Soak testing is one of the most vital yet often overlooked performance testing techniques for ensuring system stability under prolonged heavy loads mimicking production usage.
This comprehensive guide demystifies soak testing to help development and QA teams utilize it for bulletproofing applications supporting continuous 24/7 uptime requirements.
What Exactly is Soak Testing?
Soak testing evaluates application stability, reliability and performance degradation under sustained high usage loads over extended periods of time – typically multiple days to weeks.
Unlike load testing or stress testing, the goal of soak testing isn‘t to establish peak capacity thresholds.
Instead, it is to reveal memory leaks, data consistency issues, blocking defects and performance problems that only manifest themselves after lengthy continuous execution spans.
So in essence, soak testing measures system endurance rather than raw power or speed.
How Does Soak Testing Differ from Other Performance Testing?
Soak testing has some similarities with related performance testing methods but serves distinct purposes:
Load Testing
- Goal: Determine max concurrent users while meeting response time/throughput thresholds
- Duration: Hours to days
- Metrics: Hits/sec, response times at peak loads
Stress Testing
- Goal: Push system behaviour in abnormal/extreme load conditions
- Duration: Minutes to hours
- Metrics: Breaking point tracking
Soak Testing
- Goal: Test stability under expected normal usage over time
- Duration: Days to weeks
- Metrics: Trend analysis of memory, errors, throughput decay
So while load testing assesses peak capacity and stress testing evaluates robustness in overload conditions, soak testing reveals the long-term reliability issues that only arise after prolonged usage.
With soak testing, rather than overly focusing on performance numbers themselves, the key is tracking how key metrics trend over time.
Why Bother with Soak Testing?
Consider that software defects that ultimately crash systems often take repeated execution cycles to initially surface.
As a real-world example, let‘s examine Facebook‘s major outage in October 2021 that took its services offline for nearly six long hours:
During one of these routine maintenance jobs, a command was issued with the intention to assess the availability of global backbone capacity, which unintentionally took down all the connections in our backbone network, effectively disconnecting Facebook data centers globally.
Simply put, an infrastructure capacity audit triggered a crippling cascading failure throughout Facebook‘s infrastructure that risk analysis models likely never predicted.
While details were vague, broad speculation suggested that the outages resulted from latent system stability vulnerabilities that went undetected despite Facebook‘s army of world-class engineers and abundant testing resources.
This highlights why soak testing to an almost ridiculous level in production-mirroring environments provides immense insurance value for Internet-scale systems.
Soak testing would have simulated backbone capacity measurements under continuous prolonged usage, potentially exposing those weaknesses early before launch.
Industries Where Soak Testing is Critical
To drive home why serious soak testing is a must, let‘s spotlight some sectors where systems absolutely cannot afford instability under heavy perpetual loads:
Ecommerce & Retail
- On 2021 Black Friday, shoppers spent $9 billion online by evening – up 21% from 2020
- In 2020, Amazon racked up $4.8 million in sales per minute on Black Friday
- Walmart+ members placed 700 orders per second when holiday sales began in Nov 2021
Banking & Finance
- On salary pay days, Irish banks process 2.5 million payroll transactions over 24 hrs
- The 2022 Super Bowl saw 63 million payment card transactions during game hours
Gaming
- Top games handle millions of concurrent online players daily across globe
- League of Legends has peaked at over 3.9 million simultaneous players
Video Streaming
- Netflix has 222 million streaming subscribers worldwide as of Q4 2021
- YouTube serves over 1.9 billion logged-in monthly users
For these industries, unplanned outages are simply unacceptable. Billions in revenues and customer trust depend on bulletproof stability.
This is exactly why battle-hardening systems to withstand extreme continuous loads via soak testing in pre-production is mission critical.
No sane retailer would deploy an ecommerce platform without first soak testing it to endure multiple 24 hr Black Friday-sized traffic floods.
Likewise no bank would unleash a mobile payment app without confirming it works flawlessly when bombarded with hundreds of transactions per second daily during salary weeks.
So if your system or application falls into one of these categories of perpetual uptime and availability necessities, extensive soak testing is non-negotiable.
Soak Testing Uncovers Issues That Could Crash Systems Post-Launch
Beyond confirming stability and performance over time under load, comprehensive soak testing also exposes many defects and bottlenecks commonly triggered that crash or cripple systems after go-live:
Memory Leaks
- Apps failing to release allocated memory blocks eventually exhaust available RAM
Resource Starvation
- Gradual maxing out of threads, sockets, file handles etc hinders performance
Database Blocking/Locking
- Transactions piling up creates cascading bottlenecks
Software Defects
- Latent bugs manifest only under prolonged execution
Infrastructure Weaknesses
- Servers, databases etc. crumble under perpetual high intensity access
Let‘s analyze some of these issues more closely.
Memory Leaks
Memory leaks happen when application code inadvertently maintains references to objects in memory that are no longer required.
If these unused objects are not garbage collected and freed up, the application‘s memory footprint keeps growing.
Eventually, the app hits maximum heap size limits and crashes either from out of memory errors or from thrashing in its vain attempt to find any last scraps of available memory.
How leaks happen:
- Cached datasets not evicted when no longer necessary
- Unclosed connections/sessions not removed
- Unfreed temporary working data structures
- Bloated log files or analytics data pools
On their own, leaks start small. But under continuous load allowing constantly accruing new object allocations, eventually they can swallow up all available system RAM.
This is why soak tests running programs continuously for days or weeks often initially trigger otherwise dormant memory leaks.
Blocked Resources
If applications or their dependent infrastructure layers incorrectly synchronize shared resource access, deadlock conditions can happen that grind operations to a halt.
Example bottleneck troublemakers:
- Database transaction locks
- Network socket starvation
- Thread/process semaphores
- Exhausted file handles
Like leaks, these blocking defects commonly manifest only under prolonged heavy system utilization.
Short load tests may never create enough concurrent transactions to surface locks. But extended soak testing allows resource pile-ups that lead to blocks that freeze user operations.
Yet another reason why lengthy soak testing is vital for any large-scale multi-user system.
Catching blocking issues pre-launch prevents crashed services and outages post-launch.
Real-World Soak Testing War Stories
To drive home the perils of inadequate soak testing, let‘s examine some dramatic real-world examples:
Reddit‘s 2005 Holiday Outage Disaster
With over 52 million daily visitors now, Reddit is one of the Internet‘s most trafficked sites.
But back in 2005, the then fledging Web 2.0 social news site melted down completely when traffic spiked during the 2005 holiday season.
Why the catastrophic failure?
Despite extensive load testing claiming otherwise, Reddit‘s architecture was utterly unprepared to handle a surge of simultaneous user sessions.
Post-mortems revealed session bottlenecks that crippled database query throughput. Yet only continuous near-production soak testing could have surfaced these scalability flaws earlier.
Steve Huffman, Reddit‘s co-founder and now CEO, reflected:
We learned a lot about designing for scale after suffering downtime almost every day due to demand exceeding capacity. Losing users due to poor site performance hurts much more than it helps to have millions of registered users.
The crucial lesson here according to Huffman? Site stability trumps raw registered user numbers as viewing metrics for success.
Twitter‘s 2006 World Cup Tweet Storm Outages
Similarly back in its shakier early architecture days in 2006, intense spikes in World Cup game tweets repeatedly made Twitter buckle resulting in prolonged outages.
Specifically their relational database MySQL crashed over and over due to connection starvation under load it was never tested to handle.
Retrospective analysis pegged the melting point at approximately 600 tweets per second – a rate unexceptional by today‘s Twitter standards.
Yet at the time, the fledgling platform lacked solutions for connection management suited for tidal waves of time-sensitive data.
Lengthy soak tests bombarding Twitter‘s infrastructure could have exposed those fragilities. Instead, hundreds of millions of soccer fans were left tweeting into oblivion during the world‘s largest sporting event.
"If we can‘t keep up with the load during the World Cup, how can we hope to keep up with the next Presidential election?" – Greg Pass, Former Twitter CTO
H&M‘s 2009 Holiday Shopping Disappointments
Clothing giant H&M learned similar soak testing lessons during the agony of the 2009 holiday shopping season.
A flashing red warning sign manifested on Black Friday itself when shoppers browsing the H&M ecommerce store were left staring at 404 errors and dropped carts.
Outages ultimately stretched over 10+ days until December 15th costing H&M millions in lost sales.
Post-mortem forensic analysis suggested inadequate infrastructure stress testing during peak events caused low stock visibility, intermittent browse failures, and interrupted purchases.
Yet surprisingly, load tests indicated the site could handle 75,000 concurrent shoppers – far beyond the levels seen during the outages.
This again spotlights the risk of focusing solely on load tolerances rather than stability under continuous real-world conditions.
While H&M‘s systems could withstand short bursts, they lacked resilience for sustained all-day customer volumes common during holiday sales.
Extensive multi-day soak testing better reflects these Black Friday “ultra marathons”. Learning this difference the hard way cost H&M big.
Soak Test Tools
Specialized tools for running soak test typically have capabilities like:
- Simulating thousands of concurrent production users
- Reporting metrics on reliability, resource usage etc
- Automating multi-day tests spanning weeks
- Integrating with CI/CD pipelines
- Scaling test agents across environments
Popular options:
LoadRunner: An industry standard from MicroFocus for comprehensive load generation with components optimized for big data style soak testing
LoadView: Browser-based service with scripts and monitors tailored to soak testing web apps
NeoLoad: Build user journeys to test performance over time across browsers
Apache JMeterTM: Open source Java load test tool with plugin extensions for endurance testing
K6: Developer focused to soak test APIs and microservices at scale
When choosing tools, consider ease of test distribution, result analysis, metrics tracked and extensibility.
Fit the tools to match the environment under test and metrics needing inspection over duration.
Soak Test Early, Soak Test Often
With soak testing basics now covered, let‘s switch gears to winning test practices you should adopt:
Start Soak Testing Early In the SDLC
Don‘t wait until late in development to run first soak tests. Begin after initial functionality is coded.
Find surprises early so fixes are easier before layered complexity sets in later.
Schedule periodic soak testing milestones after each major feature addition.
Test Often In Pre-Production QA Environments
Mandate extended soak testing as part of standard QA procedures before committing any code.
Use scaled down versions of staging/production infrastructure for fidelity.
Compare Runs Over Time
Track how metrics like memory usage, response times etc. change release-over-release as changes roll down the pipeline.
Test Both UI and Services/APIs
Front-ends and middle-tiers can both have endurance issues so test holistically.
Use Realistic Data Volumes
If an app manipulates big user data in production, inject proportionate data samples to soak tests.
Mimic Production Usage Patterns
Understand daily, weekly and seasonal usage cycles when modeling test loads and durations.
Reflect irregular usage spikes tied to events, promotions, holidays etc. Accurately simulate disturbed demand the system must survive.
Instrument Everything
Monitor across app, database, cache, networks and OS layers to pinpoint brewing bottlenecks.
Make Soak Testing Mandatory
Treat extended soak testing on staging environments as the final gate before releases to catch stability defects.
Re-Soak Test After Fixes
When leaks or bottlenecks are uncovered and remediated, soak test again to confirm flaws are truly resolved for good.
Conclusion
In closing, hopefully this extensive exploration has armed you to unleash the power of soak testing on your most critical applications and infrastructure to ensure bulletproof reliability over the long haul under continued high intensity production conditions.
While soak testing is complex and time consuming initially, make no mistake – the early warning signs of instability exposed before customers are impacted saves massive headaches down the line.
As Internet scale pioneers like Amazon, Google, Facebook, Twitter and Uber likely profess – extensive and almost absurdly rigorous long duration stress testing across infrastructure layers lays the bedrock for world-class availability despite astronomical usage volumes.
So embrace soak testing as a centerpiece of landing rock solid production deployments capable of going the distance!
