, and
operations return quickly
P artition-tolerance: the system continues to work in spite of
network partitions
CAP Theorem
In a distributed system you can satisfy at most 2 out of the 3 guarantees.
Eventual consistency : weak consistency model
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23. Cassandra Architecture ..
CAP Theorem
CAP
Cassandra Consistency
Cassandra chooses
Consistency
and
Availability
Cassandra has consistency levels
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24. CAP Theorem ..
Consistency: No. of Replicas ..
Client is allowed to choose a consistency level for each operation
(read/write)
ANY: any server, Fastest
ALL: all replicas, Ensures strong consistency, but slowest
ONE: at least one replica, Faster than ALL
QUORUM: quorum across all replicas in all datacenters (DCs)
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25. Cassandra Architecture ..
Cassandra Gossip
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26. For further learning
Gossip Applications
Distributed
Computing/Networking
Information Dissemination
Gossip Learning
Distributed Data
Research/Projects
Gossip Protocol:
Anti-entropy (simple
epidemics)
Rumor mongering
(complex epidemics)
Eager epidemic
dissemination
Key-Value Store:
Cassandra
Redis
python-Cassandra
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27. Further Learning ..
Cassandra
Resources
http://cassandra.apache.org/
http://academy.datastax.com/
http://www.planetcassandra.org/
Use locally installed
apache-cassandra-3.4
Use Cassandra Cluster service
Python-Cassandra
Cassndra Cluster Setup
cqlengine: Cassandra CQL
object mapper for Python
Python Application
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28. References I
Atikoglu, B., Xu, Y., Frachtenberg, E., Jiang, S., and Paleczny, M.
Workload analysis of a large-scale key-value store.
In ACM SIGMETRICS Performance Evaluation Review (2012),
vol. 40, ACM, pp. 5364.
Datastax.
Python cassandra-driver[onlne] available:.
https://pypi.python.org/pypi/cassandra-driver (2015).
Gupta, I., and Meseguer, J.
Quantitative analysis of consistency in nosql key-value stores.
In Quantitative Evaluation of Systems: 12th International
Conference, QEST 2015, Madrid, Spain, September 1-3, 2015,
Proceedings (2015), vol. 9259, Springer, p. 228.
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29. References II
Jenkins, K., Hopkinson, K., and Birman, K.
A gossip protocol for subgroup multicast.
In Distributed Computing Systems Workshop, 2001 International
Conference on (2001), IEEE, pp. 2530.
Lakshman, A., and Malik, P.
Cassandra: A decentralized structured storage system.
SIGOPS Oper. Syst. Rev. 44, 2 (Apr. 2010), 3540.
van der Hoek, W.
A framework for epistemic gossip protocols.
In Multi-Agent Systems: 12th European Conference, EUMAS 2014,
Prague, Czech Republic, December 18-19, 2014, Revised Selected
Papers (2015), vol. 8953, Springer, p. 193.
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30. Thank You..
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