{"id":2537,"date":"2026-02-17T10:25:25","date_gmt":"2026-02-17T10:25:25","guid":{"rendered":"https:\/\/dataopsschool.com\/blog\/throughput\/"},"modified":"2026-02-17T15:32:06","modified_gmt":"2026-02-17T15:32:06","slug":"throughput","status":"publish","type":"post","link":"https:\/\/dataopsschool.com\/blog\/throughput\/","title":{"rendered":"What is Throughput? Meaning, Architecture, Examples, Use Cases, and How to Measure It (2026 Guide)"},"content":{"rendered":"\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Quick Definition (30\u201360 words)<\/h2>\n\n\n\n<p>Throughput is the measured rate at which a system completes work over time. Analogy: throughput is the number of cars passing a toll booth per minute. Formal technical line: throughput = successful completed units of work \/ unit time under specified conditions.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">What is Throughput?<\/h2>\n\n\n\n<p>Throughput is a performance metric describing how much useful work a system delivers per time unit. It is a system-level rate, not an instantaneous capacity; throughput depends on latency, concurrency, resource limits, contention, and backpressure mechanisms. Throughput is not the same as utilization or raw bandwidth, though those influence it.<\/p>\n\n\n\n<p>Key properties and constraints<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Throughput is time-bound: measured as operations\/sec, requests\/sec, bytes\/sec, transactions\/minute.<\/li>\n<li>It is workload-dependent: different request mixes change throughput.<\/li>\n<li>It saturates: increasing offered load hits limits where throughput plateaus or degrades.<\/li>\n<li>Backpressure and queueing affect sustained throughput and tail behavior.<\/li>\n<li>Trade-offs exist: high throughput may increase latency or error rate if not managed.<\/li>\n<\/ul>\n\n\n\n<p>Where it fits in modern cloud\/SRE workflows<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>SREs use throughput as a primary SLI for capacity planning and incident response.<\/li>\n<li>Architects design systems to meet throughput targets using horizontal scaling, batching, or sharding.<\/li>\n<li>CI\/CD and load testing validate throughput under representative traffic.<\/li>\n<li>Observability and automation (autoscaling, AI-driven anomaly detection) monitor and control throughput in real time.<\/li>\n<\/ul>\n\n\n\n<p>A text-only \u201cdiagram description\u201d readers can visualize<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Client traffic flows into an edge load balancer, which distributes to a fleet of stateless application pods behind a service mesh. Each pod queries a shared database or cache. A message queue buffers spikes. Autoscaler adjusts pod count based on throughput and CPU. Monitoring pipeline collects request rates, latencies, and error rates to dashboards; an anomaly detection system triggers scaling actions and paging if throughput drops.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Throughput in one sentence<\/h3>\n\n\n\n<p>Throughput is the sustained rate at which a system successfully processes work over time, constrained by architecture, resource limits, and workload mix.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Throughput vs related terms (TABLE REQUIRED)<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Term<\/th>\n<th>How it differs from Throughput<\/th>\n<th>Common confusion<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>T1<\/td>\n<td>Latency<\/td>\n<td>Time per request not rate<\/td>\n<td>People assume low latency = high throughput<\/td>\n<\/tr>\n<tr>\n<td>T2<\/td>\n<td>Bandwidth<\/td>\n<td>Raw data transfer capacity<\/td>\n<td>Confused with end-to-end processing rate<\/td>\n<\/tr>\n<tr>\n<td>T3<\/td>\n<td>Utilization<\/td>\n<td>Percent resource use not work output<\/td>\n<td>High utilization thought to mean high throughput<\/td>\n<\/tr>\n<tr>\n<td>T4<\/td>\n<td>IOPS<\/td>\n<td>Storage operation rate specifically<\/td>\n<td>Treated as system throughput proxy incorrectly<\/td>\n<\/tr>\n<tr>\n<td>T5<\/td>\n<td>Concurrency<\/td>\n<td>Number of simultaneous operations<\/td>\n<td>Mistaken for throughput because both grow with load<\/td>\n<\/tr>\n<tr>\n<td>T6<\/td>\n<td>Capacity<\/td>\n<td>Max potential not sustained rate<\/td>\n<td>Confused with guaranteed throughput<\/td>\n<\/tr>\n<tr>\n<td>T7<\/td>\n<td>Availability<\/td>\n<td>Uptime or successful response ratio<\/td>\n<td>Mistaken as throughput because both affect user experience<\/td>\n<\/tr>\n<tr>\n<td>T8<\/td>\n<td>Latency percentile<\/td>\n<td>Distribution snapshot not aggregate rate<\/td>\n<td>Misinterpreted as overall throughput indicator<\/td>\n<\/tr>\n<tr>\n<td>T9<\/td>\n<td>Response time<\/td>\n<td>Similar to latency but includes queuing<\/td>\n<td>Often used interchangeably with throughput<\/td>\n<\/tr>\n<tr>\n<td>T10<\/td>\n<td>Scalability<\/td>\n<td>Ability to increase throughput with resources<\/td>\n<td>Mistaken as fixed throughput metric<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if any cell says \u201cSee details below\u201d)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Why does Throughput matter?<\/h2>\n\n\n\n<p>Business impact (revenue, trust, risk)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Revenue: throughput directly affects transactional revenue and conversion velocity for e-commerce and payment systems.<\/li>\n<li>Trust: consistent throughput improves user expectations and retention.<\/li>\n<li>Risk: unexpected throughput drops cause transaction loss, revenue leakage, or legal SLA breaches.<\/li>\n<\/ul>\n\n\n\n<p>Engineering impact (incident reduction, velocity)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Predictable throughput reduces on-call churn by preventing saturation incidents.<\/li>\n<li>Proper throughput design enables faster feature delivery because capacity risks are controlled.<\/li>\n<li>Mis-measured throughput leads to wasted spend and inefficient autoscaling.<\/li>\n<\/ul>\n\n\n\n<p>SRE framing (SLIs\/SLOs\/error budgets\/toil\/on-call)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Throughput is a candidate SLI where availability or performance are tied to rate processed.<\/li>\n<li>SLOs can be throughput-based (e.g., maintain X req\/sec for Y% of time) or combined with latency SLIs.<\/li>\n<li>Error budgets may be consumed by incorrect throttling or sustained overloads that reduce throughput.<\/li>\n<li>Automating throughput handling reduces toil (autoscaling, rate limiting, circuit breakers).<\/li>\n<\/ul>\n\n\n\n<p>3\u20135 realistic \u201cwhat breaks in production\u201d examples<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>A cache eviction bug causes DB load to spike; throughput collapses as DB fails open.<\/li>\n<li>Autoscaler misconfiguration scales too slowly; burst traffic saturates pods and throughput drops.<\/li>\n<li>Thundering herd after a release creates queue build-up; throughput spikes then crashes due to CPU exhaustion.<\/li>\n<li>Network ACL change throttles inter-zone traffic; cross-region throughput reduces and increases latency, causing retries.<\/li>\n<li>Cost-optimization reduces instance sizes below needed capacity; throughput tops out and errors rise.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Where is Throughput used? (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Layer\/Area<\/th>\n<th>How Throughput appears<\/th>\n<th>Typical telemetry<\/th>\n<th>Common tools<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>L1<\/td>\n<td>Edge \/ CDN<\/td>\n<td>Requests\/sec and bytes\/sec at edge<\/td>\n<td>request rate, cache hit ratio, edge latency<\/td>\n<td>CDN logs and edge metrics<\/td>\n<\/tr>\n<tr>\n<td>L2<\/td>\n<td>Network<\/td>\n<td>Packets\/sec and bandwidth utilization<\/td>\n<td>bandwidth, packet drops, RTT<\/td>\n<td>Network telemetry and flow logs<\/td>\n<\/tr>\n<tr>\n<td>L3<\/td>\n<td>Service \/ API<\/td>\n<td>API calls\/sec and success rate<\/td>\n<td>request rate, p50\/p99 latency, errors<\/td>\n<td>API gateway and service metrics<\/td>\n<\/tr>\n<tr>\n<td>L4<\/td>\n<td>Application<\/td>\n<td>Processed jobs\/sec and task throughput<\/td>\n<td>task rate, queue depth, thread pool stats<\/td>\n<td>App logs and runtime metrics<\/td>\n<\/tr>\n<tr>\n<td>L5<\/td>\n<td>Data \/ Storage<\/td>\n<td>IOPS and bytes\/sec for DB\/storage<\/td>\n<td>IOPS, read\/write latency, queue depth<\/td>\n<td>DB metrics and storage monitoring<\/td>\n<\/tr>\n<tr>\n<td>L6<\/td>\n<td>Messaging \/ Queue<\/td>\n<td>Messages\/sec and consumer throughput<\/td>\n<td>enqueue\/dequeue rate, backlog size<\/td>\n<td>Broker metrics and consumer metrics<\/td>\n<\/tr>\n<tr>\n<td>L7<\/td>\n<td>Kubernetes<\/td>\n<td>Pod-level request\/sec and pod autoscaling<\/td>\n<td>pod CPU, pod request rate, HPA events<\/td>\n<td>K8s metrics and autoscaler<\/td>\n<\/tr>\n<tr>\n<td>L8<\/td>\n<td>Serverless \/ FaaS<\/td>\n<td>Invocations\/sec and concurrency<\/td>\n<td>invocation rate, cold starts, errors<\/td>\n<td>Serverless runtime metrics<\/td>\n<\/tr>\n<tr>\n<td>L9<\/td>\n<td>CI\/CD<\/td>\n<td>Build throughput and pipeline concurrency<\/td>\n<td>builds\/sec, queue time, failure rate<\/td>\n<td>CI system metrics<\/td>\n<\/tr>\n<tr>\n<td>L10<\/td>\n<td>Security \/ DDoS<\/td>\n<td>Attack traffic throughput and mitigation<\/td>\n<td>anomalous rate, blocked requests<\/td>\n<td>WAF and DDoS protection metrics<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">When should you use Throughput?<\/h2>\n\n\n\n<p>When it\u2019s necessary<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>When your business cares about completed transactions per time (payments, streaming, telemetry ingestion).<\/li>\n<li>When capacity planning or autoscaling targets are needed.<\/li>\n<li>When SLIs\/SLOs depend on sustained processing rates.<\/li>\n<\/ul>\n\n\n\n<p>When it\u2019s optional<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Low-traffic admin tools where latency matters more than total rate.<\/li>\n<li>Prototypes and early-stage features where single-user experience matters over scale.<\/li>\n<\/ul>\n\n\n\n<p>When NOT to use \/ overuse it<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>As a sole health indicator; throughput alone can mask latency spikes and errors.<\/li>\n<li>For systems where correctness matters more than rate (e.g., financial reconciliation jobs) unless tied with success SLIs.<\/li>\n<\/ul>\n\n\n\n<p>Decision checklist<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If traffic patterns are bursty and autoscaling is used -&gt; instrument throughput and backlog.<\/li>\n<li>If business revenue correlates with transactions per minute -&gt; set throughput SLIs.<\/li>\n<li>If latency SLOs are primary and transactions are low -&gt; focus on latency SLIs instead.<\/li>\n<\/ul>\n\n\n\n<p>Maturity ladder: Beginner -&gt; Intermediate -&gt; Advanced<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Beginner: Measure basic request\/sec and set simple dashboard panels.<\/li>\n<li>Intermediate: Correlate throughput with latency, errors, and capacity; implement autoscaling.<\/li>\n<li>Advanced: Dynamic SLOs, AI-driven autoscaling, cross-layer capacity orchestration, predictive scaling based on demand forecasting.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How does Throughput work?<\/h2>\n\n\n\n<p>Components and workflow<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Clients send requests or events.<\/li>\n<li>Ingress layer (edge\/load balancer) distributes traffic.<\/li>\n<li>Service layer accepts requests; worker threads\/processes execute logic.<\/li>\n<li>Persistence layer or downstream services respond or enqueue tasks.<\/li>\n<li>Observability pipeline records request rate, latencies, errors, and resource usage.<\/li>\n<li>Control plane (autoscaler, rate limiter) adjusts capacity or applies backpressure.<\/li>\n<\/ul>\n\n\n\n<p>Data flow and lifecycle<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Request arrives at ingress.<\/li>\n<li>Load balancer selects a healthy instance or pod.<\/li>\n<li>Instance either processes inline or offloads to a queue.<\/li>\n<li>If queue-backed, workers pull messages and complete work.<\/li>\n<li>Metrics emit for every processed unit; monitoring aggregates rates.<\/li>\n<li>Autoscaler consumes metrics and adjusts capacity.<\/li>\n<\/ol>\n\n\n\n<p>Edge cases and failure modes<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Head-of-line blocking: slow request prevents others from progressing.<\/li>\n<li>Backpressure absence: upstream continues sending causing queue growth and OOM.<\/li>\n<li>Thundering herd: simultaneous retries after outage spike throughput then errors.<\/li>\n<li>Resource contention: I\/O bottlenecks limit throughput even when CPU is free.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Typical architecture patterns for Throughput<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Horizontal stateless scaling with load balancer \u2014 use when requests are independent and latency matters.<\/li>\n<li>Queue-backed worker pool \u2014 use when smoothing bursts and retrying are required.<\/li>\n<li>Sharded partitioning by key \u2014 use when single-worker limits cause bottlenecks.<\/li>\n<li>Batching and bulk processing \u2014 use for high-rate small operations to reduce per-op overhead.<\/li>\n<li>Edge caching with origin offload \u2014 use to convert read traffic to higher effective throughput.<\/li>\n<li>Backpressure and rate limiting at ingress \u2014 use to protect downstream systems.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Failure modes &amp; mitigation (TABLE REQUIRED)<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Failure mode<\/th>\n<th>Symptom<\/th>\n<th>Likely cause<\/th>\n<th>Mitigation<\/th>\n<th>Observability signal<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>F1<\/td>\n<td>Resource saturation<\/td>\n<td>Throughput stalls and errors<\/td>\n<td>CPU, mem, or IO maxed<\/td>\n<td>Increase capacity or optimize code<\/td>\n<td>CPU% and error rate up<\/td>\n<\/tr>\n<tr>\n<td>F2<\/td>\n<td>Queue buildup<\/td>\n<td>Growing backlog and delayed processing<\/td>\n<td>Consumer slow or stopped<\/td>\n<td>Scale consumers or throttle producers<\/td>\n<td>Queue depth increases<\/td>\n<\/tr>\n<tr>\n<td>F3<\/td>\n<td>Autoscaler lag<\/td>\n<td>Temporary throughput drop after spike<\/td>\n<td>Bad metrics or slow scale policy<\/td>\n<td>Tune scaling policy and metrics<\/td>\n<td>Scale events and delay logs<\/td>\n<\/tr>\n<tr>\n<td>F4<\/td>\n<td>Hot partition<\/td>\n<td>Uneven throughput across shards<\/td>\n<td>Skewed key distribution<\/td>\n<td>Rebalance or re-shard keys<\/td>\n<td>Per-shard rate disparity<\/td>\n<\/tr>\n<tr>\n<td>F5<\/td>\n<td>Network saturation<\/td>\n<td>Packet loss and retries<\/td>\n<td>Bandwidth or NIC limits<\/td>\n<td>Increase bandwidth or reduce cross-zone traffic<\/td>\n<td>RTT, retransmits up<\/td>\n<\/tr>\n<tr>\n<td>F6<\/td>\n<td>Third-party slow<\/td>\n<td>Downstream latency limits rate<\/td>\n<td>External service throttling<\/td>\n<td>Add caching or degrade gracefully<\/td>\n<td>Downstream latency up<\/td>\n<\/tr>\n<tr>\n<td>F7<\/td>\n<td>GC pauses<\/td>\n<td>Throughput jitter and stalls<\/td>\n<td>Poor memory management<\/td>\n<td>Tune GC or reduce allocations<\/td>\n<td>Long GC pause events<\/td>\n<\/tr>\n<tr>\n<td>F8<\/td>\n<td>Misconfigured rate limits<\/td>\n<td>Throttled requests and errors<\/td>\n<td>Wrong limit or token bucket size<\/td>\n<td>Adjust limits and backoff<\/td>\n<td>429 errors spike<\/td>\n<\/tr>\n<tr>\n<td>F9<\/td>\n<td>Deployment regressions<\/td>\n<td>Sudden drop in throughput<\/td>\n<td>Bad release or config<\/td>\n<td>Rollback and run canary tests<\/td>\n<td>Deploy timestamp correlates<\/td>\n<\/tr>\n<tr>\n<td>F10<\/td>\n<td>Database contention<\/td>\n<td>Transaction slowdown<\/td>\n<td>Locking or hot rows<\/td>\n<td>Indexing, sharding, or optimistic locking<\/td>\n<td>DB lock\/wait metrics<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Key Concepts, Keywords &amp; Terminology for Throughput<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Throughput \u2014 Rate of completed work per time unit \u2014 Core performance measure \u2014 Confused with utilization.<\/li>\n<li>Request\/sec \u2014 Number of API calls per second \u2014 Common SLI \u2014 May ignore partial failures.<\/li>\n<li>Transactions\/sec \u2014 Completed transactions per second \u2014 Business-aligned rate \u2014 Hard with multi-step ops.<\/li>\n<li>Messages\/sec \u2014 Queue or broker throughput \u2014 For async systems \u2014 Can hide per-message latency.<\/li>\n<li>Bytes\/sec \u2014 Data transfer rate \u2014 Useful for streaming \u2014 Not equal to requests handled.<\/li>\n<li>IOPS \u2014 Storage operations per second \u2014 Affects DB throughput \u2014 Misused as end-to-end throughput proxy.<\/li>\n<li>Concurrency \u2014 Simultaneous executing units \u2014 Drives throughput potential \u2014 Not same as throughput.<\/li>\n<li>Capacity \u2014 Max sustainable throughput \u2014 Planning metric \u2014 Often optimistic.<\/li>\n<li>Saturation \u2014 Resource fully utilized \u2014 Predicts throughput limits \u2014 Requires monitoring.<\/li>\n<li>Backpressure \u2014 Mechanism to slow producers \u2014 Protects downstream \u2014 Can cause cascading drops.<\/li>\n<li>Throttling \u2014 Intentional rate limiting \u2014 Controls costs and abuse \u2014 Misconfig leads to user impact.<\/li>\n<li>Autoscaling \u2014 Dynamic capacity adjustment \u2014 Maintains targets \u2014 Can oscillate if misconfigured.<\/li>\n<li>Load balancing \u2014 Distributes traffic to increase throughput \u2014 Critical for horizontal scale \u2014 Misbalance creates hot nodes.<\/li>\n<li>Sharding \u2014 Split data to parallelize throughput \u2014 Improves scalability \u2014 Adds complexity.<\/li>\n<li>Batching \u2014 Grouping operations to reduce overhead \u2014 Boosts throughput \u2014 Increases latency per item.<\/li>\n<li>Queue depth \u2014 Number of waiting tasks \u2014 Indicator of insufficient throughput \u2014 Not always bad if bounded.<\/li>\n<li>Producer\/consumer ratio \u2014 Balance affects throughput \u2014 Unbalanced causes backlog \u2014 Needs tuning.<\/li>\n<li>P99\/P90 latency \u2014 Tail latency percentiles \u2014 Affects effective throughput \u2014 High tails reduce usable throughput.<\/li>\n<li>Head-of-line blocking \u2014 One slow operation blocks others \u2014 Reduces throughput \u2014 Use concurrency isolation.<\/li>\n<li>Circuit breaker \u2014 Fails fast to preserve throughput \u2014 Protects system \u2014 Aggressive config hides failures.<\/li>\n<li>Retry storms \u2014 Retries multiplying load \u2014 Crash throughput \u2014 Implement jittered backoff.<\/li>\n<li>Token bucket \u2014 Rate-limiting algorithm \u2014 Controls throughput \u2014 Poor bucket sizing harms UX.<\/li>\n<li>Leaky bucket \u2014 Alternative rate limiter \u2014 Smooths bursts \u2014 Can add delay.<\/li>\n<li>Rate limiter \u2014 Implements throughput caps \u2014 Prevents overload \u2014 Needs grace strategies.<\/li>\n<li>Service mesh \u2014 Adds observability and control \u2014 Helps route and throttle \u2014 Adds latency overhead.<\/li>\n<li>Sidecar \u2014 Observability or proxy per pod \u2014 Enables per-instance control \u2014 Resource overhead affects throughput.<\/li>\n<li>Packet loss \u2014 Network-level issue that reduces throughput \u2014 Impacts retries \u2014 Monitor retransmits.<\/li>\n<li>Bandwidth \u2014 Max data per time network can move \u2014 Upper bound for throughput \u2014 Not application-level throughput.<\/li>\n<li>I\/O bound \u2014 Work limited by disk or network IO \u2014 Throughput tied to IO \u2014 Optimize IO or use caching.<\/li>\n<li>CPU bound \u2014 Work limited by CPU cycles \u2014 Scale horizontally or optimize code \u2014 Consider async.<\/li>\n<li>Memory bound \u2014 Insufficient memory reduces throughput \u2014 Tune GC and allocations \u2014 Use memory-efficient data structures.<\/li>\n<li>GC pause \u2014 Stop-the-world events reducing throughput \u2014 Tune GC or reduce heap churn \u2014 Monitor GC metrics.<\/li>\n<li>Hot key \u2014 Frequently accessed key causing imbalance \u2014 Lowers throughput of other keys \u2014 Repartition or cache.<\/li>\n<li>Quality of Service (QoS) \u2014 Prioritization affecting throughput allocation \u2014 Ensures critical paths remain performant \u2014 Cheap jobs can starve others if misapplied.<\/li>\n<li>Observability pipeline \u2014 Metrics, logs, traces collection system \u2014 Essential to measure throughput \u2014 High-cardinality metrics cost.<\/li>\n<li>Telemetry sampling \u2014 Reduces observability cost \u2014 Must not hide throughput variance \u2014 Carefully choose sampling rates.<\/li>\n<li>Error budget \u2014 Tolerance for SLO misses \u2014 Guides throughput trade-offs \u2014 Misused budgets can mask systemic issues.<\/li>\n<li>Canary deployment \u2014 Small release to validate throughput impact \u2014 Lowers risk \u2014 Coverage depends on traffic mirroring.<\/li>\n<li>Cost-per-throughput \u2014 Money spent per unit work \u2014 Important for cloud optimization \u2014 Over-optimizing can reduce resilience.<\/li>\n<li>Predictive scaling \u2014 Forecast-based scaling for throughput \u2014 Reduces cold starts \u2014 Requires reliable demand signals.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How to Measure Throughput (Metrics, SLIs, SLOs) (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Metric\/SLI<\/th>\n<th>What it tells you<\/th>\n<th>How to measure<\/th>\n<th>Starting target<\/th>\n<th>Gotchas<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>M1<\/td>\n<td>Requests\/sec<\/td>\n<td>Rate of incoming requests<\/td>\n<td>Count successful requests per sec<\/td>\n<td>Baseline traffic avg x1.5<\/td>\n<td>Counts include retries<\/td>\n<\/tr>\n<tr>\n<td>M2<\/td>\n<td>Successes\/sec<\/td>\n<td>Rate of successful completes<\/td>\n<td>Count successful responses per sec<\/td>\n<td>Match business transactions<\/td>\n<td>May not capture partial failure<\/td>\n<\/tr>\n<tr>\n<td>M3<\/td>\n<td>Processed items\/sec<\/td>\n<td>Worker throughput<\/td>\n<td>Count completed jobs per sec<\/td>\n<td>Enough to keep queue bounded<\/td>\n<td>Batch size affects rate<\/td>\n<\/tr>\n<tr>\n<td>M4<\/td>\n<td>Queue depth<\/td>\n<td>Backlog size indicating demand<\/td>\n<td>Gauge queue length<\/td>\n<td>Keep below worker capacity<\/td>\n<td>Temporary spikes are ok<\/td>\n<\/tr>\n<tr>\n<td>M5<\/td>\n<td>Bytes\/sec<\/td>\n<td>Data throughput for streams<\/td>\n<td>Sum bytes processed per sec<\/td>\n<td>Depends on payload<\/td>\n<td>Compression skews numbers<\/td>\n<\/tr>\n<tr>\n<td>M6<\/td>\n<td>Consumer lag<\/td>\n<td>Delay between produce and consume<\/td>\n<td>Offset difference or timestamps<\/td>\n<td>Minimal for real-time systems<\/td>\n<td>Clock drift affects measure<\/td>\n<\/tr>\n<tr>\n<td>M7<\/td>\n<td>Errors\/sec<\/td>\n<td>Failed operations per sec<\/td>\n<td>Count error responses per sec<\/td>\n<td>Low relative to throughput<\/td>\n<td>High throughput can increase errors<\/td>\n<\/tr>\n<tr>\n<td>M8<\/td>\n<td>Throughput per instance<\/td>\n<td>Per-node processing capability<\/td>\n<td>Requests\/sec per pod\/instance<\/td>\n<td>Ensure headroom for bursts<\/td>\n<td>Hidden when autoscaling hides limits<\/td>\n<\/tr>\n<tr>\n<td>M9<\/td>\n<td>Saturation metrics<\/td>\n<td>Resource bottleneck signals<\/td>\n<td>CPU, mem%, IO wait<\/td>\n<td>Keep CPU below 70% typical<\/td>\n<td>Different envs vary<\/td>\n<\/tr>\n<tr>\n<td>M10<\/td>\n<td>Tail throughput<\/td>\n<td>Throughput under p99 latency conditions<\/td>\n<td>Rate during tail events<\/td>\n<td>Maintain acceptable degr rate<\/td>\n<td>Hard to collect without high-res metrics<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Best tools to measure Throughput<\/h3>\n\n\n\n<p>Choose tools that integrate with platform and produce high-cardinality but cost-managed telemetry.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Prometheus + OpenMetrics<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Throughput: request rates, counters, per-instance throughput.<\/li>\n<li>Best-fit environment: Kubernetes and cloud VMs.<\/li>\n<li>Setup outline:<\/li>\n<li>Instrument apps with client libraries.<\/li>\n<li>Expose \/metrics endpoint.<\/li>\n<li>Configure scrape jobs and retention.<\/li>\n<li>Use recording rules for rate() functions.<\/li>\n<li>Export to long-term store if needed.<\/li>\n<li>Strengths:<\/li>\n<li>Flexible and queryable.<\/li>\n<li>Good for real-time alerting.<\/li>\n<li>Limitations:<\/li>\n<li>Not ideal for high-cardinality long-term storage without remote write.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Grafana (dashboards)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Throughput: visualization of rates and correlations.<\/li>\n<li>Best-fit environment: Any metrics backend.<\/li>\n<li>Setup outline:<\/li>\n<li>Connect to metrics and tracing stores.<\/li>\n<li>Create panels for request\/sec and queue depth.<\/li>\n<li>Add annotations for deploys.<\/li>\n<li>Strengths:<\/li>\n<li>Dashboarding flexibility.<\/li>\n<li>Alerting integrations.<\/li>\n<li>Limitations:<\/li>\n<li>Visualization only; not a metric source.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Distributed tracing (OpenTelemetry)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Throughput: request flows and per-step durations.<\/li>\n<li>Best-fit environment: Microservices and serverless.<\/li>\n<li>Setup outline:<\/li>\n<li>Instrument services for traces.<\/li>\n<li>Capture spans for queuing and processing.<\/li>\n<li>Aggregate trace rates.<\/li>\n<li>Strengths:<\/li>\n<li>Correlates latency and throughput.<\/li>\n<li>Root-cause analysis for throughput drops.<\/li>\n<li>Limitations:<\/li>\n<li>Sampling reduces absolute counts unless instrumentation includes counters.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Cloud provider monitoring (varies)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Throughput: managed data like LB request\/sec, function invocations.<\/li>\n<li>Best-fit environment: Managed services and serverless.<\/li>\n<li>Setup outline:<\/li>\n<li>Enable platform metrics.<\/li>\n<li>Create alerts based on provider metrics.<\/li>\n<li>Strengths:<\/li>\n<li>Integrated with platform events.<\/li>\n<li>Limitations:<\/li>\n<li>Metric granularity and retention vary; some quotas apply.<\/li>\n<li>If unknown: Varies \/ Not publicly stated<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Logging pipeline with aggregation (e.g., streaming aggregator)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for Throughput: aggregated request counts and payload volumes.<\/li>\n<li>Best-fit environment: High-volume ingestion systems.<\/li>\n<li>Setup outline:<\/li>\n<li>Emit structured logs with request markers.<\/li>\n<li>Use streaming aggregator to compute rates.<\/li>\n<li>Strengths:<\/li>\n<li>Handles arbitrary events and business throughput.<\/li>\n<li>Limitations:<\/li>\n<li>Higher cost; latency in compute.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Recommended dashboards &amp; alerts for Throughput<\/h3>\n\n\n\n<p>Executive dashboard<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels:<\/li>\n<li>Total successful transactions per minute \u2014 revenue correlation.<\/li>\n<li>7-day throughput trend \u2014 capacity planning.<\/li>\n<li>Error budget consumption \u2014 SLO health.<\/li>\n<li>Cost-per-throughput \u2014 business metric.<\/li>\n<li>Why: Provides leadership view on throughput health and business impact.<\/li>\n<\/ul>\n\n\n\n<p>On-call dashboard<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels:<\/li>\n<li>Current request\/sec and change rate \u2014 immediate signal.<\/li>\n<li>Queue depth and consumer lag \u2014 backlog indicator.<\/li>\n<li>Per-instance throughput and saturation metrics \u2014 pinpoint overloaded nodes.<\/li>\n<li>Recent deploys and incidents annotation \u2014 context for failures.<\/li>\n<li>Why: Focused view for immediate triage.<\/li>\n<\/ul>\n\n\n\n<p>Debug dashboard<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels:<\/li>\n<li>Per-endpoint request\/sec and p50\/p99 latency \u2014 isolate hot endpoints.<\/li>\n<li>Traces for recent slow requests \u2014 trace sampling.<\/li>\n<li>DB IOPS and latency \u2014 downstream bottlenecks.<\/li>\n<li>Network retransmits and packet drops \u2014 network issues.<\/li>\n<li>Why: Deep dive for root cause analysis.<\/li>\n<\/ul>\n\n\n\n<p>Alerting guidance<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What should page vs ticket:<\/li>\n<li>Page: sustained drop in successful transactions vs baseline or SLO breach predicted quickly, and severe queue runaway causing user-visible outages.<\/li>\n<li>Ticket: gradual capacity degradation, scheduled throughput tests failing without customer impact.<\/li>\n<li>Burn-rate guidance:<\/li>\n<li>Use burn-rate alerting for SLO-based throughput with multi-window evaluation; page if burn rate &gt; 2x for short windows and &gt;1.5x for longer windows.<\/li>\n<li>Noise reduction tactics:<\/li>\n<li>Group alerts by service and cause.<\/li>\n<li>Deduplicate alerts referencing same underlying signal.<\/li>\n<li>Use suppression windows during deploys and maintenance.<\/li>\n<li>Implement statistical anomaly detectors with manual thresholds fallback.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Implementation Guide (Step-by-step)<\/h2>\n\n\n\n<p>1) Prerequisites\n&#8211; Clear business throughput targets.\n&#8211; Observability stack (metrics, traces, logs) in place.\n&#8211; Deployment automation and rollback capability.\n&#8211; Capacity for load testing.<\/p>\n\n\n\n<p>2) Instrumentation plan\n&#8211; Add counters for request starts and successful completes.\n&#8211; Emit queue depth, enqueue and dequeue times.\n&#8211; Tag metrics with service, endpoint, region, and deployment id.\n&#8211; Ensure high-resolution metrics for short windows during tests.<\/p>\n\n\n\n<p>3) Data collection\n&#8211; Centralize metrics with retention appropriate for trend analysis.\n&#8211; Export to a long-term store for capacity planning.\n&#8211; Capture traces for representative pathways.<\/p>\n\n\n\n<p>4) SLO design\n&#8211; Choose SLI (e.g., successful transactions\/min).\n&#8211; Define SLO windows (30d\/90d) and starting targets.\n&#8211; Set error budget and escalation rules.<\/p>\n\n\n\n<p>5) Dashboards\n&#8211; Build executive, on-call, and debug dashboards.\n&#8211; Add deploy annotation and capacity projection panels.<\/p>\n\n\n\n<p>6) Alerts &amp; routing\n&#8211; Create alert rules for SLO burn, queue growth, and resource saturation.\n&#8211; Route to correct teams with runbook links.<\/p>\n\n\n\n<p>7) Runbooks &amp; automation\n&#8211; Document step-by-step mitigation for throughput incidents.\n&#8211; Automate common fixes: scale-up, purge dead consumers, adjust rate limiters.<\/p>\n\n\n\n<p>8) Validation (load\/chaos\/game days)\n&#8211; Run capacity tests that match traffic patterns.\n&#8211; Conduct chaos games that throttle downstream services to test resilience.\n&#8211; Execute game days simulating autoscaler failure.<\/p>\n\n\n\n<p>9) Continuous improvement\n&#8211; Postmortem after incidents.\n&#8211; Regularly review SLOs and adjust targets.\n&#8211; Use forecasting to anticipate capacity needs.<\/p>\n\n\n\n<p>Checklists<\/p>\n\n\n\n<p>Pre-production checklist<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Instrumentation added for all paths.<\/li>\n<li>Load test scenarios defined and executed.<\/li>\n<li>Dashboards created and sanity-checked.<\/li>\n<li>Alerts configured and tested with paging disabled.<\/li>\n<li>Runbooks drafted.<\/li>\n<\/ul>\n\n\n\n<p>Production readiness checklist<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Autoscaling rules verified under real load.<\/li>\n<li>Observability retention set for trend analysis.<\/li>\n<li>Chaos-tested fallback and circuit breakers present.<\/li>\n<li>On-call team trained on runbooks.<\/li>\n<\/ul>\n\n\n\n<p>Incident checklist specific to Throughput<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify cross-correlation: requests\/sec, queue depth, per-instance saturation.<\/li>\n<li>Determine if recent deploy correlates with issue and consider rollback.<\/li>\n<li>Validate downstream service health and rate limits.<\/li>\n<li>Implement immediate mitigations (scale, apply rate limits).<\/li>\n<li>Capture traces and metrics for postmortem.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Use Cases of Throughput<\/h2>\n\n\n\n<p>Provide 8\u201312 use cases with context etc.<\/p>\n\n\n\n<p>1) High-volume API gateway\n&#8211; Context: Public API handling thousands of calls\/sec.\n&#8211; Problem: Maintain consistent processing while protecting backend.\n&#8211; Why Throughput helps: Sets autoscaling and rate-limit thresholds.\n&#8211; What to measure: requests\/sec, 429 rate, backend success\/sec.\n&#8211; Typical tools: Load balancer metrics, API gateway metrics, Prometheus.<\/p>\n\n\n\n<p>2) Telemetry ingestion service\n&#8211; Context: IoT devices sending events in bursts.\n&#8211; Problem: Spikes cause DB overload and data loss.\n&#8211; Why Throughput helps: Design buffering and autoscaling policies.\n&#8211; What to measure: messages\/sec, queue depth, consumer lag.\n&#8211; Typical tools: Message broker, streaming aggregator, metrics.<\/p>\n\n\n\n<p>3) Payment processing\n&#8211; Context: Financial transactions with SLAs.\n&#8211; Problem: Must maintain throughput without sacrificing correctness.\n&#8211; Why Throughput helps: Ensure capacity during peak shopping events.\n&#8211; What to measure: transactions\/sec, error\/sec, downstream latency.\n&#8211; Typical tools: Tracing, strict SLIs, circuit breakers.<\/p>\n\n\n\n<p>4) Video streaming ingestion\n&#8211; Context: Live video ingest service.\n&#8211; Problem: Sustaining high bytes\/sec and preventing jitter.\n&#8211; Why Throughput helps: Monitor and provision network and edge caches.\n&#8211; What to measure: bytes\/sec, packet loss, p95 latency.\n&#8211; Typical tools: CDN metrics, network telemetry.<\/p>\n\n\n\n<p>5) Batch ETL pipelines\n&#8211; Context: Nightly data processing with SLA window.\n&#8211; Problem: Late completion impacts downstream reports.\n&#8211; Why Throughput helps: Tune parallelism and partitioning.\n&#8211; What to measure: processed rows\/sec, stage durations.\n&#8211; Typical tools: Batch orchestration metrics and DB metrics.<\/p>\n\n\n\n<p>6) Serverless webhook handlers\n&#8211; Context: Serverless functions triggered by webhooks.\n&#8211; Problem: Cold starts and concurrency limits reduce throughput.\n&#8211; Why Throughput helps: Right-size concurrency and pre-warm strategies.\n&#8211; What to measure: invocations\/sec, concurrency, cold start rate.\n&#8211; Typical tools: Cloud function metrics and tracing.<\/p>\n\n\n\n<p>7) CI\/CD pipeline\n&#8211; Context: High developer velocity with many parallel builds.\n&#8211; Problem: Build queue grows, slowing developer feedback.\n&#8211; Why Throughput helps: Allocate runners and prioritize jobs.\n&#8211; What to measure: builds\/sec, queue length, avg build time.\n&#8211; Typical tools: CI metrics and runner autoscaling.<\/p>\n\n\n\n<p>8) Ad-serving system\n&#8211; Context: Real-time auction throughput at peak traffic.\n&#8211; Problem: Latency and throughput impact revenue per impression.\n&#8211; Why Throughput helps: Ensure sufficient bidder capacity and cache hit rates.\n&#8211; What to measure: bids\/sec, responses\/sec, p99 latency.\n&#8211; Typical tools: High-performance caching and low-latency infra.<\/p>\n\n\n\n<p>9) Database migration streaming\n&#8211; Context: Continuous replication with cutover window.\n&#8211; Problem: Migration needs steady throughput to catch up.\n&#8211; Why Throughput helps: Plan parallel streams and throttles.\n&#8211; What to measure: replication rows\/sec, lag, error rate.\n&#8211; Typical tools: CDC tools, streaming metrics.<\/p>\n\n\n\n<p>10) SaaS multi-tenant service\n&#8211; Context: Shared resources across tenants.\n&#8211; Problem: Noisy neighbor reduces throughput for others.\n&#8211; Why Throughput helps: Implement per-tenant rate limits and quotas.\n&#8211; What to measure: per-tenant requests\/sec, throttles.\n&#8211; Typical tools: Service mesh, per-tenant telemetry.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Scenario Examples (Realistic, End-to-End)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #1 \u2014 Kubernetes autoscaling for API throughput<\/h3>\n\n\n\n<p><strong>Context:<\/strong> An e-commerce API deployed on Kubernetes needs to handle holiday peaks.<br\/>\n<strong>Goal:<\/strong> Maintain 5,000 successful requests\/sec with p99 latency &lt; 500ms.<br\/>\n<strong>Why Throughput matters here:<\/strong> Business revenue depends on handling peak shopping traffic without failures.<br\/>\n<strong>Architecture \/ workflow:<\/strong> Ingress -&gt; API pods behind service mesh -&gt; Redis cache -&gt; Database cluster; HPA scales pods based on custom metric request\/sec and CPU.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Instrument API with counters for request start\/success and route labels.<\/li>\n<li>Expose metrics to Prometheus; create recording rules for per-pod requests\/sec.<\/li>\n<li>Configure HPA to use external metric requests\/sec per pod + CPU threshold.<\/li>\n<li>Add queue for long-running ops and worker deployment.<\/li>\n<li>Create canary deploy with 5% traffic mirror to validate throughput.<\/li>\n<li>Load test with production-like traffic pattern and adjust autoscaler cooldowns.\n<strong>What to measure:<\/strong> cluster-wide requests\/sec, per-pod throughput, queue depth, p99 latency.<br\/>\n<strong>Tools to use and why:<\/strong> Prometheus, Grafana, K8s HPA, OpenTelemetry for traces.<br\/>\n<strong>Common pitfalls:<\/strong> HPA latency causes underscale during spikes; missing pod readiness probes allow traffic to pods not yet warm.<br\/>\n<strong>Validation:<\/strong> Run synthetic peak loads and game day where autoscaler delayed.<br\/>\n<strong>Outcome:<\/strong> Stable throughput with controlled tail latency; reduced outage risk.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #2 \u2014 Serverless invoice ingestion pipeline<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Multi-tenant SaaS receives invoice webhooks processed by functions.<br\/>\n<strong>Goal:<\/strong> Sustain 2,000 events\/sec with per-tenant fairness and cost limits.<br\/>\n<strong>Why Throughput matters here:<\/strong> Real-time processing required for downstream billing analytics.<br\/>\n<strong>Architecture \/ workflow:<\/strong> API gateway -&gt; serverless functions -&gt; message queue -&gt; workers for heavy jobs; per-tenant quotas enforced at gateway.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Measure invocation\/sec at gateway and function level.<\/li>\n<li>Apply per-tenant rate limits using token bucket at gateway.<\/li>\n<li>Use queue for heavy downstream work; functions enqueue lightweight events.<\/li>\n<li>Monitor concurrency and cold start metrics; use warmers or provisioned concurrency where needed.<\/li>\n<li>Set SLOs for end-to-end processing time and throughput per tenant.\n<strong>What to measure:<\/strong> invocations\/sec, concurrency, queue depth, per-tenant throughput.<br\/>\n<strong>Tools to use and why:<\/strong> Cloud provider metrics, queue metrics, Prometheus or provider dashboard.<br\/>\n<strong>Common pitfalls:<\/strong> Provider concurrency limits throttle throughput; lack of quota enforcement allows noisy tenants to impact others.<br\/>\n<strong>Validation:<\/strong> Simulate tenant storms and observe enforced quotas.<br\/>\n<strong>Outcome:<\/strong> Predictable throughput, fair tenant experience, controlled spend.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #3 \u2014 Incident response: throughput regression post-deploy<\/h3>\n\n\n\n<p><strong>Context:<\/strong> After a deployment throughput drops 60% with higher p99 latency.<br\/>\n<strong>Goal:<\/strong> Rapidly identify cause and restore throughput.<br\/>\n<strong>Why Throughput matters here:<\/strong> Production users impacted and revenue at risk.<br\/>\n<strong>Architecture \/ workflow:<\/strong> Microservices; deployment introduced a new cache layer config.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>On-call receives SLO burn page for throughput drop.<\/li>\n<li>Check deploy annotations; correlate deploy time with metric change.<\/li>\n<li>Inspect per-service throughput and cache hit ratio metrics.<\/li>\n<li>Rollback canary if found abnormal; disable new cache config.<\/li>\n<li>Run postmortem with traces and load tests.\n<strong>What to measure:<\/strong> per-service requests\/sec, cache hit ratio, DB load.<br\/>\n<strong>Tools to use and why:<\/strong> Tracing, metrics, deploy logs.<br\/>\n<strong>Common pitfalls:<\/strong> Lack of deploy annotations slows correlation; missing per-pod metrics hide culprit.<br\/>\n<strong>Validation:<\/strong> After rollback, throughput restored; run a controlled canary to validate fix.<br\/>\n<strong>Outcome:<\/strong> Restored throughput and updated runbook for similar deploys.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #4 \u2014 Cost vs performance trade-off for streaming<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Company needs to lower cloud spend without degrading streaming throughput.<br\/>\n<strong>Goal:<\/strong> Reduce cost per GB processed while maintaining 95% of current throughput.<br\/>\n<strong>Why Throughput matters here:<\/strong> Maintain SLAs while optimizing cost.<br\/>\n<strong>Architecture \/ workflow:<\/strong> Producer -&gt; CDN -&gt; ingest cluster -&gt; stream processors.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Measure baseline bytes\/sec and cost breakdown.<\/li>\n<li>Introduce batching and compression to reduce bytes per event.<\/li>\n<li>Migrate cold pipelines to less expensive instance classes and test throughput.<\/li>\n<li>Implement predictive scaling so expensive nodes are used only during peaks.<\/li>\n<li>Validate with load tests and monitor tail latency.\n<strong>What to measure:<\/strong> bytes\/sec, cost per GB, p99 latency.<br\/>\n<strong>Tools to use and why:<\/strong> Cost accounting, metrics, load generator.<br\/>\n<strong>Common pitfalls:<\/strong> Compression increases CPU and may reduce throughput; wrong instance sizing causes noisy neighbors.<br\/>\n<strong>Validation:<\/strong> A\/B test changes on subset of traffic.<br\/>\n<strong>Outcome:<\/strong> Cost reduction with acceptable throughput impact.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Common Mistakes, Anti-patterns, and Troubleshooting<\/h2>\n\n\n\n<p>List 15\u201325 mistakes with Symptom -&gt; Root cause -&gt; Fix. Include &gt;=5 observability pitfalls.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Symptom: Throughput plateau despite adding pods -&gt; Root cause: DB connection limit reached -&gt; Fix: increase DB pool or add connection pooling proxy.<\/li>\n<li>Symptom: Sporadic throughput drops -&gt; Root cause: GC pauses -&gt; Fix: tune GC, reduce allocations, upgrade runtime.<\/li>\n<li>Symptom: High per-instance throughput variance -&gt; Root cause: Uneven load balancing -&gt; Fix: use consistent hashing or adjust LB weights.<\/li>\n<li>Symptom: Queue depth increasing steadily -&gt; Root cause: Consumers too slow -&gt; Fix: scale consumers or optimize processing.<\/li>\n<li>Symptom: Large number of 429s -&gt; Root cause: Too-strict rate limits -&gt; Fix: adjust rate limiting or implement graceful degradation.<\/li>\n<li>Symptom: Throughput drop correlates with deploy -&gt; Root cause: Config regression -&gt; Fix: rollback and implement canaries.<\/li>\n<li>Symptom: Monitoring shows low throughput but users report slowness -&gt; Root cause: Observability sampling hides events -&gt; Fix: lower sampling for suspect endpoints.<\/li>\n<li>Symptom: Alerts flood during peak -&gt; Root cause: Low alert thresholds and no grouping -&gt; Fix: tune thresholds and group alerts.<\/li>\n<li>Symptom: Aggregate throughput looks healthy but some tenants suffer -&gt; Root cause: No per-tenant telemetry -&gt; Fix: add per-tenant metrics and quotas.<\/li>\n<li>Symptom: Autoscaler oscillates -&gt; Root cause: Using the wrong metric (CPU instead of request rate) -&gt; Fix: use throughput-based scaling or combine metrics with stabilization windows.<\/li>\n<li>Symptom: Unexpected cost spike when improving throughput -&gt; Root cause: Over-provisioning without efficiency measures -&gt; Fix: enable predictive scaling and tune instance types.<\/li>\n<li>Symptom: Throughput degrades under retries -&gt; Root cause: Retry storm amplifies load -&gt; Fix: add jittered exponential backoff and circuit breakers.<\/li>\n<li>Symptom: Dashboard shows inconsistent numbers across tools -&gt; Root cause: Metric collection delays or different aggregation windows -&gt; Fix: standardize TTLs and windows.<\/li>\n<li>Symptom: Tail latency causes effective throughput loss -&gt; Root cause: Single-thread bottleneck or locking -&gt; Fix: increase parallelism or remove locks.<\/li>\n<li>Symptom: Observability costs explode with throughput -&gt; Root cause: High-cardinality metrics unbounded -&gt; Fix: reduce cardinality and use aggregated recording rules.<\/li>\n<li>Symptom: Queue consumer lag after network incidents -&gt; Root cause: Partitioned network or zone outage -&gt; Fix: add cross-zone redundancy and fallback.<\/li>\n<li>Symptom: Throughput lower than expected in serverless -&gt; Root cause: Cold starts and concurrency limits -&gt; Fix: provisioned concurrency or pre-warming strategies.<\/li>\n<li>Symptom: Metrics missing during incident -&gt; Root cause: Logging\/metrics pipeline overloaded -&gt; Fix: degrade telemetry sampling and use critical metrics only.<\/li>\n<li>Symptom: Misaligned SLOs and business needs -&gt; Root cause: SLIs not tied to business transactions -&gt; Fix: redefine SLIs to business-critical work.<\/li>\n<li>Symptom: Observability alerts ignored -&gt; Root cause: Too many noisy alerts -&gt; Fix: reduce noise and implement smarter routing.<\/li>\n<li>Symptom: Throttling of legitimate traffic -&gt; Root cause: Generic rate limiting without whitelist -&gt; Fix: implement more nuanced policies.<\/li>\n<li>Symptom: Per-shard hot spot reduces overall throughput -&gt; Root cause: Skewed data distribution -&gt; Fix: add hashing or dynamic partitioning.<\/li>\n<li>Symptom: Test environment throughput not matching production -&gt; Root cause: Synthetic traffic mismatch -&gt; Fix: mirror production traffic patterns.<\/li>\n<li>Symptom: Too much reliance on vendor metrics -&gt; Root cause: Lack of in-app instrumentation -&gt; Fix: add application-level counters.<\/li>\n<li>Symptom: Postmortem lacks throughput context -&gt; Root cause: No saved historical metrics -&gt; Fix: store historical snapshots tied to incidents.<\/li>\n<\/ol>\n\n\n\n<p>Observability-specific pitfalls included in above: sampling hides events, metrics delays, high-cardinality costs, telemetry pipeline overload, and lack of in-app instrumentation.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Best Practices &amp; Operating Model<\/h2>\n\n\n\n<p>Ownership and on-call<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Assign throughput ownership to service teams; platform team owns autoscaling primitives.<\/li>\n<li>On-call rotations should include a throughput specialist or SRE to handle rate-related incidents.<\/li>\n<\/ul>\n\n\n\n<p>Runbooks vs playbooks<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Runbooks: step-by-step remediation for specific throughput incidents.<\/li>\n<li>Playbooks: broader strategies like scaling policy changes, deploy rollbacks, and backpressure design.<\/li>\n<\/ul>\n\n\n\n<p>Safe deployments (canary\/rollback)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Always run canaries for changes affecting request handling.<\/li>\n<li>Use traffic mirroring and rollout phases to detect throughput regressions early.<\/li>\n<li>Implement automatic rollback for severe throughput degradation.<\/li>\n<\/ul>\n\n\n\n<p>Toil reduction and automation<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Automate common scaling and mitigation steps.<\/li>\n<li>Use policy-driven autoscaling and throttles.<\/li>\n<li>Automate post-incident data collection for faster RCA.<\/li>\n<\/ul>\n\n\n\n<p>Security basics<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ensure rate limiters are resistant to tampering and authenticated client IDs for per-tenant fairness.<\/li>\n<li>Monitor for throughput anomalies that can indicate DDoS or abuse.<\/li>\n<li>Protect observability endpoints to avoid telemetry poisoning.<\/li>\n<\/ul>\n\n\n\n<p>Weekly\/monthly routines<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Weekly: Review throughput trends and alert hits.<\/li>\n<li>Monthly: Capacity planning review and autoscaler policy tuning.<\/li>\n<li>Quarterly: Blast-radius tests and forecast-driven scaling exercises.<\/li>\n<\/ul>\n\n\n\n<p>What to review in postmortems related to Throughput<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Time-series of requests\/sec, error\/sec, queue depth, and per-instance saturation.<\/li>\n<li>Deploy correlation and rollout behavior.<\/li>\n<li>Root cause and whether autoscaling behaved as expected.<\/li>\n<li>Action items: instrumentation gaps, policy changes, code fixes.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Tooling &amp; Integration Map for Throughput (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Category<\/th>\n<th>What it does<\/th>\n<th>Key integrations<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>I1<\/td>\n<td>Metrics backend<\/td>\n<td>Stores and queries time-series metrics<\/td>\n<td>Exporters, instrumented apps, dashboards<\/td>\n<td>Long-term retention matters<\/td>\n<\/tr>\n<tr>\n<td>I2<\/td>\n<td>Tracing<\/td>\n<td>Correlates latency to throughput paths<\/td>\n<td>Instrumentation, sampling, tracing store<\/td>\n<td>Useful for root cause of throughput drops<\/td>\n<\/tr>\n<tr>\n<td>I3<\/td>\n<td>Logging aggregator<\/td>\n<td>Counts events and bytes for throughput<\/td>\n<td>Structured logs and parsers<\/td>\n<td>High volume requires streaming aggregation<\/td>\n<\/tr>\n<tr>\n<td>I4<\/td>\n<td>Autoscaler<\/td>\n<td>Adjusts capacity based on metrics<\/td>\n<td>K8s, cloud provider APIs, metrics<\/td>\n<td>Tune stabilization and cooldown<\/td>\n<\/tr>\n<tr>\n<td>I5<\/td>\n<td>API gateway<\/td>\n<td>Enforces rate limits and quotas<\/td>\n<td>Auth systems, logging, metrics<\/td>\n<td>Frontline for throughput control<\/td>\n<\/tr>\n<tr>\n<td>I6<\/td>\n<td>Message broker<\/td>\n<td>Buffers and mediates throughput<\/td>\n<td>Producers, consumers, monitoring<\/td>\n<td>Backpressure and durability trade-offs<\/td>\n<\/tr>\n<tr>\n<td>I7<\/td>\n<td>Load generator<\/td>\n<td>Simulates traffic for testing throughput<\/td>\n<td>CI, pipelines, test infra<\/td>\n<td>Important for realistic load tests<\/td>\n<\/tr>\n<tr>\n<td>I8<\/td>\n<td>Cost analysis<\/td>\n<td>Connects throughput to spend<\/td>\n<td>Billing data and metrics<\/td>\n<td>Critical for cost-per-throughput optimizations<\/td>\n<\/tr>\n<tr>\n<td>I9<\/td>\n<td>CD\/CI platform<\/td>\n<td>Deploys changes affecting throughput<\/td>\n<td>Observe deploy annotations, canary tools<\/td>\n<td>Use for safe rollouts and validation<\/td>\n<\/tr>\n<tr>\n<td>I10<\/td>\n<td>Security WAF<\/td>\n<td>Protects against abusive throughput<\/td>\n<td>Edge logs, rate rules<\/td>\n<td>Can impact legitimate throughput if misconfigured<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What is the best unit for measuring throughput?<\/h3>\n\n\n\n<p>Use a unit aligned to your business: requests\/sec for APIs, messages\/sec for queues, bytes\/sec for streaming.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How do I choose throughput SLIs?<\/h3>\n\n\n\n<p>Pick measures that reflect successful work completion and correlate with user\/business outcomes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Should throughput SLOs be absolute or relative?<\/h3>\n\n\n\n<p>Typically relative to baseline and seasonality; absolute targets risk being unrealistic during peaks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How does autoscaling affect throughput?<\/h3>\n\n\n\n<p>Autoscaling increases capacity to maintain throughput but requires correct metrics, stabilization settings, and warm-up considerations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How often should I sample metrics for throughput?<\/h3>\n\n\n\n<p>High-resolution (1s\u201310s) during tests and incidents, lower during steady state; balance cost and signal fidelity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can throughput be increased without more resources?<\/h3>\n\n\n\n<p>Yes: batching, caching, sharding, and algorithmic improvements can raise throughput per resource.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What&#8217;s the relationship between latency and throughput?<\/h3>\n\n\n\n<p>They are coupled; high throughput can increase latency due to queuing, and high latency can reduce achievable throughput.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to prevent retry storms affecting throughput?<\/h3>\n\n\n\n<p>Implement exponential backoff with jitter and idempotency; use client-side throttles.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Do I need per-tenant throughput metrics?<\/h3>\n\n\n\n<p>Yes for multi-tenant fairness, noisy neighbor detection, and billing accuracy.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to test throughput realistically?<\/h3>\n\n\n\n<p>Mirror production traffic patterns, use recorded traces, and test at expected peak loads including burst behavior.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How long should I retain throughput metrics?<\/h3>\n\n\n\n<p>Depends on planning needs; at minimum retain short-term high-resolution metrics and aggregated long-term metrics for trend analysis.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What are safe deployment practices for throughput-sensitive systems?<\/h3>\n\n\n\n<p>Use canary deployments, traffic mirroring, and progressive rollout with automated rollback.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How do I tune rate limits?<\/h3>\n\n\n\n<p>Start with business-affecting thresholds, monitor 429s, and iterate; add grace for critical tenants.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is throughput measurement different for serverless?<\/h3>\n\n\n\n<p>Yes: provider limits, cold starts, and billing models require tracking invocations, concurrency, and effective per-invocation throughput.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to detect DDoS versus legitimate burst?<\/h3>\n\n\n\n<p>Correlate source diversity, authenticated client IDs, and business patterns; DDoS often shows high diversity and abnormal origin distribution.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to set alert thresholds for throughput?<\/h3>\n\n\n\n<p>Use relative deltas, SLO-based burn rates, and dynamic baselines rather than static numbers when possible.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can AI help manage throughput?<\/h3>\n\n\n\n<p>Yes: predictive scaling, anomaly detection, and adaptive rate-limiting use ML for better control but require robust validation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What telemetry is most critical during a throughput incident?<\/h3>\n\n\n\n<p>Requests\/sec, errors\/sec, queue depth, per-instance resource usage, and recent deploy annotations.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>Throughput is a foundational, business-critical metric tying architecture, observability, and operations together. Proper instrumentation, SLO alignment, autoscaling, and defensive design (rate limiting, backpressure) build resilient, cost-efficient systems. Continuous validation through load tests and game days prevents surprises.<\/p>\n\n\n\n<p>Next 7 days plan (5 bullets)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Day 1: Inventory current throughput metrics and tag gaps.<\/li>\n<li>Day 2: Add\/verify instrumentation for request\/sec and queue depth.<\/li>\n<li>Day 3: Build basic executive and on-call dashboards.<\/li>\n<li>Day 4: Run baseline load test and capture results.<\/li>\n<li>Day 5\u20137: Create\/validate alerts, craft runbooks, and schedule a game day.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Appendix \u2014 Throughput Keyword Cluster (SEO)<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Primary keywords<\/li>\n<li>Throughput<\/li>\n<li>System throughput<\/li>\n<li>Throughput measurement<\/li>\n<li>Throughput vs latency<\/li>\n<li>\n<p>Throughput architecture<\/p>\n<\/li>\n<li>\n<p>Secondary keywords<\/p>\n<\/li>\n<li>Throughput SLI SLO<\/li>\n<li>Throughput in cloud<\/li>\n<li>Throughput monitoring<\/li>\n<li>Throughput autoscaling<\/li>\n<li>\n<p>Throughput best practices<\/p>\n<\/li>\n<li>\n<p>Long-tail questions<\/p>\n<\/li>\n<li>How to measure throughput in Kubernetes<\/li>\n<li>What is throughput per second vs latency<\/li>\n<li>How to calculate throughput for APIs<\/li>\n<li>How to improve throughput without scaling<\/li>\n<li>Why is throughput dropping after deployment<\/li>\n<li>How to set throughput SLOs<\/li>\n<li>How does backpressure affect throughput<\/li>\n<li>What metrics indicate throughput bottleneck<\/li>\n<li>How to test throughput with realistic traffic<\/li>\n<li>How to detect throughput regressions in CI\/CD<\/li>\n<li>How to handle throughput for multi-tenant systems<\/li>\n<li>How to throttle requests to preserve throughput<\/li>\n<li>What is the relationship between throughput and availability<\/li>\n<li>How to design throughput-resilient systems<\/li>\n<li>\n<p>How to use tracing to troubleshoot throughput issues<\/p>\n<\/li>\n<li>\n<p>Related terminology<\/p>\n<\/li>\n<li>Requests per second<\/li>\n<li>Transactions per second<\/li>\n<li>Messages per second<\/li>\n<li>Bytes per second<\/li>\n<li>IOPS<\/li>\n<li>Queue depth<\/li>\n<li>Consumer lag<\/li>\n<li>Head-of-line blocking<\/li>\n<li>Rate limiter<\/li>\n<li>Token bucket<\/li>\n<li>Leaky bucket<\/li>\n<li>Autoscaler<\/li>\n<li>HPA<\/li>\n<li>Provisioned concurrency<\/li>\n<li>Backpressure<\/li>\n<li>Circuit breaker<\/li>\n<li>Thundering herd<\/li>\n<li>Cache hit ratio<\/li>\n<li>Hot partition<\/li>\n<li>Sharding<\/li>\n<li>Batching<\/li>\n<li>Load balancing<\/li>\n<li>Observability pipeline<\/li>\n<li>Telemetry sampling<\/li>\n<li>P99 latency<\/li>\n<li>Error budget<\/li>\n<li>Burn rate<\/li>\n<li>Canary deployment<\/li>\n<li>Traffic mirroring<\/li>\n<li>Predictive scaling<\/li>\n<li>Cost-per-throughput<\/li>\n<li>Streaming ingest<\/li>\n<li>Event-driven throughput<\/li>\n<li>Serverless throughput<\/li>\n<li>CDN throughput<\/li>\n<li>Database throughput<\/li>\n<li>Network throughput<\/li>\n<li>Bandwidth limits<\/li>\n<li>Saturation metrics<\/li>\n<li>Resource contention<\/li>\n<li>Load generator<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>&#8212;<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[375],"tags":[],"class_list":["post-2537","post","type-post","status-publish","format-standard","hentry","category-what-is-series"],"_links":{"self":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/2537","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=2537"}],"version-history":[{"count":1,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/2537\/revisions"}],"predecessor-version":[{"id":2943,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/2537\/revisions\/2943"}],"wp:attachment":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=2537"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=2537"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=2537"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}