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Counting Image Views using Redis Cluster
Seandon Mooy
DevOps Engineer
@erulabs
Counting Image Views using Redis Cluster
Orā€¦. how I stopped map-reducing and learned to love the stream
Seandon Mooy
DevOps Engineer
@erulabs
3 Billion!
Delay!
Delay!
Failures!
Delay!
Failures!
Failures!
Alsoā€¦ I may not be the best zookeeper
Challenges with Hbase
Roughly 5% of all requests
through THRIFT were
failingā€¦ So many tunables!
Challenges with Hbase
Roughly 5% of all requests
through THRIFT were
failingā€¦ So many tunables!
Optimized timeouts,
added circuitbreakers, etc
Trickle of working requests
during outage means circuit
breakers are hard to designā€¦
Challenges with Hbase
Roughly 5% of all requests
through THRIFT were
failingā€¦ So many tunables!
Optimized timeouts,
added circuitbreakers, etc
Trickle of working requests
during outage means circuit
breakers are hard to designā€¦
ā€œHbase down == Imgur downā€
Downtime == sadtime :(
3 Billion!
Solution?
Redis Cluster!
Fastly
ViewCount V2 - Real time with less complexity!
TCP syslog stream
Fastly
ViewCount V2 - Real time with less complexity!
TCP syslog stream
Ingest service
Fastly
ViewCount V2 - Real time with less complexity!
TCP syslog stream
Ingest service
Parses syslog lines, reports
metrics via statsd
Fastly
ViewCount V2 - Real time with less complexity!
TCP syslog stream
Ingest service
Parses syslog lines, reports
metrics via statsd
Redis 3.2 cluster!
Fastly
ViewCount V2 - Real time with less complexity!
Ingest service
Hbase Backfill service
Fastly
ViewCount V2 - Real time with less complexity!
Ingest service
Hbase Backfill service
Internet
API service
ViewCount V2 - Results:
ViewCount V2 - Results:
Request latency:
min: 1ms
max: 16.9ms
median: 1.6ms
p95: 2.6ms
p99: 4.6ms
Codes:
200: 10000
ViewCount V2 - Results:
Request latency:
min: 1ms
max: 16.9ms
median: 1.6ms
p95: 2.6ms
p99: 4.6ms
Codes:
200: 10000
ViewCount V2 - Results:
20 billion commands!
> 400GB in memory!
Things to be aware of:
1. Redis Cluster shard maps - redirections, etc.
Monitor redirections - gracefully restart workers after shard moves
2. AOF can slow down / fail large ā€œredis-trib.rbā€ operations.
Make sure to disable before / re-enable after!
3. Not all legacy systems support Redis Cluster, and if they doā€¦
They might not support it well (PHP-FPM)!
4. Over memory capacity behavior?
Previously we would hard-crash - now weā€™d LRU old 1-view images.
Neither are good, but for us, one is much less painful
ViewCount V3?
Approaching the point of minimal gains for man-hours, but what else might be fun?
1. Moving PHP7 off NodeJS API and directly to Redis Cluster
Downsides: dealing with shard maps is complex is a stateless / process-per-request environment!
2. Using redis3's BITFIELD or HSet to save on key storage costs
Downsides: complicate the system, reduce ā€œhit-by-a-busā€ issues - keys are just hashes, values are just counts!
3. Dealing with the nature of TCP Streams (TCP is not HTTP!)
One connection to rule them all! - Nodeā€™s Cluster module helps,
but perhaps Rust or Golang?
Downsides: Vertical scaling is non-obvious on EC2
ViewCount V2 - Results:
Redis is:
Faster - Imgur response time decreased ~50ms
ViewCount V2 - Results:
Redis is:
Faster - Imgur response time decreased ~50ms
Cheaper - EC2 cost reduced by 75%
ViewCount V2 - Results:
Redis is:
Faster - Imgur response time decreased ~50ms
Cheaper - EC2 cost reduced by 75%
Simpler - No Java, no MR, no ZK, no third parties, just INCR + GET!
Redis is:
Faster - Imgur response time decreased ~50ms
Cheaper - EC2 cost reduced by 75%
Simpler - No Java, no MR, no ZK, no third parties, just INCR + GET!
More fun! - I got to talk at RedisConf17!
ViewCount V2 - Results:
Acknowledgment
Imgur DevOps Team
Imgur Platform Team

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Counting image views using redis cluster

  • 1. Counting Image Views using Redis Cluster Seandon Mooy DevOps Engineer @erulabs
  • 2. Counting Image Views using Redis Cluster Orā€¦. how I stopped map-reducing and learned to love the stream Seandon Mooy DevOps Engineer @erulabs
  • 3.
  • 4.
  • 5.
  • 7.
  • 11. Alsoā€¦ I may not be the best zookeeper
  • 12. Challenges with Hbase Roughly 5% of all requests through THRIFT were failingā€¦ So many tunables!
  • 13. Challenges with Hbase Roughly 5% of all requests through THRIFT were failingā€¦ So many tunables! Optimized timeouts, added circuitbreakers, etc Trickle of working requests during outage means circuit breakers are hard to designā€¦
  • 14. Challenges with Hbase Roughly 5% of all requests through THRIFT were failingā€¦ So many tunables! Optimized timeouts, added circuitbreakers, etc Trickle of working requests during outage means circuit breakers are hard to designā€¦ ā€œHbase down == Imgur downā€ Downtime == sadtime :(
  • 17. Fastly ViewCount V2 - Real time with less complexity! TCP syslog stream
  • 18. Fastly ViewCount V2 - Real time with less complexity! TCP syslog stream Ingest service
  • 19. Fastly ViewCount V2 - Real time with less complexity! TCP syslog stream Ingest service Parses syslog lines, reports metrics via statsd
  • 20. Fastly ViewCount V2 - Real time with less complexity! TCP syslog stream Ingest service Parses syslog lines, reports metrics via statsd Redis 3.2 cluster!
  • 21. Fastly ViewCount V2 - Real time with less complexity! Ingest service Hbase Backfill service
  • 22. Fastly ViewCount V2 - Real time with less complexity! Ingest service Hbase Backfill service Internet API service
  • 23. ViewCount V2 - Results:
  • 24. ViewCount V2 - Results: Request latency: min: 1ms max: 16.9ms median: 1.6ms p95: 2.6ms p99: 4.6ms Codes: 200: 10000
  • 25. ViewCount V2 - Results: Request latency: min: 1ms max: 16.9ms median: 1.6ms p95: 2.6ms p99: 4.6ms Codes: 200: 10000
  • 26. ViewCount V2 - Results: 20 billion commands! > 400GB in memory!
  • 27. Things to be aware of: 1. Redis Cluster shard maps - redirections, etc. Monitor redirections - gracefully restart workers after shard moves 2. AOF can slow down / fail large ā€œredis-trib.rbā€ operations. Make sure to disable before / re-enable after! 3. Not all legacy systems support Redis Cluster, and if they doā€¦ They might not support it well (PHP-FPM)! 4. Over memory capacity behavior? Previously we would hard-crash - now weā€™d LRU old 1-view images. Neither are good, but for us, one is much less painful
  • 28. ViewCount V3? Approaching the point of minimal gains for man-hours, but what else might be fun? 1. Moving PHP7 off NodeJS API and directly to Redis Cluster Downsides: dealing with shard maps is complex is a stateless / process-per-request environment! 2. Using redis3's BITFIELD or HSet to save on key storage costs Downsides: complicate the system, reduce ā€œhit-by-a-busā€ issues - keys are just hashes, values are just counts! 3. Dealing with the nature of TCP Streams (TCP is not HTTP!) One connection to rule them all! - Nodeā€™s Cluster module helps, but perhaps Rust or Golang? Downsides: Vertical scaling is non-obvious on EC2
  • 29. ViewCount V2 - Results: Redis is: Faster - Imgur response time decreased ~50ms
  • 30. ViewCount V2 - Results: Redis is: Faster - Imgur response time decreased ~50ms Cheaper - EC2 cost reduced by 75%
  • 31. ViewCount V2 - Results: Redis is: Faster - Imgur response time decreased ~50ms Cheaper - EC2 cost reduced by 75% Simpler - No Java, no MR, no ZK, no third parties, just INCR + GET!
  • 32. Redis is: Faster - Imgur response time decreased ~50ms Cheaper - EC2 cost reduced by 75% Simpler - No Java, no MR, no ZK, no third parties, just INCR + GET! More fun! - I got to talk at RedisConf17! ViewCount V2 - Results: