Amazon OpenSearch Ingestion 101: Set CloudWatch alarms for key metrics

Amazon OpenSearch Ingestion is a completely managed, serverless knowledge pipeline that simplifies the method of ingesting knowledge into Amazon OpenSearch Service and OpenSearch Serverless collections. Some key ideas embrace:

• Supply – Enter part that specifies how the pipeline ingests the information. Every pipeline has a single supply which might be both push-based and pull-based.
• Processors – Intermediate processing models that may filter, rework, and enrich information earlier than supply.
• Sink – Output part that specifies the vacation spot(s) to which the pipeline publishes knowledge. It may publish information to a number of locations.
• Buffer – It’s the layer between the supply and the sink. It serves as momentary storage for occasions, decoupling the supply from the downstream processors and sinks. Amazon OpenSearch Ingestion additionally provides a persistent buffer choice for push-based sources
• Useless-letter queues (DLQs) – Configures Amazon Easy Storage Service (Amazon S3) to seize information that fail to jot down to the sink, enabling error dealing with and troubleshooting.

This end-to-end knowledge ingestion service may help you acquire, course of, and ship knowledge to your OpenSearch environments with out the necessity to handle underlying infrastructure.

This publish offers an in-depth take a look at establishing Amazon CloudWatch alarms for OpenSearch Ingestion pipelines. It goes past our really helpful alarms to assist determine bottlenecks within the pipeline, whether or not that’s within the sink, the OpenSearch clusters knowledge is being despatched to, the processors, or the pipeline not pulling or accepting sufficient from the supply. This publish will aid you proactively monitor and troubleshoot your OpenSearch Ingestion pipelines.

Overview

Monitoring your OpenSearch Ingestion pipelines is essential for catching and addressing points early. By understanding the important thing metrics and establishing the correct alarms, you possibly can proactively handle the well being and efficiency of your knowledge ingestion workflows. Within the following sections, we offer particulars about alarm metrics for various sources, displays, and sinks. The particular values for the brink, interval, and datapoints to alarm used for alarms can differ based mostly on the person use case and necessities.

Conditions

To create an OpenSearch Ingestion pipeline, seek advice from Creating Amazon OpenSearch Ingestion pipelines. For creating CloudWatch alarms, seek advice from Create a CloudWatch alarm based mostly on a static threshold.

You’ll be able to allow logging for OpenSearch Ingestion Pipeline, which captures numerous log messages throughout pipeline operations and ingestion exercise, together with errors, warnings, and informational messages. For particulars on enabling and monitoring pipeline logs, seek advice from Monitoring pipeline logs

Sources

The entry level of your pipeline is usually the place monitoring ought to start. By setting applicable alarms for supply parts, you possibly can shortly determine ingestion bottlenecks or connection points. The next desk summarizes key alarm metrics for various sources.

Processors

The next desk offers particulars about alarm metrics for various processors.

Sinks and DLQs

The next desk comprises particulars about alarm metrics for various sinks and DLQs.

Buffer

The next desk comprises particulars about alarm metrics for buffers.

Reference Information

The next present steerage for resolving frequent pipeline points together with basic reference.

REF-001: WARN-level Log Assessment

Assessment WARN-level logs within the pipeline logs to determine the exception particulars.

REF-002: ERROR-level Log Assessment

Assessment ERROR-level logs within the pipeline logs to determine the exception particulars.

REF-003: S3 Objects Failed

When troubleshooting growing s3ObjectsFailed.depend values, monitor these particular metrics to slender down the foundation trigger:

• s3ObjectsAccessDenied.depend – This metric increments when the pipeline encounters Entry Denied or Forbidden errors whereas studying S3 objects. Widespread causes embrace:
• Inadequate permissions within the pipeline function.
• Restrictive S3 bucket coverage not permitting the pipeline function entry.
• For cross-account S3 buckets, incorrectly configured bucket_owners mapping.
• s3ObjectsNotFound.depend – This metric increments when the pipeline receives Not Discovered errors whereas trying to learn S3 objects.

For additional help with the really helpful actions, contact AWS help.

REF-004: Configuring Alarm for distinction in totalOpenShards.max and activeShardsInProcessing.worth for Amazon DynamoDB supply.

1. Open the CloudWatch console at https://console.aws.amazon.com/cloudwatch/.
2. Within the navigation pane, select Alarms, All alarms.
3. Select Create alarm.
4. Select Choose Metric.
5. Choose Supply.
6. In supply, following JSON can be utilized after updating the , and .

   {
       "metrics": [
           [ { "expression": "m1-e1", "label": "Expression2", "id": "e2", "period": 900 } ],
           [ { "expression": "FLOOR((m2/15)+0.5)", "label": "Expression1", "id": "activeShardsInProcessing", "visible": false, "period": 900 } ],
           [ "AWS/OSIS", ".dynamodb.totalOpenShards.max", "PipelineName", "", { "stat": "Maximum", "id": "m1", "visible": false } ],
           [ ".", ".dynamodb.activeShardsInProcessing.value", ".", ".", { "stat": "Average", "id": "m2", "visible": false } ]
       ],
       "view": "timeSeries",
       "stacked": false,
       "interval": 900,
       "area": ""
   }

Let’s evaluation couple of eventualities based mostly on the above metrics.

Situation 1 – Perceive and Decrease Pipeline Latency

Latency inside a pipeline is constructed up of three important parts:

• The time it takes to ship paperwork through bulk requests to OpenSearch,
• the time it takes for knowledge to undergo the pipeline processors, and
• the time that knowledge sits within the pipeline buffer

Bulk requests and processors (final two gadgets within the earlier checklist) are the foundation causes for why the buffer builds up and results in latency.

To observe how a lot knowledge is being saved within the buffer, monitor the bufferUsage.worth metric. The one approach to decrease latency inside the buffer is to optimize the pipeline processors and sink bulk request latency, relying on which of these is the bottleneck.

The bulkRequestLatency metric measures the time taken to execute bulk requests, together with retries, and can be utilized to observe write efficiency to the OpenSearch sink. If this metric reviews an unusually excessive worth, it signifies that the OpenSearch sink could also be overloaded, inflicting elevated processing time. To troubleshoot additional, evaluation the bulkRequestNumberOfRetries.depend metric to verify whether or not the excessive latency is because of rejections from OpenSearch which might be resulting in retries, equivalent to throttling (429 errors) or different causes. If doc errors are current, look at the configured DLQ to determine the failed doc particulars. Moreover, the max_retries parameter might be configured within the pipeline configuration to restrict the variety of retries. Nevertheless, if the documentErrors metric reviews zero, the bulkRequestNumberOfRetries.depend can also be zero, and the bulkRequestLatency stays excessive, it’s probably an indicator that the OpenSearch sink is overloaded. On this case, evaluation the vacation spot metrics for added particulars.

If the bulkRequestLatency metric is low (for instance, lower than 1.5 seconds) and the bulkRequestNumberOfRetries metric is reported as 0, then the bottleneck is probably going inside the pipeline processors. To observe the efficiency of the processors, evaluation the .timeElapsed.avg metric. This metric reviews the time taken for the processor to finish processing of a batch of information. For instance, if a grok processor is reporting a a lot increased worth than different processors for timeElapsed, it could be resulting from a sluggish grok sample that may be optimized and even changed with a extra performant processor, relying on the use case.

Situation 2 – Understanding and Resolving Doc Errors to OpenSearch

The documentErrors.depend metric tracks the variety of paperwork that didn’t be despatched by bulk requests. The failure can occur resulting from numerous causes equivalent to mapping conflicts, invalid knowledge codecs, or schema mismatches. When this metric reviews a non-zero worth, it signifies that some paperwork are being rejected by OpenSearch. To determine the foundation trigger, look at the configured Useless Letter Queue (DLQ), which captures the failed paperwork together with error particulars. The DLQ offers details about why particular paperwork failed, enabling you to determine patterns equivalent to incorrect discipline varieties, lacking required fields, or knowledge that exceeds measurement limits. For instance, discover the pattern DLQ objects for frequent points under:

Mapper parsing exception:

{"dlqObjects": [{
        "pluginId": "opensearch",
        "pluginName": "opensearch",
        "pipelineName": "",
        "failedData": {
            "index": "",
            "indexId": null,
            "status": 400,
            "message": "failed to parse field [] of sort [integer] in doc with id ''. Preview of discipline's worth: 'N/A' attributable to For enter string: "N/A"",
            "doc": {}
        },
        "timestamp": "…"
    }]}

Right here, OpenSearch can’t retailer the textual content string “N/A” in a discipline that’s just for numbers, so it rejects the doc and shops it within the DLQ.

Restrict of complete fields exceeded:

{"dlqObjects": [{
        "pluginId": "opensearch",
        "pluginName": "opensearch",
        "pipelineName": "",
        "failedData": {
            "index": "",
            "indexId": null,
            "status": 400,
            "message": "Limit of total fields [] has been exceeded",
            "doc": {}
        },
        "timestamp": "…"
    }]}

The index.mapping.total_fields.restrict setting is the parameter that controls the utmost variety of fields allowed in an index mapping, and exceeding this restrict will trigger indexing operations to fail. You’ll be able to verify if all these fields are required or leverage numerous processors supplied by OpenSearch Ingestion to rework the information.

As soon as these points are recognized, you possibly can both right the supply knowledge, alter the pipeline configuration to rework the information appropriately, or modify the OpenSearch index mapping to accommodate the incoming knowledge format.

Clear up

When establishing alarms for monitoring your OpenSearch Ingestion pipelines, it’s essential to be conscious of the potential prices concerned. Every alarm you configure will incur expenses based mostly on the CloudWatch pricing mannequin.

To keep away from pointless bills, we advocate fastidiously evaluating your alarm necessities and configuring them accordingly. Solely arrange the alarms which might be important in your use case, and commonly evaluation your alarm configurations to determine and take away unused or redundant alarms.

Conclusion

On this publish, we explored the excellent monitoring capabilities for OpenSearch Ingestion pipelines by CloudWatch alarms, protecting key metrics throughout numerous sources, processors, and sinks. Though this publish highlights essentially the most vital metrics, there’s extra to find. For a deeper dive, seek advice from the next sources:

Efficient monitoring by CloudWatch alarms is essential for sustaining wholesome ingestion pipelines and sustaining optimum knowledge circulate.

Concerning the authors