VictoriaMetrics supports standard PromQL including subqueries. Additionally it supports useful extensions mentioned below. Try these extensions on an editable Grafana dashboard.

  • WITH templates. This feature simplifies writing and managing complex queries. Go to WITH templates playground and try it.
  • 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.
  • default binary operator. q1 default q2 substitutes NaN values from q1 with the corresponding values from q2.
  • if binary operator. q1 if q2 removes values from q1 for NaN values from q2.
  • ifnot binary operator. q1 ifnot q2 removes values from q1 for non-NaN values from q2.
  • offset may be put anywere in the query. For instance, sum(foo) offset 24h.
  • 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"}.
  • 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).
  • Range duration and offset may be fractional. For instance, rate(node_network_receive_bytes_total[1.5m] offset 0.5d).
  • 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_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.
  • 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) - for calculating instant derivative for m.
  • 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.
  • 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(q[d]) - returns lag between the current timestamp and the timestamp from the previous data point in q over d.
  • lifetime(q[d]) - returns lifetime of q over d in seconds. It is expected that d exceeds the lifetime of q.
  • scrape_interval(q[d]) - returns the average interval in seconds between data points of q over d aka scrape interval.
  • Trigonometric functions - sin(q), cos(q), asin(q), acos(q) and pi().
  • 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) - limits the number of time series returned from q to k.
  • keep_last_value(q) - fills missing data (gaps) in q with the previous value.
  • 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.
  • histogram(q) - calculates aggregate histogram over q time series for each point on the graph.
  • 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