gossip & key value store

Gossip Protocol & Key-Value Store

Theory and Practice

Dr. SAJEEV G P

April 16, 2016

Dr. SAJEEV G P Gossip Protocol & Key-Value Store 1 / 1

Contents

Outline

I Background

I Introduction to GossipI Gossip Model

I Key-Value StoreI CassandraI CAP Theorem

I Further study


Background


Real World Applications

Resume Youtube Video

Last watched status..

Already Watched?

Youtube


Some Real World Applications

Facebook Search

Term Search

Amazon Search

Amazon Recommendations


Cloud Platform

I App Engine

I Compute Engine

I Cloud storage

I Cloud BigTable

I Google Data�ow

I Google Translate API

I Google BigQuery

I Cloud Prediction API

Gossip & Key-Value StoreI Gossip is for communication

I Key-Value store is the database


Gossip Protocol

Multicast

Multicast is group communication where information is addressed to agroup of destination computers simultaneously.

Types of Casting

I Unicast

I Multicast

I Broadcast

I Multicast in application level

I Multicast in Network level


Multicast Protocol: Centralized & Tree Based

Centralized

Tree based


Gossip Protocol: Epedemic multicast


Gossip Analysis

Basics

I Population of (n+1) individuals mixing homogeneously

I Contact rate between any individual pair is β

I At any time, each individual is either uninfected (numbering x) orinfected (numbering y)

I Then, x0 = n , y0 = 1 and at all times x+ y = n+ 1

I Infected-uninfected contact turns latter infected, and it stays infected


Gossip Analysis

Gossip Properties

I Lightweight in large groups

I Spreads quickly

I Fault-tolerant

Terms

I n+ 1 nodes

I x: # of uninfected nodes

I y: # of infected nodes

x+ y = n+ 1

Continuous time ..

I dxdt = −βxy

I β contact rate = bn

I Solution:I x = n(n+1)

n+eβ(n+1)t

I y = (n+1)1+ne−β(n+1)t


Gossip Analysis ..

No. of infected nodes ..

I At t = c log (n),

Iy ≈ (n+ 1)− 1

ncb−2

Low Latency

I Set c, b to besmall numbers

I Withint = c log (n)rounds:

I all will receivethe multicastexcept 1

ncb−2

Lightweight

I Each node hastransmitted nomore than:

I cb log (n)gossipmessages.

Fault toleranceI With 50% of packet drop

I b← b2 :

I Takes twice asmany rounds

I With 50% node failures

I n← n2 :

I Same as above


Key-Value Stores

I Simplest form of databasemanagement systems.

I They store pairs of keys andvalues as well as retrievevalues when a key is known.

Examples

I twitter.com: Tweet id ⇒information about tweet

I amazon.com: Item number ⇒information about it

I kayak.com: Flight number ⇒information about �ight, e.g.,availability

I yourbank.com: Ac number ⇒information about it


Key-Value Stores..

I It's a dictionary datastructure.

I NoSQL databaseI Insert, lookup, and delete

by keyI E.g., hash table, binary tree

I But distributed

I Key-Value stores reuse manytechniques from DHTs

NoSQL Databases

Traditional RDBMS..

I Schema-based, i.e., structured tables

I Primary key that is unique within that table

I Queried using SQL , Supports joinsDr. SAJEEV G P Gossip Protocol & Key-Value Store 14 / 1

Todays Workload

Big Data Era ..

I Data: Large and unstructured

I Lots of random reads and writes

I Sometimes write-heavy

I Foreign keys rarely needed

I Joins infrequent

Need of todays Workload

I Speed

I Avoid Single Point of Failure (SPOF)

I Low TCO (Total cost of operation)

I Fewer system administrators

I Incremental scalability, Scale out, not up


Key-Value or NoSQL

I NoSQL systems often use column-oriented storageI RDBMSs store an entire row together (on disk or at a server)I NoSQL systems typically store a column together (or a group of

columns).I Entries within a column are indexed and easy to locate, given a key

(and vice-versa)

I Why useful?I Range searches within a column are fast since you don't need to

fetch the entire databaseI E.g., get me all the blog-ids from the blog table that were updated

within the past monthI Search in the the last-updated column, fetch corresponding blog-id

columnI Don't need to fetch the other columns


CASSANDRA

What is CASSANDRA

I A distributed key-value storeIntended to run in adata-center (and also acrossDCs)

I Originally designed atFacebook

I Open-sourced later, today anApache project

In use

I Some of the companies thatuse Cassandra in theirproduction clusters

I IBM, Adobe, HP, eBay,Ericsson, Symantec

I Twitter, SpotifyI PBS KidsI Net�ix: uses Cassandra to

keep track of your currentposition in the video youârewatching


CASSANDRA

Objectives and Functions

I PerformanceI AvailabilityI ScalabilityI Fault-Tolerance

I P2P ClusterI Decentralized design

I Each node has the same role

I No single point of failureI Avoids issues of master-slave DBMS's

I No bottlenecking


Cassandra Architecture

DHT Like


Cassandra Architecture ..

Replication Strategy

I Simple Strategy: uses the Partitioner, of which there are two kindsI RandomPartitioner: Chord-like hash partitioningI ByteOrderedPartitioner: Assigns ranges of keys to servers.

I Easier for range queries (e.g., get me all twitter users starting with[a-b])

I NetworkTopologyStrategy NetworkTopologyStrategy: for multi-DCdeployments

I Two replicas per DCI Three replicas per DCI Per DC

I First replica placed according to PartitionerI Then go clockwise around ring until you hit a di�erent rack



At node

On receiving a write

I Log it in disk commit log (for failure recovery)

I Make changes to appropriate memtables

I Memtable = In-memory representation of multiple key- value pairs

Later, when memtable is full or old, �ush to disk

I Data �le: An SSTable (Sorted String Table) - list of key-valuepairs, sorted by key

I Index �le: An SSTable of (key, position in data sstable) pairs

I And a Bloom �lter (for e�cient search)



CAP

C consistency: all nodes see same data at any time, or readsreturn latest written value by any client

A vailability: the system allows operations all the time, andoperations return quickly

P artition-tolerance: the system continues to work in spite ofnetwork partitions

CAP Theorem

In a distributed system you can satisfy at most 2 out of the 3 guarantees.

Eventual consistency : weak consistency model



CAP TheoremCAP

Cassandra Consistency

I Cassandra choosesConsistency

andAvailability

I Cassandra has consistency levelsDr. SAJEEV G P Gossip Protocol & Key-Value Store 23 / 1

CAP Theorem ..

Consistency: No. of Replicas ..

I Client is allowed to choose a consistency level for each operation(read/write)

I ANY: any server, FastestI ALL: all replicas, Ensures strong consistency, but slowestI ONE: at least one replica, Faster than ALLI QUORUM: quorum across all replicas in all datacenters (DCs)



Cassandra & Gossip


For further learning

Gossip Applications

I DistributedComputing/Networking

I Information Dissemination

I Gossip Learning

I Distributed Data

Research/Projects

I Gossip Protocol:I Anti-entropy (simple

epidemics)I Rumor mongering

(complex epidemics)I Eager epidemic

dissemination

I Key-Value Store:I CassandraI RedisI python-Cassandra


Further Learning ..

Cassandra

Resources

I http://cassandra.apache.org/

I http://academy.datastax.com/

I

http://www.planetcassandra.org/

I Use locally installedapache-cassandra-3.4

I Use Cassandra Cluster service

Python-Cassandra

I Cassndra Cluster Setup

I cqlengine: Cassandra CQLobject mapper for Python

I Python Application


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. 53�64.

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 InternationalConference, QEST 2015, Madrid, Spain, September 1-3, 2015,Proceedings (2015), vol. 9259, Springer, p. 228.


References II

Jenkins, K., Hopkinson, K., and Birman, K.A gossip protocol for subgroup multicast.In Distributed Computing Systems Workshop, 2001 InternationalConference on (2001), IEEE, pp. 25�30.

Lakshman, A., and Malik, P.Cassandra: A decentralized structured storage system.SIGOPS Oper. Syst. Rev. 44, 2 (Apr. 2010), 35�40.

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 SelectedPapers (2015), vol. 8953, Springer, p. 193.


Thank You..


gossip & key value store

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