mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
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{dashboards,alerts}: subtitute {type="indexdb"} with {type=~"indexdb.*"} inside queries after 8189770c50
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337
This commit is contained in:
Aliaksandr Valialkin
parent
8189770c50
commit
f3e84b4dea
@ -1745,7 +1745,7 @@ and [cardinality explorer docs](#cardinality-explorer).
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by requesting `/internal/force_flush` http handler. This handler is mostly needed for testing and debugging purposes.
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* The last few seconds of inserted data may be lost on unclean shutdown (i.e. OOM, `kill -9` or hardware reset).
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The `-inmemoryDataFlushInterval` command-line flag allows controlling the frequency of in-memory data flush to persistent storage.
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See [this article for technical details](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
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See [storage docs](#storage) and [this article](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704) for more details.
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* If VictoriaMetrics works slowly and eats more than a CPU core per 100K ingested data points per second,
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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.
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@ -179,7 +179,7 @@
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"uid": "$ds"
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},
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"exemplar": true,
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"expr": "sum(vm_rows{job=~\"$job_storage\", type!=\"indexdb\"})",
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"expr": "sum(vm_rows{job=~\"$job_storage\", type!~\"indexdb.*\"})",
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"format": "time_series",
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"instant": true,
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"interval": "",
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@ -599,7 +599,7 @@
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"uid": "$ds"
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},
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"exemplar": true,
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"expr": "sum(vm_data_size_bytes{job=~\"$job_storage\", type!=\"indexdb\"}) / sum(vm_rows{job=~\"$job_storage\", type!=\"indexdb\"})",
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"expr": "sum(vm_data_size_bytes{job=~\"$job_storage\", type!~\"indexdb.*\"}) / sum(vm_rows{job=~\"$job_storage\", type!~\"indexdb.*\"})",
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"format": "time_series",
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"instant": true,
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"interval": "",
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@ -4484,7 +4484,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"expr": "min(vm_free_disk_space_bytes{job=~\"$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=~\"$instance\", type!=\"indexdb\"})\n )\n))",
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"expr": "min(vm_free_disk_space_bytes{job=~\"$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=~\"$instance\", type!~\"indexdb.*\"})\n )\n))",
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"format": "time_series",
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"interval": "",
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"intervalFactor": 1,
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@ -5584,7 +5584,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"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)",
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"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)",
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"format": "time_series",
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"intervalFactor": 1,
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"legendFormat": "indexdb",
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@ -5597,7 +5597,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"expr": "max(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"}) by(job, instance)\n / \n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\"}) by(job, instance)\n)",
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"expr": "max(\n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"}) by(job, instance)\n / \n sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\"}) by(job, instance)\n)",
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"format": "time_series",
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"hide": false,
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"intervalFactor": 1,
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@ -8374,7 +8374,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"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)",
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"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)",
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"format": "time_series",
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"interval": "",
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"intervalFactor": 1,
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@ -8579,7 +8579,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=\"indexdb\"}) by(job, instance)",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=~\"indexdb.*\"}) by(job, instance)",
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"format": "time_series",
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"intervalFactor": 1,
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"legendFormat": "{{job}}:{{instance}} (indexdb)",
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@ -8592,7 +8592,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"}) by(job, instance)",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"}) by(job, instance)",
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"format": "time_series",
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"hide": false,
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"intervalFactor": 1,
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@ -8791,4 +8791,4 @@
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"uid": "oS7Bi_0Wz",
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"version": 1,
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"weekStart": ""
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}
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}
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@ -225,7 +225,7 @@
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"uid": "$ds"
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},
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"exemplar": false,
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"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"})",
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"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"})",
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"format": "time_series",
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"instant": true,
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"interval": "",
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@ -3767,7 +3767,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"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 )",
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"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 )",
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"format": "time_series",
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"hide": false,
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"interval": "",
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@ -3874,7 +3874,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!=\"indexdb\"})",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"})",
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"format": "time_series",
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"interval": "",
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"intervalFactor": 1,
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@ -3900,7 +3900,7 @@
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"uid": "$ds"
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},
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"editorMode": "code",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=\"indexdb\"})",
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"expr": "sum(vm_data_size_bytes{job=~\"$job\", instance=~\"$instance\", type=~\"indexdb.*\"})",
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"format": "time_series",
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"hide": false,
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"interval": "",
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@ -4156,7 +4156,7 @@
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"type": "prometheus",
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"uid": "$ds"
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},
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"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type != \"indexdb\"})",
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"expr": "sum(vm_rows{job=~\"$job\", instance=~\"$instance\", type!~\"indexdb.*\"})",
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"format": "time_series",
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"interval": "",
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"intervalFactor": 1,
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@ -5306,4 +5306,4 @@
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"uid": "wNf0q_kZk",
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"version": 1,
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"weekStart": ""
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}
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}
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@ -18,8 +18,8 @@ groups:
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ignoring(type) rate(vm_deduplicated_samples_total{type="merge"}[1d])
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)
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* scalar(
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sum(vm_data_size_bytes{type!="indexdb"}) /
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sum(vm_rows{type!="indexdb"})
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sum(vm_data_size_bytes{type!~"indexdb.*"}) /
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sum(vm_rows{type!~"indexdb.*"})
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)
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) < 3 * 24 * 3600 > 0
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for: 30m
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@ -18,8 +18,8 @@ groups:
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ignoring(type) rate(vm_deduplicated_samples_total{type="merge"}[1d])
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)
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* scalar(
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sum(vm_data_size_bytes{type!="indexdb"}) /
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sum(vm_rows{type!="indexdb"})
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sum(vm_data_size_bytes{type!~"indexdb.*"}) /
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sum(vm_rows{type!~"indexdb.*"})
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)
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) < 3 * 24 * 3600 > 0
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for: 30m
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@ -17,7 +17,7 @@ The following tip changes can be tested by building VictoriaMetrics components f
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**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.
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**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).
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**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.
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* 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).
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* 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):
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@ -1746,7 +1746,7 @@ and [cardinality explorer docs](#cardinality-explorer).
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by requesting `/internal/force_flush` http handler. This handler is mostly needed for testing and debugging purposes.
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* The last few seconds of inserted data may be lost on unclean shutdown (i.e. OOM, `kill -9` or hardware reset).
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The `-inmemoryDataFlushInterval` command-line flag allows controlling the frequency of in-memory data flush to persistent storage.
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See [this article for technical details](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
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See [storage docs](#storage) and [this article](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704) for more details.
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* If VictoriaMetrics works slowly and eats more than a CPU core per 100K ingested data points per second,
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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.
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@ -1749,7 +1749,7 @@ and [cardinality explorer docs](#cardinality-explorer).
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by requesting `/internal/force_flush` http handler. This handler is mostly needed for testing and debugging purposes.
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* The last few seconds of inserted data may be lost on unclean shutdown (i.e. OOM, `kill -9` or hardware reset).
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The `-inmemoryDataFlushInterval` command-line flag allows controlling the frequency of in-memory data flush to persistent storage.
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See [this article for technical details](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
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See [storage docs](#storage) and [this article](https://valyala.medium.com/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704) for more details.
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* If VictoriaMetrics works slowly and eats more than a CPU core per 100K ingested data points per second,
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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.
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