Distributed Web Crawling over DHTs

Boon Thau Loo, Owen Cooper, Sailesh Krishnamurthy

CS294-4

Index

Search Today

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CrawlCrawl

What’s Wrong?Users have a limited search interface Today’s web is dynamic and growing: Timely re-crawls required. Not feasible for all web sites.

Search engines control your search results: Decide which sites get crawled:

550 billion documents estimated in 2001 (BrightPlanet)

Google indexes 3.3 billion documents. Decide which sites gets updated more

frequently May censor or skew results rankings.

Challenge: User customizable searches that scale.

Our Solution: A Distributed Crawler

P2P users donate excess bandwidth and computation resources to crawl the web.

Organized using Distributed Hash tables (DHTs) DHT and Query Processor agnostic crawler:

Designed to work over any DHT Crawls can be expressed as declarative recursive queries

Easy for user customization. Queries can be executed over PIER, a DHT-based relational P2P

Query Processor

Crawlers: PIER nodes

Crawlees: Web Servers

PotentialInfrastructure for crawl personalization:

User-defined focused crawlers Collaborative crawling/filtering (special interest

groups)

Other possibilities: Bigger, better, faster web crawler Enables new search and indexing technologies

P2P Web Search Web archival and storage (with OceanStore)

Generalized crawler for querying distributed graph structures.

Monitor file-sharing networks. E.g. Gnutella. P2P network maintenance:

Routing information. OceanStore meta-data.

Challenges that We Investigated

Scalability and Throughput DHT communication overheads. Balance network load on crawlers

2 components of network load: Download and DHT bandwidth.

Network Proximity. Exploit network locality of crawlers.

Limit download rates on web sites Prevents denial of service attacks.

Main tradeoff: Tension between coordination and communication

Balance load either on crawlers or on crawlees ! Exploit network proximity at the cost of communication.

Crawler Thread

Crawl as a Recursive Query

Downloader

Extractor

Publish WebPage(url)

Input Urls

Output Links

: Link.destUrl WebPage(url)

Redirect

CrawlWrapper

DupElim

Dup Elim

Filters

Publish Link (sourceUrl, destUrl)

Rate Throttle & Reorder

DHT Scan: WebPage(url)

Seed Urls

Crawl Distribution Strategies

Partition by URL Ensures even distribution of crawler workload. High DHT communication traffic.

Partition by Hostname One crawler per hostname. Creates a “control point” for per-server rate

throttling. May lead to uneven crawler load distribution Single point of failure:

“Bad” choice of crawler affects per-site crawl throughput.

Slight variation: X crawlers per hostname.

Redirection• Simple technique that allows a crawler to redirect or pass

on its assigned work to another crawler (and so on….)• A second chance distribution mechanism orthogonal to

the partitioning scheme. • Example: Partition by hostname

• Node responsible for google.com (red) dispatches work (by URL) to grey nodes

• Load balancing benefits of partition by URL• Control benefits of partition by hostname

• When? Policy-based• Crawler load (queue size)• Network proximity

• Why not? Cost of redirection• Increased DHT control traffic• Hence, put a limit number of redirections per URL.

www.google.com

ExperimentsDeployment

WebCrawler over PIER, Bamboo DHT, up to 80 PlanetLab nodes

3 Crawl Threads per crawler, 15 min crawl durationDistribution (Partition) Schemes

URL Hostname Hostname with 8 crawlers per unique host Hostname, one level redirection on overload.

Crawl Workload Exhaustive crawl

Seed URL: http://www.google.com 78244 different web servers

Crawl of fixed number of sites Seed URL: http://www.google.com 45 web servers within google

Crawl of single site within http://groups.google.com

Crawl of Multiple Sites I

Hostname: Can only exploit at most 45 crawlers.

Redirect (hybrid hostname/url) does the best.

Partition by Hostname shows poor imbalance (70% idle). Better off when more crawlers are busy

CDF of Per-crawler Downloads (80 nodes)

Crawl Throughput Scaleup

Crawl of Multiple Sites II

Redirection incurs higher overheads only after queue size exceeds a threshold.

Hostname incurs low overheads since crawl only looks at google.com which has lots of self-links.

Redirect: The per-URL DHT overheads hit their maximum around 70 nodes.

Per-URL DHT Overheads

Network Proximity

Sampled 5100 crawl targets and measured ping times from each of 80 PlanetLab hosts

Partition by hostname approximates random assignment

Best-3 random is “close enough” to Best-5 random

Sanity check: what if a single host crawls all targets ?

Summary of SchemesLoad- balance download bandwidth

Load- balance DHT bandwidth

Rate limitCrawlees

Network proximity

DHT Communication overheads

URL + + - - -

Hostname

- - + ? +

Redirect + ? + + --

Related WorkHerodotus, at MIT (Chord-based) Partition by URL Batching with ring-based forwarding. Experimented on 4 local machines

Apoidea, at GaTech (Chord-based) Partition by hostname. Forwards crawl to DHT neighbor closest to

website. Experimented on 12 local machines.

ConclusionOur main contributions: Propose a DHT and QP agnostic Distributed

Crawler. Express crawl as a query.

Permits user-customizable refinement of crawls Discover important trade-offs in distributed

crawling: Co-ordination comes with extra communication costs

Deployment and experimentation on PlanetLab. Examine crawl distribution strategies under different

workloads on live web sources Measure the potential benefits of network proximity.

Backup slides

Existing CrawlersCluster-based crawlers Google: Centralized dispatcher sends

urls to be crawled. Hash-based parallel crawlers.

Focused Crawlers BINGO! Crawls the web given basic training

set.

Peer-to-Peer Grub SETI@Home infrastructure. 23993 members .

Exhaustive Crawl

Partition by Hostname shows imbalance. Some crawlers are over-utilized for downloads.

Little difference in throughput. Most crawler threads are kept busy.

Single Site

URL is best, followed by redirect and hostname.

Future Work

Fault ToleranceSecuritySingle-Node ThroughputWork-Sharing between Crawl Queries Essential for overlapping users.

Crawl Global Prioritization A requirement of personalized crawls. Online relevance feedback.

Deep web retrieval.