XFilter and Distributed Data Storage

Zachary G. IvesUniversity of Pennsylvania

CIS 455 / 555 – Internet and Web Systems

April 20, 2023

Some portions derived from slides by Raghu Ramakrishnan

Readings & Reminders

Homework 2 Milestone 1 due 3/1

Please read for Wednesday: Stoica et al. “Chord” Write a 1 paragraph summary of the key ideas and

post to the discussion list

Next week: Monday will be an abbreviated (1 hour) lecture Wednesday: guest lecture by Marie Jacob on Q –

search across databases

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Recall: 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

Thinking of XPath Matching as Threads

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What Is a Thread?

It includes a promise of CPU scheduling, plus context

Suppose we do the scheduling based on events

… Then the “thread” becomes a context Active state What’s to be matched next Whether it’s a final state

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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

A Simpler Approach

Instantiate a DOM tree for each document Traverse and recursively match XPaths

Pros and cons?

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Publish-Subscribe Model Summarized

XFilter has an elegant model for matching XPaths A good deal more complex than HW2, in that it

supports wildcards (*) and //

Useful for applications with RSS (Rich Site Summary or Really Simple Syndication)

Many news sites, web logs, mailing lists, etc. use RSS to publish daily articles

An instance of a more general concept, a topic-specific crawler

Revisiting Storage and Crawlingwith a Distributed Spin

In recent weeks: Index structures primarily intended for single

machines B+ Trees

Data / document formats Basics of single-machine crawling

Seed URLs, robots.txt, etc.

Now: let’s revisit (most of) the above in a setting where there are multiple machines working together! First: storage

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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

Two strategies: Modified tree structure (e.g., BATON, Jagadish

et al.) Non-hierarchical structure

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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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Dividing Hash Tables Across Machines

Simple distribution – allocate some number of hash buckets to various machines Can give this information to every client, or provide

a central directory Can evenly or unevenly distribute buckets Lookup is very straightforward

A possible issue – data skew: some ranges of values occur frequently Can use dynamic hashing techniques Can use better hash function, e.g., SHA-1 (160-bit

key)

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Some Issues Not Solved withConventional Hashing

What if the set of servers holding the inverted index is dynamic? Our number of buckets changes How much work is required to reorganize the

hash table?

Solution: consistent hashing

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Consistent Hashing – the Basis of “Structured P2P”

Intuition: we want to build a distributed hash table where the number of buckets stays constant, even if the number of machines changes Requires a mapping from hash entries to nodes Don’t need to re-hash everything if node joins/leaves Only the mapping (and allocation of buckets) needs to

change when the number of nodes changes

Many examples: CAN, Pastry, Chord For this course, you’ll use Pastry But Chord is simpler to understand, so we’ll look at it

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Basic Ideas

We’re going to use a giant hash key space SHA-1 hash: 20B, or 160 bits We’ll arrange it into a “circular ring” (it wraps

around at 2160 to become 0)

We’ll actually map both objects’ keys (in our case, keywords) and nodes’ IP addresses into the same hash key space “abacus” SHA-1 k10 130.140.59.2 SHA-1 N12

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Chord Hashes a Key to its Successor

N32

N10

N100

N80

N60

Circularhash

ID Space

Nodes and blocks have randomly distributed IDs Successor: node with next highest ID

k52

k30

k10

k70

k99

Node ID k112

k120

k11

k33k40

k65

Key Hash

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Basic Lookup: Linear Time

N32

N10

N5

N20

N110

N99

N80

N60

N40

“Where is k70?”

“N80”

Lookups find the ID’s predecessor Correct if successors are correct

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“Finger Table” Allows O(log N) Lookups

N80

½¼

1/8

1/161/321/641/128

Goal: shortcut across the ring – binary search Reasonable lookup latency

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Node Joins

How does the node know where to go?(Suppose it knows 1

peer)

What would need to happen to maintain connectivity?

What data needs to be shipped around?

N32

N10

N5

N20

N110

N99

N80

N60

N40

N120

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A Graceful Exit: Node Leaves

What would need to happen to maintain connectivity?

What data needs to be shipped around?

N32

N10

N5

N20

N110

N99

N80

N60

N40

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What about Node Failure?

Suppose a node just dies?

What techniques have we seen that might help?

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Successor Lists Ensure Connectivity

N32

N10

N5

N20

N110

N99

N80

N60

Each node stores r successors, r = 2 log N Lookup can skip over dead nodes to find objects

N40

N10, N20, N32

N20, N32, N40

N32, N40, N60

N40, N60, N80

N60, N80, N99

N80, N99, N110

N99, N110, N5

N110, N5, N10

N5, N10, B20

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Objects are Replicated as Well

When a “dead” peer is detected, repair the successor lists of those that pointed to it

Can take the same scheme and replicate objects on each peer in the successor list Do we need to change lookup protocol to find

objects if a peer dies? Would there be a good reason to change lookup

protocol in the presence of replication?

What model of consistency is supported here? Why?

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Stepping Back for a Moment:DHTs vs. Gnutella and Napster 1.0

Napster 1.0: central directory; data on peers Gnutella: no directory; flood peers with requests Chord, CAN, Pastry: no directory; hashing scheme

to look for data

Clearly, Chord, CAN, and Pastry have guarantees about finding items, and they are decentralized

But non-research P2P systems haven’t adopted this paradigm: Kazaa, BitTorrent, … still use variations of the Gnutella

approach Why? There must be some drawbacks to DHTs..?

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Distributed Hash Tables, Summarized

Provide a way of deterministically finding an entity in a distributed system, without a directory, and without worrying about failure

Can also be a way of dividing up work: instead of sending data to a node, might send a task Note that it’s up to the individual nodes to do

things like store data on disk (if necessary; e.g., using B+ Trees)

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Applications of Distributed Hash Tables

To build distributed file systems (CFS, PAST, …)

To distribute “latent semantic indexing” (U. Rochester)

As the basis of distributed data integration (U. Penn, U. Toronto, EPFL) and databases (UC Berkeley)

To archive library content (Stanford)

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Distributed Hash Tables andYour Project

If you’re building a mini-Google, how might DHTs be useful in: Crawling + indexing URIs by keyword? Storing and retrieving query results?

The hard parts: Coordinating different crawlers to avoid redundancy Ranking different sites (often more difficult to

distribute) What if a search contains 2+ keywords?

(You’ll initially get to test out DHTs in Homework 3)

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From Chord to Pastry

What we saw was the basic data algorithms for the Chord system

Pastry is a slightly different: It uses a different mapping mechanism than

the ring (but one that works similarly) It doesn’t exactly use a hash table abstraction

– instead there’s a notion of routing messages It allows for replication of data and finds the

closest replica It’s written in Java, not C … And you’ll be using it in your projects!

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Pastry API Basics (v 1.4.3_02)

See freepastry.org for details and downloads Nodes have identifiers that will be hashed:

interface rice.p2p.commonapi.Id 2 main kinds of NodeIdFactories – we’ll use socket-based

Nodes are logical entities: can have more than one virtual node Several kinds of NodeFactories: create virtual Pastry

nodes

All Pastry nodes have built in functionality to manage routing

Derive from “common API” class rice.p2p.commonapi.Application

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Creating a P2P Network

Example code in DistTutorial.java Create a Pastry node:

Environment env = new Environment();PastryNodeFactory d = new SocketPastryNodeFactory(new

NodeFactory(keySize), env);

// Need to compute InetSocketAddress of a host to be addrNodeHandle aKnownNode =

((SocketPastryNodeFactory)d).getNodeHandle(addr);PastryNode pn = d.newNode(aKnownNode);MyApp = new MyApp(pn); // Base class of your

application! No need to call a simulator – this is real!

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Pastry Client APIs

Based on a model of routing messages Derive your message from class rice.p2p.commonapi.Message

Every node has an Id (NodeId implementation) Every message gets an Id corresponding to its key Call endpoint.route(id, msg, hint) (aka routeMsg) to

send a message (endpoint is an instance of Endpoint) The hint is the starting point, of type NodeHandle

At each intermediate point, Pastry calls a notification: forward(id, msg, nextHop)

At the end, Pastry calls a final notification: deliver(id, msg) aka messageForAppl

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IDs

Pastry has mechanisms for creating node IDs itself

Obviously, we need to be able to create IDs for keys

Need to use java.security.MessageDigest:MessageDigest md = MessageDigest.getInstance("SHA"); byte[] content = myString.getBytes();md.update(content);byte shaDigest[] = md.digest();

rice.pastry.Id keyId = new rice.pastry.Id(shaDigest);

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How Do We Create a Hash Table (Hash Map/Multiset) Abstraction?

We want the following: put (key, value) remove (key) valueSet = get (key)

How can we use Pastry to do this?

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Next Time

Distributed filesystem and database storage: GFS, PNUTS