This repository contains a tool which was developed by the TaskCluster team to
reduce inter region transfer costs in cloud object storage systems. An example
of one of these storage systems is AWS S3. In S3 it is free and fast to do an
S3 transfer within a given region, but costs a lot of money and takes a decent
amount of time if it does not exist in that region. At the time of writing, we
us-west-2 as our canonical storage region.
Cloud mirror is implemented as a very specialized proxy which copies objects into a region the first time that a machine in that region requests that resource. This tool does not act as a fully conforming HTTP proxy and conformance is not a goal. This tool should only be used for immutable objects which don't expire.
In the TaskCluster implementation, we rewrite our URLs using an algorithm which roughly resembles (python-ish psuedocode):
def rewriteUrl(inputUrl): service = determineService(inputUrl) region = determineRegion(service, inputUrl) base64Url = inputUrl.encode('base64') return 'http://cloud-mirror.taskcluster.net/v1/redirect/%s/%s/%s' % (service, region, base64Url)
Overview of design
Cloud mirror is implemented as an API front end which serves redirects and back
ends. The front and back ends communicate through SQS and use a redis cache.
It is important to note that redis is being used as a cache and not data
storage. The most important front end method is the
/v1/redirect/:service/:region/:base64(urL) method. This is the method that
the final consumer should use in place of the original url. On receiving a
request for the
/redirect/ method, the front end will check if the redis
cache contains information on the requested resource in the specified region.
If the relevant information is not found in the redis cache, the front end will
calculate the url which the object store would use and do a
HEAD request on
the object. If the object is found to exist, the front end will determine how
much longer the object will exist in the backing store and backfill the redis
cache with this information so that future requests can skip this
request. This is also how we deal with the redis cache being flushed: we'll
just backfill all resources at the low cost of an extra head request for each
If the object is found in the redis cache, it will be stored with one of the
present means that the
cache knows about the object and has the URL pointing to the backing object
ready to redirect a client to.
pending means that the cache knows about a
transfer being in progress. It contains information on where the object will
show up eventually but should not be sent as a redirect to the client. In the
pending state, the API front end will poll the cache key until the state
present. An optimization that we're considering is to use redis'
pub/sub features to monitor the keys instead of polling.
If a cache entry is in the
error state, it means that the back end tried to
copy the file but failed. A text copy of the stack trace from the failed copy
operation is also stored for easy access. As currently implemented, the front
end does not redirect in the
error state and instead retries the copy
operation as long as a request is alive using the standard polling interval.
Stack traces are not given to the client and instead kept private. This is done
to lower the risk of sensitive data leaking. A rough idea of what the error is
will be given to the client
When an object is requested to be copied into a region, a simple JSON message
is sent to an SQS queue for that given storage back end. The API front end does
not do any actual copying itself. A back end is a really simple system which
knows how to take an input URL and copy it into the object storage that the
back end represents. The
CacheManager class conatins all the logic for
managing the cache and has implementations for everything other than storage
system specific operations. The
StorageProvider class is a mostly-abstract
base class which should be completely implemented for each given storage
Contributions are very welcome and we're happy. Cloud-mirror is primiarily
developed by John Ford who can be found in
jhford. There are unit tests which should pass on your contribution as
well as an eslint configuration which declares the coding style for this
repository. We use the
await keywords to
make our async promises easier to work with. This is strongly preferred in
contributions. We also like to use
provided the needed utilities.
Running tests locally, requires a redis instance:
# This should do the trick docker run --rm -p 6379:6379 redis
taskcluster.net deployment notes
While this tool is designed to be general purpose, the TaskCluster deployment
is the first and largest. We use the docker cloud deployment tool to run the
entire system in the
us-west-2 region. Docker cloud works with stack files as
the unit of configuration. The stack file that we use has a load balancer, a
redis cache host, a front end and a back end. The load balancer does all of our
SSL termination as well as load balancing. Since the implementation we have
treats redis as a best-effort cache and not data storage, we have it included
in our stack file. Both of these images are retagged copies of the latest
version from the tutum team. We do not store the stack file in the repository
because it might contain secret data. We hope that one day, the docker cloud
team will implement a feature which lets us use something to redirect
configuration values to a private values file.
Roughly speaking cloud mirror is deployed as:
Front-end using standard taskcluster-lib-api based API. This is deployed on Heroku and is not very different to other TaskCluster systems. This heroku app is where the Redis instance we use is managed.
File copying back end. This is deployed on docker-cloud using a docker image. We use the shared
moztcaccount for this.
The two components use redis to share information between them and SQS for message passing.
We don't auto deploy the front end heroku app on push, a manual push to the Heroku git remote (https://git.heroku.com/cloud-mirror.git) is required. The back end auto deploys when pushing docker images.
The process for a deployment is roughly:
- build the docker image
- in the docker cloud UI, stop the
- push to the Heroku git remote and deploy new front-end
- push docker image
- watch docker cloud UI to ensure that things come up
- in two terminals run
heroku logs -t --app cloud-mirrorand
make prod-logsto watch initial logs.
If the change is a really small one which does not change the SQS message
format, the caching scheme or the S3 bucket/key name, then the back end does
not need to be stopped. For SQS Message format changes, it is necessary to
load the SQS Managment console and purge the
cloud-mirror-production_dead queues while the back end is not running. For
changes to the the caching scheme or s3 bucket/key, stop both the front and
back end, and run the redis
flushdb command against the database.
For metrics, this dashboard can be used.
The graphs should be reasonably named, but please ask in the
irc.mozilla.org if you are not sure what they represent.
If a resource is corrupt on the consuming side, it should be purged from the cache. First, find the queue.taskcluster.net url. Use
curl -I <url>from an ec2 instance to get the cloud-mirror url. This will be in a 302 responses
Location:header. It should look like
https://cloud-mirror.taskcluster.net/v1/redirect/:service/:region/:urlEncodedUrl. What you need to do to purge an item from the cache is change the
/redirect/portion of the URL to
/purge/and use the DELETE HTTP Method.
If there's a problem copying files, you should "redeploy" the
cloud-mirror-copierservice in cloud.docker.com. Please do note, however, that "terminate" in docker cloud terminology means "delete this service forever" and not "terminate the instances associated with this service".
Logs for the copy process currently aren't aggregated anywhere. For now use
make prod-logsin the root of the cloud-mirror repository with
export DOCKERCLOUD_USER=moztc; export DOCKERCLOUD_PASS=<snip>to follow the current logs.
If the cache becomes corrupt, you can use the flushdb redis command on the redis DB to clear it.
Service Owner: email@example.com