Amazon OpenSearch Service is a completely managed service for search, analytics, and observability workloads, serving to you index, search, and analyze massive datasets with ease. Ensuring your OpenSearch Service area is right-sized—balancing efficiency, scalability, and value—is important to maximizing its worth. An over-provisioned area wastes assets, whereas an under-provisioned one dangers efficiency bottlenecks like excessive latency or write rejections.
On this submit, we information you thru the steps to find out in case your OpenSearch Service area is right-sized, utilizing AWS instruments and greatest practices to optimize your configuration for workloads like log analytics, search, vector search, or artificial knowledge testing.
Why right-sizing your OpenSearch Service area issues
Proper-sizing your OpenSearch Service area gives optimum efficiency, reliability, and cost-efficiency. An undersized area results in excessive CPU utilization, reminiscence stress, and question latency, whereas an outsized area drives pointless spend and useful resource waste. By constantly matching area assets to workload traits similar to ingestion charge, question complexity, and knowledge development, you possibly can keep predictable efficiency with out overpaying for unused capability.
Past price and efficiency, right-sizing facilitates architectural agility. It helps be sure that your cluster scales easily throughout site visitors spikes, meets SLA targets, and sustains stability beneath altering workloads. Recurrently tuning assets to match precise demand optimizes infrastructure effectivity and helps long-term operational resilience.
Key Amazon CloudWatch metrics
OpenSearch Service gives Amazon CloudWatch metrics that supply insights into numerous facets of your area’s efficiency. These metrics fall into 16 totally different classes, together with cluster metrics, EBS quantity metrics, and occasion metrics. To find out in case your OpenSearch Service area is misconfigured, monitor these widespread signs that point out resizing or optimization could also be vital. These are brought on by imbalances in useful resource allocation, workload calls for, or configuration settings. The next desk summarizes these parameters:
| CloudWatch Metrics | Parameter |
| CPU Utilization Metrics | CPUUtilization: Common CPU utilization throughout all knowledge nodes.
Major management airplane CPU utilization (for devoted major nodes): Common CPU utilization on major nodes.
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| Reminiscence Utilization Metrics | JVMMemoryPressure: Proportion of heap reminiscence used throughout knowledge nodes.
Be aware: With Rubbish First Rubbish Collector (G1GC), JVM could delay collections to optimize efficiency. Consider
Be aware: Occasional spikes are regular throughout state updates; sustained excessive reminiscence stress warrants scaling or tuning. |
| Storage Metrics | StorageUtilization: Proportion of cupboard space used.
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Node Degree Search and Indexing Efficiency (These latencies are usually not per-request latencies or charge, however at node degree based mostly on shards assigned to a node.) |
SearchLatency: Common time for search requests.
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| Cluster Well being Indicators | ClusterStatus.yellow and ClusterStatus.pink:
Nodes
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Indicators of under-provisioning
Underneath-provisioned domains battle to deal with workload calls for, resulting in efficiency degradation and cluster instability. Search for sustained useful resource stress and operational errors that sign the cluster is working past its limits. For monitoring, you possibly can set CloudWatch alarms to catch early alerts of stress and stop outages or degraded efficiency. The next are important warning indicators:
- Excessive CPU utilization for knowledge nodes (>80%) sustained over time (similar to greater than 10 minutes)
- Excessive CPU utilization for major nodes (>60%) sustained over time (similar to greater than 10 minutes)
- JVM reminiscence stress constantly excessive (>85%) for knowledge and first nodes
- Storage utilization reaching excessive (>85%)
- Growing search latency with steady question patterns (rising by 50% from baseline)
- Frequent cluster standing yellow/pink occasions
- Node failures beneath regular load circumstances
When assets are constrained, the end-user expertise suffers with slower searches, failed indexing, and system errors. The next are key efficiency impression indicators:
Remediation suggestions
The next desk summarizes CloudWatch metric signs, attainable causes, and potential options.
| CloudWatch metric symptom | Causes and answer |
FreeStorageSpace drops <20% |
Storage stress happens when knowledge quantity outgrows native storage as a result of excessive ingestion, lengthy retention with out cleanup, or unbalanced shards. Lack of tiering (similar to UltraWarm) additional worsens capability points. Answer: Unlock area by deleting unused indexes or automating cleanup with ISM and use power merge on read-only indexes to reclaim storage. If stress persists, scale vertically or horizontally, use UltraWarm or chilly storage for older knowledge, and alter shard counts at rollover for higher steadiness. |
CPUUtilization and JVMMemoryPressure constantly >70% |
Excessive CPU or JVM stress arises when occasion sizes are too small or shard counts per node are extreme, resulting in frequent GC pauses. Inefficient shard technique, uneven distribution, and poorly optimized queries or mappings additional spike reminiscence utilization beneath heavy workloads. Answer: Tackle excessive CPU/JVM stress by scaling vertically to bigger situations (similar to from r6g.massive to r6g.xlarge) or including nodes horizontally. Optimize shard counts relative to heap dimension, clean out peak site visitors, and use sluggish logs to pinpoint and tune resource-heavy queries. |
SearchLatency or IndexingLatency spikes >500 milliseconds |
Thread pool rejections usually stem from useful resource rivalry like excessive CPU/JVM stress or GC pauses. Inefficient shard sizing, over-sharding, and overly advanced queries (deep aggregations, frequent cache evictions) additional improve overhead and push duties into rejection. Answer: Scale back question latency by optimizing queries with profiling, tuning shard sizes (10–50 GB every), and avoiding over-sharding. Enhance parallelism by scaling the cluster, including replicas for learn capability, rising cache by way of bigger nodes, and setting acceptable question timeouts. |
ThreadpoolRejected metrics point out queued requests |
Thread pool rejections happen when excessive concurrent requests overflow queues past capability, particularly with undersized nodes restricted by vCPU-based threads. Sudden unscaled site visitors spikes additional overwhelm swimming pools, inflicting duties to be dropped or delayed. Answer: Mitigate thread pool rejections by implementing shard steadiness throughout nodes, scaling horizontally to spice up thread capability, and managing shopper load with retries and decreased concurrency. Monitor search queues, right-size situations for vCPUs, and cautiously tune thread pool settings to deal with bursty workloads. |
ThroughputThrottle or IopsThrottle attain 1 |
I/O throttling arises when Amazon EBS or Amazon EC2 limits are exceeded, similar to gp3’s 125 MBps baseline, or when burst credit are depleted as a result of sustained spikes. Mismatched quantity varieties and heavy operations like bulk indexing with out optimized storage additional amplify throughput bottlenecks. Answer: Tackle I/O throttling by upgrading to gp3 volumes with larger baseline or provisioning additional IOPS and think about I/O-optimized situations like i3/i4 households whereas monitoring burst steadiness. For sustained workloads, scale nodes or schedule heavy operations throughout off-peak hours to keep away from hitting throughput caps. |
Indicators of over-provisioning
Over-provisioned clusters present constantly low utilization throughout CPU, reminiscence, and storage, suggesting assets far exceed workload calls for. Figuring out these inefficiencies helps cut back pointless spend with out impacting efficiency. You should use CloudWatch alarms to trace cluster well being and cost-efficiency metrics over 2–4 weeks to substantiate sustained underutilization:
- Low CPU utilization for knowledge and first nodes (<40%) sustained over time
- Low JVM reminiscence stress for knowledge and first nodes (<50%)
- Extreme free storage (>70% unused)
- Underutilized occasion varieties for workload patterns
Monitor cluster indexing and search latencies always because the cluster is being downsized—these latencies mustn’t improve if the cluster is eliminating unused capability. Additionally, it’s really useful to scale back nodes separately and proceed to look at latencies to proceed additional downturn. By right-sizing situations, lowering node counts, and adopting cost-efficient storage choices, you possibly can align assets to precise utilization. Optimizing shard allocation additional helps balanced efficiency at a decrease price.
Finest practices for right-sizing
On this part, we talk about greatest practices for right-sizing.
Iterate and optimize
Proper-sizing is an ongoing course of, not a one-time train. As workloads evolve, constantly monitor CPU, JVM reminiscence stress, and storage utilization utilizing CloudWatch to verify they continue to be inside wholesome thresholds. Rising latency, queue buildup, or unassigned shards usually sign capability or configuration points that require consideration.
Recurrently evaluation sluggish logs, question latency, and ingestion developments to determine efficiency bottlenecks early. If search or indexing efficiency degrades, think about scaling, rebalancing shards, or adjusting retention insurance policies. Periodic critiques of occasion sizes and node rely assist align price with demand, sustaining 200-millisecond latency targets whereas avoiding over-provisioning. Constant iteration helps your OpenSearch Service area stay performant and cost-efficient over time.
Set up baselines
Monitor for two–4 weeks after preliminary deployment and doc peak utilization patterns and seasonal differences. Document efficiency throughout totally different workload varieties. Set acceptable CloudWatch alarm thresholds based mostly in your baselines.
Common evaluation course of
Conduct weekly metric critiques throughout preliminary optimization and month-to-month assessments for steady workloads. Conduct quarterly right-sizing workout routines for price optimization.
Scaling methods
Think about the next scaling methods:
Vertical scaling (occasion varieties) – Use bigger occasion varieties when efficiency constraints stem from CPU, reminiscence, or JVM stress, and total knowledge quantity is inside a single node’s capability. Select memory-optimized situations (similar to r8g, r7g, or r7i) for heavy aggregation or indexing workloads. Use compute-optimized situations (c8g, c7g, or c7i) for CPU-bound workloads similar to query-heavy or log-processing environments. Vertical scaling is right for smaller clusters or testing environments the place simplicity and cost-efficiency are priorities.
Horizontal scaling (node rely) – Add extra knowledge nodes when storage, shard rely, or question concurrency will increase past what a single node can deal with. Preserve an odd variety of primary-eligible nodes (sometimes three or 5) and use devoted major nodes for clusters with greater than 10 knowledge nodes. Deploy throughout three Availability Zones for top availability in manufacturing. Horizontal scaling is most popular for big, production-grade workloads requiring fault tolerance and sustained development. Use _cat/allocation?v to confirm shard distribution and node steadiness:
GET /_cat/allocation/node_name_1,node_name_2,node_name_3
Optimize storage configuration
Use the newest era of Amazon EBS Common Function (gp) volumes for improved efficiency and cost-efficiency in comparison with earlier variations. Monitor storage development developments utilizing ClusterUsedSpace and FreeStorageSpace metrics. Preserve knowledge utilization beneath 50% of whole storage capability to permit for development and snapshots.
Select storage tiers based mostly on efficiency and entry patterns—for instance, allow UltraWarm or chilly storage for big, occasionally accessed datasets. Transfer older or compliance-related knowledge to cost-efficient tiers (for analytics or WORM workloads) solely after making certain the info is immutable.
Use the _cat/indices?v API to watch index sizes and refine retention or rollover insurance policies accordingly:
GET /_cat/indices/index1,index2,index3
Analyze shard configuration
Shards instantly have an effect on efficiency and useful resource utilization, so an acceptable shard technique needs to be used. The indexes which have heavy ingestion and searches ought to have a lot of shards within the order of variety of nodes for higher effectivity throughout all knowledge nodes within the cluster. We suggest preserving shard sizes between 10–30 GB for search workloads and as much as 50 GB for log analytics workloads and restrict to <20 shards per GB of JVM heap.
Run _cat/shards?v to substantiate even shard distribution and no unassigned shards. Consider over-sharding by checking JVMMemoryPressure (>80%) or SearchLatency spikes (>200 milliseconds) from extreme shard coordination. Assess under-sharding if IndexingLatency (>200 milliseconds) or low SearchRate signifies restrict parallelism. Use _cat/allocation?v to determine unbalanced shard sizes or scorching spots on nodes:
GET /_cat/allocation/node_name_1,node_name_2,node_name_3
Dealing with sudden site visitors spikes
Even nicely right-sized OpenSearch Service domains can face efficiency challenges throughout sudden workload surges, similar to log bursts, search site visitors peaks, or seasonal load patterns. To deal with such sudden spikes successfully, think about implementing the next greatest practices:
- Allow Auto-Tune – Mechanically alter cluster settings based mostly on present utilization and site visitors patterns
- Distribute shards successfully – Keep away from shard hotspots through the use of balanced shard allocation and index rollover insurance policies
- Pre-warm clusters for recognized occasions – For anticipated peak durations (end-of-month experiences, advertising campaigns), briefly scale up earlier than the spike and scale down afterward
- Monitor with CloudWatch alarms – Set proactive alarms for CPU, JVM reminiscence, and thread pool rejections to catch early stress indicators
Deploy CloudWatch alarms
CloudWatch alarms carry out an motion when a CloudWatch metric exceeds a specified worth for some period of time to take remediation motion proactively.
Conclusion
Proper-sizing is a steady strategy of observing, analyzing, and optimizing. By utilizing CloudWatch metrics, OpenSearch Dashboards, and greatest practices round shard sizing and workload profiling, you may make positive your area is environment friendly, performant, and cost-effective. Proper-sizing your OpenSearch Service area helps present optimum efficiency, cost-efficiency, and scalability. By monitoring key metrics, optimizing shards, and utilizing AWS instruments like CloudWatch, ISM, and Auto Scaling, you possibly can keep a high-performing cluster with out over-provisioning.
For extra details about right-sizing OpenSearch Service domains, consult with Sizing Amazon OpenSearch Service domains.