RedisConf17 - Redis in High Traffic Adtech Stack
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Rahul Babbar presented on Times Internet's use of Redis in their high-traffic adtech stack. Some key points: - Times Internet is India's largest digital company with 240+ million unique visitors per month. - Their adtech stack powers ads for 150+ publishers and handles 9 billion ad impressions per month. - Redis is used for caching, user profiles, analytics and more due to its low latency and ability to handle high volumes. - Their Redis clusters consist of over 160 nodes across 4 clusters, serving over 2 million queries per second with 99% under 2ms response times.
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RedisConf17 - Redis in High Traffic Adtech Stack
- 1. Utilizing Redis in high traffic Adtech stack Rahul Babbar Arjun Satya Times Internet Ltd
- 2. About me • Rahul Babbar • Chief Manager – Technology, Adtech Colombia • Times Internet Ltd • Technology, soccer, philosophy, travel enthusiast.
- 3. Agenda • About Times Internet • About Colombia Adtech Stack. • Where we use redis. • Load testing, design decisions, cluster setup and configuration. • Monitoring and more. • Good practices • Challenges
- 4. Times Internet Ltd • Digital arm of Times Group • 240+ Million Unique Visitors per month. • Evolved from a digital media company to a digital products company.
- 6. Colombia • Complete adtech stack • Ad server • Data Management Platform (DMP) • Demand mediation • Recommendation Service • Billing, automation and self service • powers ads on ~150 publishers, monetizes ~55% of news traffic in India. • ~9 billion ad impressions per month.
- 8. Simplified ad serving flow 9
- 9. Redis to the rescue Redis helped in achieving • Low latency • 99% of requests in under 2 ms • Central caching layer • Smart analytics
- 10. Redis Stacks • Runtime cluster (User Profile) • Master Slave (Central Caching Layer) • Operational Cluster (for DMP)
- 11. Central Caching Layer • Implemented JSR 107 specification(JCache) • Write through Cache • Helps in keeping the metadata in all ad components in sync. • Uses redis pub-sub.
- 13. Data Management Platform • User : Category : Date => frequency • Analytics / HLL • Collocation of site data using redis hash tagging. • Lua scripting
- 14. Load Testing(~2016) • Customized for cluster and our use case. • test the network • test the java clients also. • ~20K requests per second.
- 15. Load testing design • Redis cluster of 15 master nodes across 3 machines (5 nodes/machine) • Java Client -> • java client using a jar file • use case => get the user profile and set an attribute in user profile • while(true){ • Execute the use case • Print the current time in HHMMSS:averagetime to get : average time to set • } • 3 client machines • Each client machine ran 4 instances of java client
- 16. Load testing output • Machine 1 file 1 • (Hour:Minute:Second:TimeToGet(microsec):TimeToSet(microsec)) • 10:00:20:1500:1000 • 10:00:20:1450:950 • ….. • 15:00:20:909:800 • .. • … • ………. • Machine 3 file 4 • 10:00:20:1100:900
- 17. Load test continued. • 3 client machines(4 java client instances per machine) • ~15,000 operations per second. • 6 client machines(4 java client instances per machine) • ~28,000 operations per second. • ~ Linear Increase(confidence that redis cluster could work for our use case)
- 18. Decisions • 512 GB memory • How many redis nodes • How much memory per node • Number of slaves/master • Appropriate Java client(Jedis/Lettuce)
- 19. Memory per node/Nodes per machine(512 GB) • less nodes/machine => more memory/node • 5 nodes => ~100 GB/node • easy to manage. • Utilizing only 5 cores • Slow startup of all nodes on the machine • more nodes/machine => Less memory/node • 20 nodes => ~25 GB/node • Fast startup • more core utilization. • Difficult to manage
- 20. Our configuration(Cluster) • Each machine(512 GB) • 20 nodes/machine • 10 masters + 10 slaves per machine. • ~24 GB/node • 7 such machines for Runtime cluster • 6 such machines for operational cluster • 1 slave/master • Jedis + Lettuce(Async calls)
- 22. Monitoring • All software systems will fail at some point or the other because of dependency on other systems. What matters is how fast can we detect/predict such a failure and auto-heal it if possible.
- 23. Node level monitoring • A script runs on all the machines which have redis nodes. • checks every 30 seconds that 1 redis instance is running on each of the ports 7000…7019 • If No, starts the instance and raises an alert. • What if the machine(s) itself is down, so no alert.
- 24. Stack Level(Global) Monitoring • A script runs on 2 machines. • It tries to “set” a key and “get” a key in each stack. • If it fails, it raises an alert. • If 2+ machines are down leading to the failure of redis stack, “set” fails, it generates an alert.
- 25. Hourly health stats check • Check the below every hour per node per stack • Used memory • No of keys • No of connections • Memory fragmentation ratio • Slow queries • Last background save was successful. • Slaves are online and not lagging behind masters • Raise an alert if any of these is abnormal. • Email the report twice a day(10 AM, 6 PM) to make sure the script is running.
- 26. Sample
- 27. Cluster masters distribution script • Script checks whether each machine has equal no of masters(10 in our case) • Raise alert if not.
- 28. Graphs and more!!! • Stats from ”info all” commands are pushed to graphite, and graphs are created from grafana. • Stats pushed. • Memory • No of keys. • each type of command • No of calls. • CPU time • Connected clients. • New Keys • Persistent keys. • Input/Output bytes
- 29. Graphs
- 30. Graphs
- 31. Graphs
- 32. Daily Stack report from random node • Pick a random node • Scan all records. • Scrutinize key prefixes. • Analyze data as per business.
- 34. Good practices • Disabled save, nightly saves one after the other. • Ensure TTL for keys • Renamed commands • Setting timeout for idle connections. • ‘hz’ parameter. • Application strategy in case of redis slowdown/failure.
- 36. Overall stats • 4 clusters, 1 master-slave-sentinel • ~160 + nodes, 2+ TB of master data. • 1 slave per master node. • 99+% requests served under 2 ms. • DMP stack serves more than 2 million QPS with pipelining.
- 37. Challenges • Tracking rogue clients. • Who deleted my data? • Who executed this slow query? • Scan instead of keys helpful? Scan 0 match * count 1000000 • Who modified my cluster. • What we did for security? • private ips • IP-tables
- 38. Questions