--- title: Optimize your data ingest source: https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/operational-efficiency/data-governance-optimize-ingest-guide --- ![Optimize](https://docs.newrelic.com/images/oma-oe-dg_icon_optimizing.webp) **Data ingest governance** is a practice of getting optimal value for the telemetry data collected by an organization. This is especially important for a complex organization that has numerous business units and working groups. This is the third part of a four-part guide to optimizing your New Relic data ingest, and is part of [our series on observability maturity](https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/introduction). ## Before you start [#before-start] This guide contains detailed recommendations for optimizing your data ingest. Before using this guide, we recommend you review [our general data management docs](https://docs.newrelic.com/docs/data-apis/manage-data/manage-your-data). ## Desired outcome [#desired-outcome] Maximize the observability value of your data by optimizing data ingest. Reduce non-essential ingest data so you can stay within your budget. ## Process [#process] The process will include these steps: - [Prioritize your observability objectives](#prioritize-objectives) - [Develop an optimization plan](#develop-plan) - [Use data reduction techniques to execute your plan](#use-reduction-techniques) We'll explain these steps in more detail. ## Prioritize your observability objectives [#prioritize-objectives] One of the most important parts of the data ingest governance framework is to align collected telemetry with **observability value drivers**. You need to ensure that you understand the primary observability objective is when you configure new telemetry. When you introduce new telemetry you want to understand what it delivers to your overall observability solution. Your new data might overlap with other data. If you consider introducing telemetry that you can't align to any of the key objectives you may reconsider introducing that data. Objectives include: - Meeting an internal SLA - Meeting an external SLA - Supporting feature innovation (A/B performance and adoption testing) - Monitor customer experience - Hold vendors and internal service providers to their SLA - Business process health monitoring - Other compliance requirements Alignment to these objectives are what allow you to make flexible and intuitive decisions about prioritizing one set of data over another and helping guide teams know where to start when instrumenting new platforms and services. ## Develop an optimization plan [#develop-plan] For this section, we'll make two core assumptions: - You have the tools and techniques from the [Baseline your data ingest](https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/operational-efficiency/dg-baselining) section to have a proper accounting of where your data is coming from. - You have a good understanding of the [observability maturity value drivers](https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/introduction). This will be crucial in applying a value and a priority to groups of telemetry. Use the following examples to help you visualize how you would assess your own telemetry ingest and make the sometimes hard decisions that are needed to get within budget. Although each of these examples tries to focus on a value driver, most instrumentation serves more than one value driver. This is the hardest part of data ingest governance. **Example 1: Focus on uptime and reliability** An account is ingesting about 20% more than they'd budgeted for. They've been asked by a manager to find some way to reduce consumption. Their most important value driver is **Uptime, performance, and reliability**. ![Observability value drivers with a focus on uptime and reliability](https://docs.newrelic.com/images/oma-oe-dg_diagram_value-driver-uptime.webp "Observability value drivers with a focus on uptime and reliability") Observability value drivers with a focus on uptime and reliability. Their estate includes: - APM (dev, staging, prod) - Distributed tracing - Browser - Infrastructure monitoring of 100 hosts - K8s monitoring (dev, staging, prod) - Logs (dev, staging, prod - including debug) > #### πŸ’‘ OPTIMIZATION PLAN > > - Omit debug logs (knowning they can be turned on if there is an issue) (saves 5%) > - Omit several K8s state metrics which are not required to display the Kubernetes cluster explorer (saves 10%) > - Drop some custom Browser events they were collecting back when they were doing a lot of A/B testing of new features (saves 10%) After executing those changes the team is 5% below their budget and has freed up some space to do a NPM pilot. Their manager is satisfied they're not losing any significant uptime and reliability observability. > #### πŸ’‘ FINAL OUTCOME > > - 5% under their original budget > - Headroom created for an NPM pilot which serves uptime, performance, and reliability objectives > - Minimal loss of uptime and reliability observability **Example 2: Focus on customer experience** A team responsible for a new user-facing platform with an emphasis on mobile monitoring and browser monitoring is running 50% over budget. They'll need to right-size their ingest, but they're adamant about not sacrificing any **Customer experience** observability. ![Observability value drivers with a focus on customer experience](https://docs.newrelic.com/images/oma-oe-dg_diagram_value-driver-customer.webp "Observability value drivers with a focus on customer experience") Observability value drivers with a focus on **Customer experience** Their estate includes: - Mobile - Browser - APM - Distributed tracing - Infrastructure on 30 hosts, including process samples - Serverless monitoring for some backend asynchronous processes - Logs from their serverless functions - Various cloud integrations > #### πŸ’‘ OPTIMIZATION PLAN > > - Omit the serverless logs (they are basically redundant to what they get from their Lambda integration) > - Decrease the process sample rate on their hosts to every one minute > - Drop process sample data in DEV environments > - Turn off EC2 integration which is highly redundant with other infrastructure monitoring provided by the New Relic infra agent. > #### πŸ’‘ FINAL OUTCOME > > - 5% over their original budget > - Sufficient to get them through peak season > - No loss of customer experience observability After executing the changes they're now just 5% over their original budget, but they conclude this will be sufficient to carry them through peak season. **Example 3: Focus on innovation** A team is in the process of refactoring a large Python monolith to four microservices. The monolith shares much infrastructure with the new architecture including a customer database, and a cache layer. They are 70% over budget and have two months to go before they can officially decommission the monolith. ![Observability value drivers with a focus on Innovation and Growth](https://docs.newrelic.com/images/oma-oe-dg_diagram_value-driver-innovation.webp "Observability value drivers with a focus on Innovation and Growth") Observability value drivers with a focus on **Innovation and growth**. Their estate includes: - K8s monitoring (microservices) - New Relic host monitoring (monolith) - APM (microservices and host monitoring) - Distributed tracing (microservices and host monitoring) - Postgresql (shared) - Redis(shared) - MSSQL (future DB for the microservice architecture) - Load balancer logging (microservices and host monitoring) > #### πŸ’‘ OPTIMIZATION PLAN > > - Configure load balancer logging to only monitor 5xx response codes (monolith) > - Custom sample rate on `ProcessSample`, `StorageSample`, and `NetworkSample` to 60s for hosts running the monolith > - Disable MSSQL monitoring since currently the new architecture does not use it. > - Disable distributed tracing for the monolith as it's far less useful then it will be for the microservice architecture. > #### πŸ’‘ FINAL OUTCOME > > - 1% under their original budget > - No loss of **Innovation and growth** observability > #### πŸ’‘ TIP > > We recommend you to track the plan in a task managing tool you're familiar with. This helps to manage the optimization plan, and also to understand the effect each optimization task takes. You can use this [Data optimization plan template](https://docs.google.com/spreadsheets/d/1CimLpALwl1Z9f41vzbNWx00bGcED9XPV3s4ROqVEnr0/copy). ## Use data reduction techniques to execute your plan [#use-reduction-techniques] At this stage you've given thought to all of the kinds of telemetry in your account(s) and how it relates to your value drivers. This section will provide detailed technical instructions and examples on how to reduce a variety of telemetry types. There are two main ways to approach data reduction: - Through configuration - Through using drop rules ### Optimization through configuration [#optimization-through-configuration] This section includes various ways to configure New Relic features to optimize data reporting and ingest: **APM agent** > #### πŸ’‘ GROWTH DRIVERS > > - Monitored transactions > - Error activity > - Custom events The volume of data generated by the APM agent will be determined by several factors: - The amount of organic traffic generated by the application (for example, all things being equal an application being called one million times per day will generate more data than one being called one thousand times per day) - Some of the characteristics of the underlying transaction data itself (length and complexity of URLs) - Whether the application is reporting database queries - Whether the application has transactions with many (or any) custom attributes - The error volume for the application - Whether the application agent is configured for distributed tracing ### Managing volume While you can assume that all calls to an application are needed to support the business, it's possible that you could be more thrifty in your overall architecture. In an extreme case you may have a user profile microservice that is called every 10 seconds by its clients. This helps reduce latency if some user information is updated by other clients. However, one lever we have is reducing the frequency of calls to this service to, for example, every minute. ### Custom attributes Any [custom attributes](https://docs.newrelic.com/docs/data-apis/custom-data/custom-events/collect-custom-attributes/) added using a call to an APM API [`addCustomParameter`](https://developer.newrelic.com/collect-data/custom-attributes/) will add an additional attribute to the transaction payload. These are often useful, but as application logic and priorities change the data can become less valuable or even obsolete. The Java agent captures the following `request.headers` by default: - `request.headers.referer` - `request.headers.accept` - `request.headers.contentLength` - `request.headers.host` - `request.headers.userAgent` Developers might also use `addCustomParameter` to capture additional information (potentially more verbose headers). For an example of the rich configuration that's available in relation to APM see our [Java agent documentation](https://docs.newrelic.com/docs/apm/agents/java-agent/attributes/java-agent-attributes/#requestparams) ### Error events It's possible to determine how errors will be handled by APM. This can reduce the volume of data in some cases. For example there may be a high volume but harmless error that cannot be removed at the present time. We have the ability to `collect`, `ignore`, or `mark as expected`. For more information, see [Manage APM errors](https://docs.newrelic.com/docs/apm/agents/manage-apm-agents/agent-data/manage-errors-apm-collect-ignore-or-mark-expected). ### Database queries One highly variable aspect of APM instances is the number of database calls and what configurations we have set. We have a fair amount of control on how verbose database query monitoring is. These queries will show up in the transaction traces page. Common database query setting changes include: - [Collecting raw query data instead of obfuscated or turning off query collection](https://docs.newrelic.com/docs/apm/transactions/transaction-traces/configure-transaction-traces#record-sql) - Changing the stack trace threshold - Turning on query explain plan collection For more details, see [Transaction traces database queries page](https://docs.newrelic.com/docs/apm/transactions/transaction-traces/transaction-traces-database-queries-page/#settings). ### Setting event limits Our APM and mobile agents have limits on how many events can be reported per harvest cycle. If there were no limit, a very large number of events being sent could impact the performance of your application or of New Relic. When the limit is reached, the agents begin sampling events in order to give a representative sample of events across the harvest cycle. Different agents have different limits. Events that are capped and subject to sampling include: - Custom events reported via agent API (for example, the .NET agent's `RecordCustomEvent`) - `Mobile` - `MobileCrash` - `MobileHandledException` - `MobileRequest` - `Span` (see distributed tracing sampling) - `Transaction` - `TransactionError` Most agents have configuration options for changing the event limit on sampled transactions. For example, the Java agent uses [`max_samples_stored`](https://docs.newrelic.com/docs/apm/agents/java-agent/configuration/java-agent-configuration-config-file/#ae-max_samples_stored). The default value for `max_samples_stored` is `2000` and the max is `10000`. This value governs how many sampled events can be reported every 60 seconds from an agent instance. For a full explanation of event sampling limits, see [Event limits](https://docs.newrelic.com/docs/using-new-relic/data/understand-data/new-relic-event-limits-sampling). You can compensate for sampled events via the NRQL [`EXTRAPOLATE` operator](https://docs.newrelic.com/docs/query-your-data/nrql-new-relic-query-language/get-started/nrql-syntax-clauses-functions/#extrapolate). Before attempting to change how sampling occurs, please read these caveats and recommendations: - The more events you report, the more memory your agent will use. - You can usually get the data you need without raising an agent's event-reporting limit. - The payload size limit is 1MB (10^6 bytes) (compressed), so the number of events may still be affected by that limit. To determine if events are being dropped, see the agent log for a `413 HTTP` status message. ### Log sampling rate Newer versions of the New Relic APM language agents can forward logs directly to New Relic. In some cases you may want to govern some limits of how big logging spikes can be from each APM agent instance. For details on APM agent log sampling, see [Log forwarders](#log-forwarders). ### Transaction traces > #### πŸ’‘ GROWTH DRIVERS > > - Number of connected services > - Number of monitored method calls per connected services In APM, [transaction traces](https://docs.newrelic.com/docs/apm/transactions/transaction-traces/transaction-traces) record in-depth details about your application's transactions and database calls. You can edit the default settings for transaction traces. This is also highly configurable via [transaction trade configuration](https://docs.newrelic.com/docs/apm/transactions/transaction-traces/configure-transaction-traces). The level and mode of configurability will be language-specific in many cases. Transaction trace settings available using server-side configuration will differ depending on the New Relic agent you use. The UI includes descriptions of each. Settings in the UI may include: - Transaction tracing and threshold - Record SQL, including recording level and input fields - Log SQL and stack trace threshold - SQL query plans and threshold - Error collection, including HTTP code and error class - Slow query tracing - Thread profiler ### Distributed tracing Distributed tracing configuration has some language-specific differences. Distributed tracing can be disabled as needed. This is an example for Java agent `newrelic.yml`: ```yml distributed_tracing: enabled: false ``` This is a node.js example for `newrelic.js` ```js distributed_tracing: { enabled: false } ``` Data volume will also vary based on whether you are using [Infinite Tracing](https://docs.newrelic.com/docs/distributed-tracing/infinite-tracing/introduction-infinite-tracing). Standard distributed tracing for APM agents (above) captures up to 10% of your traces, but if you want us to analyze all your data and find the most relevant traces, you can set up Infinite Tracing. This alternative to standard distributed tracing is available for all APM language agents. The main parameters that could drive a small increase in monthly ingest are: - Configure trace observer monitoring - Configure span attribute trace filter - Configure random trace filter **Browser agent** > #### πŸ’‘ GROWTH DRIVERS > > - Page loads > - Ajax calls > - Error activity For [browser agent version 1211](https://docs.newrelic.com/docs/release-notes/new-relic-browser-release-notes/browser-agent-release-notes) or higher, all network requests made by a page are recorded as `AjaxRequest` events. You can use the deny list configuration options in the application settings UI page to filter which requests record events. Regardless of this filter, all network requests are captured as metrics and available in the AJAX page. ### Using the deny list Requests can be blocked in three ways: - To block recording of all `AjaxRequest` events, add an asterisk `*` as a wildcard. - To block recording of `AjaxRequest` events to a domain, enter just the domain name. Example: `example.com` - To block recording of `AjaxRequest` events to a specific domain and path, enter the domain and path. Example: `example.com/path` - The protocol, port, search and hash of a URL are ignored by the deny list. To validate whether the filters you have added work as expected, run a NRQL query for `AjaxRequest` events matching your filter. ### Accessing the deny list To update the deny list of URLs your application will filter from creating events, go to the app settings UI page: 1. Go to **[one.newrelic.com](https://one.newrelic.com/all-capabilities)**, and click Browser. 2. Select an app. 3. On the left navigation, click **App settings**. 4. Under **Ajax request deny list**, add the filters you'd like to apply. 5. Select **Save application settings** to update the agent configuration. 6. Redeploy the browser agent (either restarting the associated APM agent or updating the copy/paste browser installation). ### Validating ```sql FROM AjaxRequest SELECT * WHERE requestUrl LIKE `%example.com%` ``` **Mobile agent** > #### πŸ’‘ GROWTH DRIVERS > > - Monthly active users > - Crash events > - Number of events per user ### Android All settings, including the call to invoke the agent, are called in the `onCreate` method of the `MainActivity` class. To change settings, call the setting in one of two ways (if the setting supports it): ```java NewRelic.disableFeature(FeatureFlag.DefaultInteractions); NewRelic.enableFeature(FeatureFlag.CrashReporting); NewRelic.withApplicationToken(NEW_RELIC_TOKEN).start(this.getApplication()); ``` [Analytics settings](https://docs.newrelic.com/docs/mobile-monitoring/new-relic-mobile-android/android-sdk-api/android-agent-configuration-feature-flags#analytics-settings) enable or disable collection of event data. These events are reported to New Relic and used in the **Crash analysis** page. It's also possible to configure [agent logging](https://docs.newrelic.com/docs/mobile-monitoring/new-relic-mobile-android/android-sdk-api/android-agent-configuration-feature-flags#logging-settings-logging) to be more or less verbose. ### iOS Like with Android, New Relic's iOS configuration allows to enable and disable [feature flags](https://docs.newrelic.com/docs/mobile-monitoring/new-relic-mobile-android/android-sdk-api/android-agent-configuration-feature-flags). The following feature flags can be configured: #### Crash and error reporting - `NRFeatureFlag_CrashReporting` - `NRFeatureFlag_HandleExceptionEvents` - `NRFeatureFlag_CrashReporting` #### Distributed tracing - `NRFeatureFlag_DistributedTracing` #### Interactions - `NRFeatureFlag_DefaultInteractions` - `NRFeatureFlag_InteractionTracing` - `NRFeatureFlag_SwiftInteractionTracing` #### Network feature flags - `NRFeatureFlag_ExperimentalNetworkInstrumentation` - `NRFeatureFlag_NSURLSessionInstrumentation` - `NRFeatureFlag_NetworkRequestEvents` - `NRFeatureFlag_RequestErrorEvents` - `NRFeatureFlag_HttpResponseBodyCapture` For more details, see [Feature flags](https://docs.newrelic.com/docs/mobile-monitoring/new-relic-mobile-ios/ios-sdk-api/ios-agent-configuration-feature-flags). **Infrastructure agent** > #### πŸ’‘ GROWTH DRIVERS > > - Hosts and containers monitored > - Sampling rates for core events > - Process sample configurations > - Custom attributes > - Number and type of on-host integrations installed > - Log forwarding configuration New Relic's [infrastructure agent configuration file](https://docs.newrelic.com/docs/infrastructure/install-infrastructure-agent/configuration/infrastructure-agent-configuration-settings) contains a couple of powerful ways to control ingest volume. The most important ingest control is configuring sampling rates. There are several distinct sampling rate configurations that can be adjusted. In addition it's possible to create regular expressions to control what gets collected from certain collectors such as for `ProcessSample` and `NetworkSample`. ### Configurable Sampling rates There are a number of sampling rates that can be configured in infrastructure, but these are the most commonly used. | Parameter | Default | Disable | | ----------------------------- | ------- | ------- | | `metrics_storage_sample_rate` | 5 | -1 | | `metrics_process_sample_rate` | 20 | -1 | | `metrics_network_sample_rate` | 10 | -1 | | `metrics_system_sample_rate` | 5 | -1 | | `metrics_nfs_sample_rate` | 5 | -1 | ### Process samples Process samples can be the single most high volume source of data from the infrastructure agent. This is because it will send information about any running process on a host. They're disabled by default but they can be enabled as follows: ```yaml enable_process_metrics: true ``` This has the same effect as setting `metrics_process_sample_rate` to `-1`. By default, processes using low memory are excluded from being sampled. For more information, see `disable-zero-mem-process-filter`. You can control how much data is sent to New Relic by configuring `include_matching_metrics`, which allows you to restrict the transmission of metric data based on the values of metric [attributes](https://docs.newrelic.com/docs/query-your-data/nrql-new-relic-query-language/nrql-query-tutorials/query-infrastructure-dimensional-metrics-nrql#naming-conventions). You include metric data by defining literal or partial values for any of the attributes of the metric. For example, you can choose to send the `host.process.cpuPercent` of all processes whose `process.name` matches the `^java` regular expression. In this example, we include process metrics using executable files and names: ```yaml include_matching_metrics: # You can combine attributes from different metrics process.name: - regex "^java" # Include all processes starting with "java" process.executable: - "/usr/bin/python2" # Include the Python 2.x executable - regex "\\System32\\svchost" # Include all svchost executables ``` You can also use this filter for the Kubernetes integration: ```yaml env: - name: NRIA_INCLUDE_MATCHING_METRICS value: | process.name: - regex "^java" process.executable: - "/usr/bin/python2" - regex "\\System32\\svchost" ``` An `exclude_matching_metrics` is available that works similarly for excluding metric data. ### Network interface filter > #### πŸ’‘ GROWTH DRIVERS > > - Number of network interfaces monitored The configuration uses a simple pattern-matching mechanism that can look for interfaces that start with a specific sequence of letters or numbers following either pattern: - `{name}[other characters]` - `[number]{name}[other characters]`, where you specify the name using the `index-1` option ```yaml network_interface_filters: prefix: - dummy - lo index-1: - tun ``` Default network interface filters for Linux: - Network interfaces that start with `dummy`, `lo`, `vmnet`, `sit`, `tun`, `tap`, or `veth` - Network interfaces that contain `tun` or `tap` Default network interface filters for Windows: - Network interfaces that start with `Loop`, `isatap`, or `Local` To override defaults include your own filter in the config file: ```yaml network_interface_filters: prefix: - dummy - lo index-1: - tun ``` ### Custom attributes [Custom attributes](https://docs.newrelic.com/docs/data-apis/custom-data/custom-events/collect-custom-attributes) are key-value pairs (similar to tags in other tools) used to annotate the data from the infrastructure agent. You can use this metadata to build filter sets, group your results, and annotate your data. For example, you might indicate a machine's environment (staging or production), the service a machine hosts (login service, for example), or the team responsible for that machine. Example of custom attributes from `newrelic.yml` ```yaml custom_attributes: environment: production service: billing team: alpha-team ``` They're powerful and useful but if the data is not well organized or has become obsolete in any way you should consider streamling these. **Kubernetes integration** > #### πŸ’‘ GROWTH DRIVERS > > - Number of `pods` and `containers` monitored > - Frequency and number of kube state metrics collected > - Logs generated per cluster It's not surprising that a complex and decentralized system like Kubernetes has the potential to generate a lot of telemetry fast. There are a few good approaches to managing data ingest in Kubernetes. These will be very straightforward if you are using observability as code in your K8s deployments. We highly recommend you install this Kubernetes data ingest analysis dashboard before making any decisions about reducing K8s ingest. To get this dashboard, see the [Infrastructure integrations quickstart](https://newrelic.com/instant-observability/infrastructure-integrations-data-analysis/8e31a0ae-81c0-4df0-a119-a0ada9ec16fa). ### Scrape interval Depending on your observability objectives you may consider adjusting the scrape interval. The default is 15s. Now the Kubernetes cluster explorer only refreshes every 45s. If your primary use of the K8s data is to support the KCE visualizations you may consider changing your scrape interval to 20s. That change from 15s to 20s can have a substantial impact. For more details about managing this, see our [Helm integration scrape interval docs](https://docs.newrelic.com/docs/kubernetes-pixie/kubernetes-integration/installation/install-kubernetes-integration-using-helm/#scrape-interval). ### Filtering Namespaces The Kubernetes Integration v3 and above allows filtering which namespaces are scraped by labelling them. By default all namespaces are scraped. We use the `namespaceSelector` in the same way Kubernetes does. In order to include only namespaces matching a label, change the `namespaceSelector` by adding the following to your `values-newrelic.yaml`, under the `newrelic-infrastructure` section: ```yaml common: config: namespaceSelector: matchLabels: key1 : "value1" ``` In this example only namespaces with the label `newrelic.com/scrape` set to `true` will be scraped: ```yaml global: licenseKey: YOUR_NEW_RELIC_LICENSE_KEY cluster: K8S_CLUSTER_NAME # ... Other settings as shown above # Configuration for newrelic-infrastructure newrelic-infrastructure: # ... Other settings as shown above common: config: namespaceSelector: matchLabels: newrelic.com/scrape: "true" ``` You can also use Kubernetes match expressions to include or exclude namespaces. The valid operators are: - In - NotIn - Exists - DoesNotExist The general structure the `matchExpressions` section is one or more of the following lines: ```yaml {key: VALUE, operator: OPERATOR, values: LIST_OF_VALUES} ``` Here's a complete example: ```yaml common: config: namespaceSelector: matchExpressions: - {key: newrelic.com/scrape, operator: NotIn, values: ["false"]} ``` > #### πŸ’‘ TIP > > More than one line can be included in the `matchExpresions` section, and the expressions are concatenated. All must be true for the filter to apply. Labels and match expressions are explained in more detail [here](https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/). In this example, namespaces with the label `newrelic.com/scrape` set to `false` will be excluded: ```yaml global: licenseKey: YOUR_NEW_RELIC_LICENSE_KEY cluster: K8S_CLUSTER_NAME # ... Other settings as shown above # Configuration for newrelic-infrastructure newrelic-infrastructure: # ... Other settings as shown above common: config: namespaceSelector: matchExpressions: - {key: newrelic.com/scrape, operator: NotIn, values: ["false"]} ``` See a full list of the settings that can be modified in the [chart's README file](https://github.com/newrelic/nri-kubernetes/tree/main/charts/newrelic-infrastructure). #### How can I know which namespaces are excluded? [#excluded-namespaces] All the namespaces within the cluster are listed thanks to the `K8sNamespace` sample. The `nrFiltered` attribute determines whether the data related to the namespace is going to be scraped. Use this query to know which namespaces are being monitored: ```sql FROM K8sNamespaceSample SELECT displayName, nrFiltered WHERE clusterName = INSERT_NAME_OF_CLUSTER SINCE 2 MINUTES AGO ``` #### What data is being discarded from the excluded namespaces? [#namespaces-discarded-data] The following samples won't be available for the excluded namespaces: - `K8sContainerSample` - `K8sDaemonsetSample` - `K8sDeploymentSample` - `K8sEndpointSample` - `K8sHpaSample` - `K8sPodSample` - `K8sReplicasetSample` - `K8sServiceSample` - `K8sStatefulsetSample` - `K8sVolumeSample` ### Kube state metrics The Kubernetes cluster explorer requires only the following kube state metrics (KSM): - Container data - Cluster data - Node data - Pod data - Volume data - API server data1 - Controller manager data1 - ETCD data1 - Scheduler data1 1 Not collected in a managed Kubernetes environment (EKS, GKE, AKS, etc.) You may consider disabling some of the following: - DaemonSet data - Deployment data - Endpoint data - Namespace data - ReplicaSet data2 - Service data - StatefulSet data 2 Used in the default alert: β€œReplicaSet doesn't have desired amount of pods” #### Example of updating state metrics in manifest (Deployment) ```shell [spec] [template] [spec] [containers] [name=kube-state-metrics] [args] #- --collectors=daemonsets #- --collectors=deployments #- --collectors=endpoints #- --collectors=namespaces #- --collectors=replicasets #- --collectors=services #- --collectors=statefulsets ``` _Example of updating state metrics in manifest (ClusterRole)_ ````shell [rules] # - apiGroups: ["extensions", "apps"] # resources: # - daemonsets # verbs: ["list", "watch"] # - apiGroups: ["extensions", "apps"] # resources: # - deployments # verbs: ["list", "watch"] # - apiGroups: [""] # resources: # - endpoints # verbs: ["list", "watch"] # - apiGroups: [""] # resources: # - namespaces # verbs: ["list", "watch"] # - apiGroups: ["extensions", "apps"] # resources: # - replicasets # verbs: ["list", "watch"] # - apiGroups: [""] # resources: # - services # verbs: ["list", "watch"] # - apiGroups: ["apps"] # resources: # - statefulsets # verbs: ["list", "watch"] ``` ### Config `lowDataMode` in `nri-bundle` chart Our Helm charts support setting an option to reduce the amount of data ingested at the cost of dropping detailed information. To enable it, set `global.lowDataMode` to `true` in the `nri-bundle` chart. `lowDataMode` affects three specific components of the `nri-bundle` chart: 1. Increase infrastructure agent interval from `15` to `30` seconds. 2. Prometheus OpenMetrics integration will exclude a few metrics as indicated in the Helm doc below. 3. Labels and annotations details will be dropped from logs. You can find more details about this configuration in our [Helm doc](/docs/kubernetes-pixie/kubernetes-integration/installation/install-kubernetes-integration-using-helm/#reducedataingest). ```` **On-host integrations** New Relic's on-host integrations represent a diverse set of integrations for third party services such as Postgresql, MySQL, Kafka, RabbitMQ etc. It's not possible to provide every optimization technique in the scope of this document, but we can provide these generally applicable techniques: - Manage sampling rate - Manage those parts of the config that can increase or decrease breadth of collection - Manage those parts of the config that allow for custom queries - Manage the infrastructure agents' custom attributes because they'll be applied to all on-host integration data. We'll use a few examples to demonstrate. ### [PostgreSQL integration](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/postgresql-monitoring-integration/#example-postgresSQL-collection-config) > #### πŸ’‘ GROWTH DRIVERS > > - Number of tables monitored > - Number of indices monitored The PostgreSQL on-host integration configuration provides these adjustable settings that can help manage data volume: - `interval`: Default is 15s - `COLLECTION_LIST`: list of tables to monitor (use ALL to monitor ALL) - `COLLECT_DB_LOCK_METRICS`: Collect `dblock` metrics - `PGBOUNCER`: Collect `pgbouncer` metrics - `COLLECT_BLOAT_METRICS`: Collect bloat metrics - `METRICS`: Set to `true` to collect only metrics - `INVENTORY`: Set to `true` to enable only inventory collection - `CUSTOM_METRICS_CONFIG`: Config file containing custom collection queries **Sample config:** ```yaml integrations: - name: nri-postgresql env: USERNAME: postgres PASSWORD: pass HOSTNAME: psql-sample.localnet PORT: 6432 DATABASE: postgres COLLECT_DB_LOCK_METRICS: false COLLECTION_LIST: '{"postgres":{"public":{"pg_table1":["pg_index1","pg_index2"],"pg_table2":[]}}}' TIMEOUT: 10 interval: 15s labels: env: production role: postgresql inventory_source: config/postgresql ``` ### [Kafka integration](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/kafka-monitoring-integration/) > #### πŸ’‘ GROWTH DRIVERS > > - Number of brokers in cluster > - Number of topics in cluster The Kafka on-host integration configuration provides these adjustable settings that can help manage data volume: - `interval`: Default is 15s - `TOPIC_MODE`: Determines how many topics we collect. Options are `all`, `none`, `list`, or `regex`. - `METRICS`: Set to `true` to collect only metrics - `INVENTORY`: Set to `true` to enable only inventory collection - `TOPIC_LIST`: JSON array of topic names to monitor. Only in effect if topic_mode is set to list. - `COLLECT_TOPIC_SIZE`: Collect the metric Topic size. Options are `true` or `false`, defaults to `false`. - `COLLECT_TOPIC_OFFSET`:Collect the metric Topic offset. Options are `true` or `false`, defaults to `false`. It should be noted that collection of topic level metrics especially offsets can be resource intensive to collect and can have an impact on data volume. It's very possible that a cluster's ingest can increase by an order of magnitude simply by the addition of new Kafka topics to the cluster. ### [MongoDB Integration](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/mongodb-monitoring-integration) > #### πŸ’‘ GROWTH DRIVERS > > - Number of databases monitored The MongoDB integration provides these adjustable settings that can help manage data volume: - `interval`: Default is 15s - `METRICS`: Set to `true` to collect only metrics - `INVENTORY`: Set to `true` to enable only inventory collection - `FILTERS`: A JSON map of database names to an array of collection names. If empty, it defaults to all databases and collections. For any on-host integration you use it's important to be aware of parameters like `FILTERS` where the default is to collect metrics from all databases. This is an area where you can use your monitoring priorities to streamline collected data. **Example configuration with different intervals for METRIC and INVENTORY:** ```yaml integrations: - name: nri-mongodb env: METRICS: true CLUSTER_NAME: my_cluster HOST: localhost PORT: 27017 USERNAME: mongodb_user PASSWORD: mongodb_password interval: 15s labels: environment: production - name: nri-mongodb env: INVENTORY: true CLUSTER_NAME: my_cluster HOST: localhost PORT: 27017 USERNAME: mongodb_user PASSWORD: mongodb_password interval: 60s labels: environment: production inventory_source: config/mongodb ``` ### [Elasticsearch integration](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/elasticsearch-monitoring-integration) > #### πŸ’‘ GROWTH DRIVERS > > - Number of nodes in cluster > - Number of indices in cluster The Elasticsearch integration provides these adjustable settings that can help manage data volume: - `interval`: Default is 15s - `METRICS`: Set to `true` to collect only metrics - `INVENTORY`: Set to `true` to enable only inventory collection - `COLLECT_INDICES`: Signals whether to collect indices metrics or not. - `COLLECT_PRIMARIES`: Signals whether to collect primary metrics or not. - `INDICES_REGEX`: Filter which indices are collected. - `MASTER_ONLY`: Collect cluster metrics on the elected master only. **Example configuration with different intervals for `METRICS` and `INVENTORY`:** ```yaml integrations: - name: nri-elasticsearch env: METRICS: true HOSTNAME: localhost PORT: 9200 USERNAME: elasticsearch_user PASSWORD: elasticsearch_password REMOTE_MONITORING: true interval: 15s labels: environment: production - name: nri-elasticsearch env: INVENTORY: true HOSTNAME: localhost PORT: 9200 USERNAME: elasticsearch_user PASSWORD: elasticsearch_password CONFIG_PATH: /etc/elasticsearch/elasticsearch.yml interval: 60s labels: environment: production inventory_source: config/elasticsearch ``` ### [JMX integration](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/jmx-monitoring-integration) > #### πŸ’‘ GROWTH DRIVERS > > - Metrics listed in `COLLECTION_CONFIG` The JMX integration is inherently generic. It allows us to scrape metrics from any JMX instance. We have a good amount of control over what gets collected by this integration. In some enterprise New Relic environments JMX metrics reprepresent a relatively high proportion of all data collected. The JMX integration provides these adjustable settings that can help manage data volume: - `interval`: Default is 15s - `METRICS`: Set to `true` to collect only metrics - `INVENTORY`: Set to `true` to enable only inventory collection - `METRIC_LIMIT`: Number of metrics that can be collected per entity. If this limit is exceeded the entity will not be reported. A limit of 0 implies no limit. - `LOCAL_ENTITY`: Collect all metrics on the local entity. Only used when monitoring localhost. - `COLLECTION_FILES`: A comma-separated list of full file paths to the metric collection definition files. For on-host install, the default JVM metrics collection file is at `/etc/newrelic-infra/integrations.d/jvm-metrics.yml`. - `COLLECTION_CONFIG`: Configuration of the metrics collection as a JSON. It's the `COLLECTION_CONFIG` entries that most govern the amount of data ingested. Understanding the JMX model you are scraping will help you optimize. _`COLLECTION_CONFIG` example for JVM metrics_ ```java COLLECTION_CONFIG='{"collect":[{"domain":"java.lang","event_type":"JVMSample","beans":[{"query":"type=GarbageCollector,name=*","attributes":["CollectionCount","CollectionTime"]},{"query":"type=Memory","attributes":["HeapMemoryUsage.Committed","HeapMemoryUsage.Init","HeapMemoryUsage.Max","HeapMemoryUsage.Used","NonHeapMemoryUsage.Committed","NonHeapMemoryUsage.Init","NonHeapMemoryUsage.Max","NonHeapMemoryUsage.Used"]},{"query":"type=Threading","attributes":["ThreadCount","TotalStartedThreadCount"]},{"query":"type=ClassLoading","attributes":["LoadedClassCount"]},{"query":"type=Compilation","attributes":["TotalCompilationTime"]}]}]}' ``` Omitting any one entry from that config such as `NonHeapMemoryUsage.Init` will have a tangible impact on the overall data volume collected. _`COLLECTION_CONFIG` example for Tomcat metrics_ ```java COLLECTION_CONFIG={"collect":[{"domain":"Catalina","event_type":"TomcatSample","beans":[{"query":"type=UtilityExecutor","attributes":["completedTaskCount"]}]}]} ``` ### Other on-host integrations There are many other on-host integrations with configuration options that will help you optimize collection. Some commonly used ones are: - [NGINX](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/nginx/nginx-integration/) - [MySQL](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/mySQL/mysql-integration) - [Redis](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/redis-monitoring-integration) - [Apache](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/apache-monitoring-integration) - [RabbitMQ](https://docs.newrelic.com/docs/infrastructure/host-integrations/host-integrations-list/rabbitmq-monitoring-integration) This is a good [starting point](https://docs.newrelic.com/docs/infrastructure/infrastructure-integrations/get-started/introduction-infrastructure-integrations#on-host) to learn more. **Network performance monitoring (NPM)** > #### πŸ’‘ GROWTH DRIVERS > > Monitored devices driven by: > > - hard configured devices > - CIDR scope in discovery section > - traps configured This section focuses on New Relic's network performance monitoring which relies on the `ktranslate` agent from Kentik. This agent is quite sophisticated and it's important to fully understand the [advanced configuration docs](https://docs.newrelic.com/docs/network-performance-monitoring/advanced/advanced-config) before major optimization efforts. Configuration options include: - `mibs_enabled`: Array of all active MIBs the KTranslate docker image will poll. This list is automatically generated during discovery if the `discovery_add_mibs` attribute is `true`. MIBs not listed here will not be polled on any device in the configuration file. You can specify a SNMP table directly in a MIB file using `MIB-NAME.tableName` syntax. Ex: `HOST-RESOURCES-MIB.hrProcessorTable`. - `user_tags`: key:value pair attributes to give more context to the device. Tags at this level will be applied to all devices in the configuration file. - `devices`: Section listing devices to be monitored for flow - `traps`: configures IP and ports to be monitored with SNMP traps (default is `127.0.0.1:1162`) - `discovery`: configures how endpoints can be discovered. Under this section the following parameters will do the most to increase or decrease scope: - `cidrs`: Array of target IP ranges in [CIDR notation](https://en.wikipedia.org/wiki/Classless_Inter-Domain_Routing#CIDR_notation). - `ports`: Array of target ports to scan during SNMP polling. - `debug`: Indicates whether to enable debug level logging during discovery. By default, it's set to `false` - `default_communities`: Array of SNMPv1/v2c community strings to scan during SNMP polling. This array is evaluated in order and discovery accepts the first passing community. To support filtering of data that does not create value for your observability needs, you can set the `global.match_attributes.{}` and/or `devices..match_attributes.{}` attribute map. This will provide filtering at the KTranslate level, before shipping data to New Relic, giving you granular control over monitoring of things like interfaces. For more details, see [Network performance monitoring configuration](https://docs.newrelic.com/docs/network-performance-monitoring/advanced/advanced-config/#match_attributes-attribute). **Log forwarders** > #### πŸ’‘ GROWTH DRIVERS > > - Logs forwarded > - Average size of forward log records Logs represent one of the most flexible sources of telemetry in that we are typically routing logs through a dedicated forwarding layer with its own routing and transform rules. Because there are a variety of forwarders, we'll focus on the most commonly used ones: - APM language agents (recent versions) - Fluentd - Fluentbit - New Relic infrastructure agent (built-in Fluentbit) - Logstash ### APM agent log sampling Recent versions of the New Relic language agents can forward logs directly to New Relic. In some cases you may want to govern some limits of how big logging spikes can be from each APM agent instance. We can enable sampling with the environment variable `NEW_RELIC_APPLICATION_LOGGING_FORWARDING_MAX_SAMPLES_STORED` It's configured simply by providing the max number of logs that will be stored in the APM agents logging queue. It operates based on a custom priority queue. All log messages are given a priority. Logs that occur within a transaction get the transaction's priority. The queue for logs sorts based on the priority and when the log arrives. Higher priority wins and if needed, the newest log will win (we keep a count for each entry in the queue). Logs are added individually to the queue (even ones in a transaction) and when the limit is reached, the log at the end of the queue is pushed out in favor of the newer log. In the resources section below there is a [quickstart dashboard](https://onenr.io/0Bj3BlEZkRX) that helps you track log volume in a simple way. Tracking log volume will enable you to adjust or disable sampling rate to suit your observability needs. ### Configuring filters in Fluentd or Fluentbit Most general forwarders provide a fairly complete [routing workflow](https://docs.fluentd.org/configuration/routing-examples) that includes filtering, and transformation. New Relic's infrastructure agent provides some very simple patterns for filtering unwanted logs. Regular expression for filtering records. Only supported for the `tail`, `systemd`, `syslog`, and `tcp` (only with format none) sources. This field works in a way similar to `grep -E` in Unix systems. For example, for a given file being captured, you can filter for records containing either `WARN` or `ERROR` using: ```yaml ``` - name: only-records-with-warn-and-error file: /var/log/logFile.log pattern: WARN|ERROR ``` If you have pre-written Fluentd configurations for Fluentbit that do valuable filtering or parsing, you can import them into our New Relic logging config. To do this, use the `config_file` and `parsers` parameters in any `.yaml` file in your `logging.d` folder: ``` - `config_file`: path to an existing Fluent Bit configuration file. Any overlapping source results in duplicate messages in New Relic's log management. - `parsers_file`: path to an existing Fluent Bit parsers file. The following parser names are reserved: `rfc3164`, `rfc3164-local` and `rfc5424`. Learning how to inject attributes (or tags) into your logs in your data pipeline and to perform transformations can help with downstream features dropping using New Relic drop rules. By augmenting your logs with metadata about the source, we can make centralized and easily reversible decisions about what to drop on the backend. At a minimum, make sure the following attributes are present in your logs in some form: - Team - Environment (dev/stage/prod) - Application - Data center - Log level Below are some detailed routing and filtering resources: - [Common filter and routing patterns in Fluentd](https://docs.fluentd.org/configuration/routing-examples) - [Fluentbit data pipeline](https://docs.fluentbit.io/manual/concepts/data-pipeline) - [Forwarding logs with New Relic infrastructure agent](https://docs.newrelic.com/docs/logs/forward-logs/forward-your-logs-using-infrastructure-agent/) ### Adjusting the infrastructure agent's default attribute set The infrastructure agent adds some attributes by default, including any custom tags added to the host. It's possible that your configurations bring in many more than that, including a large number of AWS tags, which appear in New Relic with the form `aws.[attributename]`. These attributes are important for optimal observability, so it's highly recommended you evaluate your visualization, analytics, and alerting needs in light of any planned configuration changes. For example, logs from a Kubernetes cluster won't likely be useful without metadata such as: - `cluster_name` - `pod_name` - `container_name` - `node_name` **Prometheus metrics sources** > #### πŸ’‘ GROWTH DRIVERS > > - Number of metrics exported from apps > - Number of metrics transferred via remote write or POMI New Relic provides two primary options for sending Prometheus metrics to New Relic. The best practices for managing metric ingest in this document will be focused primarily on option 2 - the Prometheus OpenMetrics integration (POMI) - because this component was created by New Relic. ### Option 1: [Prometheus remote write integration](https://docs.newrelic.com/docs/integrations/prometheus-integrations/install-configure-remote-write/set-your-prometheus-remote-write-integration) For Prometheus server scrape config options, see [the Prometheus config docs](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#scrape_config). These scrape configs determine which metrics are collected by Prometheus server. By configuring the [`remote_write`](https://docs.newrelic.com/docs/integrations/prometheus-integrations/install-configure-remote-write/set-your-prometheus-remote-write-integration) parameter, the collected metrics can be written to the New Relic database (NRDB) via the New Relic Metric API. ### Option 2: [Prometheus OpenMetrics integration (POMI)](https://docs.newrelic.com/docs/integrations/prometheus-integrations/install-configure-openmetrics/install-update-or-uninstall-your-prometheus-openmetrics-integration) POMI is a standalone integration that scrapes metrics from both dynamically discovered and static Prometheus endpoints. POMI then sends this data to NRDB via the New Relic Metric API. This integration is ideal for customers not currently running Prometheus Server. #### POMI: scrape label POMI will discover any Prometheus endpoint containing the label or annotation `prometheus.io/scrape=true` by default. Depending upon what's deployed in the cluster, this can be a large number of endpoints and thus, a large number of metrics ingested. It's suggested that the `scrape_enabled_label` parameter be modified to something custom (e.g. `newrelic/scrape`) and that the Prometheus endpoints be selectively labeled or annotated when data ingest is of utmost concern. For the latest reference config, see [nri-prometheus-latest.yaml](https://download.newrelic.com/infrastructure_agent/integrations/kubernetes/nri-prometheus-latest.yaml). **POMI config parameter:** ```yaml # Label used to identify scrapable targets. # Defaults to "prometheus.io/scrape" scrape_enabled_label: "prometheus.io/scrape" ``` POMI will discover any Prometheus endpoint exposed at the node-level by default. This typically includes metrics coming from Kubelet and cAdvisor. If you're running the New Relic Kubernetes Daemonset, it's important that you set `require_scrape_enabled_label_for_nodes: true` so that POMI doesn't collect duplicate metrics. For the endpoints targeted by the New Relic Kubernetes Daemonset, see [our Kubernetes README on GitHub](https://github.com/newrelic/nri-kubernetes/blob/main/README.md). #### POMI: scrape label for nodes POMI will discover any Prometheus endpoint exposed at the node-level by default. This typically includes metrics coming from Kubelet and cAdvisor. If you're running the New Relic Kubernetes Daemonset, it's important that you set `require_scrape_enabled_label_for_nodes: true` so that POMI doesn't collect duplicate metrics. For the endpoints targeted by the New Relic Kubernetes Daemonset, see [our Kubernetes README on GitHub](https://github.com/newrelic/nri-kubernetes/blob/main/README.md). _POMI config parameters_ ```yaml # Whether k8s nodes need to be labeled to be scraped or not. # Defaults to false. require_scrape_enabled_label_for_nodes: false ``` #### POMI: co-existing with `nri-kubernetes` New Relic's [Kubernetes integration](https://docs.newrelic.com/docs/integrations/kubernetes-integration/get-started/introduction-kubernetes-integration) collects a [number of metrics](https://docs.newrelic.com/docs/integrations/kubernetes-integration/understand-use-data/find-use-your-kubernetes-data#metrics) out of the box. However, it doesn't collect every possible metric available from a Kubernetes cluster. In the POMI config, you'll see a section similar to this which will **disable** metric collection for a subset of metrics that the New Relic Kubernetes integration is already collecting from **Kube State Metrics**. It's also very important to set `require_scrape_enabled_label_for_node: true` so that Kubelet and cAdvisor metrics are not duplicated. **POMI config parameters:** ```yaml transformations: - description: "Uncomment if running New Relic Kubernetes integration" ignore_metrics: - prefixes: - kube_daemonset_ - kube_deployment_ - kube_endpoint_ - kube_namespace_ - kube_node_ - kube_persistentvolume_ - kube_persistentvolumeclaim_ - kube_pod_ - kube_replicaset_ - kube_service_ - kube_statefulset_ ``` #### POMI: request/limit settings When running POMI, it's recommended to apply the following [resource limits](https://kubernetes.io/docs/tasks/configure-pod-container/assign-memory-resource/) for clusters generating approximately 500k DPM: - CPU limit: 1 core (1000m) - Memory limit: 1Gb 1024 (1G) The resource request for CPU and memory should be set at something reasonable so that POMI receives enough resources from the cluster. Setting this to something extremely low (e.g. cpu: 50m) can result in cluster resources being consumed by "noisy neighbors." **POMI config parameter:** ```yaml spec: serviceAccountName: nri-prometheus containers: - name: nri-prometheus image: newrelic/nri-prometheus:2.2.0 resources: requests: memory: 512Mi cpu: 500m limits: memory: 1G cpu: 1000m ``` ### POMI: estimating cardinality Although cardinality is not associated directly with billable per GB ingest, New Relic does maintain certain rate limits on cardinality. Being able to visualize cardinality from a Prometheus cluster can be very important. > #### πŸ’‘ TIP > > New Relic accounts have a 1M cardinality limit, but you can request up to 15M cardinality. To request changes, contact your New Relic account representative. For more information, see [Metric API limits](https://docs.newrelic.com/docs/data-apis/ingest-apis/metric-api/metric-api-limits-restricted-attributes). If you're already running Prometheus Server, you can run cardinality estimates there prior to enabling POMI or `remote_write`. **Top 20 metrics (highest cardinality):** ```promql topk(20, count by (**name**, job)({__name__=~".+"})) ``` **Cloud integrations** > #### πŸ’‘ GROWTH DRIVERS > > - Number of metrics exported per integration > - Polling frequency (for polling based integration) Some New Relic cloud integrations get data from cloud providers' APIs. With this implementation, data is generally collected from monitoring APIs such as AWS CloudWatch, Azure Monitor, and GCP Stackdriver, and inventory metadata is collected from the specific services' APIs. Other cloud integrations get their data from streaming metrics (or "pushed" metrics) that are pushed via a streaming service such as AWS Kinesis. ### Polling API-based integrations If you want to report more or less data from your cloud integrations, or if you need to control the use of the cloud providers' APIs to prevent reaching rate and throttling limits in your cloud account, you can change the configuration settings to modify the amount of data they report. The two main controls are: - [Change polling frequency](https://docs.newrelic.com/docs/infrastructure/infrastructure-integrations/cloud-integrations/configure-polling-frequency-data-collection-cloud-integrations/#polling) - [Change what data is reported](https://docs.newrelic.com/docs/infrastructure/infrastructure-integrations/cloud-integrations/configure-polling-frequency-data-collection-cloud-integrations/#filter-data) Examples of business reasons for wanting to change your polling frequency include: - **Billing**: If you need to manage your AWS CloudWatch bill, you may want to decrease the polling frequency. Before you do this, make sure that any alert conditions set for your cloud integrations are not affected by this reduction. - **New services**: If you're deploying a new service or configuration and you want to collect data more often, you may want to increase the polling frequency temporarily. > #### ⚠️ CAUTION > > Changing the configuration settings for your integrations may impact alert conditions and chart trends. For more details, see [Configure polling](https://docs.newrelic.com/docs/infrastructure/infrastructure-integrations/cloud-integrations/configure-polling-frequency-data-collection-cloud-integrations). ### "Streaming" or "pushed" metrics More and more cloud integrations are offering the option of having the data pushed via a [streaming service](https://docs.newrelic.com/docs/infrastructure/amazon-integrations/aws-integrations-list/aws-metric-stream) instead of using API polling. This has proven to cut down on latency drastically. One issue some users have observed is that it's not as easy to control volume because sampling rate cannot be configured. New Relic rules for dropping data will be well covered in the next section. They're the primary way of filtering out streaming metrics that are too high volume. However, there are some things that can be done on the cloud provider side to limit the stream volume somewhat. For example in AWS it's possible to use condition keys to [limit access to CloudWatch\* namespaces](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/iam-cw-condition-keys-namespace.html). The following policy limits the user to publishing metrics only in the namespace named `MyCustomNamespace`: ```json { "Version": "2012-10-17", "Statement": { "Effect": "Allow", "Resource": "*", "Action": "cloudwatch:PutMetricData", "Condition": { "StringEquals": { "cloudwatch:namespace": "MyCustomNamespace" } } } } ``` The following policy allows the user to publish metrics in any namespace except for `CustomNamespace2`: ```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Resource": "*", "Action": "cloudwatch:PutMetricData" }, { "Effect": "Deny", "Resource": "*", "Action": "cloudwatch:PutMetricData", "Condition": { "StringEquals": { "cloudwatch:namespace": "CustomNamespace2" } } } ] } ``` #### Optimization with drop rules [#optimization-with-drop-rules] A simple rule for understanding what you can do with drop rules is: **If you can query it you can drop it.** Drop filter rules help you accomplish several important goals: - Lower costs by storing only the logs relevant to your account. - Protect privacy and security by removing personal identifiable information (PII). - Reduce noise by removing irrelevant events and attributes. **A note of caution**: When creating drop rules, you're responsible for ensuring that the rules accurately identify and discard the data that meets the conditions that you've established. You're also responsible for monitoring the rule, as well as the data you disclose to New Relic. Always test and retest your queries and, after the drop rule is installed, make sure it works as intended. Creating a dashboard to monitor your data pre- and post-drop will help. Here's some guidance for using drop rules to optimize data ingest for specific tools: **Logs** All New Relic drop rules are implemented by the same backend data model and API. However New Relic log management provides a powerful UI that makes it very easy to create and monitor drop rules. In our previous section on prioritizing telemetry we ran through some exercises to show ways in which we could deprecate certain data. Let's revisit this example: ``` Omit debug logs (knowing they can be turned on if there is an issue) (saves 5%) ``` #### Method 1: [Log UI](https://docs.newrelic.com/docs/logs/ui-data/drop-data-drop-filter-rules) - Identify the logs we care about using a filter in the Log UI: `level: DEBUG`. - Make sure it finds the logs we want to drop. - Check some alternative syntax such as `level:debug` and `log_level:Debug`. These variations are common. - Under **Manage data**, click **Drop filters**, and create and enable a filter named 'Drop debug logs'. - Verify the rule works. #### Method 2: [Our NerdGraph API](https://docs.newrelic.com/docs/data-apis/manage-data/drop-data-using-nerdgraph/) - Create the relevant NRQL query: ```sql SELECT count(*) FROM Log WHERE `level` = 'DEBUG' ``` - Make sure it finds the logs we want to drop. - Check variations on the attribute name and value (`Debug` vs `DEBUG`). - Execute the following NerdGraph statement and make sure it works: ```graphql mutation { nrqlDropRulesCreate(accountId: YOUR_ACCOUNT_ID, rules: [ { action: DROP_DATA nrql: "SELECT * FROM Log WHERE `level` = 'DEBUG'" description: "Drops DEBUG logs. Disable if needed for troubleshooting." } ]) { successes { id } failures { submitted { nrql } error { reason description } } } } ``` **Process samples** Let's implement the recommendation: `Drop process sample data in DEV environments`. - Create the relevant query: ```sql SELECT * FROM ProcessSample WHERE `env` = 'DEV' ``` - Make sure it finds the process samples we want to drop - Check for other variations on `env` such as `ENV` and `Environment` - Check for various of `DEV` such as `Dev` and `Development` - Use our NerdGraph API to execute the following statement and make sure it works: ```graphql mutation { nrqlDropRulesCreate(accountId: YOUR_ACCOUNT_ID, rules: [ { action: DROP_DATA nrql: "SELECT * FROM ProcessSample WHERE `env` = 'DEV'" description: "Drops ProcessSample from development environments" } ]) { successes { id } failures { submitted { nrql } error { reason description } } } } ``` **Cloud metrics** In some cases we can economize on data where we have redundant coverage. For example: in an environment where you have the AWS RDS integration running as well as one of the New Relic on-host integrations that monitor SQL databases such as `nri-mysql` or `nri-postgresql` you may be able to discard some overlapping metrics. For quick exploration we can run a query like this: ```sql FROM Metric SELECT count(*) WHERE metricName LIKE 'aws.rds%' FACET metricName LIMIT MAX ``` That will show us all `metricName` values matching the pattern. We see from the results there's a high volume of metrics of the pattern `aws.rds.cpu%`. Let's drop those because we have other instrumentation for those: - Create the relevant query: ```sql FROM Metric SELECT * WHERE metricName LIKE 'aws.rds.cpu%' FACET metricName LIMIT MAX SINCE 1 day ago ``` - Make sure it finds the process samples we want to drop. - Use the NerdGraph API to execute the following statement and make sure it works: ```graphql mutation { nrqlDropRulesCreate(accountId: YOUR_ACCOUNT_ID, rules: [ { action: DROP_DATA nrql: "FROM Metric SELECT * WHERE metricName LIKE 'aws.rds.cpu%' FACET metricName LIMIT MAX SINCE 1 day ago" description: "Drops rds cpu related metrics" } ]) { successes { id } failures { submitted { nrql } error { reason description } } } } ``` **Drop specific attributes** One powerful thing about drop rules is that we can configure a rule that drops specific attributes but maintains the rest of the data intact. Use this to remove private data from NRDB, or to drop excessively large attributes. For example, stack traces or large chunks of JSON in log records can sometimes be very large. To set these drop rules, change the `action` field to `DROP_ATTRIBUTES` instead of `DROP_DATA`. ```graphql mutation { nrqlDropRulesCreate(accountId: YOUR_ACCOUNT_ID, rules: [ { action: DROP_ATTRIBUTES nrql: "SELECT stack_trace, json_data FROM Log WHERE appName='myApp'" description: "Drops large fields from logs for myApp" } ]) { successes { id } failures { submitted { nrql } error { reason description } } } } ``` **Drop random sample of events** > #### ⚠️ CAUTION > > Use this approach carefully, and only in situations where there are no other alternatives, because it can alter statistical inferrences made from your data. However, for events with massive sample size, you may do with only a portion of your data as long as you understand the consequences. In this example we'll take advantage of the relative distribution of certain trace IDs to approximate random sampling. We can use the `RLIKE` operator to check for the leading values of a span's `trace.id` attribute. The following example could drop about 25% of spans: ```sql SELECT * FROM Span WHERE trace.id RLIKE r'.*[0-3]' AND appName = 'myApp' ``` Useful expressions include: - `r'.*0'` approximates 6.25% - `r'.*[0-1]'` approximates 12.5% - `r'.*[0-2]'` approximates 18.75% - `r'.*[0-3]'` approximates 25.0% Here's an example of a full mutation: ```graphql mutation { nrqlDropRulesCreate(accountId: YOUR_ACCOUNT_ID, rules: [ { action: DROP_ATTRIBUTES nrql: "SELECT * FROM Span WHERE trace.id RLIKE r'.*[0-3]' AND appName = 'myApp'" description: "Drops approximately 25% of spans for myApp" } ]) { successes { id } failures { submitted { nrql } error { reason description } } } } ``` **Other events and metrics** The preceding examples should show you all you need to know to use these techniques on any other event or metric in NRDB. If you can query it you can drop it. Reach out to New Relic if you have questions about the precise way to structure a query for a drop rule. ## Exercise [#exercise] Answering the following questions will help you develop confidence in your ability to develop and execute optimization plans. You may want to use the [Data ingest baseline](https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/operational-efficiency/dg-baselining#install-dashboard) and [Data ingest entity breakdown](https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/operational-efficiency/dg-baselining#install-entity-breakdown-dashboard) dashboards from the `Baselining` section. Install those dashboards as described and see how many of these questions you can answer. | Questions | | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Show three drop rules in which you could reduce this organization's ingest by at least 5% per month? Include the NerdGraph syntax for your drop rule in your response. | | Suggest three instrumentation configuration changes you could implement to reduce this organization's ingest by at least 5% per month? Include the configurations snippets in your response. | | What are three things you could do to reduce data volume from K8s monitoring? How much data reduction could you achieve? What are potential trade-offs of this reduction? (for example, would they lose any substantial observability?) | | 1. Use the data ingest governance baseline dashboard to identify an account that's sending a large amount of log data to New Relic.2. Find and select that account from the account switcher.3. Navigate to the **logs** page of the account and select **patterns** from the left-side menu.4. Review the log patterns shown and give some examples of the low value log patterns. What makes them low value? How much total reduction you could achieve by dropping these logs? | | Based on your overall analysis of this organization, what telemetry is underutilized? | ## Conclusion [#conclusion] The process section showed us how to associate our telemetry with specific observability value drivers or objectives. This can make the hard decision of optimizing our account ingest somewhat easier. We learned how to describe a high level [optimization plan](https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/operational-efficiency/dg-optimizing#develop-plan) that optimizes ingest while protecting our objectives. Finally we were introduced a [rich set of recipes](https://docs.newrelic.com/docs/new-relic-solutions/observability-maturity/operational-efficiency/dg-optimizing#use-reduction-techniques) for configuration and drop-rule-based ingest optimizations.