{"id":3578,"date":"2026-02-17T16:38:33","date_gmt":"2026-02-17T16:38:33","guid":{"rendered":"https:\/\/dataopsschool.com\/blog\/yarn\/"},"modified":"2026-02-17T16:38:33","modified_gmt":"2026-02-17T16:38:33","slug":"yarn","status":"publish","type":"post","link":"https:\/\/dataopsschool.com\/blog\/yarn\/","title":{"rendered":"What is YARN? 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>YARN is the resource management and job scheduling layer of Hadoop that separates resource allocation from data processing. Analogy: YARN is the cluster receptionist that assigns rooms and time slots to workers. Formal: YARN provides resource negotiation and application lifecycle management for distributed data processing frameworks.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">What is YARN?<\/h2>\n\n\n\n<p>YARN (Yet Another Resource Negotiator) is the cluster resource management and job scheduling architecture originally introduced in Hadoop 2.x. It is NOT a data processing engine itself but a platform that allows engines like MapReduce, Tez, and Spark to run as applications on a shared cluster. YARN manages resources, schedules containers, tracks application lifecycle, enforces queues and priorities, and provides a framework for multi-tenant compute in large data clusters.<\/p>\n\n\n\n<p>Key properties and constraints:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Centralized resource negotiation via ResourceManager and distributed NodeManagers.<\/li>\n<li>Container-based isolation for CPU, memory, and optionally GPUs.<\/li>\n<li>Queue-based multi-tenancy with capacity or fair schedulers.<\/li>\n<li>Fault-tolerance via application-level managers and recovery for ResourceManager in HA mode.<\/li>\n<li>Not designed as a full cloud-native orchestrator; limited pod-level isolation compared to Kubernetes.<\/li>\n<li>Performance sensitive to heartbeat frequency, container launch latency, and YARN scheduler configuration.<\/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>Works as the resource substrate in Hadoop and on-prem big data environments.<\/li>\n<li>Can integrate with Kubernetes via YARN-on-Kubernetes or run alongside Kubernetes in a hybrid stack.<\/li>\n<li>In SRE practices, YARN is viewed like any critical control plane: monitor RM health, NodeManager reachability, scheduler latency, container failures, and job SLA compliance.<\/li>\n<li>Useful when teams need tight locality for HDFS-based workloads, predictable queueing, and multi-tenant resource governance.<\/li>\n<\/ul>\n\n\n\n<p>Diagram description (text-only):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A single ResourceManager cluster fronting multiple NodeManagers.<\/li>\n<li>Users submit applications to the ResourceManager.<\/li>\n<li>ResourceManager assigns an ApplicationMaster per application.<\/li>\n<li>ApplicationMaster negotiates containers from ResourceManager.<\/li>\n<li>NodeManagers host containers and report heartbeats to ResourceManager.<\/li>\n<li>HDFS and object storage sit beside the cluster for data access.<\/li>\n<li>Monitoring and auth services (Kerberos) are cross-cutting.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">YARN in one sentence<\/h3>\n\n\n\n<p>YARN is the distributed resource negotiator that lets multiple data processing frameworks share a cluster by managing containers, scheduling, and application lifecycle.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">YARN 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 YARN<\/th>\n<th>Common confusion<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>T1<\/td>\n<td>Hadoop<\/td>\n<td>Hadoop is an ecosystem; YARN is Hadoop&#8217;s resource manager<\/td>\n<td>People call entire Hadoop &#8220;YARN&#8221;<\/td>\n<\/tr>\n<tr>\n<td>T2<\/td>\n<td>MapReduce<\/td>\n<td>MapReduce is a processing engine; YARN schedules its containers<\/td>\n<td>MapReduce often runs under YARN<\/td>\n<\/tr>\n<tr>\n<td>T3<\/td>\n<td>Kubernetes<\/td>\n<td>Kubernetes is a cloud-native orchestrator; YARN is for big data resource negotiation<\/td>\n<td>Both schedule containers but differ in scope<\/td>\n<\/tr>\n<tr>\n<td>T4<\/td>\n<td>Spark<\/td>\n<td>Spark is a data processing framework; YARN is one of its cluster managers<\/td>\n<td>Spark can run on YARN or Kubernetes<\/td>\n<\/tr>\n<tr>\n<td>T5<\/td>\n<td>HDFS<\/td>\n<td>HDFS is storage; YARN is compute orchestration<\/td>\n<td>Data locality is commonly conflated<\/td>\n<\/tr>\n<tr>\n<td>T6<\/td>\n<td>Mesos<\/td>\n<td>Mesos is a general cluster manager; YARN targets Hadoop workloads<\/td>\n<td>Overlap on scheduling but different APIs<\/td>\n<\/tr>\n<tr>\n<td>T7<\/td>\n<td>ResourceManager<\/td>\n<td>ResourceManager is a YARN component; YARN is the whole architecture<\/td>\n<td>RM often called YARN by mistake<\/td>\n<\/tr>\n<tr>\n<td>T8<\/td>\n<td>NodeManager<\/td>\n<td>NodeManager is a YARN agent; YARN includes RM and NM<\/td>\n<td>NM not a standalone product<\/td>\n<\/tr>\n<tr>\n<td>T9<\/td>\n<td>ApplicationMaster<\/td>\n<td>AM is per-app coordinator; YARN is the platform hosting AMs<\/td>\n<td>AM mistaken for scheduler<\/td>\n<\/tr>\n<tr>\n<td>T10<\/td>\n<td>Container<\/td>\n<td>Container is the execution unit; YARN manages containers<\/td>\n<td>People assume containers are Docker only<\/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 YARN matter?<\/h2>\n\n\n\n<p>Business impact:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Revenue: Data products and analytics pipelines driving pricing, recommendations, and reporting rely on predictable job completion. YARN controls job throughput and fairness, directly affecting time-to-insight and downstream revenue-facing features.<\/li>\n<li>Trust: Consistent SLAs for nightly ETL or real-time analytics maintain stakeholder trust in dashboards and models.<\/li>\n<li>Risk: Scheduler misconfiguration or resource starvation causes missed SLAs, regulatory reporting delays, and potential financial penalties.<\/li>\n<\/ul>\n\n\n\n<p>Engineering impact:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Incident reduction: Proper queueing and resource limits reduce noisy-neighbor incidents.<\/li>\n<li>Velocity: Multi-tenant scheduling allows separate teams to share hardware without blocking each other, improving resource utilization and deployment speed.<\/li>\n<li>Efficiency: Dynamic containers decrease resource waste compared to static allocations.<\/li>\n<\/ul>\n\n\n\n<p>SRE framing:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>SLIs\/SLOs: Useful SLIs include job success rate, job runtime P95, container allocation latency, and RM availability.<\/li>\n<li>Error budget: Use job SLA breaches to consume error budget; allow controlled testing windows.<\/li>\n<li>Toil\/on-call: Automate container restarts, auto-scaling of YARN cluster nodes, and alerting to reduce manual remediation.<\/li>\n<\/ul>\n\n\n\n<p>What breaks in production (realistic examples):<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Scheduler misconfiguration leads to a high-priority tenant monopolizing resources, starving nightly ETL.<\/li>\n<li>NodeManager memory leaks cause progressive node failures and container launch backlog.<\/li>\n<li>ResourceManager HA not configured; RM crash causes total job submission outage.<\/li>\n<li>Kerberos ticket renewal failure prevents authentication to HDFS, causing widespread job failures.<\/li>\n<li>Excessive container churn from bad application behavior triggers high GC and I\/O, increasing job latencies.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Where is YARN 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 YARN 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>Data layer<\/td>\n<td>Schedules compute near HDFS blocks<\/td>\n<td>Container allocation, locality metrics, job runtime<\/td>\n<td>Hadoop HDFS, MapReduce<\/td>\n<\/tr>\n<tr>\n<td>L2<\/td>\n<td>Batch processing<\/td>\n<td>Job executor for ETL pipelines<\/td>\n<td>Job success, queue wait time, container failures<\/td>\n<td>Airflow, Oozie<\/td>\n<\/tr>\n<tr>\n<td>L3<\/td>\n<td>ML training<\/td>\n<td>Resource manager for distributed training<\/td>\n<td>GPU allocation, memory, job preemption<\/td>\n<td>Spark, TensorFlow on YARN<\/td>\n<\/tr>\n<tr>\n<td>L4<\/td>\n<td>Hybrid cloud<\/td>\n<td>On-prem cluster managed with cloud burst<\/td>\n<td>Node provisioning events, cluster utilization<\/td>\n<td>Cloud APIs, YARN federations<\/td>\n<\/tr>\n<tr>\n<td>L5<\/td>\n<td>CI\/CD<\/td>\n<td>Integration tests that require large data sets<\/td>\n<td>Job durations, queue depth<\/td>\n<td>Jenkins, GitLab CI<\/td>\n<\/tr>\n<tr>\n<td>L6<\/td>\n<td>Observability<\/td>\n<td>Telemetry source for cluster health<\/td>\n<td>RM NMs heartbeats, scheduler latency<\/td>\n<td>Prometheus, Grafana<\/td>\n<\/tr>\n<tr>\n<td>L7<\/td>\n<td>Security<\/td>\n<td>Enforces Kerberos and ACLs for jobs<\/td>\n<td>Auth failures, audit logs<\/td>\n<td>Kerberos, Ranger<\/td>\n<\/tr>\n<tr>\n<td>L8<\/td>\n<td>Kubernetes interop<\/td>\n<td>YARN-on-Kubernetes or K8s as scheduler<\/td>\n<td>Pod\/container mapping metrics<\/td>\n<td>Kubernetes, YARN adapters<\/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 YARN?<\/h2>\n\n\n\n<p>When it\u2019s necessary:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>You have HDFS-centric data locality needs that improve job performance.<\/li>\n<li>You run legacy Hadoop ecosystems or tools designed for YARN.<\/li>\n<li>You need robust queue-based multi-tenancy with capacity\/fair scheduling.<\/li>\n<\/ul>\n\n\n\n<p>When it\u2019s optional:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>New cloud-native apps where Kubernetes-native scheduling suffices.<\/li>\n<li>Batch jobs that can run on cloud managed services with autoscaling.<\/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>For generic microservice orchestration or stateless HTTP workloads.<\/li>\n<li>If you require fine-grained pod-level networking, service mesh, or Kubernetes CRD-based operators.<\/li>\n<li>When running small, ephemeral functions where serverless is cheaper.<\/li>\n<\/ul>\n\n\n\n<p>Decision checklist:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If you depend on HDFS locality and run MapReduce\/Tez jobs -&gt; Use YARN.<\/li>\n<li>If you prefer cloud-managed autoscaling and container orchestration -&gt; Consider Kubernetes first.<\/li>\n<li>If you have GPU-heavy ML on cloud-native frameworks -&gt; Evaluate Kubernetes or managed ML services.<\/li>\n<\/ul>\n\n\n\n<p>Maturity ladder:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Beginner: Single-cluster YARN with default CapacityScheduler and basic monitoring.<\/li>\n<li>Intermediate: HA ResourceManager, custom queues, preemption, Kerberos, and Prometheus metrics.<\/li>\n<li>Advanced: YARN federation, mixed on-prem and cloud bursting, GPU scheduling, autoscaling, and integrated SRE runbooks.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How does YARN work?<\/h2>\n\n\n\n<p>Components and workflow:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ResourceManager (RM): Global scheduler and cluster resource authority.<\/li>\n<li>NodeManager (NM): Per-node agent that launches and monitors containers.<\/li>\n<li>ApplicationMaster (AM): Per-application coordinator that negotiates with RM to request containers and tracks job progress.<\/li>\n<li>Containers: Execution units with allocated CPU and memory, running tasks.<\/li>\n<li>Scheduler: Implements capacity, fair, or FIFO scheduling policies inside RM.<\/li>\n<li>Timeline Server \/ JobHistory: Stores application logs and job metadata.<\/li>\n<\/ul>\n\n\n\n<p>Typical workflow:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>User submits an application to the ResourceManager.<\/li>\n<li>RM allocates a container for the ApplicationMaster and launches AM on a NodeManager.<\/li>\n<li>AM registers with RM and requests containers for tasks.<\/li>\n<li>RM assigns containers based on available resources and scheduler policy.<\/li>\n<li>NM launches containers and reports health back to RM.<\/li>\n<li>AM coordinates task execution and handles job-level failures and retries.<\/li>\n<li>On completion, AM informs RM; logs and history are persisted.<\/li>\n<\/ol>\n\n\n\n<p>Data flow and lifecycle:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Heartbeats: NMs send periodic heartbeats to RM with container statuses and resource availability.<\/li>\n<li>Allocation requests: AMs request and release containers dynamically.<\/li>\n<li>Container lifecycle: Allocated -&gt; Launched -&gt; Running -&gt; Completed\/Failed.<\/li>\n<li>Recovery: RM HA or AM recovery mechanisms re-establish state after failures.<\/li>\n<\/ul>\n\n\n\n<p>Edge cases and failure modes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Slow heartbeats cause RM to mark NMs dead and evacuate containers.<\/li>\n<li>AM crash leaves tasks unmanaged unless AM recovery is enabled.<\/li>\n<li>Misestimated container sizes cause OOMs or CPU contention.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Typical architecture patterns for YARN<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Single-cluster dedicated for data platform: Use for centralized analytics teams.<\/li>\n<li>Multi-tenant cluster with capacity scheduler: Use when multiple business units share cluster.<\/li>\n<li>YARN federation: Multiple YARN clusters federated for scale and isolation.<\/li>\n<li>YARN-on-Kubernetes: Run NodeManagers as pods to leverage cloud-native infra.<\/li>\n<li>Hybrid burst model: On-prem YARN with cloud burst to ephemeral YARN clusters in cloud.<\/li>\n<li>GPU-augmented YARN: NodeManagers advertise GPUs and scheduler uses GPU-aware allocation.<\/li>\n<\/ul>\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>RM crash<\/td>\n<td>No job submissions accepted<\/td>\n<td>Single RM without HA<\/td>\n<td>Enable RM HA and automatic failover<\/td>\n<td>RM uptime, leader changes<\/td>\n<\/tr>\n<tr>\n<td>F2<\/td>\n<td>NodeManager down<\/td>\n<td>Containers lost and rescheduled<\/td>\n<td>NM process OOM or host crash<\/td>\n<td>Auto-restart NM and cordon node for investigation<\/td>\n<td>NM heartbeats missing<\/td>\n<\/tr>\n<tr>\n<td>F3<\/td>\n<td>Container OOM<\/td>\n<td>Task failures with OOM logs<\/td>\n<td>Wrong memory settings for container<\/td>\n<td>Tune memory or enable swapless configs<\/td>\n<td>Container exit codes and logs<\/td>\n<\/tr>\n<tr>\n<td>F4<\/td>\n<td>Scheduler starvation<\/td>\n<td>Low-priority jobs never start<\/td>\n<td>Misconfigured queues or weights<\/td>\n<td>Adjust queue capacities and enable preemption<\/td>\n<td>Queue wait time percentiles<\/td>\n<\/tr>\n<tr>\n<td>F5<\/td>\n<td>Kerberos failures<\/td>\n<td>Authentication errors for many apps<\/td>\n<td>Expired tickets or KDC unavailable<\/td>\n<td>Monitor ticket renewal and KDC HA<\/td>\n<td>Auth failure rate in logs<\/td>\n<\/tr>\n<tr>\n<td>F6<\/td>\n<td>Excessive container churn<\/td>\n<td>High CPU and IO churn<\/td>\n<td>Bad application retry or short-lived tasks<\/td>\n<td>Batch tasks into fewer containers<\/td>\n<td>Container start rate metric<\/td>\n<\/tr>\n<tr>\n<td>F7<\/td>\n<td>Log server overload<\/td>\n<td>Delays in job history availability<\/td>\n<td>Central log sink overloaded<\/td>\n<td>Scale timeline or offload logs to object store<\/td>\n<td>Timeline processing lag<\/td>\n<\/tr>\n<tr>\n<td>F8<\/td>\n<td>Disk pressure<\/td>\n<td>NM rejects container starts<\/td>\n<td>Local disk fill from logs or shuffle<\/td>\n<td>Clean up logs and temporary directories<\/td>\n<td>Node disk utilization<\/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 YARN<\/h2>\n\n\n\n<p>(Glossary 40+ terms. Each entry: Term \u2014 1\u20132 line definition \u2014 why it matters \u2014 common pitfall)<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>ResourceManager \u2014 Central authority handling resource allocation and scheduling \u2014 It controls cluster-wide scheduling \u2014 Confused with ApplicationMaster.<\/li>\n<li>NodeManager \u2014 Node agent that launches and monitors containers \u2014 Executes containers locally \u2014 Fails when disk fills.<\/li>\n<li>ApplicationMaster \u2014 Per-application coordinator that negotiates for containers \u2014 Manages app-specific lifecycle \u2014 Rarely implemented correctly by custom frameworks.<\/li>\n<li>Container \u2014 Allocated compute unit with CPU and memory \u2014 Core execution unit \u2014 Assumed to be Docker only by mistake.<\/li>\n<li>Scheduler \u2014 Policy engine inside RM (Capacity\/Fair\/FIFO) \u2014 Controls fairness and capacity \u2014 Misconfiguration causes starvation.<\/li>\n<li>CapacityScheduler \u2014 Queue-oriented scheduler for multi-tenancy \u2014 Good for rigid quotas \u2014 Complex to tune.<\/li>\n<li>FairScheduler \u2014 Attempts to equalize resources across jobs \u2014 Better for flexible sharing \u2014 Can lead to instability without limits.<\/li>\n<li>FIFO Scheduler \u2014 Simple first-in-first-out policy \u2014 Predictable for single-tenant runs \u2014 Not fair for multi-tenant clusters.<\/li>\n<li>YARN queue \u2014 Logical partition of resources for tenants \u2014 Enforces limits and priorities \u2014 Overly strict queues block utilization.<\/li>\n<li>ApplicationAttempt \u2014 Single try of an ApplicationMaster \u2014 Useful for tracking retries \u2014 Multiple attempts hide root failure.<\/li>\n<li>ContainerToken \u2014 Security token for container launches \u2014 Prevents unauthorized starts \u2014 Token expiry issues cause failures.<\/li>\n<li>Heartbeat \u2014 Periodic NM message to RM with status \u2014 Essential for liveness detection \u2014 Heartbeat lag causes false node death.<\/li>\n<li>NodeLabel \u2014 Node grouping with labels for specialized resources \u2014 Enables workload placement \u2014 Label misuse leads to fragmentation.<\/li>\n<li>Preemption \u2014 Forcible reclamation of containers to satisfy higher priority queues \u2014 Protects SLAs \u2014 Causes job interruption.<\/li>\n<li>ResourceRequest \u2014 AM request for containers with size and locality \u2014 Drives allocation decisions \u2014 Poor estimation harms efficiency.<\/li>\n<li>Locality \u2014 Data proximity classification like NODE\/RACK\/ANY \u2014 Impacts job performance \u2014 Ignoring locality increases network IO.<\/li>\n<li>YARN federation \u2014 Combining clusters to scale and isolate workloads \u2014 Improves isolation and scale \u2014 Complex to operate.<\/li>\n<li>High Availability (HA) \u2014 RM configured for failover \u2014 Prevents single-point of control \u2014 Requires quorum and shared storage.<\/li>\n<li>Timeline Server \u2014 Stores job and application metadata \u2014 Useful for audits and debugging \u2014 Can become bottleneck.<\/li>\n<li>JobHistoryServer \u2014 Provides completed job logs and counters \u2014 Necessary for postmortem \u2014 If down, historical data unavailable.<\/li>\n<li>Shuffle \u2014 MapReduce data transfer stage \u2014 Heavy network and disk use \u2014 Causes disk pressure if unbounded.<\/li>\n<li>AM Container \u2014 The container that runs ApplicationMaster \u2014 Critical for application coordination \u2014 AM failure often kills job.<\/li>\n<li>Client \u2014 The user-facing submission client \u2014 Starts application submission flow \u2014 Misconfigured clients lead to failed submissions.<\/li>\n<li>Distributed Cache \u2014 Mechanism to distribute small files to nodes \u2014 Useful for job dependencies \u2014 Cache bloat causes disk issues.<\/li>\n<li>Resource Calculator \u2014 Calculates CPU\/memory units on nodes \u2014 Ensures correct allocation \u2014 Wrong config misallocates resources.<\/li>\n<li>NodeManager log aggregation \u2014 Collects and ships container logs to central store \u2014 Essential for debugging \u2014 Fails when log dirs full.<\/li>\n<li>User ACLs \u2014 Access control for job submission and queue operations \u2014 Ensures tenant isolation \u2014 Misconfigured ACLs block users.<\/li>\n<li>Kerberos \u2014 Authentication protocol commonly used with YARN \u2014 Secures identity and tickets \u2014 Ticket expiry breaks jobs.<\/li>\n<li>Delegation tokens \u2014 Short-lived credentials for accessing HDFS \u2014 Avoids long-lived keys \u2014 Expiry during job causes failures.<\/li>\n<li>Shuffle Service \u2014 Auxiliary service to serve intermediate data \u2014 Enables container restarts without losing shuffle \u2014 Unavailable shuffle service breaks map-reduce shuffle.<\/li>\n<li>NodeManager container executor \u2014 The process launching containers \u2014 Can be container-executor or Docker \u2014 Wrong permissions cause start failures.<\/li>\n<li>Disk Types \u2014 Local disk vs SSD or ephemeral \u2014 Affects shuffle performance \u2014 Using wrong disk type hurts throughput.<\/li>\n<li>CPU cores vcores \u2014 Virtual cores used for allocation \u2014 Matches container CPU shares \u2014 Mismatch causes contention.<\/li>\n<li>Memory overhead \u2014 Extra memory reserved beyond container limit \u2014 Prevents OOM at JVM level \u2014 Not accounting for it leads to crashes.<\/li>\n<li>Admission control \u2014 Limits to prevent overload on RM or NMs \u2014 Protects stability \u2014 Too strict blocks valid jobs.<\/li>\n<li>Resource isolation \u2014 Mechanisms to prevent noisy neighbors \u2014 Protects tenants \u2014 Incomplete isolation leads to interference.<\/li>\n<li>YarnClient API \u2014 Programmatic interface to interact with RM \u2014 Enables custom submissions \u2014 API changes can break clients.<\/li>\n<li>ApplicationReport \u2014 API object describing app state \u2014 Useful for monitoring \u2014 Misinterpreting fields causes wrong alerts.<\/li>\n<li>ContainerRetryPolicy \u2014 How containers retried on failure \u2014 Balances resilience and load \u2014 Aggressive retries cause churn.<\/li>\n<li>Queue Abuse \u2014 When users hog queues with many small jobs \u2014 Reduces cluster efficiency \u2014 Enforce quotas and throttling.<\/li>\n<li>FederationStateStore \u2014 Stores federation metadata \u2014 Enables multi-cluster view \u2014 Corruption affects federation routing.<\/li>\n<li>Resource Profiles \u2014 Profiles specifying different resource shapes \u2014 Useful for diverse workloads \u2014 Incorrect profiles cause mismatch.<\/li>\n<li>GPU scheduling \u2014 YARN extension to schedule GPUs \u2014 Enables ML workloads \u2014 Vendor and driver issues complicate use.<\/li>\n<li>NodeLabelsManager \u2014 Manages node labels in RM \u2014 Helps workload placement \u2014 Label leaks lead to misplacement.<\/li>\n<li>Yarn-Application-ACL \u2014 Application-level permissions \u2014 Controls cancel\/kill operations \u2014 Misconfig causes unauthorized kills.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How to Measure YARN (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>RM availability<\/td>\n<td>RM uptime and leader health<\/td>\n<td>RM leader metric and process up<\/td>\n<td>99.95% monthly<\/td>\n<td>RM HA must be enabled<\/td>\n<\/tr>\n<tr>\n<td>M2<\/td>\n<td>NM heartbeat success<\/td>\n<td>Node reachability to RM<\/td>\n<td>Heartbeat success rate per NM<\/td>\n<td>99.9%<\/td>\n<td>Network partitions skew metric<\/td>\n<\/tr>\n<tr>\n<td>M3<\/td>\n<td>Container allocation latency<\/td>\n<td>Time from request to container start<\/td>\n<td>Histogram of allocation-to-launch times<\/td>\n<td>P95 &lt; 5s<\/td>\n<td>High load increases latency<\/td>\n<\/tr>\n<tr>\n<td>M4<\/td>\n<td>Job success rate<\/td>\n<td>Percent of completed jobs without failures<\/td>\n<td>Completed successful jobs \/ total jobs<\/td>\n<td>99% for critical jobs<\/td>\n<td>Small retries inflate success<\/td>\n<\/tr>\n<tr>\n<td>M5<\/td>\n<td>Queue wait time<\/td>\n<td>Time job waits before first container<\/td>\n<td>Job submit to first container time<\/td>\n<td>P95 &lt; 10m for batch<\/td>\n<td>Bursty submissions spike waits<\/td>\n<\/tr>\n<tr>\n<td>M6<\/td>\n<td>Container OOM rate<\/td>\n<td>Frequency of OOM exits for containers<\/td>\n<td>Count of exit code OOM per time<\/td>\n<td>&lt;0.1%<\/td>\n<td>JVM heap vs container memory mismatch<\/td>\n<\/tr>\n<tr>\n<td>M7<\/td>\n<td>Scheduler utilization<\/td>\n<td>Percent of cluster resources used<\/td>\n<td>Allocated resources \/ total resources<\/td>\n<td>70\u201390%<\/td>\n<td>High utilization increases frag<\/td>\n<\/tr>\n<tr>\n<td>M8<\/td>\n<td>Preemption events<\/td>\n<td>Number of preemptions per period<\/td>\n<td>Preemption counter per queue<\/td>\n<td>Minimal for stable clusters<\/td>\n<td>Preemption may hide queue probs<\/td>\n<\/tr>\n<tr>\n<td>M9<\/td>\n<td>Log aggregation lag<\/td>\n<td>Time logs become available centrally<\/td>\n<td>Timestamp diff of log write<\/td>\n<td>&lt;5m<\/td>\n<td>Slow sinks or object store throttling<\/td>\n<\/tr>\n<tr>\n<td>M10<\/td>\n<td>Kerberos auth failures<\/td>\n<td>Auth errors count<\/td>\n<td>Count auth failure logs<\/td>\n<td>0 for critical systems<\/td>\n<td>Transient KDC issues common<\/td>\n<\/tr>\n<tr>\n<td>M11<\/td>\n<td>Container start rate<\/td>\n<td>Containers started per minute<\/td>\n<td>Start counter per minute<\/td>\n<td>Varies by workload<\/td>\n<td>Sudden spikes indicate churn<\/td>\n<\/tr>\n<tr>\n<td>M12<\/td>\n<td>RM GC pause time<\/td>\n<td>RM GC duration and frequency<\/td>\n<td>GC pause times for RM process<\/td>\n<td>Keep GC &lt;500ms<\/td>\n<td>JVM tuning may be needed<\/td>\n<\/tr>\n<tr>\n<td>M13<\/td>\n<td>Shuffle bytes per job<\/td>\n<td>Network and disk usage of shuffle<\/td>\n<td>Bytes transferred during shuffle<\/td>\n<td>Varies by job<\/td>\n<td>High shuffle implies poor partitioning<\/td>\n<\/tr>\n<tr>\n<td>M14<\/td>\n<td>GPU allocation ratio<\/td>\n<td>GPU containers allocated vs available<\/td>\n<td>Allocated GPUs \/ total GPUs<\/td>\n<td>80\u201395% for GPU pools<\/td>\n<td>Driver mismatches cause false free<\/td>\n<\/tr>\n<tr>\n<td>M15<\/td>\n<td>Application latency SLI<\/td>\n<td>P95 job completion time per class<\/td>\n<td>Job runtime percentiles<\/td>\n<td>Define per workload<\/td>\n<td>Outliers can skew mean<\/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 YARN<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Prometheus + JMX Exporter<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for YARN: RM and NM JVM metrics, scheduler counters, container allocations.<\/li>\n<li>Best-fit environment: On-prem and cloud clusters with Prometheus stack.<\/li>\n<li>Setup outline:<\/li>\n<li>Deploy JMX exporter on RM and NMs.<\/li>\n<li>Scrape RM and NM endpoints into Prometheus.<\/li>\n<li>Add recording rules for SLI calculations.<\/li>\n<li>Configure Grafana dashboards.<\/li>\n<li>Strengths:<\/li>\n<li>Flexible querying and alerting.<\/li>\n<li>Widely adopted and extensible.<\/li>\n<li>Limitations:<\/li>\n<li>Needs JVM metrics mapping and maintenance.<\/li>\n<li>Scrape scaling for large clusters.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Grafana<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for YARN: Visualization for SLOs and cluster health.<\/li>\n<li>Best-fit environment: Any environment with Prometheus or time-series backend.<\/li>\n<li>Setup outline:<\/li>\n<li>Create dashboards for RM, NM, applications, queues.<\/li>\n<li>Add alert panels and links to runbooks.<\/li>\n<li>Strengths:<\/li>\n<li>Rich visualization and templating.<\/li>\n<li>Limitations:<\/li>\n<li>Not a datastore; relies on metrics backend.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Elastic Stack (ELK)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for YARN: Centralized logs, audit trails, and RM event ingestion.<\/li>\n<li>Best-fit environment: Teams needing full-text search on logs.<\/li>\n<li>Setup outline:<\/li>\n<li>Ship NM and RM logs to Logstash or Beats.<\/li>\n<li>Index logs in Elasticsearch.<\/li>\n<li>Build Kibana dashboards for auth and error patterns.<\/li>\n<li>Strengths:<\/li>\n<li>Powerful search and analysis.<\/li>\n<li>Limitations:<\/li>\n<li>Storage and retention costs can be high.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Apache Ambari (or Ranger for security)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for YARN: Cluster management, config, and security integrations.<\/li>\n<li>Best-fit environment: Hadoop distributions and on-prem clusters.<\/li>\n<li>Setup outline:<\/li>\n<li>Install Ambari server and agents on nodes.<\/li>\n<li>Use Ambari metrics sink for dashboards.<\/li>\n<li>Strengths:<\/li>\n<li>Integrated management for Hadoop components.<\/li>\n<li>Limitations:<\/li>\n<li>Tightly coupled to Hadoop ecosystem.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Cloud provider monitoring<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for YARN: Node-level health, autoscaling events when running on cloud VMs.<\/li>\n<li>Best-fit environment: Cloud-burst or hybrid clusters.<\/li>\n<li>Setup outline:<\/li>\n<li>Integrate cloud metrics with Prometheus or alerting pipelines.<\/li>\n<li>Use cloud autoscaler to map YARN utilization to node scaling.<\/li>\n<li>Strengths:<\/li>\n<li>Native VM lifecycle insights.<\/li>\n<li>Limitations:<\/li>\n<li>May not capture application-level granularity.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Recommended dashboards &amp; alerts for YARN<\/h3>\n\n\n\n<p>Executive dashboard:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels: Cluster utilization, RM availability, job success rate, queue-level SLA compliance, cost by cluster.<\/li>\n<li>Why: Provides stakeholders a quick health summary and SLA posture.<\/li>\n<\/ul>\n\n\n\n<p>On-call dashboard:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels: RM process health, NM heartbeat map, top failing applications, queue wait times, top OOM jobs.<\/li>\n<li>Why: Rapid triage and root cause identification for incidents.<\/li>\n<\/ul>\n\n\n\n<p>Debug dashboard:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels: Container allocation latency heatmap, per-node resource usage, recent RM GC events, log aggregation lag, AM attempt counts.<\/li>\n<li>Why: Deep analysis during incidents and optimization.<\/li>\n<\/ul>\n\n\n\n<p>Alerting guidance:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Page vs ticket:<\/li>\n<li>Page: RM down, RM leader flip failing, NM heartbeat loss &gt; X nodes, Kerberos KDC unreachable, critical queue SLA breach.<\/li>\n<li>Ticket: Individual job failures, non-critical queue latency, minor log aggregation lag.<\/li>\n<li>Burn-rate guidance:<\/li>\n<li>Use error budget burn windows for scheduled testing; page on burn rate crossing 5x expected for critical SLIs.<\/li>\n<li>Noise reduction tactics:<\/li>\n<li>Deduplicate alerts by grouping by cluster and issue type.<\/li>\n<li>Suppression during planned maintenance windows.<\/li>\n<li>Use correlation rules to avoid paging on downstream symptom when the RM is the root.<\/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; Inventory node types, disk and network capabilities.\n&#8211; Storage decisions for logs and job history.\n&#8211; Security model: Kerberos, delegation tokens, ACLs.\n&#8211; Define tenant queues and resource quotas.<\/p>\n\n\n\n<p>2) Instrumentation plan\n&#8211; Expose RM and NM JMX metrics.\n&#8211; Configure log aggregation to object store.\n&#8211; Define SLI and SLO owners and alert thresholds.<\/p>\n\n\n\n<p>3) Data collection\n&#8211; Deploy Prometheus exporters and log shippers.\n&#8211; Ensure retention policies for metrics and logs meet compliance.<\/p>\n\n\n\n<p>4) SLO design\n&#8211; Group jobs by criticality and define SLOs per group.\n&#8211; Set SLI measurement windows and error budget policies.<\/p>\n\n\n\n<p>5) Dashboards\n&#8211; Build executive, on-call, and debug dashboards with templating.\n&#8211; Include runbook links directly on panels.<\/p>\n\n\n\n<p>6) Alerts &amp; routing\n&#8211; Map alerts to teams and define on-call rotation.\n&#8211; Implement escalation and suppression rules.<\/p>\n\n\n\n<p>7) Runbooks &amp; automation\n&#8211; Create runbooks for RM failover, NM cordon, Kerberos renewals.\n&#8211; Automate common fixes: NM restart, log rotation, auto-scaling.<\/p>\n\n\n\n<p>8) Validation (load\/chaos\/game days)\n&#8211; Run game days for RM failover, node loss, and auth failures.\n&#8211; Perform load tests for allocation latency and container churn.<\/p>\n\n\n\n<p>9) Continuous improvement\n&#8211; Regularly review SLO breaches and refine queue configs.\n&#8211; Automate runbook steps after repeated manual fixes.<\/p>\n\n\n\n<p>Pre-production checklist:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>RM HA configured and tested.<\/li>\n<li>NodeManagers installed and heartbeats validated.<\/li>\n<li>Metrics and log forwarding working.<\/li>\n<li>Security credentials and ACLs validated.<\/li>\n<li>Synthetic jobs to validate queue behavior.<\/li>\n<\/ul>\n\n\n\n<p>Production readiness checklist:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Alerting integrated with on-call rotations.<\/li>\n<li>Runbooks and automation tested.<\/li>\n<li>Capacity plan and autoscaling thresholds set.<\/li>\n<li>Backup and recovery for JobHistory and state.<\/li>\n<\/ul>\n\n\n\n<p>Incident checklist specific to YARN:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Check RM leader state and logs.<\/li>\n<li>Verify NM heartbeats and node map.<\/li>\n<li>Inspect ApplicationMaster attempts and container exit codes.<\/li>\n<li>Check Kerberos and delegation token status.<\/li>\n<li>Execute runbook: cordon nodes, restart NM, failover RM if needed.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Use Cases of YARN<\/h2>\n\n\n\n<p>(8\u201312 concise use cases)<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p>Nightly ETL pipelines\n&#8211; Context: Large-scale batch transformations on HDFS.\n&#8211; Problem: High throughput ETL with predictable windows.\n&#8211; Why YARN helps: Queueing, locality, and container sizing optimize throughput.\n&#8211; What to measure: Job completion P95, queue wait times, container OOM rate.\n&#8211; Typical tools: MapReduce, Tez, Airflow.<\/p>\n<\/li>\n<li>\n<p>Multi-tenant analytics platform\n&#8211; Context: Multiple teams run ad-hoc and scheduled jobs.\n&#8211; Problem: Resource contention and noisy neighbors.\n&#8211; Why YARN helps: CapacityScheduler enforces quotas and guarantees.\n&#8211; What to measure: Queue utilization and fairness metrics.\n&#8211; Typical tools: Hive on Tez, Presto with YARN-managed connectors.<\/p>\n<\/li>\n<li>\n<p>Large-scale ML training on GPU nodes\n&#8211; Context: Distributed training needing GPUs.\n&#8211; Problem: Coordinating GPU resource allocation and scheduling.\n&#8211; Why YARN helps: GPU-aware schedulers and node labeling support placement.\n&#8211; What to measure: GPU allocation ratio and training job success rate.\n&#8211; Typical tools: TensorFlow on YARN, Spark with GPU support.<\/p>\n<\/li>\n<li>\n<p>Ad-hoc research clusters\n&#8211; Context: Data scientists needing burst compute for experiments.\n&#8211; Problem: Temporary workloads that shouldn&#8217;t affect production ETL.\n&#8211; Why YARN helps: Isolated queues and preemption policies allow burst without long-term risk.\n&#8211; What to measure: Preemption events, job runtimes.\n&#8211; Typical tools: Spark, Zeppelin notebooks tied into YARN.<\/p>\n<\/li>\n<li>\n<p>On-prem to cloud burst\n&#8211; Context: Peak-season compute demand spikes.\n&#8211; Problem: Under-provisioning cost vs peak demand.\n&#8211; Why YARN helps: Federation or ephemeral YARN clusters in cloud for burst.\n&#8211; What to measure: Time to provision nodes and job completion during burst.\n&#8211; Typical tools: Cloud APIs, autoscaling scripts.<\/p>\n<\/li>\n<li>\n<p>CI for large datasets\n&#8211; Context: Integration tests that process sizeable sample data.\n&#8211; Problem: Tests need many cores and memory temporarily.\n&#8211; Why YARN helps: Temporarily allocate large containers without long lived nodes.\n&#8211; What to measure: Job success rate and test latency.\n&#8211; Typical tools: Jenkins executor integrating with YARN.<\/p>\n<\/li>\n<li>\n<p>Secure regulated workloads\n&#8211; Context: Jobs that require strict audit and access controls.\n&#8211; Problem: Need for Kerberos and audit trails.\n&#8211; Why YARN helps: Kerberos integration and audit logs via timeline server.\n&#8211; What to measure: Auth failure rates and ACL violations.\n&#8211; Typical tools: Kerberos, Ranger.<\/p>\n<\/li>\n<li>\n<p>Real-time-ish streaming frameworks\n&#8211; Context: Low-latency streaming analytics on HDFS-backed sources.\n&#8211; Problem: Low-latency requires small containers and fast scheduling.\n&#8211; Why YARN helps: Long-running containers for streaming frameworks like Storm on YARN.\n&#8211; What to measure: Task latency, container restart frequency.\n&#8211; Typical tools: Storm, Flink (on YARN).<\/p>\n<\/li>\n<\/ol>\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 integration for a legacy Hadoop cluster<\/h3>\n\n\n\n<p><strong>Context:<\/strong> A company wants to modernize by running NodeManagers inside Kubernetes to standardize infra.\n<strong>Goal:<\/strong> Keep existing YARN apps running while leveraging K8s scheduling and autoscaling.\n<strong>Why YARN matters here:<\/strong> Preserves application compatibility while enabling cloud-native benefits.\n<strong>Architecture \/ workflow:<\/strong> Kubernetes runs NodeManager pods; ResourceManager runs on VMs; NMs use hostPath or CSI for local-disk access; Prometheus scrapes both.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Containerize NodeManager with proper permissions.<\/li>\n<li>Configure NodeManager to register with RM external endpoint.<\/li>\n<li>Expose persistent volumes or CSI for local storage.<\/li>\n<li>Adjust container-executor settings for pod boundaries.<\/li>\n<li>Test with synthetic jobs and validate locality.\n<strong>What to measure:<\/strong> Container allocation latency, node pod restarts, disk latency.\n<strong>Tools to use and why:<\/strong> Kubernetes for pod lifecycle, Prometheus for metrics, Grafana for dashboards.\n<strong>Common pitfalls:<\/strong> Local disk semantics differ and can break locality; network overlay increases latency.\n<strong>Validation:<\/strong> Run job set emphasizing locality; compare runtime to baseline.\n<strong>Outcome:<\/strong> Centralized infra with gradual migration path to cloud-native deployments.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #2 \u2014 Serverless managed-PaaS job submission<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Teams want to submit Spark jobs from a managed serverless interface that abstracts cluster details.\n<strong>Goal:<\/strong> Allow developers to run ad-hoc jobs without cluster management.\n<strong>Why YARN matters here:<\/strong> Underneath managed PaaS, YARN still provides resource governance and locality.\n<strong>Architecture \/ workflow:<\/strong> Serverless API validates job, pushes application to YARN via client gateway, AM runs on cluster, results persisted to object store.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Implement submission gateway with authentication and quota checks.<\/li>\n<li>Create job templates and resource profiles.<\/li>\n<li>Use delegation tokens for object store access.<\/li>\n<li>Monitor submission throughput and map to queue usage.\n<strong>What to measure:<\/strong> API success rate, job start latency, cost per job.\n<strong>Tools to use and why:<\/strong> Gateway service, Prometheus, centralized logging for audits.\n<strong>Common pitfalls:<\/strong> Token handling for long jobs and quota exhaustion.\n<strong>Validation:<\/strong> Developer self-service trials and security review.\n<strong>Outcome:<\/strong> Developer productivity increased while maintaining resource governance.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #3 \u2014 Incident response: RM failure and failover<\/h3>\n\n\n\n<p><strong>Context:<\/strong> ResourceManager crashed during peak job submission window.\n<strong>Goal:<\/strong> Failover to standby RM with minimal job disruption.\n<strong>Why YARN matters here:<\/strong> RM is the control plane; its failure impacts cluster operability.\n<strong>Architecture \/ workflow:<\/strong> RM in HA mode with ZK-based leader election and shared state store; standby RM ready to take over.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Detect RM down via RM heartbeat and process monitors.<\/li>\n<li>Trigger automatic failover using ZooKeeper fencing.<\/li>\n<li>Reconnect NodeManagers and ApplicationMasters to new RM.<\/li>\n<li>Validate application states and resume queue scheduling.\n<strong>What to measure:<\/strong> RM failover duration, number of lost application attempts, queue backlog.\n<strong>Tools to use and why:<\/strong> Alerting system, automated failover scripts, runbook.\n<strong>Common pitfalls:<\/strong> Shared storage not available to new RM; lingering locks blocking startup.\n<strong>Validation:<\/strong> Periodic failover game days and postmortem.\n<strong>Outcome:<\/strong> Reduced RM downtime with documented runbook and automation.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #4 \u2014 Cost vs performance optimization<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Team needs to reduce infrastructure cost while preserving job latency for critical reports.\n<strong>Goal:<\/strong> Tune container sizes and queue priorities to save cost.\n<strong>Why YARN matters here:<\/strong> YARN&#8217;s scheduling policies and container shapes directly affect resource efficiency.\n<strong>Architecture \/ workflow:<\/strong> Right-sizing containers, implementing mixed queue priorities, enabling preemption and autoscaling of nodes via utilization signals.\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Analyze job profiles for CPU and memory usage.<\/li>\n<li>Create resource profiles and adjust container sizes.<\/li>\n<li>Tune scheduler queue capacities and enable preemption for critical workloads.<\/li>\n<li>Configure autoscaling based on scheduler utilization metrics.\n<strong>What to measure:<\/strong> Cost per job, job latency P95, cluster utilization.\n<strong>Tools to use and why:<\/strong> Prometheus for utilization, cost analytics tools, Grafana dashboards.\n<strong>Common pitfalls:<\/strong> Over-aggressive packing causing OOMs; preemption causing important job churn.\n<strong>Validation:<\/strong> A\/B testing on a staging cluster and measured cost delta.\n<strong>Outcome:<\/strong> Lower infrastructure cost with maintained SLA for critical jobs.<\/li>\n<\/ul>\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; symptom -&gt; root cause -&gt; fix)<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Symptom: Jobs stuck in queue indefinitely -&gt; Root cause: Queue misconfiguration or quota exhaustion -&gt; Fix: Rebalance queue capacities and enable preemption.<\/li>\n<li>Symptom: Many container OOMs -&gt; Root cause: Underestimated container memory -&gt; Fix: Increase container memory and account for JVM overhead.<\/li>\n<li>Symptom: RM unresponsive -&gt; Root cause: GC thrashing or disk saturation -&gt; Fix: Tune JVM, increase heap, or provision faster disks.<\/li>\n<li>Symptom: NodeManagers disappearing -&gt; Root cause: Network flapping or NM process crash -&gt; Fix: Harden network and configure NM auto-restart.<\/li>\n<li>Symptom: High container launch latency -&gt; Root cause: Admission control throttling or overloaded RM -&gt; Fix: Tune admission settings and scale RM resources.<\/li>\n<li>Symptom: JobHistory data missing -&gt; Root cause: Timeline Server or history server misconfigured -&gt; Fix: Check storage and service health.<\/li>\n<li>Symptom: Auth errors across jobs -&gt; Root cause: Kerberos ticket expiry or KDC outage -&gt; Fix: Ensure KDC HA and automated renewal.<\/li>\n<li>Symptom: Excessive preemptions -&gt; Root cause: Overlapping queue policies or mis-set priorities -&gt; Fix: Revisit queue policies and lower preemption aggressiveness.<\/li>\n<li>Symptom: Disk full on NMs -&gt; Root cause: Log aggregation or temporary directories not cleaned -&gt; Fix: Implement log rotation and cleanup job.<\/li>\n<li>Symptom: GPU tasks failing to launch -&gt; Root cause: Driver mismatch or incorrect node labels -&gt; Fix: Standardize drivers and validate node labels.<\/li>\n<li>Symptom: Metrics gaps -&gt; Root cause: JMX exporter misconfigured or scrape targets missing -&gt; Fix: Validate exporter endpoints and Prometheus configs.<\/li>\n<li>Symptom: Duplicate logs in index -&gt; Root cause: Multiple log shippers without dedupe -&gt; Fix: Add unique identifiers and dedupe in ingestion.<\/li>\n<li>Symptom: Users bypassing queues -&gt; Root cause: Weak ACL enforcement -&gt; Fix: Enforce application ACLs and submit gates.<\/li>\n<li>Symptom: High shuffle I\/O -&gt; Root cause: Poor partitioning in jobs -&gt; Fix: Repartition and minimize shuffle by optimizing jobs.<\/li>\n<li>Symptom: Frequent AM restarts -&gt; Root cause: Application bugs or memory exhaustion -&gt; Fix: Review application logs and allocate more resources.<\/li>\n<li>Symptom: Slow container startup on Windows nodes -&gt; Root cause: Unsupported platform or permissions -&gt; Fix: Use Linux nodes or validated Windows configurations.<\/li>\n<li>Symptom: Elevated RM leader switches -&gt; Root cause: ZooKeeper instability -&gt; Fix: Harden ZK ensemble and network.<\/li>\n<li>Symptom: Jobs completed but wrong output -&gt; Root cause: Non-deterministic job logic or partial failures -&gt; Fix: Add data validation in job pipelines.<\/li>\n<li>Symptom: Observability blind spots -&gt; Root cause: Not instrumenting AM or client metrics -&gt; Fix: Instrument AM and client with custom metrics.<\/li>\n<li>Symptom: Alerts flood during maintenance -&gt; Root cause: Missing suppression windows -&gt; Fix: Schedule maintenance windows and suppress non-critical alerts.<\/li>\n<li>Symptom: Slow authentication for many clients -&gt; Root cause: KDC overloaded -&gt; Fix: Scale KDC or use caching where safe.<\/li>\n<li>Symptom: Overly conservative container sizes -&gt; Root cause: Lack of profiling -&gt; Fix: Profile tasks and right-size containers.<\/li>\n<li>Symptom: Job retries causing load spike -&gt; Root cause: Aggressive retry policy -&gt; Fix: Implement exponential backoff and limit retries.<\/li>\n<li>Symptom: Missing audit trails -&gt; Root cause: Log aggregation misroute -&gt; Fix: Ensure central logging includes RM and AM audit events.<\/li>\n<\/ol>\n\n\n\n<p>Observability pitfalls (at least 5 included above):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Missing AM metrics.<\/li>\n<li>Incomplete JMX coverage.<\/li>\n<li>No container-level logging aggregation.<\/li>\n<li>Alert fatigue from ungrouped events.<\/li>\n<li>Lack of contextual traces linking RM events to job IDs.<\/li>\n<\/ul>\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>Clear ownership for the YARN control plane: RM, NMs, and scheduling policies.<\/li>\n<li>Dedicated rotation for platform SREs with defined escalation to data engineering.<\/li>\n<li>On-call playbooks for RM failover, NM cordoning, and Kerberos issues.<\/li>\n<\/ul>\n\n\n\n<p>Runbooks vs playbooks:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Runbook: Step-by-step remediation for single failure modes (e.g., RM failover).<\/li>\n<li>Playbook: Higher-level decision guide for incidents requiring coordination across teams.<\/li>\n<\/ul>\n\n\n\n<p>Safe deployments:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Canary scheduler config changes in low-traffic queues first.<\/li>\n<li>Use feature flags for preemption or resource profile changes.<\/li>\n<li>Maintain rollback playbooks.<\/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 NM auto-restarts and cordoning.<\/li>\n<li>Auto-scale node pools based on scheduler utilization.<\/li>\n<li>Scheduled cleanup jobs for log dirs and temp data.<\/li>\n<\/ul>\n\n\n\n<p>Security basics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enable Kerberos and enforce delegation tokens best practices.<\/li>\n<li>Audit job submissions and queue ACLs.<\/li>\n<li>Use least privilege for service accounts and secure RM endpoints.<\/li>\n<\/ul>\n\n\n\n<p>Weekly\/monthly routines:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Weekly: Check failed jobs by owner and queue, clean NM disks, review DAG patterns.<\/li>\n<li>Monthly: Review SLO performance, update capacity planning, verify Kerberos ticket lifecycles.<\/li>\n<\/ul>\n\n\n\n<p>Postmortem reviews should include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Timeline of RM and NM metrics.<\/li>\n<li>Container allocation latencies and queue behaviors.<\/li>\n<li>Root cause analysis and mitigation tasks assigned for YARN-specific items.<\/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 YARN (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<\/td>\n<td>Collects RM NM and JVM metrics<\/td>\n<td>Prometheus, JMX Exporter<\/td>\n<td>Use recording rules for SLIs<\/td>\n<\/tr>\n<tr>\n<td>I2<\/td>\n<td>Logging<\/td>\n<td>Aggregates NM and AM logs centrally<\/td>\n<td>Elasticsearch, Object store<\/td>\n<td>Ensure unique job identifiers<\/td>\n<\/tr>\n<tr>\n<td>I3<\/td>\n<td>Security<\/td>\n<td>Manages auth and policies<\/td>\n<td>Kerberos, Ranger<\/td>\n<td>Enforce queue and app ACLs<\/td>\n<\/tr>\n<tr>\n<td>I4<\/td>\n<td>Orchestration<\/td>\n<td>Runs NodeManagers or integrates with K8s<\/td>\n<td>Kubernetes, Mesos<\/td>\n<td>YARN-on-K8s requires storage mapping<\/td>\n<\/tr>\n<tr>\n<td>I5<\/td>\n<td>Scheduler UI<\/td>\n<td>Visualizes queues and apps<\/td>\n<td>Custom dashboards<\/td>\n<td>Useful for ops and tenants<\/td>\n<\/tr>\n<tr>\n<td>I6<\/td>\n<td>Alerting<\/td>\n<td>Notifies on SLO breaches and crashes<\/td>\n<td>Pager tools, Email<\/td>\n<td>Use grouping and suppression<\/td>\n<\/tr>\n<tr>\n<td>I7<\/td>\n<td>Autoscaler<\/td>\n<td>Scales node pools by utilization<\/td>\n<td>Cloud APIs, Custom scripts<\/td>\n<td>Map YARN util to cloud scale actions<\/td>\n<\/tr>\n<tr>\n<td>I8<\/td>\n<td>Job Orchestration<\/td>\n<td>Manages DAGs and scheduling<\/td>\n<td>Airflow, Oozie<\/td>\n<td>Job templates submit to YARN<\/td>\n<\/tr>\n<tr>\n<td>I9<\/td>\n<td>Cost Analytics<\/td>\n<td>Tracks cost per cluster or job<\/td>\n<td>Billing APIs, Analytics tools<\/td>\n<td>Attribution can be approximate<\/td>\n<\/tr>\n<tr>\n<td>I10<\/td>\n<td>Backup<\/td>\n<td>Persists JobHistory and RM state<\/td>\n<td>Object store, HDFS<\/td>\n<td>Required for RM HA recovery<\/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 difference between YARN and Hadoop?<\/h3>\n\n\n\n<p>YARN is the resource manager in the Hadoop ecosystem; Hadoop refers to the larger ecosystem including HDFS, MapReduce, and other components.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can Spark run on YARN?<\/h3>\n\n\n\n<p>Yes, Spark can run on YARN as a cluster manager; it can also run on Kubernetes and standalone modes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is YARN cloud-native?<\/h3>\n\n\n\n<p>Not originally; YARN predates cloud-native patterns but can be integrated with Kubernetes or run in cloud VMs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Should new projects use YARN or Kubernetes?<\/h3>\n\n\n\n<p>Depends on data locality needs and existing tooling. For HDFS-heavy jobs, YARN may be better; for microservices and modern orchestration, Kubernetes is preferred.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How do I monitor YARN health?<\/h3>\n\n\n\n<p>Monitor RM availability, NM heartbeats, container allocation latency, queue wait times, and job success rates.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What causes container OOMs?<\/h3>\n\n\n\n<p>Common causes are underestimated memory settings, JVM heap not accounting for overhead, or host-level memory pressure.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to secure YARN?<\/h3>\n\n\n\n<p>Enable Kerberos, use delegation tokens, enforce queue ACLs, and centralize audit logs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is ResourceManager HA?<\/h3>\n\n\n\n<p>A configuration where multiple RM instances exist with leader election for failover; necessary for production resilience.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How many NodeManagers per cluster is ideal?<\/h3>\n\n\n\n<p>Varies \/ depends on hardware profiles and workload; scale to balance recovery domain and management overhead.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can YARN schedule GPUs?<\/h3>\n\n\n\n<p>Yes, with GPU-aware extensions and node labeling, though setup involves drivers and scheduler config.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is preemption in YARN?<\/h3>\n\n\n\n<p>Preemption forcibly reclaims containers to satisfy higher-priority queues or applications.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to reduce noisy neighbor issues?<\/h3>\n\n\n\n<p>Use fine-grained queues, enforce resource limits, enable preemption sparingly, and monitor per-queue metrics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to handle log aggregation at scale?<\/h3>\n\n\n\n<p>Ship logs to object storage and scale timeline server; ensure dedupe and retention policies to manage costs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is federation in YARN?<\/h3>\n\n\n\n<p>A method to combine multiple YARN clusters for scale and isolation; operationally complex.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How often should I run RM failover tests?<\/h3>\n\n\n\n<p>At least quarterly, and after any major config or version change.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What SLIs matter most for YARN?<\/h3>\n\n\n\n<p>RM availability, container allocation latency, job success rate, and queue wait times.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How do I debug slow jobs?<\/h3>\n\n\n\n<p>Check locality, shuffle sizes, container resource usage, and AM logs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is Hadoop YARN still relevant in 2026?<\/h3>\n\n\n\n<p>Yes for HDFS-centric workloads and large legacy ecosystems, but consider hybrid strategies with cloud-native orchestration.<\/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>YARN remains a foundational resource management system for large-scale Hadoop ecosystems. For SREs and cloud architects it represents a control plane requiring the same production rigor as other orchestration systems: monitoring, HA, security, and automation. Modern adoption often combines YARN with cloud-native patterns such as Kubernetes integration, autoscaling, and improved observability.<\/p>\n\n\n\n<p>Next 7 days plan:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Day 1: Inventory current YARN clusters, RM\/NM counts, and queue configs.<\/li>\n<li>Day 2: Ensure RM HA and basic monitoring are in place.<\/li>\n<li>Day 3: Deploy JMX exporters and validate key SLIs like RM availability and container latency.<\/li>\n<li>Day 4: Create executive and on-call dashboard templates.<\/li>\n<li>Day 5: Run a failover and node loss game day and update runbooks.<\/li>\n<li>Day 6: Review queue policies and right-size top 10 job resource profiles.<\/li>\n<li>Day 7: Automate a routine task (disk cleanup or NM restart) and add to CI.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Appendix \u2014 YARN Keyword Cluster (SEO)<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Primary keywords<\/li>\n<li>YARN<\/li>\n<li>Hadoop YARN<\/li>\n<li>Yet Another Resource Negotiator<\/li>\n<li>YARN architecture<\/li>\n<li>YARN ResourceManager<\/li>\n<li>YARN NodeManager<\/li>\n<li>YARN ApplicationMaster<\/li>\n<li>YARN scheduler<\/li>\n<li>YARN containers<\/li>\n<li>\n<p>YARN monitoring<\/p>\n<\/li>\n<li>\n<p>Secondary keywords<\/p>\n<\/li>\n<li>YARN metrics<\/li>\n<li>YARN SLIs<\/li>\n<li>YARN SLOs<\/li>\n<li>YARN HA<\/li>\n<li>YARN federation<\/li>\n<li>YARN on Kubernetes<\/li>\n<li>YARN GPU scheduling<\/li>\n<li>YARN security Kerberos<\/li>\n<li>CapacityScheduler YARN<\/li>\n<li>\n<p>FairScheduler YARN<\/p>\n<\/li>\n<li>\n<p>Long-tail questions<\/p>\n<\/li>\n<li>What does YARN do in Hadoop<\/li>\n<li>How to monitor YARN ResourceManager<\/li>\n<li>How to reduce YARN container OOMs<\/li>\n<li>How to configure YARN HA<\/li>\n<li>How to run Spark on YARN<\/li>\n<li>YARN vs Kubernetes for big data<\/li>\n<li>How to enable GPU scheduling in YARN<\/li>\n<li>YARN container allocation latency tuning<\/li>\n<li>How to secure YARN with Kerberos<\/li>\n<li>Best practices for YARN queue management<\/li>\n<li>How to scale YARN clusters to cloud<\/li>\n<li>How to integrate YARN with Prometheus<\/li>\n<li>How to aggregate YARN logs to object store<\/li>\n<li>How to migrate Hadoop jobs from YARN to Kubernetes<\/li>\n<li>How to configure YARN NodeManager disk cleanup<\/li>\n<li>What is YARN ApplicationMaster role<\/li>\n<li>How to do RM failover in YARN<\/li>\n<li>How to measure YARN job success rate<\/li>\n<li>YARN timeline server troubleshooting<\/li>\n<li>\n<p>How to set SLOs for YARN job types<\/p>\n<\/li>\n<li>\n<p>Related terminology<\/p>\n<\/li>\n<li>ResourceManager<\/li>\n<li>NodeManager<\/li>\n<li>ApplicationMaster<\/li>\n<li>Container<\/li>\n<li>CapacityScheduler<\/li>\n<li>FairScheduler<\/li>\n<li>JobHistoryServer<\/li>\n<li>Timeline Server<\/li>\n<li>Kerberos<\/li>\n<li>Delegation tokens<\/li>\n<li>Shuffle service<\/li>\n<li>Locality<\/li>\n<li>Preemption<\/li>\n<li>Node labels<\/li>\n<li>Container tokens<\/li>\n<li>Heartbeat<\/li>\n<li>Container-executor<\/li>\n<li>Distributed cache<\/li>\n<li>Shuffle bytes<\/li>\n<li>VM autoscaling<\/li>\n<li>FederationStateStore<\/li>\n<li>Timeline aggregation<\/li>\n<li>Job orchestration<\/li>\n<li>Log aggregation<\/li>\n<li>JVM tuning<\/li>\n<li>GC pauses<\/li>\n<li>Admission control<\/li>\n<li>Resource profiles<\/li>\n<li>GPU nodes<\/li>\n<li>Disk pressure<\/li>\n<li>Container retry policy<\/li>\n<li>Queue ACLs<\/li>\n<li>Auditing<\/li>\n<li>Metrics exporter<\/li>\n<li>Prometheus JMX<\/li>\n<li>Grafana dashboard<\/li>\n<li>RM leader election<\/li>\n<li>NodeManager cordon<\/li>\n<li>Preemption events<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>&#8212;<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-3578","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/3578","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=3578"}],"version-history":[{"count":0,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/3578\/revisions"}],"wp:attachment":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=3578"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=3578"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=3578"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}