If you're using this tool with Grafana Mimir, please use the new mimirtool instead:
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.
- chunktool: Interacts with chunks stored and indexed in Cortex storage backends.
- 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/cortextoolRefer 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.
| Env Variables | Flag | Description |
|---|---|---|
| CORTEX_ADDRESS | address |
Address of the API of the desired Cortex cluster. |
| CORTEX_API_USER | user |
In cases where the Cortex API is set behind a basic auth gateway, a user can be set as a basic auth user. If empty and CORTEX_API_KEY is set, CORTEX_TENANT_ID will be used instead. |
| CORTEX_API_KEY | key |
In cases where the Cortex API is set behind a basic auth gateway, a key can be set as a basic auth password. |
| CORTEX_AUTH_TOKEN | authToken |
In cases where the Cortex API is set behind gateway authenticating by bearer token, a token can be set as a bearer token header. |
| CORTEX_TENANT_ID | id |
The tenant ID of the Cortex instance to interact with. |
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.
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:
-iwhich allows you to edit in place, otherwise a a new file with a.outputextension is created with the results of the run.-lwhich allows you to specify the label you want to add for your aggregations, which isclusterby 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 0The 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.
| Env Variables | Flag | Description |
|---|---|---|
| GRAFANA_ADDRESS | address |
Address of the Grafana instance. |
| GRAFANA_API_KEY | key |
The API Key for the Grafana instance. Create a key using the following instructions: https://grafana.com/docs/grafana/latest/http_api/auth/ |
| __ | output |
The output file path. metrics-in-grafana.json by default. |
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.
| Env Variables | Flag | Description |
|---|---|---|
| CORTEX_ADDRESS | address |
Address of the Prometheus instance. |
| CORTEX_TENANT_ID | id |
If you're using Grafana Cloud this is your instance ID. |
| CORTEX_API_KEY | key |
If you're using Grafana Cloud this is your API Key. |
| __ | output |
The output file path. metrics-in-ruler.json by default. |
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.
| Env Variables | Flag | Description |
|---|---|---|
| CORTEX_ADDRESS | address |
Address of the Prometheus instance. |
| CORTEX_TENANT_ID | id |
If you're using Grafana Cloud this is your instance ID. |
| CORTEX_API_KEY | key |
If you're using Grafana Cloud this is your API Key. |
| __ | grafana-metrics-file |
The dashboard metrics input file path. metrics-in-grafana.json by default. |
| __ | ruler-metrics-file |
The rules metrics input file path. metrics-in-ruler.json by default. |
| __ | output |
The output file path. prometheus-metrics.json by default. |
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 ...This repo also contains the chunktool. A client meant to interact with chunks stored and indexed in cortex backends.
The delete command currently cleans all index entries pointing to chunks in the specified index. Only bigtable and the v10 schema are currently fully supported. This will not delete the entire index entry, only the corresponding chunk entries within the index row.
The migrate command helps with migrating chunks across cortex clusters. It also takes care of setting right index in the new cluster as per the specified schema config.
As of now it only supports Bigtable or GCS as a source to read chunks from for migration. For writing it supports all the storages that Cortex supports.
More details about it here
The chunk validate-index and chunk clean-index command allows users to scan their index and chunk backends for invalid entries. The validate-index command will find invalid entries and ouput them to a CSV file. The clean-index command will take that CSV file as input and delete the invalid entries.
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.