Sharded system recipes for ZooKeeper
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README.md

PettingZoo

A collection of sharded system recipes for ZooKeeper written in Python.

Why Petting Zoo?

We need to be able to do stream processing of observations of student interactions with course material. This involves multiple models that have interdependent parameters. This requires:

  • Sharding along different axes dependent upon the models
  • Subscriptions between models for parameters
  • Dynamic reconfiguration of the environment to deal with current load

How does this help us scale?

  • Makes discovery of dependencies simple
  • Adds to reliability of system by quickly removing dead resources
  • Makes dynamic reconfiguration simple as additional resources become available

Current State

  • Relies on zc.zk (will eventually be merged with the unified Kazoo project)
  • Documented, doc strings
  • Tests (mock ZooKeeper)
  • All recipes implemented in a Java version as well

Recipes


Distributed Discovery

Allows services in a dynamic, distributed environment to be able to be quickly alerted of service address changes.

  • Most service discovery recipes only contain host:port, Distributed Discovery can share arbitrary data as well (using yaml)
  • Can handle load balancing through random selection of config
  • Handles rebalancing on pool change

Why Distributed Discovery?

  • Makes discovery of dependencies simple
  • Adds to reliability of system by quickly removing dead resources
  • Makes dynamic reconfiguration simple as additional resources become available

DD Example: Write

from pettingzoo.discovery import write_distributed_config
from pettingzoo.utils import connect_to_zk
conn = connect_to_zk('localhost:2181')
config = {
	'header': {
		'service_class': 'kestrel', 'metadata': {
			'protocol': 'memcached', 'version': 1.0
		}
	},
	'host': 'localhost',
	'port': 22133
}
write_distributed_config(conn, 'kestrel', 'platform', config)

DD Example: Read

from pettingzoo.discovery import DistributedMultiDiscovery
from pettingzoo.utils import connect_to_zk
conn = connect_to_zk('localhost:2181')
dmd = DistributedMultiDiscovery(conn)
conf = None
def update_configs(path, updated_configs):
	conf.update(updated_configs)
conf = dmd.load_config('kestrel', 'platform', callback=update_configs)

Distributed Bag

A recipe for a distributed bag (dbag) that allows processes to share a collection. Any participant can post or remove data, alerting all others.

  • Used as a part of Role Match
  • Useful for any case where processes need to share configuration determined at runtime

dbag

Why Distributed Bag?

  • Can quickly alert processes as to who is subscribing to them
  • Reduces load by quickly yanking dead subscriptions
  • Provides event based subscriptions, making implementation simpler

Dbag Details

  • Sequential items contain the actual data
  • Can be ephemeral
  • Clients set delete watch on discrete items
  • Token is set to id of highest item
  • Clients set a child watch on the "Tokens" node
  • Can determine exact adds and deletes with a constant number of messages per delta

Distributed Bag diagram

Dbag Example

import yaml
from pettingzoo.dbag import DistributedBag
from pettingzoo.utils import connect_to_zk

...
conn = connect_to_zk('localhost:2181')
dbag = DistributedBag(conn, '/some/data')
docs = {}
def acb(cb_dbag, cb_id):
	docs[cb_id] = cb_dbag.get(cb_id)
def rcb(cb_dbag, cb_id):
	docs.remove(cb.id)

dbag.add_listeners(add_callback=acb, remove_callback=rcb)
add_id = dbag.add(yaml.load(document), ephemeral=True)
docs[add_id] = document

Leader Queue

Recipe is similar to Leader Election, but makes it easy to monitor your spare capacity.

  • Used in Role Match
  • As services are ready to do work, they create an ephemeral, sequential node in the queue.
  • Any member always knows if either they are in the queue or at the front
  • Watch lets leader know when it is elected

Why Leader Queue?

  • Gives a convenient method of assigning work
  • Makes monitoring current excess capacity easy

LQ Details

  • Candidates register with sequential, ephemeral nodes
  • Candidate sets delete watch on predecessor
  • Candidate is elected when it is the smallest node
  • When elected, candidate takes over its new role
  • When ready, candidate removes itself from the queue
  • Only one candidate needs to call get_children upon any node exiting

Leader Queue diagram

LQ Example

from pettingzoo.leader_queue import LeaderQueue, Candidate
from pettingzoo.utils import connect_to_zk

class SomeCandidate(Candidate):
	def on_elected(self):
		<do something sexy>
	
conn = connect_to_zk('localhost:2181')
leaderq = LeaderQueue(conn)
leaderq.add_candidate(SomeCandidate())

Role Match

Allows systems to expose needed, long lived jobs, and for services to take over those jobs until all are filled.

  • Dbag used to expose jobs
  • Leader queue used to hold applicants
  • Records which jobs are presently held with ephemeral node
  • Lets a new process take over if a worker dies
  • We use it for sharding/segmentation to dynamically adjust the shards as needed due to load

Why Role Match?

  • Core of our ability to dynamically adjust shards
  • Lets the controlling process adjust problem spaces and have those tasks become automatically filled
  • Monitoring is easy to identify who is working on what, when

RM Details

  • Leader monitors for open jobs
  • Job holder creates an ephemeral assignment
  • Assignment id matches job id, indicating that it is claimed

Role Match diagram


Future: Distributed Config

  • Allows service config to be recorded and changed with a yaml config
  • Every process that connects creates a child node of the appropriate service
  • Any change in a child node's config overrides the overall service config for that process
  • Any change of the parent or child fires a watch to let the process know that it's config has changed