Cases2 cases

1 to 2 of 2

1. IL-0301 Basecamp 3's primary database went into total gridlock at the top of the hour and Basecamp 3 went down twice

   37signals2022

   Symptoms

   At 13:00 UTC on Monday, June 27, 2022, Basecamp 3 became extremely slow, then showed error pages, and then stopped working altogether. Basecamp 2 and Basecamp Classic kept working normally.

   Lock contention or deadlockDatabase overloadHigh latencyHigh error rateService outage

   Trigger

   At 13:00 UTC, when Basecamp 3 sends scheduled notifications in massive batches, a rush of demanding queries reached the primary database and started a cascade of heavy lock contention.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No capacity planningHarmful manual actionMonitoring blind spot

   Detection

   The team saw the Basecamp 3 primary database become completely congested within 30 seconds of 13:00 UTC and responded immediately.

   Remediation

   • Paused and disabled parts of Basecamp 3, such as delaying progress report emails
   • Failed over from the primary database to a replica, which brought Basecamp 3 back until it tipped over again
   • Put Basecamp 3 into maintenance mode
   • Set hard limits on database query time
   • Brought features back online step by step with the query limits in place

   Action items

   • Keep hard limits on database query time so slow queries fail instead of piling up
   • Tune the limits on concurrency
   • Treat top-of-the-hour database load spikes from scheduled batches as unacceptable
   • Reduce the number of parallel workers for some scheduled jobs
   • Let some scheduled activities run over several minutes instead of all at once
   • Move bursty queries from the primary database to read-only replicas
   • Fix the quiet performance regressions found in a review of the past year of database metrics
   • Find and spread out other bursty activities that concentrate load on the database
   • Tune the database for more predictable behaviour under heavy load and high concurrency
   • Add observability into the database's internal locking and transaction states
   • Build tools to switch off high-load Basecamp 3 features selectively during an incident
   • Run operational fire drills for a wedged database
   • Evaluate upgrading the database software and hardware
   • Set service level objectives for the components behind Basecamp 3
   • Hold quarterly performance and load reviews to catch slow, subtle changes

   Lessons

   A system running just past its limits can flip from stable to unstable without any particular trigger, and steady growth plus small regressions that raised no alarm had brought Basecamp 3's primary database to that point. Under lock contention, terminating queries does not relieve the database, because rolling back their transactions waits on the same locks. The team struggled to respond with confidence because there was no single clear cause and little visibility into the database's internals.

   View case
2. IL-0282 DynamoDB in US-East reached about 55% request errors as storage servers overloaded its metadata service

   Amazon Web Services2015

   Symptoms

   Early on Sunday 20 September 2015, the error rate of customer requests to Amazon DynamoDB in the US-East Region rose far beyond any level of the previous 3 years by 2:37am PDT and settled at about 55%. Some other AWS services in the region that depend on DynamoDB were affected as well.

   High error rateDatabase overload

   Trigger

   At 2:19am PDT a brief network disruption affected a portion of DynamoDB's storage servers, and they all requested their membership data from the metadata service at the same time.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No capacity planningMonitoring blind spotRetry stormCircular dependencyExcessive blast radius

   Detection

   For the errors that remained after the main recovery, a growing number of customer support cases on Monday about tables stuck in the updating or deleting stage and higher than normal error rates showed that the low overall error rate was hitting some customers hard.

   Remediation

   • Paused requests to the metadata service, which cut the retries and relieved its load
   • Added significant capacity to the metadata service once it could answer administrative requests
   • Turned requests to the metadata service back on and returned storage servers to the customer request path
   • Restored a metadata partition that was still not taking its full traffic, ending the remaining errors on Monday

   Action items

   • Significantly increased the capacity of the metadata service, which AWS had already done by the time of the summary
   • Add stricter monitoring on performance dimensions such as membership size, to plan capacity ahead
   • Reduce the rate at which storage nodes request membership data
   • Lengthen the time allowed to process membership queries
   • Segment DynamoDB into many metadata service instances, each serving only part of the storage server fleet
   • Change SQS metadata caching so send and receive keep working without long access to the metadata table
   • Partition work across the Auto Scaling servers to process a backlog of scaling jobs in parallel
   • Prune older scaling activities that have been superseded
   • Increase the capacity available to process scaling activities
   • Change CloudWatch caching of DynamoDB group membership data to refresh only the smallest set of metrics
   • Deliver CloudWatch metrics faster through write-through caching
   • Roll out the shorter timeout on the DynamoDB-dependent API call in Console login

   Lessons

   A low overall error rate can hide disproportionately high error rates for a few customers, and AWS should have posted to the dashboard sooner. A call that does not need to succeed, like the DynamoDB-dependent API call in Console login, should fail quickly instead of blocking on a very long timeout. Splitting the metadata service into many instances, each serving part of the fleet, is meant to contain the impact of software, capacity or infrastructure failures.

   View case

By root cause1