If you're using this tool with Grafana Mimir, please use the new mimirtool instead:

• mimirtool documentation
• mimirtool releases

This repo contains tools used for interacting with Cortex.

• benchtool: A powerful YAML driven tool for benchmarking Cortex write and query API.
• cortextool: Interacts with user-facing Cortex APIs and backend storage components.
• logtool: Tool which parses Cortex query-frontend logs and formats them for easy analysis.
• e2ealerting: Tool that helps measure how long an alert takes from scrape of sample to Alertmanager notification delivery.

The various binaries are available for macOS, Windows, and Linux.

cortextool is available on macOS via Homebrew:

$ brew install grafana/grafana/cortextool

Refer to the latest release for installation intructions on these.

This tool is designed to interact with the various user-facing APIs provided by Cortex, as well as, interact with various backend storage components containing Cortex data.

Config commands interact with the Cortex api and read/create/update/delete user configs from Cortex. Specifically, a user's alertmanager and rule configs can be composed and updated using these commands.

The following commands are used by users to interact with their Cortex alertmanager configuration, as well as their alert template files.

cortextool alertmanager get

cortextool alertmanager load ./example_alertmanager_config.yaml

cortextool alertmanager load ./example_alertmanager_config.yaml template_file1.tmpl template_file2.tmpl

The following commands are used by users to interact with their Cortex ruler configuration. They can load prometheus rule files, as well as interact with individual rule groups.

Note: If you are interacting with a Loki ruler, be sure to add the flag --backend=loki to all commands.

This command will retrieve all of the rule groups stored in the specified Cortex instance and print each one by rule group name and namespace to the terminal.

cortextool rules list

This command will retrieve all of the rule groups stored in the specified Cortex instance and print them to the terminal.

cortextool rules print

This command will retrieve the specified rule group from Cortex and print it to the terminal.

cortextool rules get example_namespace example_rule_group

This command will delete the specified rule group from the specified namespace.

cortextool rules delete example_namespace example_rule_group

This command will load each rule group in the specified files and load them into Cortex. If a rule already exists in Cortex it will be overwritten, if a diff is found.

cortextool rules load ./example_rules_one.yaml ./example_rules_two.yaml  ...

This command lints a rules file. The linter's aim is not to verify correctness but just YAML and PromQL expression formatting within the rule file. This command always edits in place, you can use the dry run flag (-n) if you'd like to perform a trial run that does not make any changes. This command does not interact with your Cortex cluster.

cortextool rules lint -n ./example_rules_one.yaml ./example_rules_two.yaml ...

This command prepares a rules file for upload to Cortex. It lints all your PromQL expressions and adds an specific label to your PromQL query aggregations in the file. This command does not interact with your Cortex cluster.

cortextool rules prepare -i ./example_rules_one.yaml ./example_rules_two.yaml ...

There are two flags of note for this command:

• -i which allows you to edit in place, otherwise a a new file with a .output extension is created with the results of the run.
• -l which allows you to specify the label you want to add for your aggregations, which is cluster by default.

At the end of the run, the command tells you whenever the operation was a success in the form of

INFO[0000] SUCESS: 194 rules found, 0 modified expressions

It is important to note that a modification can be a PromQL expression lint or a label add to your aggregation.

This commands checks rules against the recommended best practices for rules. This command does not interact with your Cortex cluster.

cortextool rules check ./example_rules_one.yaml

Cortex exposes a Remote Read API which allows access to the stored series. The remote-read subcommand of cortextool allows interacting with its API, to find out which series are stored.

The remote-read stats command summarizes statistics of the stored series matching the selector.

cortextool remote-read stats --selector '{job="node"}' --address http://demo.robustperception.io:9090 --remote-read-path /api/v1/read
INFO[0000] Create remote read client using endpoint 'http://demo.robustperception.io:9090/api/v1/read'
INFO[0000] Querying time from=2020-12-30T14:00:00Z to=2020-12-30T15:00:00Z with selector={job="node"}
INFO[0000] MIN TIME                           MAX TIME                           DURATION     NUM SAMPLES  NUM SERIES   NUM STALE NAN VALUES  NUM NAN VALUES
INFO[0000] 2020-12-30 14:00:00.629 +0000 UTC  2020-12-30 14:59:59.629 +0000 UTC  59m59s       159480       425          0                     0

The remote-read dump command prints all series and samples matching the selector.

cortextool remote-read dump --selector 'up{job="node"}' --address http://demo.robustperception.io:9090 --remote-read-path /api/v1/read
{__name__="up", instance="demo.robustperception.io:9100", job="node"} 1 1609336914711
{__name__="up", instance="demo.robustperception.io:9100", job="node"} NaN 1609336924709 # StaleNaN
[...]

The remote-read export command exports all series and samples matching the selector into a local TSDB. This TSDB can then be further analysed with local tooling like prometheus and promtool.

# Use Remote Read API to download all metrics with label job=name into local tsdb
cortextool remote-read export --selector '{job="node"}' --address http://demo.robustperception.io:9090 --remote-read-path /api/v1/read --tsdb-path ./local-tsdb
INFO[0000] Create remote read client using endpoint 'http://demo.robustperception.io:9090/api/v1/read'
INFO[0000] Created TSDB in path './local-tsdb'
INFO[0000] Using existing TSDB in path './local-tsdb'
INFO[0000] Querying time from=2020-12-30T13:53:59Z to=2020-12-30T14:53:59Z with selector={job="node"}
INFO[0001] Store TSDB blocks in './local-tsdb'
INFO[0001] BLOCK ULID                  MIN TIME                       MAX TIME                       DURATION     NUM SAMPLES  NUM CHUNKS   NUM SERIES   SIZE
INFO[0001] 01ETT28D6B8948J87NZXY8VYD9  2020-12-30 13:53:59 +0000 UTC  2020-12-30 13:59:59 +0000 UTC  6m0.001s     15950        429          425          105KiB867B
INFO[0001] 01ETT28D91Z9SVRYF3DY0KNV41  2020-12-30 14:00:00 +0000 UTC  2020-12-30 14:53:58 +0000 UTC  53m58.001s   143530       1325         425          509KiB679B

# Examples for using local TSDB
## Analyzing contents using promtool
promtool tsdb analyze ./local-tsdb

## Dump all values of the TSDB
promtool tsdb dump ./local-tsdb

## Run a local prometheus
prometheus --storage.tsdb.path ./local-tsdb --config.file=<(echo "")

The Overrides Exporter allows to continuously export per tenant configuration overrides as metrics. It can also, optionally, export a presets file (cf. example override config file and presets file).

cortextool overrides-exporter --overrides-file overrides.yaml --presets-file presets.yaml

This lets you generate the header which can then be used to enforce access control rules in GME / GrafanaCloud.

./cortextool acl generate-header --id=1234 --rule='{namespace="A"}'

Run analysis against your Prometheus, Grafana and Cortex to see which metrics being used and exported. Can also extract metrics from dashboard JSON and rules YAML files.

This command will run against your Grafana instance and will download its dashboards and then extract the Prometheus metrics used in its queries. The output is a JSON file.

cortextool analyse grafana --address=<grafana-address> --key=<API-Key>

{
  "metricsUsed": [
    "apiserver_request:availability30d",
    "workqueue_depth",
    "workqueue_queue_duration_seconds_bucket",
    ...
  ],
  "dashboards": [
    {
      "slug": "",
      "uid": "09ec8aa1e996d6ffcd6817bbaff4db1b",
      "title": "Kubernetes / API server",
      "metrics": [
        "apiserver_request:availability30d",
        "apiserver_request_total",
        "cluster_quantile:apiserver_request_duration_seconds:histogram_quantile",
        "workqueue_depth",
        "workqueue_queue_duration_seconds_bucket",
        ...
      ],
      "parse_errors": null
    }
  ]
}

This command will run against your Grafana Cloud Prometheus instance and will fetch its rule groups. It will then extract the Prometheus metrics used in the rule queries. The output is a JSON file.

cortextool analyse ruler --address=https://prometheus-blocks-prod-us-central1.grafana.net --id=<1234> --key=<API-Key>

{
  "metricsUsed": [
    "apiserver_request_duration_seconds_bucket",
    "container_cpu_usage_seconds_total",
    "scheduler_scheduling_algorithm_duration_seconds_bucket"
    ...
  ],
  "ruleGroups": [
    {
      "namspace": "prometheus_rules",
      "name": "kube-apiserver.rules",
      "metrics": [
        "apiserver_request_duration_seconds_bucket",
        "apiserver_request_duration_seconds_count",
        "apiserver_request_total"
      ],
      "parse_errors": null
    },
    ...
}

This command will run against your Prometheus / Cloud Prometheus instance. It will then use the output from analyse grafana and analyse ruler to show you how many series in the Prometheus server are actually being used in dashboards and rules. Also, it'll show which metrics exist in Grafana Cloud that are not in dashboards or rules. The output is a JSON file.

cortextool analyse prometheus --address=https://prometheus-blocks-prod-us-central1.grafana.net --id=<1234> --key=<API-Key> --log.level=debug

{
  "total_active_series": 38184,
  "in_use_active_series": 14047,
  "additional_active_series": 24137,
  "in_use_metric_counts": [
    {
      "metric": "apiserver_request_duration_seconds_bucket",
      "count": 11400,
      "job_counts": [
        {
          "job": "apiserver",
          "count": 11400
        }
      ]
    },
    {
      "metric": "apiserver_request_total",
      "count": 684,
      "job_counts": [
        {
          "job": "apiserver",
          "count": 684
        }
      ]
    },
    ...
  ],
  "additional_metric_counts": [
    {
      "metric": "etcd_request_duration_seconds_bucket",
      "count": 2688,
      "job_counts": [
        {
          "job": "apiserver",
          "count": 2688
        }
      ]
    },
    ...

This command accepts Grafana dashboard JSON files as input and extracts Prometheus metrics used in the queries. The output is a JSON file compatible with analyse prometheus.

cortextool analyse dashboard ./dashboard_one.json ./dashboard_two.json ...

This command accepts Prometheus rule YAML files as input and extracts Prometheus metrics used in the queries. The output is a JSON file compatible with analyse prometheus.

cortextool analyse rule-file ./rule_file_one.yaml ./rule_file_two.yaml ...

A CLI tool to parse Cortex query-frontend logs and formats them for easy analysis.

Options:
  -dur duration
        only show queries which took longer than this duration, e.g. -dur 10s
  -query
        show the query
  -utc
        show timestamp in UTC time

Feed logs into it using logcli from Loki, kubectl for Kubernetes, cat from a file, or any other way to get raw logs:

Loki logcli example:

$ logcli query '{cluster="us-central1", name="query-frontend", namespace="dev"}' --limit=5000 --since=3h --forward -o raw | ./logtool -dur 5s
https://logs-dev-ops-tools1.grafana.net/loki/api/v1/query_range?direction=FORWARD&end=1591119479093405000&limit=5000&query=%7Bcluster%3D%22us-central1%22%2C+name%3D%22query-frontend%22%2C+namespace%3D%22dev%22%7D&start=1591108679093405000
Common labels: {cluster="us-central1", container_name="query-frontend", job="dev/query-frontend", level="debug", name="query-frontend", namespace="dev", pod_template_hash="7cd4bf469d", stream="stderr"}

Timestamp                                TraceID           Length    Duration       Status  Path
2020-06-02 10:38:40.34205349 -0400 EDT   1f2533b40f7711d3  12h0m0s   21.92465802s   (200)   /api/prom/api/v1/query_range
2020-06-02 10:40:25.171649132 -0400 EDT  2ac59421db0000d8  168h0m0s  16.378698276s  (200)   /api/prom/api/v1/query_range
2020-06-02 10:40:29.698167258 -0400 EDT  3fd088d900160ba8  168h0m0s  20.912864541s  (200)   /api/prom/api/v1/query_range

$ cat query-frontend-logs.log | ./logtool -dur 5s
Timestamp                                TraceID           Length    Duration       Status  Path
2020-05-26 13:51:15.0577354 -0400 EDT    76b9939fd5c78b8f  6h0m0s    10.249149614s  (200)   /api/prom/api/v1/query_range
2020-05-26 13:52:15.771988849 -0400 EDT  2e7473ab10160630  10h33m0s  7.472855362s   (200)   /api/prom/api/v1/query_range
2020-05-26 13:53:46.712563497 -0400 EDT  761f3221dcdd85de  10h33m0s  11.874296689s  (200)   /api/prom/api/v1/query_range

A tool for benchmarking a Prometheus remote-write backend and PromQL compatible API. It allows for metrics to be generated using a workload file.

Licensed Apache 2.0, see LICENSE.