Docs » Available host and application monitors » Configure application receivers for hosts and servers » Elasticsearch query

Elasticsearch query 🔗

Description 🔗

This receiver is in beta.

This receiver metricizes aggregated responses from Elasticsearch. The receiver constructs Splunk Observability Cloud data points based on Elasticsearch aggregation types and also aggregation names.

The following is a sample configuration:

monitors:
- type: elasticsearch-query
 host: localhost
 port: 9200
 index: <name_of_index>
 elasticsearchRequest: |
    {
      "query" : {
        "range" : {
          "@timestamp" : {
            "gte": "now-5m"
          }
        }
      },
      "aggs": {
        "avg_cpu_utilization": {
          "avg": {
            "field": "cpu_utilization"
          }
        }
      }
    }
intervalSeconds: 300

The elasticsearchRequest takes in a string request in the format specified in elasticsearch documentation.

The above query is performed against an index containing documents that take the following form:

{
   'cpu_utilization':87,
   'memory_utilization':94,
   'host':'helsinki',
   'service':'android',
   'container_id':'macbook',
   '@timestamp':1580321240579
}

The query specified in elasticsearchRequest returns the average value of cpu_utilization across all documents with a @timestamp in the last five minutes. This value is metricized to the following form in Splunk Observability Cloud:

{
metric_name: avg_cpu_utilization,
dimensions:
  index: <name_of_index>
  metric_aggregation_type: avg
}

Data model transformation 🔗

This receiver transforms Elasticsearch responses into Splunk Observability Cloud data points.

At high level, this receiver metricizes responses of the following types:

  1. Metric aggregations inside one or more Bucket aggregations such as the terms and filters aggregations. Dimensions on a data point are determined by the aggregation name (dimension name) and the key of each bucket (dimension value). The metric name is derived from the type of metric aggregation name and its values in case of multi-value aggregations. A dimension called metric_aggregation_typeis also set on the corresponding data points.

  2. Metric aggregations applied without any Bucket aggregation are transformed in the same way.

  3. Bucket aggregations that do not have any Metric aggregations as sub aggregations are transformed to a metric called <name_of_aggregation>.doc_count and have the bucket_aggregation_name dimension apart from the key of each bucket.

Note: Since Bucket aggregations determine dimensions in Splunk Observability Cloud, in most cases Bucket aggregations should be performed on string fields that represent a slice of the data from Elasticsearch.

Examples 🔗

  1. avg metric aggregation as a sub-aggregation of terms bucket aggregation:

    {
"aggs":{
    "host" : {
    "terms":{"field" : "host"},
    "aggs": {
        "average_cpu_usage": {
        "avg": {
            "field": "cpu_utilization"
        }
        }
    }
    }
}
}

    This query results in a metric called elasticsearch_query.average_cpu_usage, where the data point has a host dimension with its value being the key of a bucket in the response. The type of the metric aggregation (avg) is set on the data oint as the metric_aggregation_type dimension.

    For instance, the json below provides 4 data points, each with a different value for host:

    "aggregations" : {
"host" : {
    "doc_count_error_upper_bound" : 0,
    "sum_other_doc_count" : 0,
    "buckets" : [
    {
        "key" : "helsinki",
        "doc_count" : 13802,
        "average_cpu_usage" : {
        "value" : 49.77438052456166
        }
    },
    {
        "key" : "lisbon",
        "doc_count" : 13802,
        "average_cpu_usage" : {
        "value" : 49.919866685987536
        }
    },
    {
        "key" : "madrid",
        "doc_count" : 13802,
        "average_cpu_usage" : {
        "value" : 49.878350963628456
        }
    },
    {
        "key" : "nairobi",
        "doc_count" : 13802,
        "average_cpu_usage" : {
        "value" : 49.99789885523837
        }
    }
    ]
}
}

  2. extended_stats metric aggregation as a sub-aggregation of terms bucket aggregation

    {
"aggs":{
"host" : {
    "terms":{"field" : "host"},
    "aggs": {
    "cpu_usage_stats": {
        "extended_stats": {
        "field": "cpu_utilization"
        }
    }
    }
}
}
}

    "aggregations" : {
"host" : {
    "doc_count_error_upper_bound" : 0,
    "sum_other_doc_count" : 0,
    "buckets" : [
    {
        "key" : "helsinki",
        "doc_count" : 13996,
        "cpu_usage_stats" : {
        "count" : 13996,
        "min" : 0.0,
        "max" : 100.0,
        "avg" : 49.86660474421263,
        "sum" : 697933.0
        }
    },
    {
        "key" : "lisbon",
        "doc_count" : 13996,
        "cpu_usage_stats" : {
        "count" : 13996,
        "min" : 0.0,
        "max" : 100.0,
        "avg" : 49.88225207202058,
        "sum" : 698152.0
        }
    },
    {
        "key" : "madrid",
        "doc_count" : 13996,
        "cpu_usage_stats" : {
        "count" : 13996,
        "min" : 0.0,
        "max" : 100.0,
        "avg" : 49.92469276936267,
        "sum" : 698746.0
        }
    },
    {
        "key" : "nairobi",
        "doc_count" : 13996,
        "cpu_usage_stats" : {
        "count" : 13996,
        "min" : 0.0,
        "max" : 100.0,
        "avg" : 49.98320948842527,
        "sum" : 699565.0
        }
    }
    ]
}
}

    In this case, each bucket results in 5 metrics:

    1. cpu_usage_stats.count

    2. cpu_usage_stats.min

    3. cpu_usage_stats.max

    4. cpu_usage_stats.avg

    5. cpu_usage_stats.sum

    The dimensions are derived in the same manner as the previous example.

Benefits 🔗

After you configure the integration, you can access these features:

  • View metrics. You can create your own custom dashboards, and most monitors provide built-in dashboards as well. For information about dashboards, see View dashboards in Observability Cloud.

  • View a data-driven visualization of the physical servers, virtual machines, AWS instances, and other resources in your environment that are visible to Infrastructure Monitoring. For information about navigators, see Splunk Infrastructure Monitoring navigators.

  • Access the Metric Finder and search for metrics sent by the monitor. For information, see Use the Metric Finder.

Installation 🔗

Follow these steps to deploy this integration:

  1. Deploy the Splunk Distribution of OpenTelemetry Collector to your host or container platform:

  2. Configure the monitor, as described in the Configuration section.

  3. Restart the Splunk Distribution of OpenTelemetry Collector.

Configuration 🔗

To use this Smart Agent monitor with the Collector, include the smartagent receiver and service pipeline in your configuration file. The Smart Agent receiver is fully supported only on x86_64/amd64 platforms.

See the examples below for more details.

receivers:
  smartagent/elasticsearch-query: 
    type: elasticsearch-query
    ... # Additional config

To complete the integration, include the receiver with this monitor type in a metrics pipeline. To do this, add the receiver to the service > pipelines > metrics > receivers section of your configuration file.

service:
  pipelines:
    metrics:
      receivers: [smartagent/elasticsearch-query]

See the configuration example in GitHub for specific use cases that show how the Splunk Distribution of OpenTelemetry Collector can integrate and complement existing environments.

For Kubernetes, see the kubernetes.yaml in GitHub for the Agent and Gateway YAML files.

For Prometheus, see Prometheus Federation Endpoint Example in GitHub for an example of how the OTel Collector works with Splunk Enterprise and an existing Prometheus deployment.

Configuration settings 🔗

The following table shows the configuration options for this receiver:

Option

Required

Type

Description

httpTimeout

no

int64

HTTP timeout duration for both read and writes. This should be a duration string that is accepted by https://golang.org/pkg/time/#ParseDuration (default: 10s)

username

no

string

Basic Auth username to use on each request, if any.

password

no

string

Basic Auth password to use on each request, if any.

useHTTPS

no

bool

If true, the agent will connect to the server using HTTPS instead of plain HTTP. (default: false)

httpHeaders

no

map of strings

A map of HTTP header names to values. Comma separated multiple values for the same message-header is supported.

skipVerify

no

bool

If useHTTPS is true and this option is also true, the exporter TLS cert will not be verified. (default: false)

caCertPath

no

string

Path to the CA cert that has signed the TLS cert, unnecessary if skipVerify is set to false.

clientCertPath

no

string

Path to the client TLS cert to use for TLS required connections

clientKeyPath

no

string

Path to the client TLS key to use for TLS required connections

host

yes

string

port

yes

string

index

no

string

Index that’s being queried. If none is provided, given query will be applied across all indexes. To apply the search query to multiple indices, provide a comma separated list of indices (default: _all)

elasticsearchRequest

yes

string

Takes in an Elasticsearch request body search request. See the Elasticsearch documentation at https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-body.html for details.

Metrics 🔗

The Splunk Distribution of OpenTelemetry Collector does not do any built-in filtering of metrics for this receiver.

Get help 🔗

If you are not able to see your data in Splunk Observability Cloud, try these tips:

To learn about even more support options, see Splunk Customer Success.