Cases6 cases

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1. IL-0296 Transloadit's us-east Redis cluster lost queued jobs and heartbeats, raising WORKER_JOB_ERROR errors for 81 customers

   Transloadit2016

   Symptoms

   Reports came in of more WORKER_JOB_ERROR errors, which tell Transloadit there is a communication problem between its Uploaders, the machines that receive files and orchestrate Assemblies, and its Drones, the machines that run encoding tasks. Many Assemblies became much slower because retries with exponential back-off kicked in, and a few could not be replayed.

   High error rateHigh latencyLoad imbalance

   Trigger

   A steep increase in HLS traffic, together with more Drones online processing the same volume faster, which put more load on the central Redis nodes.

   Root cause

   Capacity exhaustion

   Component

   In-memory cacheDatabase replication

   Contributing factor

   Monitoring blind spotUnverified assumptionHarmful manual action

   Detection

   Reports of an increased number of WORKER_JOB_ERROR errors.

   Remediation

   • Re-added lost messages and relaxed heartbeat monitoring as a stopgap
   • Upgraded Redis to the latest stable version
   • Doubled the Redis cluster to six nodes and then added three more, which made it worse
   • Brought the number of read replicas down to one, which cut traffic and failure rates
   • Capped how far Drones scale up in each region

   Action items

   • Add logging and alarms for approaching saturation
   • Find ways to decentralise the traffic between the Redis nodes and the Drones and Uploaders
   • Add sharding per Uploader on top of the sharding per region

   Lessons

   To handle more traffic Transloadit had to use fewer Redis nodes, not more: each added replica multiplied the replication traffic from the single leader that takes every write, so scaling up the number of nodes was the wrong call. Bandwidth had not been among the monitored Redis metrics.

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2. IL-0319 Dropping databases on the MySQL replica mysql2 also dropped them on mysql1, as the pair actually ran master-master

   Oregon State University Open Source Lab2015

   Symptoms

   On July 30th, a few minutes after the OSU Open Source Lab started reloading the MySQL server mysql2 to fix its replication errors, the team saw that databases were being dropped on mysql1 as well. The script was stopped, and the MySQL service behind mysql1-vip stayed down through a long restore.

   Data lossService outageReplication lag

   Trigger

   On July 30th at 4:00PM PDT the team stopped slave replication on mysql2 and began dropping its databases one at a time to reload it and fix replication errors.

   Root cause

   Operator error

   Component

   DatabaseDatabase replicationDatabase backups and snapshots

   Contributing factor

   Unverified assumptionUntested backup

   Detection

   The team watching the reload noticed after a few minutes that mysql1 was also dropping databases; the delays between drops in the script gave them that time.

   Remediation

   • Stopped the script that was dropping databases
   • Restored the databases from the mysql-zrm backups
   • Pulled each database that failed to restore out of the 200GB backup file and restored it separately
   • Recovered most data with the binary logs from the 17th through the 30th

   Action items

   • Evaluate better replication strategies, including row-based replication
   • Store binlogs as a backup on a separate server
   • Take backups with Percona XtraBackup for much faster full restores
   • Use mydumper instead of mysql-zrm for faster logical backups
   • Improve documentation and training for complex systems
   • Test full restores regularly on a spare server as part of the backup process
   • Gather more accurate time estimates for the restore process
   • Audit the hosted databases regularly, since test and very large databases delayed the restore
   • Migrate to a bigger, more powerful MySQL cluster
   • Set a policy and tools to keep the community informed of outages, maintenance and resource use, with dashboards
   • Define and monitor capacity limits for core services
   • Implement redundancy and restore practices for core services, including staff drills
   • Migrate more core services to Chef
   • Review documentation, policies and performance metrics periodically
   • Find better ways to draw on community expertise
   • Raise funds to replace the oldest infrastructure and reduce technical debt

   Lessons

   The lab had stretched its resources to host as many projects as possible, and that came with tradeoffs such as heavily compressed backups to save disk and less redundancy than wanted; the heavy compression made this restore CPU-bound. The lab concluded that it needs to change how it plans, monitors and manages resources, and that the length of the outage and the lack of progress reports were not acceptable.

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