Pegasus: A Modular, Durable Web Crawler For Clojure

Pegasus is a durable, multithreaded web-crawler for clojure.

I wrote Pegasus after the existing choices in the Java ecosystem left me frustrated.

The more popular crawler projects (Heritrix and Nutch) are clunky and not easy to configure. I have often wanted to be able to supply my own extractors, save payloads directly to a database and so on. Short of digging into large codebases, there isn’t much of an option there.

Tiny crawlers hold all their data structures in memory and are incapable of crawling the entire web. A simple crash somewhere causes you to lose all state built over a long-running crawl. I also want to be able to (at times) modify critical data-structures and functions mid-crawl.

Pegasus gives you the following:

  1. Parallelism using the excellent core.async library.
  2. Disk-backed data structures that allow crawls to survive crashes, system restarts etc. (I am still implementing the restart bits).
  3. Implements the bare minimum politeness needed in crawlers (support for robots.txt and rel='nofollow').

A crawl is configured using a config object - a clojure map. All crawl state such as references to important data-structures, function definitions, crawl-specific vars, is stored in this config.

The config is available to all critical functions and allows us to (i) maintain important crawl-state, and (ii) update important data-structures on-the-fly.

Some critical pieces:

Process Pipeline

core.async allows us to specify a pipeline using pipeline-blocking. The default pipeline in pegasus consists of a set of simple functions. These routines are invoked by producers and consumers that produce and consume from, well core.async channels.

schema’s are used to enforce structural requirements for all components.

Here’s a simple example of the pipeline:

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{:frontier default-frontier-fn
   :extractor default-extractor-fn
   :writer default-writer-fn
   :enqueue queue/enqueue-pipeline
   :update-state default-update-state
   :update-stats default-update-stats
   :test-and-halt default-stop-check
   :filter default-filter
   :stop-sequence [close-wrtr mark-stop]
   :pipeline [[:frontier s/Str 5]
              [:extractor {:url s/Str,
                           :body s/Str,
                           :time s/Int} 5]
              [:update-state {:url s/Str,
                              :body s/Str,
                              :time s/Int
                              :extracted [s/Str]} 5]
              [:filter {:url s/Str
                        :body s/Str
                        :time s/Int
                        :extracted [s/Str]} 5]
              [:writer {:url s/Str
                        :body s/Str
                        :time s/Int
                        :extracted [s/Str]} 5]
              [:enqueue {:url s/Str
                         :body s/Str
                         :time s/Int
                         :extracted [s/Str]} 5]
              [:update-stats {:url s/Str
                              :body s/Str
                              :time s/Int
                              :extracted [s/Str]} 5]
              [:test-and-halt s/Any 5]]}

Each component specifies a function-name whose implementation is available from the config, a schema that specifies the input structure and the parallelism (i.e. the number of concurrent threads).

Politeness

Politeness is achieved using a combination of:

  1. A minimum delay between successive requests to a host.
  2. Robots.txt directives.
  3. Not following rel="nofollow" urls.

Durability

A web crawl is a breadth-first search on the web-graph. The critical data-structure is a queue. I decided to use factual’s excellent durable-queue.

Every host gets its own named queue and a custom worker pulls from each queue and pushes it through the pipeline.

Each component accepts an object and attaches a key to it.

In addition, it is important to be able to access the queue’s contents at random. For instance to check if a URL has already been crawled. The typical approach involves a bloom filter but I chose to go with a key-value store whose results can be saved to disk - JCS.

The Heritrix project has successfully used BerkeleyDB. I might write a variant of pegasus that uses BerkeleyDB in the future.

Examples

In this section, I am going to implement some simple crawl tasks. These should give you a flavor for what’s possible.

Enlive selectors for crawling this blog

This snippet crawls all submissions on this blog and the pagination.

XPath selectors for crawling this blog

This snippet performs the exact same task but with XPaths.

Pegasus is still a WIP and the interfaces exposed are subject to some change but I should have them fleshed out soon (and I will be spending time on better docs).

Resources

If there are any features you want to see or suggestions you have, do let me know.


Per Intellectum, Vis
(c) Shriphani Palakodety 2013-2016