Dancing with publish/subscribe

Dancing with
publish/subscribe
(Distributed event based systems)
Lightening Talk on Top-k publish/subscribe
By Y.S. Horawalavithana
BSc(Hons.) Computer Science
MSc. Distributed System 1

Today
For who?
Outline
Discussion

Communication paradigms
Point-to-point communication
• Participants need to exist at the
same time
• Direct coupling
• Strict Identity management
• Not good for volatile
environment
• Not a good way to communicate
with several participants
Indirect communication
• Communication through an
intermediary between sender(s)
& receiver(s)
• No direct coupling
• Space uncoupling
• Anonymity
• Time uncoupling
• Independent lifetimes
• Through persistent communication
channel

Indirect communication
• Scenarios where users connect and disconnect very often
• Mobile environments, messaging services, forums
• Event dissemination where receivers may be unknown and change
often
• RSS, events feeds in financial services
• Scenarios with very large number of participants
• Google Ads system, Spotify
• Commonly used in cases when change is anticipated
• Need to provide dependable services

Taxonomy
Indirect
Communication
Communication
based
Group
communication
Message Queues
Publish/subscribe
State based
Tuple spaces
Distributed Shared
Memory

Publish/Subscribe
‘’ Notify me of all stock quotes of Google from
NYSE if the price is greater than 150 ’’

Introduction: pub/sub systems
• Information consumers express their interests in information with
subscriptions, identifying which items are of interest.
• Information producers, publish information by submitting
publications (a.k.a. publication events or event notifications).
• A pub/sub system:
• Subscription processing: Indexing and storing subscriptions.
• Event processing: upon event arrival, access subscription indices and identify
all matched subscriptions.
• Event delivery: deliver event to clients with matched subscriptions..

Programming model
MSc. Distributed System
8
Figure adapted from Instructor’s Guide for Coulouris, Dollimore, Kindbergand Blair, Distributed Systems: Concepts and Design Edn. 5 © Pearson Education 2012

Introduction: DB view at pub/sub
• Events correspond to data (“data-carrying events”).
• Subscriptions correspond to continuous queries:
• Define predicates on attributes
• Fundamentally different model:
• Instead of storing/indexing data and issuing queries to access it
• Queries (subscriptions) are stored/indexed and incoming data (events) is
matched against stored queries.

Introduction: Communications view at
pub/sub
• Akin to multicasting (group IPC, 1-N communication)
• Each publisher (through its events) communicates to a large number of subscribers.
• However, communication is,
• Anonymous
• Subscribers do not “know” publishers and vice versa
• Asynchronous
• publishers and subscribers do not block when publishing/subscribing
• Mutually out-of-sync: no rendezvous in time
• Heterogeneous
• can be used to connect heterogeneous components

Example: Real-world Implementation

Pub/sub: System Space
12
Figure adapted from K. Pripuºi, I. Podnaršarko, and K. Aberer, Top-k/w publish/subscribe 2012

Pub/sub: Subscription models
Content based
Type based
Topic based
• Context Type
• Object Types
• Independent Channels
• Hierarchical Topics
• (Un)structured queries
• Complex Event Processing

Pub/sub: Real-world Applications
• Too numerous…some representative application classes
• News alerts
• Online stock quotes
• Internet games
• Sensor networks
• Location-based services
• Network management
• Internet auctions
• …….

Case study: Dealing Room

Case study: Spotify

Spotify at First glance…
• End-to-end architecture to support social interaction
• Topic-based subscriptions
• Friends (Spotify + Facebook): FB friends who are Spotify users and by sharing
music
• Playlists (URI): other users playlists (updates), “Collaborative” playlists or only
modifiable by creator
• Artists pages (follow artist): new albums or news related to artist

Spotify at First glance…
• Hybrid engine
• Relay events to online users in real
time
• Store and forward selected events to
offline users
• DHT based overlay
• 3 sites: Stockholm Sweden, London
UK, Ashburn USA
• Design to scale
• Stores approx., 600 million
subscriptions at any given time
• Matches billions of publication events
every day

Large scale publish/subscribe systems

Boolean matching at pub/sub
• Assume the dealer room system implemented on top of pub/sub
paradigm
• Dealer submits a subscription
• [Name = ‘Google’ , price > 150 , volume < 5000]
• Stock Exchange publishes a stock quote (publication)
• [Name = ‘Google’ and price = 200 and volume = 3000]

Drawbacks at Boolean pub/sub
Drawbacks
A subscriber may be either
overloaded with publications or
receive too few publications
Impossible to compare different
matching publications as
ranking functions are not
defined, and
Partial matching between
subscriptions and publications
is not supported.

Real-world Requirements: Sensor Web
• Real-time environmental monitoring
• Environmental scientists would like to identify and monitor up to 10 sites with
the largest pollution readings over the course of a single day - NSF's Ocean
Observatories Initiative (OOI)
• Identify 10 sensors closest to a particular location measuring the largest
pollution levels over time (e.g. top-10 readings are provided on hourly basis) -
SNSF’s Sensor Scope project
• Power grid monitoring
• Operators would like to monitor over time 100 sites with the largest or the
lowest power production using solar panel current and voltage readings so
that they to identify power grid hot-spots

Real-world Requirements: Forest Fire rescue

Real-world Requirements: Social Media
• Personalized newspaper
• Facebook user is approximately exposed to more than 1500 stories per day,
but an average user only engaged with 100 stories from the current news
feed.
• What if to have a personalized news-paper at the end of day
• Social Annotation of news-stories
• Serving of Yahoo! News page-views with a fresh set of Top-k tweets, by
considering news-story as a subscription while tweets as incoming
publications

Top-k publish/subscribe
‘’ Notify me of all Top-10 stock quotes of Google hourly from NYSE if
the price is greater than 150 ’’

Top-k publish/subscribe
• How many matching publications will be delivered to a subscriber
during a period of time?
• Actually we don’t know in state-of-the-art pub/sub systems
• Top-k pub/sub models are powered by,
• Expressive stateful query processing engines
• User defined parameter k restricts the delivered publications
• Time (in)dependent Top-k computing methods
• Sliding window model for handling streaming publications
• Methods to deliver Top-k notifications
• Pro-active
• On-demand

Abstract Top-k/w matching
• Limit the number of matching and delivered publications to k best within a sliding
window of size w
𝑃1 𝑃2 𝑃3 𝑃4 𝑃5 𝑃6 𝑃7 𝑃8 𝑃9 𝑃10 ....
𝑃1 𝑃2 𝑃3 𝑃4 𝑃5 𝑃6 𝑃7 𝑃8 𝑃9 𝑃10 ....
𝑃1 𝑃2 𝑃3 𝑃4 𝑃5 𝑃6 𝑃7 𝑃8 𝑃9 𝑃10 ....
𝑃5 𝑃1
𝑃5 𝑃6
𝑃5 𝑃9
Top-2
Matching publication stream
h=1
h=3
Jumping
step
(h)

[Pripužić 2012] Top-k/w model: DaZaLaPS
• Subscriber controls the number of publications it receives per
subscription (top-k) within a sliding window
• Subscription is defined by
• Totally-ordered and time-independent scoring function
• Parameter k ∈N
• Parameter w ∈R+*(time-based)or n ∈ N (count-based sliding window).
• Ranks publications according to the degree of relevance (score) to a
subscription
• Each publication is competing with other publications from the sliding
window for a position among top-k publications

[Pripužić 2012] Top-k/w model: DaZaLaPS
• When can a publication become a Top-k object in the subscription
window?
• Immediately upon publication
• Later on when it becomes a Top-k object in the subscription window
• Maintain a set of candidate
(potential Top-k) publications
in memory!

[Pripužić 2012] Distributed Top-k/w model
• Network of processing nodes, where each node is responsible for
computing Top-k/w publications
• Publication Flooding
A
B
C D
E
F
subscribe(s)
change(𝑡ℎ 𝑠)
publish(p)
p
p
p
p
p

• Subscription Flooding
• Proxy subscriptions:
• Replicas of original publications which to be advertised
over the network
A
B
C D
E
F
subscribe(s)
publish(p)
𝑠 𝑝
𝑠 𝑝
𝑠 𝑝
𝑠 𝑝
𝑠 𝑝
𝑡ℎ 𝑠
𝑡ℎ 𝑠
𝑡ℎ 𝑠
𝑡ℎ 𝑠
𝑡ℎ 𝑠

• Rendezvous routing
• Often implemented on top of a structured peer-to-peer network
• Rendezvous node is responsible for
• Matching mapped publications & subscriptions
• Delivering matching publications to subscribers directly
A
B
C D
E
F
subscribe(s)
publish(p)
s
s
sp
p

• Basic gossiping
• Similar to publication flooding, but randomly spread through an overlay
network as a gossip
• Cannot provide any guarantee regarding publication delivery
• Purely probabilistic
A
B
C D
E
F
subscribe(s)
publish(p)
p
p
p

• Informed gossiping
• Each node additionally stores subscriptions of its close neighbors and also
processes the subscriptions of its neighbors
• Partially probabilistic and partially deterministic
A
B
C D
E
F
subscribe(s)
publish(p)
p
p
𝑠 𝑝
𝑡ℎ 𝑠
p

[Shrarer 2014] Google Top-k pub/sub
MSc. Distributed System 35News-story as a subscription Tweets as publications

[Shrarer 2014] Google Top-k pub/sub
• Annotating news stories with social updates (tweets), at a news website
serving high volume of page-views
• Billions page-views at Yahoo News! per day
• More than 100 millions related tweets per day
• Top-k pub/sub approach
• stories are standing subscriptions on tweets
• Story Index is queried frequently,
• but it is updated infrequently
• based on DAAT, TAAT algorithms
• Tweet Index updated frequently
• but queried only for new stories

[Drosou 2009] PrefSIENA
• Say Addison is more interested in horror movies than comedies
• Addison would like to receive notifications about (various) comedies only if
there are no (or just a few) notifications about horror movies
title = The Godfather
genre = drama
showing time = 21:10 title = Ratatouille
genre = comedy
showing time = 21:15
title = Fight Club
genre = drama
title = Casablanca
genre = drama
showing time = 23:10title = Vertigo
genre = drama
Published events User subscriptions
genre = drama
genre = horror

• To express some form of ranking among subscriptions, PrefSIENA
allow users to define priorities among them
• To do this, they introduce preferential subscriptions
• Based on preferential subscriptions, we deliver to users only the k most
interesting events
• Covering/Matching relation
string director = Peter Jackson
time release date > 1 Jan 2003
string director = Steven Spielberg
string genre = fantasy
string release date > 1 Jan 2003
string title = LOTR: The Return of the King
time release date = 1 Dec 2003
string genre = fantasy
integer oscars = 11



• Ordering subscriptions
• To order user subscriptions according to the preference relation, they use
the winnow operator1, applying it on various levels
• Step 01: Construct DAG
genre = drama genre = horror
User preferences
genre = comedy genre = romance
genre = romance genre = action
≻genre = drama genre = horror
≻genre = comedy genre = romance
≻genre = romance genre = action
≻genre = comedy genre = horror
genre = drama genre = comedy
genre = horror genre = romance
genre = action
Preference graph

• Step 02: perform a topological sort to compute winnow levels. The
subscriptions of level i are associated with a preference rank 𝒢(i):
• 𝒢 is a monotonically decreasing function with 𝒢 → [0, 1]
• e.g. for 𝒢 = (D +1 – (l -1)) / (D +1)
genre = drama genre = comedy
genre = horror genre = romance
genre = action
Preference graph
Preference rank = 1
Preference rank = 2/3
Preference rank = 1/3

• Step 03: Computing Event Ranks
• Step 04: Based on the ranks, they deliver to users only the k most
interesting events
• Continuous, periodic & sliding window
User subscriptions
genre = adventure 0.9
director = Peter Jackson 0.7
string title = King Kong
string genre = adventure
string title = King Kong
string genre = adventure
0.9
ℱ = max

[Drosou 2009] PrefSIENA: Sliding window
Delivery
title = The Big Parade
genre = romance
title = The Apartment
genre = comedy
genre = drama
title = Forrest Gump
genre = romance
title = Jaws
genre = horror
title = Vertigo
genre = horror
title = Psycho
genre = horror
title = Pulp Fiction
genre = drama
User subscriptions
genre = comedy 0.9
genre = romance 0.9
genre = drama 0.8
genre = horror 0.6
20:00
20:15
20:22
20:25
20:50
20:40
20:45
20:55
k = 2
w = 4
title = The Big Parade
genre = romance
title = The Apartment
genre = comedy
title = Forrest Gump
genre = romance
genre = drama
title = Psycho
genre = horror
title = Pulp Fiction
genre = drama
Matching events Delivered events

[Drosou 2009] PrefSIENA But wait..
• The most highly ranked events may be very similar to each other…
• We wish to retrieve results on a broader variety of user interests
• Two different perspectives on achieving diversity:
• Avoid overlap: choose notifications that are dissimilar to each other
• Increase coverage: choose notifications that cover as many user interests as possible
• How to measure diversity?
• Many alternative ways
• Common ground: measure similarity/distance among the selected items

Diversity: Top-k representative set
Representative Top-kDrawback
(without diversity)
What we want
(with diversity)
Method to retrieve Top-k publications from matching publications

MAX* k-diversity problem
where
1. P = {p1, …, pn}
2. k ≤ n
3. d: a distance metric
4. f: a diversity function
),(argmax*
dSfS
k|S|
PS



Find:

Proposed: MAXDIVREL k-diversity problem
 
  S-Pinrelevancy&similarity-distheminimize,,
Sinrelevancy&similarity-disthemaximize,,g
),,(
),,(
maxarg),,(argmax*




rdSh
rdS
rdSh
rdSg
rdSfS
PS
where
1. P = {p1, …, pn}
3. r: a relevance metric
4. f: a diversity function

Formal Definition: MAXDIVREL k-diversity
 
  






SPpSp
ji
i
j
Spp
ji
i
j
ji
ji
ppd
pr
pr
SP
rdSh
ppd
pr
pr
S
rdS
,
,
dominanceholds),(
)(
)(
||
1
,,argmin
ceindependenholds),(
)(
)(
||
1
,,gargmax
where
1. P = {p1, …, pn}
3. r: a relevance metric
4. 𝛼 > 0
Independence condition:
∀𝑝𝑖, 𝑝𝑗 ∈ 𝑆, 𝑑 𝑝𝑖, 𝑝𝑗 > 𝛼
Dominance condition:
∀𝑝𝑖 ∈ 𝑃, ∃𝑝𝑗 ∈ 𝑆 𝑠. 𝑡. 𝑑 𝑝𝑖, 𝑝𝑗 ≤ 𝛼; 𝑖 ≠ 𝑗

NP-Hardness:
Minimum independent-dominating set
𝑝1
𝑝2
𝑝3
𝑝4
𝑝5
𝑣1
𝑣4
𝑣3
𝑣5
𝑣2
𝛼
𝑣1
𝑣4
𝑣3
𝑣5
𝑣2

𝑣1
𝑣4
𝑣3
𝑣2
𝑣5
𝑣1
𝑣4
𝑣3
𝑣2
𝑣5
  jijiji ppppdppodNeighborho  ,|)(
𝑣1
𝑣4
𝑣3𝑣2
𝑣5
Publication
space
Graph
model
Independent, dominating Independent, dominating Independent, dominating Dominating, not independent

NAÏVE Greedy argmax
𝑟(𝑝𝑖)2
𝑝 𝑗∈𝑁(𝑝 𝑖) 𝑟(𝑝𝑗) × 𝑑(𝑝𝑖, 𝑝𝑗)

Handling streaming publications
𝑝1
𝑝2
𝑝3
𝑝4
𝑝5
𝑣1
𝑣4
𝑣3
𝑣5
𝑣2𝛼
𝑝6
𝑣1
𝑣4
𝑣3
𝑣5
𝑣2𝑣6
Continuity Requirements
1. Durability
an item is selected as diversified in 𝑖 𝑡ℎ window may still have the chance to be in 𝑖 + 1 𝑡ℎ window
if it's not expired & other valid items in 𝑖 + 1 𝑡ℎ
window are failed to compete with it.
2. Order
Publication stream follow the chronological order
We avoid the selection of item j as diverse later, when we already selected an item i which is not-
older than j.

MAXDIVREL continuous k-diversity
𝑃1 𝑃2 𝑃3 𝑃4 .. 𝑃𝑗 𝑃𝑗+1 .. .. .. ....
Matching publication stream
𝑃1 𝑃2 𝑃3 𝑃4 .. 𝑃𝑗 𝑃𝑗+1 .. .. .. ....
ith window
(i+1)th window
𝑆𝑖
∗
𝑆𝑖+1
∗
MAXDIVREL k-diversity
MAXDIVREL k-diversity
Independence
Dominance
Durability
Order
 Straightforward solution:
 Apply naïve greedy method at each instance
 Propose incremental index mechanism!
 Avoid the curse of re-calculating neighborhood

Locality Sensitive Hashing (LSH)
 Simple Idea
 if two points are close together, then after a “projection” operation these two
points will remain close together

LSH Analysis
 For any given points 𝑝, 𝑞 ∈ 𝑅 𝑑
𝑃 𝐻 ℎ 𝑝 = ℎ 𝑞 ≥ 𝑃1 𝑓𝑜𝑟 𝑝 − 𝑞 ≤ 𝑑1
𝑃 𝐻 ℎ 𝑝 = ℎ 𝑞 ≤ 𝑃2 𝑓𝑜𝑟 𝑝 − 𝑞 ≥ 𝑐𝑑1 = 𝑑2
• Hash function h is (𝑑1, 𝑑2, 𝑃1, 𝑃2) sensitive,
• Ideally we need
• (𝑃1−𝑃2) to be large
• (𝑑1−𝑑2) to be small

LSH in MAXDIVREL:
Publications as categorical data

LSH in MAXDIVREL:
Characteristic Matrix

LSH in MAXDIVREL:
Minhashing
 No Publications any more!
 Signature to represent
 Technique
 Randomly permute the rows at
characteristic matrix m times
 Take the number of the 1st row, in
the permuted order,
 which the column has a 1 for
the correspondent column of
publications.
First permutation of rows at characteristic matrix
 Advantage:
 Reduce the dimensions into a small
minhash signature

LSH in MAXDIVREL:
Signature Matrix
Fast-minhashing
Select m number of random hash
functions
To model the effect of m number of
random permutation
Mathematically proved only when,
The number of rows is a prime.

LSH in MAXDIVREL:
LSH Buckets
 Take r sized
signature vectors
 From m sized
minhash-
signature
 Map them into,
 L Hash-Tables
 Each with
arbitrary b
number of
buckets

LSH in MAXDIVREL:
How to select L, r?
For two vectors x,y
𝐽𝐷 𝑥, 𝑦 = 1 − 𝐽𝑆𝐼𝑀 𝑥, 𝑦 ;
𝑤ℎ𝑒𝑟𝑒, 𝐽𝑆𝐼𝑀 𝑥, 𝑦 =
𝑥 ∩ 𝑦
𝑥 ∪ 𝑦
1. 𝐿 × 𝑟 = 𝑚
2. ?
2) 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑(𝑠) ≈
1
𝐿
1
𝑟

LSH in MAXDIVREL:
Analysis
For two vectors x,y
𝐽𝐷 𝑥, 𝑦 = 1 − 𝐽𝑆𝐼𝑀 𝑥, 𝑦 ;
𝑤ℎ𝑒𝑟𝑒, 𝐽𝑆𝐼𝑀 𝑥, 𝑦 =
𝑥 ∩ 𝑦
𝑥 ∪ 𝑦
 For publications x & y
𝐽𝑆𝐼𝑀 𝑥, 𝑦 ∝ 𝑃𝑟𝑜𝑏 𝐻 𝑥 = 𝐻 𝑦
 At a particular hash table
 x & y map into the same bucket:
𝐽𝑆𝐼𝑀 𝑥, 𝑦 𝑏
 x & y does not map into the same bucket:
1 − 𝐽𝑆𝐼𝑀 𝑥, 𝑦 𝑏
 At L Hash-tables
 x & y does not map into the same bucket:
(1 − 𝐽𝑆𝐼𝑀 𝑥, 𝑦 𝑏
) 𝐿 1 − (1 − 𝐽𝑆𝐼𝑀 𝑥, 𝑦 𝑏) 𝐿
True near neighbors will
be unlikely to be unlucky
in all the projections

LSH in MAXDIVREL:
Batch-wise Top-k computation
 Bucket “Winner” – a publication which has the
highest relevancy score
 Winner is dominant to represent it's bucket
neighborhood
 Top-k "winners“ that have a majority of votes
 k winners are independent
𝑃𝐴 𝑃𝐵 𝑃𝐶 𝑃 𝐷 𝑃𝐸 𝑃𝐹 𝑃𝐺 𝑃 𝐻 . .
ith
window

LSH in MAXDIVREL:
Incremental Top-k computation
𝑁𝑒𝑤 𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑖 𝑈𝑝𝑑𝑎𝑡𝑒 𝑖 𝑡ℎ
𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐 𝑣𝑒𝑐𝑡𝑜𝑟
Characteristic
Matrix
𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑖 𝑡ℎ
𝑚𝑖𝑛ℎ𝑎𝑠ℎ 𝑠𝑖𝑔𝑛𝑎𝑡𝑢𝑟𝑒
Signature
Matrix
Map 𝑖 𝑡ℎ
signature
into L hash-tables
Update “Winner” at
bucket 𝑖 𝑡ℎ
signature
maps into
Vote 𝑇𝑜𝑝 − 𝑘 𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒

LSH in MAXDIVREL:
When new publication F arrives…
 Only buckets 𝐵13
, 𝐵23
, 𝐵32
, 𝐵43
will vote
 Follow continuity requirements
 Durability
 Order
𝑃𝐴 𝑃𝐵 𝑃𝐶 𝑃 𝐷 𝑃𝐸 𝑃𝐹 𝑃𝐺 𝑃 𝐻 . .
ith
window
(i+1)th
window


Implementation

Cloud service modules
Source: Amazon Kinesis Source: Amazon Elastic-cache

Top-k pub/sub: DEMO

P2P Pub/Sub
• Scribe: topic-based, built on top of Pastry, stateful, rendezvous.
• Hermes: topic & content-based, built on top of Pastry(-like) net, stateful,
rendezvous & flooding-like.
• Meghdoot: content-based, built on top of CAN, stateful, rendezvous.
• Tera: topic-based, built on unstructured P2P net, stateful, random walk-
based-flooding.
• Sub2Sub: content-based, built on unstructured P2P net, stateful, flooding-
like.
• DHTStrings: content-based, DHT-independent, string support, stateless,
rendezvous.
• OP-DHT Pub/Sub: content-based, (can be) built on top of
Chord/Pastry/Bamboo.

DHT based pub/sub: Scribe
• Topic Based
• Based on DHT (Pastry)
• Rendezvous event routing
• A random identifier is assigned to each topic
• The pastry node with the identifier closest to the one of the topic
becomes responsible for that topic

DHT based pub/sub: Meghdoot
• Content Based
• Based on Structured Overlay CAN
• Mapping the subscription language and the event space to CAN
space
• Subscription and event Routing exploit CAN routing algorithms

Top-k publish/subscribe at P2P
• Stateful approaches introduce some kind of state at (intermediate) nodes.
State can refer to :
• State needed to support specialized structures built on top of the network structure
• E.g. trees (parent, children pointers)
• Routing state – for ‘content-based routing’:
• Subscription paths to be followed by matching publications
• Subscriptions (meta)data: not just forward pointers to be followed and subscription
content (its predicates), but also possible info as to
• What about query inherent diversification?
• The controlled parameters (k & w) can change
• Updates and the need to maintain state consistency may stress the
system and revoke any benefits..
• So we’ll be left with the complexity …

Future work
• Apply Top-k diversification modules at (un)structured P2P
• Exploiting overlap among diversified results of users who have similar interest
• Develop LSH based index over multi-threaded distributed
environment
• Develop large scale Top-k pub/sub applications by exploring other
suitable use-cases E.g.
• Personalized newspaper for every Facebook user
• Diverse set of personalized Twitter trends
• Social annotation of news-stories

Thank you!
sam2010ucsc@acm.org
@SamTube405
http://geektube405.wordpress.com

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