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Project Discontinued

For additional context see:


Overview

What is this?

A distributed solution for retrieving images from a list of URLs fed to a message queue. Async workers consume messages from this queue and store the images at the configured resolution in S3. The workers also scrape metadata, including image resolution and EXIF tags, and publish the results back to Kafka.

Performance is horizontally scalable; workers can be run in any number of processes on any number of machines.

How do I start it?

docker-compose up --build

How do I run the tests?

pipenv install
pipenv shell
PYTHONPATH=. pytest

Use pytest -s to include debug logs.

You may need to install ImageMagick on your development machine; execute the tests inside of the container to avoid this requirement.

How do I feed images to it?

See dummy_producer.py for an example.

If you are running docker-compose, you must run the producer from within the docker-compose network. Enter the worker container and run it from there.

docker exec -it image_get_worker_1 /bin/bash
pipenv run python dummy_producer.py

Input and output topics

Input and output into the cluster is controlled through message topics.

Input topics

inbound_images topic

inbound_images is the point of entry for scheduling image downloads.

The cluster expects a JSON message with the following structure:

{
    "url": "https://example.gov/example.jpg",
    "uuid": "7563efd4-58d0-41eb-9a4f-3903d36a5225",
    "source": "example"
}

url: The URL of the image

uuid: Our unique identifier for an image.

source: The source of an image (in our case, this should match the provider field in our data schema). This is used to determine what rate limit policy should apply to the URL. The source must appear in https://api.creativecommons.engineering/v1/sources, or else the URL will not be crawled (we need to know roughly how large a source is to automatically estimate an appropriate rate limit).

Output topics

image_metadata_updates

The image_metadata_updates topic contains resolution metadata discovered from crawled images.

Example: discovering the quality of an image

{
    "height": 1024,
    "width": 768,
    "compression_quality": 85,
    "filesize": 15623,
    "identifier": "7563efd4-58d0-41eb-9a4f-3903d36a5225"
}

The event contains the resolution of the image, the amount of compression (if applicable; otherwise null), and the size of the file in bytes.

Example: discovering the EXIF metadata of an image. The below example contains an artist named Alden Page and a Flash of value 0, indicating it was not used. For more details on decoding EXIF, see the list of EXIF tags and PIL's EXIF tag list.

{
    "identifier": "7563efd4-58d0-41eb-9a4f-3903d36a5225",
    "exif": {
        "0x13b": "Alden Page",
        "0x9209": 0
    }
}

link_rot

The link_rot topic contains the identifiers of images that appear to have been deleted from their original source.

Example: a 404'd image.

{
    "identifier": "7563efd4-58d0-41eb-9a4f-3903d36a5225",
    "time": "2020-06-23T14:30:22.129148"
}

Monitoring the crawl

The crawl_monitor logs useful information about the crawl in a machine-friendly format. There are several different types of events that will appear in the logger.

monitoring_update

monitoring_update is the most common event, and will appear every 5 seconds.

{
   "event" : "monitoring_update",
   "time" : "2020-04-17T20:22:56.837232",
   "general" : {
      "global_max_rps" : 193.418869804698,
      "error_rps" : 0,
      "processing_rate" : 0,
      "success_rps" : 0,
      "circuit_breaker_tripped" : [],
      "num_resized" : 13224,
      "resize_errors" : 0,
      "split_rate" : 0
   },
   "specific" : {
      "flickr" : {
         "successful" : 13188,
         "last_50_statuses" : {
            "200" : 50
         },
         "rate_limit" : 178.375147633876,
         "error" : 0
      },
      "animaldiversity" : {
         "last_50_statuses" : {
            "200" : 18
         },
         "successful" : 18,
         "error" : 0,
         "rate_limit" : 0.206215440554406
      },
      "phylopic" : {
         "rate_limit" : 0.2,
         "error" : 0,
         "successful" : 18,
         "last_50_statuses" : {
            "200" : 18
         }
      }
   }
}

Desciption of non-obvious keys:

General statistics give the operator an idea of how crawling is progressing globally across all workers and domains.

global_max_rps is the theoretical maximum throughput of the cluster within scheduled rate limits.

processing_rate is the rate of completing image processing tasks.

error_rps is the rate of HTTP errors occurring over the last monitoring interval.

circuit_breaker_tripped lists sources that the cluster has stopped crawling due to a surge in errors. See the "Configuration and Operation" section for more details.

num_resized is the total number of successfully resized images.

split_rate is the rate that the inbound_urls topic is being split into separate topics for scheduling. See the "Architecture" section for more details. This statistic is only updated once for every 1000 URLs inserted.

The specific statistics are statistics that are specific to a given source; the key names are generally self-explanatory.

crawl_halted

crawl_halted events indicate that crawling has stopped, temporarily or permanently, for a single source. Temporary halts are resolved automatically, while permanent halts require intervention from an operator. See "The error circuit breaker" section for details.

An example crawl_halted message:

{
   "time" : "2020-04-17T16:57:52.135155",
   "type" : "temporary",
   "event" : "crawl_halted",
   "msg" : "example tripped temporary halt. Response codes: {\"b'500\": 11}",
   "source" : "example"
}

Configuration and Operation

Configuring the crawl can be achieved by setting the corresponding keys in Redis.

Kafka

It is critical to ensure that each source_urls topic (or any other topic the worker machines read from) has at least as many partitions as there are workers. If default partition settings are used, only one consumer will receive messages.

The simplest way to achieve this is to set the Kafka-wide num.partitions equal to the number of workers upon deployment of the system. This will ensure that multiple consumers can read from any topic in parallel.

Overriding automatically computed rate limits

Rate limits are set in proportion to the number of artifacts we are crawling from a given source. The logic behind this is that sites with more images have more infrastructure in place for serving high traffic. If the automatic crawl rate isn't satisfactory for whatever reason, it can be manually overridden.

redis-cli
> set override-rate:example 10
# Sets rate limit for `example` to 10 requests per second

The error circuit breaker

The crawler is designed to operate with minimal manual intervention; if everything has been set up properly, inserting URLs into the inbound_urls queue should kick off crawling. In situations where there are brief upticks in errors, crawling will be temporarily halted for 60 seconds before resuming automatically.

One exception where operators must intervene is if a source trips the error circuit breaker. If any source has 50 failed requests in a row, all crawling for that source will be stopped automatically. To resume crawling, the operator should inspect the crawl_monitor logs to find the nature of the error message, manually lower the rate limit for the source if necessary, and reset the circuit breaker using the Redis client.

For instance, if the source "example" has tripped the circuit breaker, this is the procedure for resuming crawling:

redis-cli
# View which sources have tripped the circuit breaker
> smembers halted
example
# Manually override rate limit to 5 requests per second
> set override_rate:example 5
# Clear error window for the source
> del statuslast50req:example
# Remove the source from the halted set
> srem halted example

Technical Architecture

Image crawler architecture

Images URLs are scheduled for crawling in the inbound_images topic. The Splitter process in the crawl monitor funnels URLs into queues based on their source keys (e.g. source: flickr, source: met get put into the respective flickr_urls and met_urls topics). Splitting by source is necessary to allow the worker to crawl all domains simultaneously and prevent starvation from "slow" sources with low rate limits.

Rate limits for each source are determined by the crawl monitor. It sets rate limits in proportion to the size of each domain (number of images), which it learns from the CC Catalog API sources endpoint. When a target rate limit has been established, the crawl monitor regulates worker rate limits by replenishing token buckets for each source every second. Before making a request from a source, the worker checks the token bucket for the source. If no token is available, the request will block until tokens are replenished.

Once an image has been downloaded, the worker performs several other operations on the image:

  • The image is resized to settings.TARGET_RESOLUTION and uploaded to S3.
  • The resolution of the image is collected and pushed to the image_metadata_updates topic.
  • The exif metadata of the image is collected and pushed to the image_metadata_updates topic.

Once the task has been completed, stats are pushed to Redis. While this is occurring, the Crawl Monitor is listening for errors; if an unsafe number of errors occurs (as described in "The error circuit breaker" section), crawling is halted. The crawl monitor also keeps a detailed log of the status of the crawl as it progresses.

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A polite image crawler that can thumbnail and extract metadata from images at scale

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