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{dashboards,alerts}: subtitute {type="indexdb"} with {type=~"indexdb.*"} inside queries after 8189770c50

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Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337
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Aliaksandr Valialkin 2022-12-05 15:59:52 -08:00
parent 8189770c50
commit f3e84b4dea
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8 changed files with 23 additions and 23 deletions
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2
README.md
View file

@ -1745,7 +1745,7 @@ and [cardinality explorer docs](#cardinality-explorer).
by requesting `/internal/force_flush` http handler. This handler is mostly needed for testing and debugging purposes.
* The last few seconds of inserted data may be lost on unclean shutdown (i.e. OOM, `kill -9` or hardware reset).
The `-inmemoryDataFlushInterval` command-line flag allows controlling the frequency of in-memory data flush to persistent storage.
See [this article for technical details](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
See [storage docs](#storage) and [this article](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704) for more details.
* If VictoriaMetrics works slowly and eats more than a CPU core per 100K ingested data points per second,
then it is likely you have too many [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series) for the current amount of RAM.

18
dashboards/victoriametrics-cluster.json
View file

@ -179,7 +179,7 @@
"uid": "$ds"
},
"exemplar": true,
"expr": "sum(vm_rows{job=~\"$job_storage\", type!=\"indexdb\"})",
"expr": "sum(vm_rows{job=~\"$job_storage\", type!~\"indexdb.*\"})",
"format": "time_series",
"instant": true,
"interval": "",
@ -599,7 +599,7 @@
"uid": "$ds"
},
"exemplar": true,
"expr": "sum(vm_data_size_bytes{job=~\"$job_storage\", type!=\"indexdb\"}) / sum(vm_rows{job=~\"$job_storage\", type!=\"indexdb\"})",
"expr": "sum(vm_data_size_bytes{job=~\"$job_storage\", type!~\"indexdb.*\"}) / sum(vm_rows{job=~\"$job_storage\", type!~\"indexdb.*\"})",
"format": "time_series",
"instant": true,
"interval": "",
@ -4484,7 +4484,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "min(vm_free_disk_space_bytes{job=~\"$job_storage\", instance=~\"$instance\"} \n/ \nignoring(path) (\n (\n rate(vm_rows_added_to_storage_total{job=~\"$job_storage\", instance=~\"$instance\"}[1d])\n - \n ignoring(type) rate(vm_deduplicated_samples_total{job=~\"$job_storage\", instance=~\"$instance\", type=\"merge\"}[1d])\n ) * scalar(\n sum(vm_data_size_bytes{job=~\"$job_storage\", instance=~\"$instance\", type!=\"indexdb\"})\n / \n sum(vm_rows{job=~\"$job_storage\", instance=~\"$instance\", type!=\"indexdb\"})\n )\n))",
"expr": "min(vm_free_disk_space_bytes{job=~\"$job_storage\", instance=~\"$instance\"} \n/ \nignoring(path) (\n (\n rate(vm_rows_added_to_storage_total{job=~\"$job_storage\", instance=~\"$instance\"}[1d])\n - \n ignoring(type) rate(vm_deduplicated_samples_total{job=~\"$job_storage\", instance=~\"$instance\", type=\"merge\"}[1d])\n ) * scalar(\n sum(vm_data_size_bytes{job=~\"$job_storage\", instance=~\"$instance\", type!~\"indexdb.*\"})\n / \n sum(vm_rows{job=~\"$job_storage\", instance=~\"$instance\", type!~\"indexdb.*\"})\n )\n))",
"format": "time_series",
"interval": "",
"intervalFactor": 1,
@ -5584,7 +5584,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "max(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=\"indexdb\"}) by(job, instance)\n / \n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\"}) by(job, instance)\n)",
"expr": "max(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=~\"indexdb.*\"}) by(job, instance)\n / \n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\"}) by(job, instance)\n)",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "indexdb",
@ -5597,7 +5597,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "max(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"}) by(job, instance)\n / \n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\"}) by(job, instance)\n)",
"expr": "max(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"}) by(job, instance)\n / \n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\"}) by(job, instance)\n)",
"format": "time_series",
"hide": false,
"intervalFactor": 1,
@ -8374,7 +8374,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "vm_free_disk_space_bytes{job=~\"$job_storage\", instance=~\"$instance\"} \n/ \nignoring(path) (\n (\n rate(vm_rows_added_to_storage_total{job=~\"$job_storage\", instance=~\"$instance\"}[1d])\n - \n ignoring(type) rate(vm_deduplicated_samples_total{job=~\"$job_storage\", instance=~\"$instance\", type=\"merge\"}[1d])\n ) * scalar(\n sum(vm_data_size_bytes{job=~\"$job_storage\", instance=~\"$instance\", type!=\"indexdb\"})\n / \n sum(vm_rows{job=~\"$job_storage\", instance=~\"$instance\", type!=\"indexdb\"})\n )\n)",
"expr": "vm_free_disk_space_bytes{job=~\"$job_storage\", instance=~\"$instance\"} \n/ \nignoring(path) (\n (\n rate(vm_rows_added_to_storage_total{job=~\"$job_storage\", instance=~\"$instance\"}[1d])\n - \n ignoring(type) rate(vm_deduplicated_samples_total{job=~\"$job_storage\", instance=~\"$instance\", type=\"merge\"}[1d])\n ) * scalar(\n sum(vm_data_size_bytes{job=~\"$job_storage\", instance=~\"$instance\", type!~\"indexdb.*\"})\n / \n sum(vm_rows{job=~\"$job_storage\", instance=~\"$instance\", type!~\"indexdb.*\"})\n )\n)",
"format": "time_series",
"interval": "",
"intervalFactor": 1,
@ -8579,7 +8579,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=\"indexdb\"}) by(job, instance)",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=~\"indexdb.*\"}) by(job, instance)",
"format": "time_series",
"intervalFactor": 1,
"legendFormat": "{{job}}:{{instance}} (indexdb)",
@ -8592,7 +8592,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"}) by(job, instance)",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"}) by(job, instance)",
"format": "time_series",
"hide": false,
"intervalFactor": 1,
@ -8791,4 +8791,4 @@
"uid": "oS7Bi_0Wz",
"version": 1,
"weekStart": ""
}
}

12
dashboards/victoriametrics.json
View file

@ -225,7 +225,7 @@
"uid": "$ds"
},
"exemplar": false,
"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"})",
"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"})",
"format": "time_series",
"instant": true,
"interval": "",
@ -3767,7 +3767,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "vm_free_disk_space_bytes{job=~\"$job\", instance=~\"$instance\"} \n/ ignoring(path) (\n (\n rate(vm_rows_added_to_storage_total{job=~\"$job\", instance=~\"$instance\"}[1d]) \n - ignoring(type) rate(vm_deduplicated_samples_total{job=~\"$job\", instance=~\"$instance\", type=\"merge\"}[1d])\n ) * scalar(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"}) \n / sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"})\n )\n )",
"expr": "vm_free_disk_space_bytes{job=~\"$job\", instance=~\"$instance\"} \n/ ignoring(path) (\n (\n rate(vm_rows_added_to_storage_total{job=~\"$job\", instance=~\"$instance\"}[1d]) \n - ignoring(type) rate(vm_deduplicated_samples_total{job=~\"$job\", instance=~\"$instance\", type=\"merge\"}[1d])\n ) * scalar(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"}) \n / sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"})\n )\n )",
"format": "time_series",
"hide": false,
"interval": "",
@ -3874,7 +3874,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"})",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"})",
"format": "time_series",
"interval": "",
"intervalFactor": 1,
@ -3900,7 +3900,7 @@
"uid": "$ds"
},
"editorMode": "code",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=\"indexdb\"})",
"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=~\"indexdb.*\"})",
"format": "time_series",
"hide": false,
"interval": "",
@ -4156,7 +4156,7 @@
"type": "prometheus",
"uid": "$ds"
},
"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type != \"indexdb\"})",
"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"})",
"format": "time_series",
"interval": "",
"intervalFactor": 1,
@ -5306,4 +5306,4 @@
"uid": "wNf0q_kZk",
"version": 1,
"weekStart": ""
}
}

4
deployment/docker/alerts-cluster.yml
View file

@ -18,8 +18,8 @@ groups:
ignoring(type) rate(vm_deduplicated_samples_total{type="merge"}[1d])
)
* scalar(
sum(vm_data_size_bytes{type!="indexdb"}) /
sum(vm_rows{type!="indexdb"})
sum(vm_data_size_bytes{type!~"indexdb.*"}) /
sum(vm_rows{type!~"indexdb.*"})
)
) < 3 * 24 * 3600 > 0
for: 30m

4
deployment/docker/alerts.yml
View file

@ -18,8 +18,8 @@ groups:
ignoring(type) rate(vm_deduplicated_samples_total{type="merge"}[1d])
)
* scalar(
sum(vm_data_size_bytes{type!="indexdb"}) /
sum(vm_rows{type!="indexdb"})
sum(vm_data_size_bytes{type!~"indexdb.*"}) /
sum(vm_rows{type!~"indexdb.*"})
)
) < 3 * 24 * 3600 > 0
for: 30m

2
docs/CHANGELOG.md
View file

@ -17,7 +17,7 @@ The following tip changes can be tested by building VictoriaMetrics components f
**Update note 1:** this release drops support for direct upgrade from VictoriaMetrics versions prior [v1.28.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.28.0). Please upgrade to `v1.84.0`, wait until `finished round 2 of background conversion` line is emitted to log by single-node VictoriaMetrics or by `vmstorage`, and then upgrade to newer releases.
**Update note 2:** this release splits `type="indexdb"` metrics into `type="indexdb/inmemory"` and `type="indexdb/file"` metrics. This may break old dashboards and alerting rules, which contain label filters on `{type="indexdb"}`. It is recommended upgrading to the latest available dashboards and alerting rules mentioned in [these docs](https://docs.victoriametrics.com/#monitoring).
**Update note 2:** this release splits `type="indexdb"` metrics into `type="indexdb/inmemory"` and `type="indexdb/file"` metrics. This may break old dashboards and alerting rules, which contain [label filter](https://docs.victoriametrics.com/keyConcepts.html#filtering) on `{type="indexdb"}`. Such label filter must be substituted with `{type=~"indexdb.*"}`, so it matches `indexdb` from the previous releases and `indexdb/inmemory` + `indexdb/file` from new releases. It is recommended upgrading to the latest available dashboards and alerting rules mentioned in [these docs](https://docs.victoriametrics.com/#monitoring), since they already contain fixed label filters.
* FEATURE: add `-inmemoryDataFlushInterval` command-line flag, which can be used for controlling the frequency of in-memory data flush to disk. The data flush frequency can be reduced when VictoriaMetrics stores data to low-end flash device with limited number of write cycles (for example, on Raspberry PI). See [this feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337).
* FEATURE: expose additional metrics for `indexdb` and `storage` parts stored in memory and for `indexdb` parts stored in files (see [storage docs](https://docs.victoriametrics.com/#storage) for technical details):

2
docs/README.md
View file

@ -1746,7 +1746,7 @@ and [cardinality explorer docs](#cardinality-explorer).
by requesting `/internal/force_flush` http handler. This handler is mostly needed for testing and debugging purposes.
* The last few seconds of inserted data may be lost on unclean shutdown (i.e. OOM, `kill -9` or hardware reset).
The `-inmemoryDataFlushInterval` command-line flag allows controlling the frequency of in-memory data flush to persistent storage.
See [this article for technical details](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
See [storage docs](#storage) and [this article](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704) for more details.
* If VictoriaMetrics works slowly and eats more than a CPU core per 100K ingested data points per second,
then it is likely you have too many [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series) for the current amount of RAM.

2
docs/Single-server-VictoriaMetrics.md
View file

@ -1749,7 +1749,7 @@ and [cardinality explorer docs](#cardinality-explorer).
by requesting `/internal/force_flush` http handler. This handler is mostly needed for testing and debugging purposes.
* The last few seconds of inserted data may be lost on unclean shutdown (i.e. OOM, `kill -9` or hardware reset).
The `-inmemoryDataFlushInterval` command-line flag allows controlling the frequency of in-memory data flush to persistent storage.
See [this article for technical details](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
See [storage docs](#storage) and [this article](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704) for more details.
* If VictoriaMetrics works slowly and eats more than a CPU core per 100K ingested data points per second,
then it is likely you have too many [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-an-active-time-series) for the current amount of RAM.