finding what we want: dns and xpath-based pub-sub zachary g. ives university of pennsylvania cis 455...
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![Page 1: Finding What We Want: DNS and XPath-Based Pub-Sub Zachary G. Ives University of Pennsylvania CIS 455 / 555 – Internet and Web Systems February 12, 2008](https://reader035.vdocument.in/reader035/viewer/2022062519/5697bfd51a28abf838cad250/html5/thumbnails/1.jpg)
Finding What We Want: DNS and XPath-Based Pub-Sub
Zachary G. IvesUniversity of Pennsylvania
CIS 455 / 555 – Internet and Web Systems
February 12, 2008
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Today
Reminder: HW1 Milestone 2 due tonight
Directories: DNS
Flooding: Gnutella
XML filtering for pub-sub: XFilter
2
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The Backbone of Internet Naming:Domain Name Service
A simple, hierarchical name system with a distributed database – each domain controls its own names
edu
columbia upenn berkeley
com
www cis sas
www wwwwww
amazon
www
……
……
…… …
…
Top LevelDomains
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Top-Level Domains (TLDs)
Mostly controlled by Network Solutions, Inc. today .com: commercial .edu: educational institution .gov: US government .mil: US military .net: networks and ISPs (now also a number of other
things) .org: other organizations 244, 2-letter country suffixes, e.g., .us, .uk, .cz, .tv, … and a bunch of new suffixes that are not very common,
e.g., .biz, .name, .pro, …
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Finding the Root
13 “root servers” store entries for all top level domains (TLDs)
DNS servers have a hard-coded mapping to root servers so they can “get started”
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Excerpt from DNS Root Server Entries
This file is made available by InterNIC registration services under anonymous FTP as ; file /domain/named.root ; ; formerly NS.INTERNIC.NET ; . 3600000 IN NS A.ROOT-
SERVERS.NET. A.ROOT-SERVERS.NET. 3600000 A 98.41.0.4 ; ; formerly NS1.ISI.EDU ; . 3600000 NS B.ROOT-
SERVERS.NET.B.ROOT-SERVERS.NET. 3600000 A 128.9.0.107 ; ; formerly C.PSI.NET ; . 3600000 NS C.ROOT-
SERVERS.NET.C.ROOT-SERVERS.NET. 3600000 A 192.33.4.12
(13 servers in total, A through M)
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Supposing We Were to Build DNS
How would we start? How is a lookup performed?
(Hint: what do you need to specify when you add a client to a network that doesn’t do DHCP?)
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Issues in DNS
We know that everyone wants to be “my-domain”.com How does this mesh with the assumptions
inherent in our hierarchical naming system?
What happens if things move frequently? What happens if we want to provide
different behavior to different requestors (e.g., Akamai)?
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Directories Summarized
An efficient way of finding data, assuming: Data doesn’t change too often, hence it can be
replicated and distributed Hierarchy is relatively “wide and flat” Caching is present, helping with repeated queries
Directories generally rely on names at their core
Sometimes we want to search based on other means, e.g., predicates or filters over content…
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Pushing the Search to the Network:Flooding Requests – Gnutella
Node A wants a data item; it asks B and C If B and C don’t have it, they ask their
neighbors, etc. What are the implications of this model?
AC B
D
EF
G
I
H
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Bringing the Data to the “Router”: Publish-Subscribe
Generally, too much data to store centrally – but perhaps we only need a central coordinator!
Interested parties register a profile with the system (often in a central server) In, for instance, XPath!
Data gets aggregated at some sort of router or by a crawler, and then gets disseminated to individuals Based on match between content and the profile Data changes often, but queries don’t!
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An Example: XML-Based Information Dissemination
Basic model (XFilter, YFilter, Xyleme): Users are interested in data relating to a particular topic,
and know the schema/politics/usa//body
A crawler-aggregator reads XML files from the web (or gets them from data sources) and feeds them to interested parties
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Engine for XFilter [Altinel & Franklin 00]
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How Does It Work?
Each XPath segment is basically a subset of regular expressions over element tags Convert into finite state automata
Parse data as it comes in – use SAX API Match against finite state machines
Most of these systems use modified FSMs because they want to match many patterns at the same time
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Path Nodes and FSMs
XPath parser decomposes XPath expressions into a set of path nodes
These nodes act as the states of corresponding FSM A node in the Candidate List denotes the current state The rest of the states are in corresponding Wait Lists
Simple FSM for /politics[@topic=“president”]/usa//body:
politics usa body
Q1_1 Q1_2 Q1_3
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Decomposing Into Path Nodes
Query IDPosition in state machineRelative Position (RP) in tree:
0 for root node if it’s not preceded by “//”
-1 for any node preceded by “//”
Else =1+ (no of “*” nodes from predecessor node)
Level:If current node has fixed
distance from root, then 1+ distance
Else if RP = –1, then –1, else 0Finaly, NextPathNodeSet points to
next node
Q1=/politics[@topic=“president”]/usa//body
Q1 Q1 Q1
1 2 3
0 1 -1
1 2 -1Q1-1 Q1-2 Q1-3
Q2 Q2 Q2
1 2 3
-1 2 1-1 0 0
Q2-1 Q2-2 Q2-3
Q2=//usa/*/body/p
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Query Index Query index entry
for each XML tag Two lists:
Candidate List (CL) and Wait List (WL) divided across the nodes
“Live” queries’ states are in CL; “pending” queries + states are in WL
Events that cause state transition are generated by the XML parser
politics
usa
body
p
Q1-1
Q2-1
Q1-3 Q2-2
Q2-3
X
X
X
X
X
X
X
X CLWL
Q1-2
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Encountering an Element
Look up the element name in the Query Index and all nodes in the associated CL
Validate that we actually have a match
Q1
1
0
1Q1-1politics
Q1-1X
X
WL
startElement: politics
CL
Query IDPositionRel.
PositionLevelEntry in Query Index:
NextPathNodeSet
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Validating a Match
We first check that the current XML depth matches the level in the user query: If level in CL node is less than 1, then ignore
height else level in CL node must = height
This ensures we’re matching at the right point in the tree!
Finally, we validate any predicates against attributes (e.g., [@topic=“president”])
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Processing Further Elements
Queries that don’t meet validation are removed from the Candidate Lists
For other queries, we advance to the next state We copy the next node of the query from the
WL to the CL, and update the RP and level When we reach a final state (e.g., Q1-3), we
can output the document to the subscriber
When we encounter an end element, we must remove that element from the CL
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Publish-Subscribe Model Summarized
Currently not commonly used Partly because XML isn’t that widespread This may change with the adoption of an XML
format called RSS (Rich Site Summary or Really Simple Syndication)
Many news sites, web logs, mailing lists, etc. use RSS to publish daily articles
Seems like a perfect fit for publish-subscribe models!
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Finding a Happy Medium
We’ve seen two approaches: Do all the work at the data stores: flood the network
with requests Do all the work via a central crawler: record profiles
and disseminate matches
An alternative, two-step process: Build a content index over what’s out there Typically limited in what kinds of queries can be
supported Most common instance: an index of document
keywords
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Inverted Indices
A conceptually very simple data structure:
<keyword, {list of occurrences}>
In its simplest form, each occurrence includes a document pointer (e.g., URI), perhaps a count and/or position
Requires two components, an indexer and a retrieval system
We’ll consider cost of building the index, plus searching the index using a single keyword
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How Do We Lay Out an Inverted Index?
Some options: Unordered list Ordered list Tree Hash table
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Unordered and Ordered Lists
Assume that we have entries such as:<keyword, #items, {list of occurrences}>
What does ordering buy us?
Assume that we adopt a model in which we use:<keyword, item><keyword, item>
Do we get any additional benefits?
How about:<keyword, {items}> where we fix the size
of thekeyword and the number
of items?
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Tree-Based Indices
Trees have several benefits over lists: Potentially, logarithmic search time, as with
a well-designed sorted list, IF it’s balanced Ability to handle variable-length records
We’ve already seen how trees might make a natural way of distributing data, as well
How does a binary search tree fare? Cost of building? Cost of finding an item in it?
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B+ Tree: A Flexible, Height-Balanced, High-Fanout Tree Insert/delete at log F N cost
(F = fanout, N = # leaf pages) Keep tree height-balanced
Minimum 50% occupancy (except for root) Each node contains d <= m <= 2d entries
d is called the order of the tree Can search efficiently based on equality (or also
range, though we don’t need that here)Index Entries
Data Entries("Sequence set")
(Direct search)
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Example B+ Tree
Data (inverted list ptrs) is at leaves; intermediate nodes have copies of search keys
Search begins at root, and key comparisons direct it to a leaf
Search for be↓, bobcat↓ ...
Based on the search for bobcat*, we know it is not in the tree!
Root
best but dog
a↓ am ↓ an↓ ant↓ art↓ be↓ best↓ bit↓ bob↓ but↓can↓cry↓ dog↓ dry↓ elf↓ fox↓
art
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B+ Trees in Practice
Typical order: 100. Typical fill-factor: 67%. average fanout = 133
Typical capacities: Height 4: 1334 = 312,900,700 records Height 3: 1333 = 2,352,637 records
Can often hold top levels in a cache: Level 1 = 1 page = 8 Kbytes Level 2 = 133 pages = 1 Mbyte Level 3 = 17,689 pages = 133 MBytes
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Inserting Data into a B+ Tree
Find correct leaf L Put data entry onto L
If L has enough space, done! Else, must split L (into L and a new node L2)
Redistribute entries evenly, copy up middle key Insert index entry pointing to L2 into parent of L
This can happen recursively To split index node, redistribute entries evenly, but push
up middle key. (Contrast with leaf splits.) Splits “grow” tree; root split increases height
Tree growth: gets wider or one level taller at top
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Inserting “and↓” into Example B+ Tree
Observe how minimum occupancy is guaranteed in both leaf and index page splits
Recall that all data items are in leaves, and partition values for keys are in intermediate nodesNote difference between copy-up and push-up
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Inserting “and↓” Example: Copy up
Want to insert here; no room, so split & copy up:
a↓ am ↓ an↓ ant↓ and↓
an
Entry to be inserted in parent node.(Note that key “an” is copied up andcontinues to appear in the leaf.)
and↓
Root
best but dog
a↓ am ↓ an↓ ant↓ art↓ be↓ best↓ bit↓ bob↓ but↓can↓cry↓ dog↓ dry↓ elf↓ fox↓
art
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Inserting “and↓” Example: Push up 1/2
Root
art↓ be↓ best↓ bit↓ bob↓ but↓can↓ cry↓
an
Need to split node & push up
best but dogart
a↓ am ↓ dog↓ dry↓ elf↓ fox↓
an↓ ant↓ and↓
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Inserting “and↓” Example: Push up 2/2
Root
art↓ be↓ best↓ bit↓ bob↓ but↓can↓ cry↓
an but dog
best
art
Entry to be inserted in parent node.(Note that best is pushed up and onlyappears once in the index. Contrastthis with a leaf split.)
a↓ am ↓ dog↓ dry↓ elf↓ fox↓
an↓ ant↓ and↓
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Copying vs. Splitting, Summarized
Every keyword (search key) appears in at most one intermediate node Hence, in splitting an intermediate node, we push
up
Every inverted list entry must appear in the leaf We may also need it in an intermediate node to
define a partition point in the tree We must copy up the key of this entry
Note that B+ trees easily accommodate multiple occurrences of a keyword
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Virtues of the B+ Tree
B+ tree and other indices are quite efficient: Height-balanced; logF N cost to search
High fanout (F) means depth rarely more than 3 or 4 Almost always better than maintaining a sorted file Typically, 67% occupancy on average
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How Do We Distribute a B+ Tree?
We need to host the root at one machine and distribute the rest
What are the implications for scalability? Consider building the
index as well as searching
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Eliminating the Root
Sometimes we don’t want a tree-structured system because the higher levels can be a central point of congestion or failure
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A “Flatter” Scheme: Hashing
Start with a hash function with a uniform distribution of values: h(name) a value (e.g., 32-
bit integer)
Map from values to hash buckets Generally using mod (#
buckets)
Put items into the buckets May have “collisions” and
need to chain
0
1
2
3
0
4812
…
buckets
{h(x) values
overflow chain
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Next: Data Distribution
Going from hashing to distributed hashing