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ServiceNow Cloud Observability (formerly Lightstep)

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ServiceNow Cloud Observability (formerly Lightstep) features distributed tracing that can be used to rapidly pinpoint the causes of failures and poor performance across the deeply complex dependencies among services, teams, and workloads in modern production systems. Nobl9 integration with ServiceNow Cloud Observability facilitates organizations to establish service level objectives from performance data captured through distributed traces in the ServiceNow Cloud Observability platform.

Scope of support​

ServiceNow Cloud Observability parameters and supported features in Nobl9
General support:
Release channel: Stable, Beta
Connection method: Agent, Direct
Replay and SLI Analyzer: Historical data limit 30 days
Event logs: Supported
Query checker: Not supported
Query parameters retrieval: Supported
Timestamp cache persistence: Supported
Query parameters:
Query interval: 1 min
Query delay: 2 min
Jitter: 15 sec
Timeout: 30 sec
Agent details and minimum required versions for supported features:
Plugin name: n9lightstep
Query delay environment variable: LS_QUERY_DELAY
Replay and SLI Analyzer: 0.65.0
Query parameters retrieval: 0.73.2
Timestamp cache persistence: 0.65.0

You can configure Nobl9 SLOs with ServiceNow Cloud Observability as follows:

  • Threshold metrics:
    • UQL: constant, metrics, and spans query types
    • Latency Threshold
    • Error Threshold
  • Ratio metrics:
    • UQL: constant, metrics, and spans query types
    • Error Ratio
caution Unsupported query types

No support for spans_sample and assemble UQL queries.

Creating SLOs with ServiceNow Cloud Observability​

Nobl9 Web​

Follow the instructions below to create your SLOs with ServiceNow Cloud Observability on the Nobl9 Web:

  1. Navigate to Service Level Objectives.
  2. Click .
  3. Select a Service.
    It will be the location for your SLO in Nobl9.
  4. Select your ServiceNow Cloud Observability data source.
  5. Modify Period for Historical Data Retrieval, when necessary.
    • This value defines how far back in the past your data will be retrieved when replaying your SLO based on ServiceNow Cloud Observability.
    • A longer period can extend the data loading time for your SLO.
    • Must be a positive whole number up to the maximum period value you've set when adding the ServiceNow Cloud Observability data source.
  6. Select the Metric type:
    • Threshold metric: a single time series is evaluated against a threshold.
    • Ratio metric: two-time series for comparison for good events and total events.
      For ratio metrics, select the Data count method: incremental or non-incremental.
UQL
UQL metric lets you retrieve the constant, metrics, and spans data.
Read more about Unified Query Language (UQL) and available metric types.
Latency and Error thresholds
These metric types let you monitor your required stream and measure the latency and total number of errors, accordingly.
Stream ID is your required metric stream ID created in ServiceNow Cloud Observability.
Percentile is required for the Latency Threshold and refers to the n-th percentile of latency in milliseconds you consider acceptable for your threshold.
Read more about the ways to retrieve the Stream ID.
Missing data vs. 0 value

ServiceNow Cloud Observability does not recognize the distinction between missing data and valid data with a 0 value in the stream. In such cases, ServiceNow Cloud Observability considers these values equal and returns the 0 value.

  1. Define the Time window for your SLO:
  2. Configure the Error budget calculation method and Objectives:
    • Occurrences method counts good attempts against the count of total attempts.
    • Time Slices method measures how many good minutes were achieved (when a system operates within defined boundaries) during a time window.
    • You can define up to 12 objectives for an SLO.

      • Similar threshold values for objectives
      To use similar threshold values for different objectives in your SLO, we recommend differentiating them by setting varying decimal points for each objective.
      For example, if you want to use threshold value 1 for two objectives, set it to 1.0000001 for the first objective and to 1.0000002 for the second one.
      Learn more about threshold value uniqueness.
  3. Add the Display name, Name, and other settings for your SLO:
    • Name identifies your SLO in Nobl9. After you save the SLO, its name becomes read-only.
      Use only lowercase letters, numbers, and dashes.
    • Create composite SLO: with this option selected, you create a composite SLO 1.0. Composite SLOs 1.0 are deprecated. They're fully operable; however, we encourage you to create new composite SLOs 2.0.
      You can create composite SLOs 2.0 with sloctl using the provided template. Alternatively, you can create a composite SLO 2.0 with Nobl9 Terraform provider.
    • Set Notifications on data. With it, Nobl9 will notify you in the cases when SLO won't be reporting data for more than 15 minutes.
    • Add alert policies, labels, and links, if required.
      Up to 20 items of each type per SLO is allowed.
  4. Click CREATE SLO.

    5. SLO configuration use case
    Check the SLO configuration use case for a real-life SLO example.
SLI values for good and total
When choosing the query for the ratio SLI (countMetrics), keep in mind that the values ​​resulting from that query for both good and total:
  • Must be positive.
  • While we recommend using integers, fractions are also acceptable.
    • If using fractions, we recommend them to be larger than 1e-4 = 0.0001.
  • Shouldn't be larger than 1e+20.

sloctl​

Here’s an example of ServiceNow Cloud Observability using rawMetric (threshold metric) with Metrics as the configuration type:

YAML definition for a metrics threshold SLO
- apiVersion: n9/v1alpha
  kind: SLO
  metadata:
    name: api-server-slo
    # Optional
    #displayName: API Server SLO
    project: default
    # Labels and annotations are optional
    #labels:
    #  area:
    #    - latency
    #    - slow-check
    #  env:
    #    - prod
    #    - dev
    #  region:
    #    - us
    #    - eu
    #  team:
    #    - green
    #    - sales
    #annotations:
    #  area: latency
    #  env: prod
    #  region: us
    #  team: sales
  spec:
    description: Example ServiceNow Cloud Observability SLO
    indicator:
      metricSource:
        name: lightstep
        project: default
        kind: Agent
    budgetingMethod: Occurrences
    objectives:
      - displayName: Good response (200)
        value: 200.0
        name: ok
        target: 0.95
        rawMetric:
          query:
            lightstep:
              typeOfData: metric
              uql: metric cpu.utilization | rate | group_by [], mean
        op: lte
        primary: true
    service: api-server
    timeWindows:
      - unit: Month
        count: 1
        isRolling: false
        calendar:
          startTime: 2022-12-01 00:00:00
          timeZone: UTC
    # Alert policies, attachments, and anomaly notifications are optional
    #alertPolicies:
    #  - fast-burn-5x-for-last-10m
    #attachments:
    #  - url: https://docs.nobl9.com
    #    displayName: Nobl9 Documentation
    #anomalyConfig:
    #  noData:
    #    alertMethods:
    #      - name: slack-notification
    #        project: default

When ServiceNow Cloud Observability is used as ratio (count) metric, then the field incremental under spec.objectives.countMetrics must be set to false.

Metric specification from ServiceNow Cloud Observability has three fields:

  • streamID – mandatory, string. For instructions on how to retrieve it, go to Authentication section.

  • typeOfData – accepts one of the following values: metric, latency, error_rate, good, total. For more detailed information, refer to the Scope of support section of the documentation.

    Description of values for typeOfData fields:

    • metric - metrics queries with which you can use ServiceNow Cloud Observability's Query Language (UQL) to retrieve and process your metric data by creating your query in the ServiceNow Cloud Observability UI and copying and pasting the query into Nobl9. This type can be used both for rawMetric and countMetrics SLO types.

    • latency – the n-th percentile (look at field percentile) of latency in milliseconds. This type can be used only as rawMetric. The value of value under spec.objective must also represent milliseconds.

    • error_rate – a single value representing the percentage of errors. This type can be used only as rawMetric. The value of value under spec.objectives must be between 0 and 1.

    • good – the number of successful events (operations). It is calculated as total operations minus the number of errors. This value is only allowed in the ratio (count) metric.

    • total – the number of all events (operations). This value is only allowed in ratio (count) metric.

  • percentile – number of percentiles of latency. The value must be greater than 0 and less or equal to 99.99. This field is mandatory when you use typeOfData: latency, and is forbidden otherwise.

ServiceNow Cloud Observability UQL​

You can use ServiceNow Cloud Observability Unified Query Language (UQL) to retrieve and process your metric data. Nobl9 supports the constant, metrics, and spans query types in the UQL.

Create your query in the ServiceNow Cloud Observability UI and copy and paste the query into Nobl9. Nobl9 then passes the query to the query_timeseries ServiceNow Cloud Observability API to retrieve the time series data.

constant queries​

constant fetches a gauge time series where all points have value literal-value.

To build a query of the constant type, specify the required value:

constant 100

metrics queries​

You can build the UQL queries using the following ServiceNow Cloud Observability metric kinds:

  • Gauge, an instantaneous measurement, for example,
    metric memory.utilized | latest | group_by [], sum,
    spans count | delta | group_by [], sum.

  • Delta, a measurement of the change in metrics from point to point. For the delta-kind queries, you must choose one of the operators or appropriate reducer to convert the distribution values into scalar values to build SLI on it, for example,
    metric request.size.bytes | delta | group_by [], sum | point dist_count(value)
    For more information, refer to the Using distributions in UQL | ServiceNow Cloud Observability documentation.

  • If you select a percentile as an operator in a query for the threshold metric SLI type, we recommend using the 100th percentile for best results as Nobl9 uses percentiles to display the data in the SLI chart. The following is an example metric query with Delta metric kind:
    metric request.size.bytes | delta | group_by [], sum | point percentile(value, 100.0)

  • If you define many aggregation values, Nobl9 will fetch data for the first aggregation value defined in your query. For example, Nobl9 will fetch data for the 100th percentile in the below query:
    metric request.size.bytes | delta | group_by [], sum | point percentile(value, 100.0), percentile(value, 99.9)

lightstep:
  typeOfData: metric
  uql: metric cpu.utilization | rate | group_by [], mean

spans queries​

Limitations:

ServiceNow Cloud Observability UQL spans queries supported in the public API must have retained data in ServiceNow Cloud Observability streams.

For example, when spans is not retained in a stream, the following query:

spans latency | delta | filter ((service == "adservice") || (service == "frontend")) | group_by [], sum | point percentile(value, 99.9)

will return the following error when querying the API:

"rpc error: code = InvalidArgument desc = public API only supports retained spans TQL queries at this time, please create a retained span query first"

However, when spans is retained in a stream, after creating a stream for a given filter, API starts returning a metric. For example, the following UQL query will return the metric:

spans latency | delta | filter (service == "frontend") | group_by [], sum | point percentile(value, 99.9)

if service IN ("frontend") is an existing stream.

tip

You can test your spans query whether it has retained data in the stream in the ServiceNow Cloud Observability API Reference documentation.

caution

Retention period:

  • for UQL spans queries retained in the stream, the retention period is set from 28 days, up to two years.

For more information, refer to the ServiceNow Cloud Observability documentation.

Metric YAML sample:

lightstep:
  typeOfData: metric
  uql: spans count | delta | group_by [], sum

SLOs explained​

Latency threshold​

The Latency threshold SLO configuration uses the threshold metric method under the hood with the SLI equal to the specific percentile value defined in SLO configuration.
Learn more about performance investigation in ServiceNow Cloud Observability.

Nobl9 retrieves percentile values from ServiceNow Cloud Observability API under data.attributes[].latencies[].

These values are represented in ServiceNow Cloud Observability on the following chart (the Latency section):

Metric YAML sample:

lightstep:
  streamID: DzpxcSRh
  typeOfData: latency
  percentile: 95

Error threshold​

The Error threshold SLO configuration uses the threshold metric method under the hood with the SLI equal to the percentage of errors for a given stream.

Nobl9 retrieves te ops-counts and error-counts values from ServiceNow Cloud Observability API and uses them to calculate the value:

value = error-counts / ops-counts

Such calculated values are used as an SLI for SLOs configured with this method.

They are represented in ServiceNow Cloud Observability on the following chart (the Err% section):

Metric YAML sample:

lightstep:
  streamID: DzpxcSRh
  typeOfData: error_rate

Error ratio​

This SLO configuration uses count (ratio) metric method under the hood. Each count metric SLO needs two data streams: good and total.

With this configuration, Nobl9 retrieves the error-counts and ops-counts values from ServiceNow Cloud Observability API and calculates those data streams as following:

Good = ops-counts - error-counts
Total = ops-counts

By default, ServiceNow Cloud Observability does not show these values on chart. It shows operations per second instead.

Nobl9 doesn’t use Rate to calculate error budgets for any SLO. Events counts are used instead (calculated from ops-counts and error-counts that are retrieved from the API).

Metric YAML sample:

countMetrics:
  incremental: false
  good:
    lightstep:
      streamID: DzpxcSRh
      typeOfData: good
  total:
    lightstep:
      streamID: DzpxcSRh
      typeOfData: total

Querying the ServiceNow Cloud Observability API​

The Nobl9 agent makes calls the ServiceNow Cloud Observability API once every 60 seconds.

API rate limits​

ServiceNow Cloud Observability has low rate limits for its Streams Timeseries API. For Community, Professional, and Enterprise licenses it’s 60, 200, 600 requests per hour respectively. The Nobl9 agent makes requests once every 60s, which allows for one ServiceNow Cloud Observability organization to use only 1, 3, or 10 unique metric specifications. For more information, refer to the Rate Limits | ServiceNow Cloud Observability documentation.

ServiceNow Cloud Observability users can request an increase of rate limits via ServiceNow Cloud Observability customer support.

For a more in-depth look, consult additional resources:
Add ServiceNow Cloud Observability as a data sourceAdding data sourcesMonitor Metric DataServiceNow Cloud Observability docsInvestigate a Latency RegressionServiceNow Cloud Observability docsInvestigate an Error Rate IncreaseServiceNow Cloud Observability docsInvestigate a Metric DeviationServiceNow Cloud Observability docsStreams TimeseriesServiceNow Cloud Observability docsQuery TimeseriesServiceNow Cloud Observability docsRate LimitsServiceNow Cloud Observability docsUnified Query Language (UQL)ServiceNow Cloud Observability docsMetric KindsServiceNow Cloud Observability docsUsing distributions in UQLServiceNow Cloud Observability docsCreating SLOs via TerraformThe Nobl9 Terraform provider docs
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