Heinrich Hartmann opinion consulting about

Distributed Scraper Design

« 2014-01-15 »

We are am confronted with the following situation:

We search an architecture meeting the following requirements:

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We will use the zmq messaging library [1] for message passing and queing.

Draft 1: All in one

Scraper and Worker are executed in one process, and comunicate directly with the collector process.

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Discussion

The architecture is clearly not optimal, although it might get you a long way. There can be many ScraperWorkers, that give room for parallelism. In my application the download is the bottleneck, so that there is not too much time wasted by waiting for the worker to finish. The network can be easily kept saturated with a few more ScraperWorker tasks.

There is a architecture principle, that is violated here, of spreading out the work in as may intermediate steps as possible. In this way testing and monitoring becomes easy. Process steps can be reused, and paralellism can be added preciesly where the current bottlenecks are. When the implementation is matured several steps of the architecture might be squashed to a single step.

Draft 2: Pass everything

Pass the donwloaded blops as messages to the worker. Add a proxy to allow dynamic allocation of workers and scrapers.

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Discussion

Draft 3: Central storage

Scraper store downloaded blops on a centralized storage node. The central storage could be a file system location or a database. The passed messages contain only paths to files on the storage node.

The storage node could be mounted at the boxes running the scrapers and the workers, so that the details of the storage solutions are abstracted from the components.

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Draft 3b: Distributed storage

Scraper store downloaded blops to a distributed file system. The files will be cut into containers and distributed around all available storage nodes. The storage nodes can be different from the nodes running the scrapers/workers but do not have to be.

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There is additional juice in this option as it allows to bring the computation to the storage. It should be possible to run the worker tasks processing the downloaded files on the node that runs the scraper. This would allow the scraper box to use its computing power for something sensible while waiting for the network.

Since HDFS [3] can be mounted to a folder, this solution can be interchanged with draft 3.

Draft 4: Scraper storage

Scrapers store downloaded blops locally. The network locations ($HOST:$PATH) of the downloaded files are passed as messages. The worker thread reads the contents of the file directly from the scraper’s file system (e.g. via ssh)

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Final Discussion

Currently I like the Draft 3 option best, since it is reasonably simple, while allowing parallelism and compying to the separation of concerns principle. It allows to resume work when the service is stopped or crashes rather easily, since the downloaded files are available on a central location. When the storage becomes a problem it is possible to upgrade to Draft 3b, which uses a distributed file system.

References

  1. http://zeromq.org
  2. http://en.wikipedia.org/wiki/Separation_of_concerns
  3. http://en.wikipedia.org/wiki/Apache_Hadoop#Hadoop_distributed_file_system