© 2011Geeknet Inc

Realtime Analytics using MongoDB, Python, Gevent,

and ZeroMQ

Rick Copeland@rick446

rick@geek.net

SourceForge s MongoDB

- Tried CouchDB – liked the dev model, not so much the performance

- Migrated consumer-facing pages (summary, browse, download) to MongoDB and it worked great (on MongoDB 0.8 no less!)

- Built an entirely new tool platform around MongoDB (Allura)

The Problem We’re Trying to Solve

- We have lots of users (good)

- We have lots of projects (good)

- We don’t know what those users and projects are doing (not so good)

- We have tons of code in PHP, Perl, and Python (not so good)

Introducing Zarkov 0.0.1

Asynchronous TCP server for event logging with gevent

Turn OFF “safe” writes, turn OFF Ming validation (or do it in the client)

Incrementally calculate aggregate stats based on event log using mapreduce with {‘out’:’reduce’}

Zarkov Architecture

MongoDB

BSON over

ZeroMQ

Journal Greenlet

Commit Greenlet

Write-ahead log

Write-ahead log

Aggregation

Greenlet

Technologies

- MongoDB- Fast (10k+ inserts/s single-threaded)

- ZeroMQ- Built-in buffering- PUSH/PULL sockets (push never blocks, easy to distribute work)

- BSON- Fast Python/C implementation- More types than JSON

- Gevent- “green threads” for Python

“Wow, it’s really fast; can it replace…”

- Download statistics?- Google Analytics?- Project realtime statistics?

“Probably, but it’ll take some

work….”

Moving towards production....

- MongoDB MapReduce: convenient, but not so fast- Global JS Interpreter Lock per mongod- Lots of writing to temp collections (high lock %)- Javascript without libraries (ick!)

- Hadoop? Painful to configure, high latency, non-seamless integration with MongoDB

Zarkov’s already doing a lot…

So we added a lightweight map/reduce framework

- Write your map/reduce jobs in Python- Input/Output is MongoDB- Intermediate files are local .bson files- Use ZeroMQ for job distribution

Quick Map/reduce Refresherdef map_reduce(input_collection, query, output_collection,

map, reduce):

objects = input_collection.find(query)

map_results = list(map(objects))

map_results.sort(key=operator.itemgetter(0))

for key, kv_pairs in itertools.groupby(

(map_results, operator.itemgetter(0)):

value = reduce(key, [ v for k,v in kv_pairs ])

output_collection.save(

{"_id":key, "value":value})

Quick Map/reduce Refresherdef map_reduce(input_collection, query, output_collection,

map, reduce):

objects = input_collection.find(query)

map_results = list(map(objects))

map_results.sort(key=operator.itemgetter(0))

for key, kv_pairs in itertools.groupby(

(map_results, operator.itemgetter(0)):

value = reduce(key, [ v for k,v in kv_pairs ])

output_collection.save(

{"_id":key, "value":value})

Parallel

Zarkov Map/Reduce Architecture

map_in_#.bson

Query

Map

Sort

Reduce Commit

map_out_#.bson

reduce_in.bson

JobMgr

Zarkov Map/Reduce

- Phases managed by greenlets

- Map and reduce jobs parceled out to remote workers via zmq PUSH/PULL

- Adaptive timeout/retry to support dead workers

- Sort phase is local (big mergesort) but still done in worker processes

Zarkov Web Service

- We’ve got the data in, now how do we get it out?

- Zarkov includes a tiny HTTP server

$ curl -d foo='{"c":"sfweb", "b":"date/2011-07-01/", "e":"date/2011-07-04"}' http://localhost:8081/q

{"foo": {"sflogo": [[1309579200000.0, 12774], [1309665600000.0, 13458], [1309752000000.0, 13967]], "hits": [[1309579200000.0, 69357], [1309665600000.0, 68514], [1309752000000.0, 68494]]}}

- Values come out tweaked for use in flot

Zarkov Deployment at SF.net

© 2011Geeknet Inc

Lessons learned at

MongoDB Tricks

- Autoincrement integers are harder than in MySQL but not impossible

- Unsafe writes, insert > update

class IdGen(object): @classmethod def get_ids(cls, inc=1): obj = cls.query.find_and_modify( query={'_id':0}, update={ '$inc': dict(inc=inc), }, upsert=True, new=True) return range(obj.inc - inc, obj.inc)

MongoDB Pitfalls

- $addToSet is nice but nothing beats an integer range query

- Avoid Javascript like the plague (mapreduce, group, $where)

- Indexing is nice, but slows things down; use _id when you can

- mongorestore is fast, but locks a lot

Open Source

Minghttp://sf.net/projects/

merciless/MIT License

Allurahttp://sf.net/p/allura/

Apache License

Zarkovhttp://sf.net/p/zarkov/

Apache License

Future Work

Remove SPoF

Better way of expressing aggregates Suggestions?

Better web integration WebSockets/Socket.io

Maybe trigger aggs based on event activity?

© 2011Geeknet Inc

Rick Copeland@rick446

rick@geek.net

Credits

- http://www.flickr.com/photos/jprovost/5733297977/in/photostream/