VictoriaMetrics/docs/MetricsQL.md

MetricsQL

VictoriaMetrics implements MetricsQL - query language inspired by PromQL. It is backwards compatible with PromQL, so Grafana dashboards backed by Prometheus datasource should work the same after switching from Prometheus to VictoriaMetrics. Standalone MetricsQL package can be used for parsing MetricsQL in external apps.

If you are unfamiliar with PromQL, then it is suggested reading this tutorial for beginners.

The following functionality is implemented differently in MetricsQL comparing to PromQL in order to improve user experience:

• MetricsQL takes into account the previous point before the window in square brackets for range functions such as rate and increase. It also doesn't extrapolate range function results. This addresses this issue from Prometheus.
• MetricsQL returns the expected non-empty responses for requests with step values smaller than scrape interval. This addresses this issue from Grafana.
• MetricsQL treats scalar type the same as instant vector without labels, since subtle difference between these types usually confuses users. See the corresponding Prometheus docs for details.
• MetricsQL removes all the NaN values from the output, so some queries like (-1)^0.5 return empty results in VictoriaMetrics, while returning a series of NaN values in Prometheus. Note that Grafana doesn't draw any lines or dots for NaN values, so usually the end result looks the same for both VictoriaMetrics and Prometheus.
• MetricsQL keeps metric names after applying functions, which don't change the meaining of the original time series. For example, min_over_time(foo) or round(foo) leave foo metric name in the result. See this issue for details.

Other PromQL functionality should work the same in MetricsQL. File an issue if you notice discrepancies between PromQL and MetricsQL results other than mentioned above.

MetricsQL provides additional functionality mentioned below, which is aimed towards solving practical cases. Feel free filing a feature request if you think MetricsQL misses certain useful functionality.

Note that the functionality mentioned below doesn't work in PromQL, so it is impossible switching back to Prometheus after you start using it.

This functionality can be tried at an editable Grafana dashboard.

• WITH templates. This feature simplifies writing and managing complex queries. Go to WITH templates playground and try it.

• Graphite-compatible filters can be passed via {__graphite__="foo.*.bar"} syntax. This is equivalent to {__name__=~"foo[.][^.]*[.]bar"}, but usually works faster and is easier to use when migrating from Graphite to VictoriaMetrics.

• Range duration in functions such as 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).

• All the aggregate functions support optional limit N suffix in order to limit the number of output series. For example, sum(x) by (y) limit 10 limits the number of output time series after the aggregation to 10. All the other time series are dropped.

• 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.

• offset, range duration and step value for range vector may refer to the current step aka $__interval value from Grafana. 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.

• offset may be put anywere in the query. For instance, sum(foo) offset 24h.

• offset may be negative. For example, q offset -1h.

• Range duration and offset may be fractional. For instance, rate(node_network_receive_bytes_total[1.5m] offset 0.5d).

• default binary operator. q1 default q2 fills gaps in q1 with the corresponding values from q2.

• Most aggregate functions accept arbitrary number of args. For example, avg(q1, q2, q3) would return the average values for every point across q1, q2 and q3.

• 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.

• if binary operator. q1 if q2 removes values from q1 for missing values from q2.

• ifnot binary operator. q1 ifnot q2 removes values from q1 for existing values from q2.

• 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.

• String literals may be concatenated. This is useful with WITH templates: WITH (commonPrefix="long_metric_prefix_") {__name__=commonPrefix+"suffix1"} / {__name__=commonPrefix+"suffix2"}.

• Comments starting with # and ending with newline. For instance, up # this is a comment for 'up' metric.

• 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 values for all the m data points over d duration.

• 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.

• union(q1, ... qN) function for building multiple graphs for q1, ... qN subqueries with a single query. The union function name may be skipped - the following queries are equivalent: union(q1, q2) and (q1, q2).

• 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 metrics.

• 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.

• Functions for label manipulation:

  • alias(q, name) for setting metric name across all the time series q.
  • label_set(q, label1, value1, ... labelN, valueN) for setting the given values for the given labels on q.
  • label_map(q, label, srcValue1, dstValue1, ... srcValueN, dstValueN) for mapping label values from src* to dst*.
  • label_uppercase(q, label1, ... labelN) for uppercasing values for the given labels.
  • label_lowercase(q, label2, ... labelN) for lowercasing value for the given labels.
  • label_del(q, label1, ... labelN) for deleting the given labels from q.
  • label_keep(q, label1, ... labelN) for deleting all the labels except the given labels from q.
  • label_copy(q, src_label1, dst_label1, ... src_labelN, dst_labelN) for copying label values from src_* to dst_*.
  • label_move(q, src_label1, dst_label1, ... src_labelN, dst_labelN) for moving label values from src_* to dst_*.
  • label_transform(q, label, regexp, replacement) for replacing all the regexp occurences with replacement in the label values from q.
  • label_value(q, label) - returns numeric values for the given label from q.

• label_match(q, label, regexp) and label_mismatch(q, label, regexp) for filtering time series with labels matching (or not matching) the given regexps.

• sort_by_label(q, label1, ... labelN) and sort_by_label_desc(q, label1, ... labelN) for sorting time series by the given set of labels.

• step() function for returning the step in seconds used in the query.

• start() and end() functions for returning the start and end timestamps of the [start ... end] range used in the query.

• integrate(m[d]) for returning integral over the given duration d for the given metric m.

• ideriv(m[d]) - for calculating instant derivative for the metric m over the duration d.

• deriv_fast(m[d]) - for calculating fast derivative for m based on the first and the last points from duration d.

• running_ functions - running_sum, running_min, running_max, running_avg - for calculating running values on the selected time range.

• 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. Note that global value is based on calculated datapoints for the inner query. The calculated datapoints can differ from raw datapoints stored in the database. See these docs for details.

• smooth_exponential(q, sf) - smooths q using exponential moving average with the given smooth factor sf.

• remove_resets(q) - removes counter resets from q.

• lag(m[d]) - returns lag between the current timestamp and the timestamp from the previous data point in m over d.

• lifetime(m[d]) - returns lifetime of q over d in seconds. It is expected that d exceeds the lifetime of m.

• scrape_interval(m[d]) - returns the average interval in seconds between data points of m over d aka scrape interval.

• Trigonometric functions - sin(q), cos(q), asin(q), acos(q) and pi().

• 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]).

• median_over_time(m[d]) - calculates median values for m over d time window. Shorthand to quantile_over_time(0.5, m[d]).

• median(q) - median aggregate. Shorthand to quantile(0.5, q).

• limitk(k, q) by (group_labels) - limits the number of time series returned from q to k per each group_labels. The returned set of k time series per each group_labels can change with each call.

• any(q) by (x) - returns any time series from q for each group in x.

• keep_last_value(q) - fills missing data (gaps) in q with the previous non-empty value.

• keep_next_value(q) - fills missing data (gaps) in q with the next non-empty value.

• interpolate(q) - fills missing data (gaps) in q with linearly interpolated values.

• distinct_over_time(m[d]) - returns distinct number of values for m data points over d duration.

• distinct(q) - returns a time series with the number of unique values for each timestamp in q.

• sum2_over_time(m[d]) - returns sum of squares for all the m values over d duration.

• sum2(q) - returns a time series with sum of square values for each timestamp in q.

• geomean_over_time(m[d]) - returns geomean value for all the m value over d duration.

• geomean(q) - returns a time series with geomean value for each timestamp in q.

• rand(), rand_normal() and rand_exponential() functions - for generating pseudo-random series with even, normal and exponential distribution.

• increases_over_time(m[d]) and decreases_over_time(m[d]) - returns the number of m increases or decreases over the given duration d.

• prometheus_buckets(q) - converts VictoriaMetrics histogram buckets to Prometheus buckets with le labels.

• buckets_limit(k, q) - limits the number of buckets (Prometheus-style or VictoriaMetrics-style) per each metric returned by by q to k. It also converts VictoriaMetrics-style buckets to Prometheus-style buckets, i.e. the end result are buckets with with le labels.

• histogram(q) - calculates aggregate histogram over q time series for each point on the graph. See this article for more details.

• histogram_over_time(m[d]) - calculates VictoriaMetrics histogram for m over d. For example, the following query calculates median temperature by country over the last 24 hours: histogram_quantile(0.5, sum(histogram_over_time(temperature[24h])) by (vmbucket, country)).

• histogram_share(le, buckets) - returns share (in the range 0..1) for buckets that fall below le. Useful for calculating SLI and SLO. For instance, the following query returns the share of requests which are performed under 1.5 seconds during the last 5 minutes: histogram_share(1.5, sum(rate(request_duration_seconds_bucket[5m])) by (le)).

• 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 - see this article for details.

  • topk_min(k, q) - returns top K time series with the max minimums on the given time range
  • topk_max(k, q) - returns top K time series with the max maximums on the given time range
  • topk_avg(k, q) - returns top K time series with the max averages on the given time range
  • topk_median(k, q) - returns top K time series with the max medians on the given time range
  • bottomk_min(k, q) - returns bottom K time series with the min minimums on the given time range
  • bottomk_max(k, q) - returns bottom K time series with the min maximums on the given time range
  • bottomk_avg(k, q) - returns bottom K time series with the min averages on the given time range
  • bottomk_median(k, q) - returns bottom K time series with the min medians on the given time range.

  All the topk_* and bottomk_* functions accept optional third argument - label name for the sum of the remaining time series outside top K or bottom K time series. For example, topk_max(3, process_resident_memory_bytes, "remaining_sum") would return up to 3 time series with the maximum value for process_resident_memory_bytes plus fourth time series with the sum of the remaining time series if any. The fourth time series will contain remaining_sum="remaining_sum" additional label.

• 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. 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.

• 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. Example: share_gt_over_time(up[24h], 0) - returns service availability for the last 24 hours.

• count_le_over_time(m[d], le) - returns the number of raw samples for m over d, which don't exceed le.

• count_gt_over_time(m[d], gt) - returns the number of raw samples for m over d, which are bigger than gt.

• count_eq_over_time(m[d], N) - returns the number of raw samples for m over d with values equal to N.

• count_ne_over_time(m[d], N) - returns the number of raw samples for m over d with values not equal to N.

• tmin_over_time(m[d]) - returns timestamp for the minimum value for m over d time range.

• tmax_over_time(m[d]) - returns timestamp for the maximum value for m over d time range.

• tfirst_over_time(m[d]) - returns timestamp for the first sample for m over d time range.

• tlast_over_time(m[d]) - returns timestamp for the last sample for m over d time range.

• aggr_over_time(("aggr_func1", "aggr_func2", ...), m[d]) - simultaneously calculates all the listed aggr_func* for m over d time range. aggr_func* can contain any functions that accept range vector. For instance, aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d]) would calculate min_over_time, max_over_time and rate for m[d].

• hoeffding_bound_upper(phi, m[d]) and hoeffding_bound_lower(phi, m[d]) - return upper and lower Hoeffding bounds for the given phi in the range [0..1].

• last_over_time(m[d]) - returns the last value for m on the time range d.

• first_over_time(m[d]) - returns the first value for m on the time range d.

• outliersk(N, q) by (group) - returns up to N outlier time series for q in every group. Outlier time series have the highest deviation from the median(q). This aggregate function is useful to detect anomalies across groups of similar time series.

• ascent_over_time(m[d]) - returns the sum of positive deltas between adjancent data points in m over d. Useful for tracking height gains in GPS track.

• descent_over_time(m[d]) - returns the absolute sum of negative deltas between adjancent data points in m over d. Useful for tracking height loss in GPS track.

• mode_over_time(m[d]) - returns mode for m values over d. It is expected that m values are discrete.

• mode(q) by (x) - returns mode for each point in q grouped by x. It is expected that q points are discrete.

• rate_over_sum(m[d]) - returns rate over the sum of m values over d duration.

• zscore_over_time(m[d]) - returns z-score for m values over d duration. Useful for detecting anomalies in time series comparing to historical samples.

• zscore(q) by (group) - returns independent z-score values for every point in every group of q. Useful for detecting anomalies in the group of related time series.