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This is implementation of https://github.com/prometheus/prometheus/pull/1533 for VictoriaMetrics.
111 lines
14 KiB
Markdown
111 lines
14 KiB
Markdown
# MetricsQL
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VictoriaMetrics implements MetricsQL - query language inspired by [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/).
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It is backwards compatible with PromQL, so Grafana dashboards backed by Prometheus datasource should work the same after switching from Prometheus to VictoriaMetrics.
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[Standalone MetricsQL package](https://godoc.org/github.com/VictoriaMetrics/VictoriaMetrics/lib/metricsql) can be used for parsing MetricsQL in external apps.
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The following functionality is implemented differently in MetricsQL comparing to PromQL in order to improve user experience:
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* MetricsQL takes into account the previous point before the window in square brackets for range functions such as `rate` and `increase`.
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It also doesn't extrapolate range function results. This addresses [this issue from Prometheus](https://github.com/prometheus/prometheus/issues/3746).
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* MetricsQL returns the expected non-empty responses for requests with `step` values smaller than scrape interval. This addresses [this issue from Grafana](https://github.com/grafana/grafana/issues/11451).
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* MetricsQL treats `scalar` type the same as `instant vector` without labels, since subtle difference between these types usually confuses users.
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See [the corresponding Prometheus docs](https://prometheus.io/docs/prometheus/latest/querying/basics/#expression-language-data-types) for details.
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Other PromQL functionality should work the same in MetricsQL. [File an issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues)
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if you notice discrepancies between PromQL and MetricsQL results other than mentioned above.
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MetricsQL provides additional functionality mentioned below, which is aimed towards solving practical cases.
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Feel free [filing a feature request](https://github.com/VictoriaMetrics/VictoriaMetrics/issues) if you think MetricsQL misses certain useful functionality.
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*Note that the functionality mentioned below doesn't work in PromQL, so it is impossible switching back to Prometheus after you start using it.*
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This functionality can be tried at [an editable Grafana dashboard](http://play-grafana.victoriametrics.com:3000/d/4ome8yJmz/node-exporter-on-victoriametrics-demo).
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- [`WITH` templates](https://play.victoriametrics.com/promql/expand-with-exprs). This feature simplifies writing and managing complex queries. Go to [`WITH` templates playground](https://victoriametrics.com/promql/expand-with-exprs) and try it.
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- Metric names and metric labels may contain escaped chars. For instance, `foo\-bar{baz\=aa="b"}` is valid expression. It returns time series with name `foo-bar` containing label `baz=aa` with value `b`. Additionally, `\xXX` escape sequence is supported, where `XX` is hexadecimal representation of escaped char.
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- `offset`, range duration and step value for range vector may refer to the current step aka `$__interval` value from Grafana.
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For instance, `rate(metric[10i] offset 5i)` would return per-second rate over a range covering 10 previous steps with the offset of 5 steps.
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- `offset` may be put anywere in the query. For instance, `sum(foo) offset 24h`.
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- `offset` may be negative. For example, `q offset -1h`.
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- `default` binary operator. `q1 default q2` substitutes `NaN` values from `q1` with the corresponding values from `q2`.
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- `histogram_quantile` accepts optional third arg - `boundsLabel`. In this case it returns `lower` and `upper` bounds for the estimated percentile. See [this issue for details](https://github.com/prometheus/prometheus/issues/5706).
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- `if` binary operator. `q1 if q2` removes values from `q1` for `NaN` values from `q2`.
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- `ifnot` binary operator. `q1 ifnot q2` removes values from `q1` for non-`NaN` values from `q2`.
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- Trailing commas on all the lists are allowed - label filters, function args and with expressions. For instance, the following queries are valid: `m{foo="bar",}`, `f(a, b,)`, `WITH (x=y,) x`. This simplifies maintenance of multi-line queries.
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- String literals may be concatenated. This is useful with `WITH` templates: `WITH (commonPrefix="long_metric_prefix_") {__name__=commonPrefix+"suffix1"} / {__name__=commonPrefix+"suffix2"}`.
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- Range duration in functions such as [rate](https://prometheus.io/docs/prometheus/latest/querying/functions/#rate()) may be omitted. VictoriaMetrics automatically selects range duration depending on the current step used for building the graph. For instance, the following query is valid in VictoriaMetrics: `rate(node_network_receive_bytes_total)`.
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- [Range duration](https://prometheus.io/docs/prometheus/latest/querying/basics/#range-vector-selectors) and [offset](https://prometheus.io/docs/prometheus/latest/querying/basics/#offset-modifier) may be fractional. For instance, `rate(node_network_receive_bytes_total[1.5m] offset 0.5d)`.
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- Comments starting with `#` and ending with newline. For instance, `up # this is a comment for 'up' metric`.
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- Rollup functions - `rollup(m[d])`, `rollup_rate(m[d])`, `rollup_deriv(m[d])`, `rollup_increase(m[d])`, `rollup_delta(m[d])` - return `min`, `max` and `avg`
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values for all the `m` data points over `d` duration.
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- `rollup_candlestick(m[d])` - returns `open`, `close`, `low` and `high` values (OHLC) for all the `m` data points over `d` duration. This function is useful for financial applications.
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- `union(q1, ... qN)` function for building multiple graphs for `q1`, ... `qN` subqueries with a single query. The `union` function name may be skipped -
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the following queries are equivalent: `union(q1, q2)` and `(q1, q2)`.
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- `ru(freeResources, maxResources)` function for returning resource utilization percentage in the range `0% - 100%`. For instance, `ru(node_memory_MemFree_bytes, node_memory_MemTotal_bytes)` returns memory utilization over [node_exporter](https://github.com/prometheus/node_exporter) metrics.
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- `ttf(slowlyChangingFreeResources)` function for returning the time in seconds when the given `slowlyChangingFreeResources` expression reaches zero. For instance, `ttf(node_filesystem_avail_byte)` returns the time to storage space exhaustion. This function may be useful for capacity planning.
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- Functions for label manipulation:
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- `alias(q, name)` for setting metric name across all the time series `q`.
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- `label_set(q, label1, value1, ... labelN, valueN)` for setting the given values for the given labels on `q`.
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- `label_del(q, label1, ... labelN)` for deleting the given labels from `q`.
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- `label_keep(q, label1, ... labelN)` for deleting all the labels except the given labels from `q`.
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- `label_copy(q, src_label1, dst_label1, ... src_labelN, dst_labelN)` for copying label values from `src_*` to `dst_*`.
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- `label_move(q, src_label1, dst_label1, ... src_labelN, dst_labelN)` for moving label values from `src_*` to `dst_*`.
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- `label_transform(q, label, regexp, replacement)` for replacing all the `regexp` occurences with `replacement` in the `label` values from `q`.
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- `label_value(q, label)` - returns numeric values for the given `label` from `q`.
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- `label_match(q, label, regexp)` and `label_mismatch(q, label, regexp)` for filtering time series with labels matching (or not matching) the given regexps.
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- `sort_by_label(q, label)` and `sort_by_label_desc(q, label)` for sorting time series by the given `label`.
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- `step()` function for returning the step in seconds used in the query.
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- `start()` and `end()` functions for returning the start and end timestamps of the `[start ... end]` range used in the query.
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- `integrate(m[d])` for returning integral over the given duration `d` for the given metric `m`.
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- `ideriv(m)` - for calculating `instant` derivative for `m`.
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- `deriv_fast(m[d])` - for calculating `fast` derivative for `m` based on the first and the last points from duration `d`.
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- `running_` functions - `running_sum`, `running_min`, `running_max`, `running_avg` - for calculating [running values](https://en.wikipedia.org/wiki/Running_total) on the selected time range.
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- `range_` functions - `range_sum`, `range_min`, `range_max`, `range_avg`, `range_first`, `range_last`, `range_median`, `range_quantile` - for calculating global value over the selected time range.
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- `smooth_exponential(q, sf)` - smooths `q` using [exponential moving average](https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average) with the given smooth factor `sf`.
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- `remove_resets(q)` - removes counter resets from `q`.
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- `lag(q[d])` - returns lag between the current timestamp and the timestamp from the previous data point in `q` over `d`.
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- `lifetime(q[d])` - returns lifetime of `q` over `d` in seconds. It is expected that `d` exceeds the lifetime of `q`.
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- `scrape_interval(q[d])` - returns the average interval in seconds between data points of `q` over `d` aka `scrape interval`.
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- Trigonometric functions - `sin(q)`, `cos(q)`, `asin(q)`, `acos(q)` and `pi()`.
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- `range_over_time(m[d])` - returns value range for `m` over `d` time window, i.e. `max_over_time(m[d])-min_over_time(m[d])`.
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- `median_over_time(m[d])` - calculates median values for `m` over `d` time window. Shorthand to `quantile_over_time(0.5, m[d])`.
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- `median(q)` - median aggregate. Shorthand to `quantile(0.5, q)`.
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- `limitk(k, q)` - limits the number of time series returned from `q` to `k`.
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- `keep_last_value(q)` - fills missing data (gaps) in `q` with the previous non-empty value.
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- `keep_next_value(q)` - fills missing data (gaps) in `q` with the next non-empty value.
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- `distinct_over_time(m[d])` - returns distinct number of values for `m` data points over `d` duration.
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- `distinct(q)` - returns a time series with the number of unique values for each timestamp in `q`.
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- `sum2_over_time(m[d])` - returns sum of squares for all the `m` values over `d` duration.
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- `sum2(q)` - returns a time series with sum of square values for each timestamp in `q`.
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- `geomean_over_time(m[d])` - returns [geomean](https://en.wikipedia.org/wiki/Geometric_mean) value for all the `m` value over `d` duration.
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- `geomean(q)` - returns a time series with [geomean](https://en.wikipedia.org/wiki/Geometric_mean) value for each timestamp in `q`.
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- `rand()`, `rand_normal()` and `rand_exponential()` functions - for generating pseudo-random series with even, normal and exponential distribution.
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- `increases_over_time(m[d])` and `decreases_over_time(m[d])` - returns the number of `m` increases or decreases over the given duration `d`.
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- `prometheus_buckets(q)` - converts [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) buckets to Prometheus buckets with `le` labels.
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- `histogram(q)` - calculates aggregate histogram over `q` time series for each point on the graph. See [this article](https://medium.com/@valyala/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350) for more details.
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- `histogram_over_time(m[d])` - calculates [VictoriaMetrics histogram](https://godoc.org/github.com/VictoriaMetrics/metrics#Histogram) for `m` over `d`.
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For example, the following query calculates median temperature by country over the last 24 hours:
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`histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmbucket, country))`.
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- `histogram_share(le, buckets)` - returns share (in the range 0..1) for `buckets`. Useful for calculating SLI and SLO.
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For instance, the following query returns the share of requests which are performed under 1.5 seconds: `histogram_share(1.5, sum(request_duration_seconds_bucket) by (le))`.
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- `topk_*` and `bottomk_*` aggregate functions, which return up to K time series. Note that the standard `topk` function may return more than K time series -
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see [this article](https://www.robustperception.io/graph-top-n-time-series-in-grafana) for details.
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- `topk_min(k, q)` - returns top K time series with the max minimums on the given time range
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- `topk_max(k, q)` - returns top K time series with the max maximums on the given time range
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- `topk_avg(k, q)` - returns top K time series with the max averages on the given time range
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- `topk_median(k, q)` - returns top K time series with the max medians on the given time range
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- `bottomk_min(k, q)` - returns bottom K time series with the min minimums on the given time range
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- `bottomk_max(k, q)` - returns bottom K time series with the min maximums on the given time range
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- `bottomk_avg(k, q)` - returns bottom K time series with the min averages on the given time range
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- `bottomk_median(k, q)` - returns bottom K time series with the min medians on the given time range
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- `share_le_over_time(m[d], le)` - returns share (in the range 0..1) of values in `m` over `d`, which are smaller or equal to `le`. Useful for calculating SLI and SLO.
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Example: `share_le_over_time(memory_usage_bytes[24h], 100*1024*1024)` returns the share of time series values for the last 24 hours when memory usage was below or equal to 100MB.
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- `share_gt_over_time(m[d], gt)` - returns share (in the range 0..1) of values in `m` over `d`, which are bigger than `gt`. Useful for calculating SLI and SLO.
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Example: `share_gt_over_time(up[24h], 0)` - returns service availability for the last 24 hours.
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- `tmin_over_time(m[d])` - returns timestamp for the minimum value for `m` over `d` time range.
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- `tmax_over_time(m[d])` - returns timestamp for the maximum value for `m` over `d` time range.
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- `aggr_over_time(("aggr_func1", "aggr_func2", ...), m[d])` - simultaneously calculates all the listed `aggr_func*` for `m` over `d` time range.
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`aggr_func*` can contain any functions that accept range vector. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`
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would calculate `min_over_time`, `max_over_time` and `rate` for `m[d]`.
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- `hoeffding_bound_upper(phi, m[d])` and `hoeffding_bound_lower(phi, m[d])` - return upper and lower [Hoeffding bounds](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality)
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for the given `phi` in the range `[0..1]`.
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