Chirayu Patel shres his experience in building SnappyFingers.com

1. Lessons from building a search
engine with Amazon Web Services
Chirayu Patel
chirayu@snappyfingers.com

2. Chirayu Patel
Developer
SnappyFingers
Question and Answer Search Engine
100% in the cloud
5-Apr-09 CloudCamp - Bangalore

3. My experiences
What worked?
What didn’t?
What could be done better?
What did I miss?
5-Apr-09 CloudCamp - Bangalore

4. Recap - AWS?
• EC2 – Elastic Compute Cloud
• S3 – Simple Storage Service
• SQS – Simple Queue Service
• SDB – SimpleDB
5-Apr-09 CloudCamp - Bangalore

5. Why AWS?
• Computing Power requirements unknown
• Cheap
• Availability of multiple services
• Easy to implement SnappyFingers architecture
using AWS services
5-Apr-09 CloudCamp - Bangalore

6. SnappyFingers
• Information Retrieval System (IRS)
• FrontEnd
– Nothing unique here
5-Apr-09 CloudCamp - Bangalore

7. Three motivations (behind my decisions)
• Reluctance to learn
• Cost Conscious
• I write buggy code
5-Apr-09 CloudCamp - Bangalore

8. Architectural Requirements
• Loose Coupled
• Scalable
• Fault Tolerant
• Budget dependent
5-Apr-09 CloudCamp - Bangalore

9. IRS Architecture
Pipeline
SQS
Pipe Crawler Pipe Parser Pipe Indexer
EC2
Crawler Parser Indexer
EC2 + S3 SDB
Data Store Errors
5-Apr-09 CloudCamp - Bangalore

10. Pipes and Pipelines
5-Apr-09 CloudCamp - Bangalore

11. Pipes and Pipelines
• Pipes contain jobs
• Pipeline is a group of pipe
• Easy to create pipelines and add pipes
5-Apr-09 CloudCamp - Bangalore

12. Job ORM
SQS API
class CrawlerJob (JobBase):
SDB API
class SDBInterfaceConfig:
domain_name = settings.CRAWLER_JOB_DOMAIN
class SQSInterfaceConfig:
queue_name = settings.CRAWLER_JOB_QUEUE
timeout = settings.CRAWLER_JOB_TIMEOUT
class AWSMetaData:
action = CharField (...)
url = CharField (...)
...
...
Default attributes of each Job:
• Pipeline Name
• Status
• Start Time
• End Time
• Id
5-Apr-09 CloudCamp - Bangalore

13. Job Processing
for i in range (num_of_jobs):
try:
job = cls.jobclass.sqs_get() # process job
...
except Exception, e:
job.job_processing_complete(…)
fsdebug.mail_admins (..)
end_transaction(rollback = True)
job.sdb_save() # save in error store
finally:
job.sqs_del() # delete the job
5-Apr-09 CloudCamp - Bangalore

14. The Good
• Architecture easy to extend
• ORM approach is a big time saver
• Simple to add new services
5-Apr-09 CloudCamp - Bangalore

15. The Bad
• Messages may be lost
– Service Failure
– SQS deletes messages after 4 days.
Imp: System should be able to recreate jobs
5-Apr-09 CloudCamp - Bangalore

16. Storage
5-Apr-09 CloudCamp - Bangalore

17. What do we store?
• Crawler Data – Web Pages
• Extracted Content – Questions/Answers
• Backups
5-Apr-09 CloudCamp - Bangalore

18. Storage Structure
Meta Data Key + Value
Postgres S3
5-Apr-09 CloudCamp - Bangalore

19. ORM
• Extended Django ORM to support S3
class S3WebPage (S3Model):
_allowed_attrs = [quot;urlquot;, quot;contentquot;, ..]
_name = quot;S3WebPage“
...
...
5-Apr-09 CloudCamp - Bangalore

20. The Good
• Extremely scalable
• Possible to store Python objects in S3
• Latency issues can be solved by using a
caching layer
• No need to backup S3 data
• Storage is cheap
5-Apr-09 CloudCamp - Bangalore

21. The Bad
• Postgres + S3 is not an elegant solution
– Periodic syncing of Postgres and S3 required
• High transaction costs
– $.01 per 1000 PUT,COPY,POST or LIST Requests
– $.01 per 10000 GET Requests
5-Apr-09 CloudCamp - Bangalore

22. Computing
5-Apr-09 CloudCamp - Bangalore

23. EC2 – The Good
• Computing needs are not constant
• Data transfer to other AWS services is free
• AMI’s per node type
5-Apr-09 CloudCamp - Bangalore

24. The bad
• Missed having a nerve center
– Budget
– Job Load
– CPU load
• Low cost 64bit severs are not available
5-Apr-09 CloudCamp - Bangalore

25. Thank You