Merge pull request #43 from prometheus/beorn7/doc-improve

Document histograms and histogram_quantile.

content/docs/concepts/metric_types.md

@@ -24,7 +24,7 @@ tasks completed, errors occurred, etc. Counters should not be used to expose
 current counts of items whose number can also go down, e.g. the number of
 currently running goroutines. Use gauges for this use case.
 
-See the client library usage documentation for counters:
+Client library usage documentation for counters:
 
    * [Go](http://godoc.org/github.com/prometheus/client_golang/prometheus#Counter)
    * [Java](https://github.com/prometheus/client_java/blob/master/client/src/main/java/io/prometheus/client/metrics/Counter.java)
@@ -41,7 +41,7 @@ Gauges are typically used for measured values like temperatures or current
 memory usage, but also "counts" that can go up and down, like the number of
 running goroutines.
 
-See the client library usage documentation for gauges:
+Client library usage documentation for gauges:
 
    * [Go](http://godoc.org/github.com/prometheus/client_golang/prometheus#Gauge)
    * [Java](https://github.com/prometheus/client_java/blob/master/client/src/main/java/io/prometheus/client/metrics/Gauge.java)
@@ -49,26 +49,52 @@ See the client library usage documentation for gauges:
    * [Ruby](https://github.com/prometheus/client_ruby#gauge)
    * [Python](https://github.com/prometheus/client_python#gauge)
 
-## Summaries
+## Histogram
 
-A _summary_ samples observations (usually things like request durations) over
-sliding windows of time and provides instantaneous insight into their
-distributions, frequencies, and sums.
+A _histogram_ samples observations (usually things like request durations or
+response sizes) and counts them in configurable buckets. It also provides a sum
+of all observed values.
+
+A histogram with a base metric name of `<basename>` exposes multiple time series
+during a scrape:
+
+  * cumulative counters for the observation buckets, exposed as `<basename>_bucket{le="<upper inclusive bound>"}`
+  * the **total sum** of all observed values, exposed as `<basename>_sum`
+  * the **count** of events that have been observed, exposed as `<basename>_count` (identical to `<basename>_bucket{le="+Inf"}` above)
+
+Use the [`histogram_quantile()`
+function](/docs/querying/functions/#histogram_quantile()) to calculate
+quantiles from histograms or even aggregations of histograms. A
+histogram is also suitable to calculate an [Apdex
+score](http://en.wikipedia.org/wiki/Apdex). See [histograms and
+summaries](/docs/practices/histograms) for details of histogram usage
+and differences to [summaries](#summary).
+
+Client library usage documentation for histograms:
+
+   * [Go](http://godoc.org/github.com/prometheus/client_golang/prometheus#Histogram)
+   * [Java](https://github.com/prometheus/client_java/blob/master/simpleclient/src/main/java/io/prometheus/client/Histogram.java) (histograms are only supported by the simple client but not by the legacy client)
+   * [Python](https://github.com/prometheus/client_python#histogram)
+
+## Summary
+
+Similar to a _histogram_, a _summary_ samples observations (usually things like
+request durations and response sizes). While it also provides a total count of
+observations and a sum of all observed values, it calculates configurable
+quantiles over a sliding time window.
 
 A summary with a base metric name of `<basename>` exposes multiple time series
 during a scrape:
 
-  * streaming **quantile values** of observed events, exposed as `<basename>{quantile="<quantile label>"}`
+  * streaming **φ-quantiles** (0 ≤ φ ≤ 1) of observed events, exposed as `<basename>{quantile="<φ>"}`
   * the **total sum** of all observed values, exposed as `<basename>_sum`
   * the **count** of events that have been observed, exposed as `<basename>_count`
 
-This is quite convenient, for if you are interested in tracking latencies of an
-operation in real time, you get three types of information reported for free
-with one metric.
-
-A typical use-case is the observation of request latencies or response sizes.
+See [histograms and summaries](/docs/practices/histograms) for
+detailed explanations of φ-quantiles, summary usage, and differences
+to [histograms](#histogram).
 
-See the client library usage documentation for summaries:
+Client library usage documentation for summaries:
 
    * [Go](http://godoc.org/github.com/prometheus/client_golang/prometheus#Summary)
    * [Java](https://github.com/prometheus/client_java/blob/master/client/src/main/java/io/prometheus/client/metrics/Summary.java)

content/docs/introduction/roadmap.md

@@ -23,17 +23,6 @@ detailed local views.
 
 GitHub issue: [#9](https://github.com/prometheus/prometheus/issues/9)
 
-**Aggregatable histograms**
-
-The current client-side [summary
-types](/docs/concepts/metric_types/#summaries) do not
-support aggregation of quantiles. For example, it is [statistically
-incorrect](http://latencytipoftheday.blogspot.de/2014/06/latencytipoftheday-you-cant-average.html)
-to average over the 90th percentile latency of multiple monitored instances.
-We plan to implement server-side histograms which will allow for this use case.
-
-GitHub issue: [#480](https://github.com/prometheus/prometheus/issues/480)
-
 **More flexible label matching in binary operations**
 
 [Binary operations](/docs/querying/operators/) between time series vectors

content/docs/practices/alerting.md

 ---
 title: Alerting
-sort_rank: 4
+sort_rank: 5
 ---
 
 # Alerting

content/docs/practices/consoles.md

@@ -3,7 +3,7 @@ title: Consoles and dashboards
 sort_rank: 3
 ---
 
-## Consoles and dashboards
+# Consoles and dashboards
 
 It can be tempting to display as much data as possible on a dashboard, especially
 when a system like Prometheus offers the ability to have such rich

content/docs/practices/instrumentation.md

@@ -144,9 +144,9 @@ gauge for how long the collection took in seconds and another for the number of
 errors encountered.
 
 This is one of the two cases when it is okay to export a duration as a gauge
-rather than a summary, the other being batch job durations. This is because both
-represent information about that particular push/scrape, rather than
-tracking multiple durations over time.
+rather than a summary or a histogram, the other being batch job durations. This
+is because both represent information about that particular push/scrape, rather
+than tracking multiple durations over time.
 
 ## Things to watch out for
 
@@ -191,10 +191,14 @@ processing system.
 If you are unsure, start with no labels and add more
 labels over time as concrete use cases arise.
 
-### Counter vs. gauge vs. summary
+### Counter vs. gauge, summary vs. histogram
 
-It is important to know which of the three main metric types to use for a given
-metric. There is a simple rule of thumb: if the value can go down, it's a gauge.
+It is important to know which of the four main metric types to use for
+a given metric.
+
+To pick between counter and gauge, there is a simple rule of thumb: if
+-metric To pick between counter and gauge, there is a simple rule of
+thumb: if the value can go down, it is a gauge.
 
 Counters can only go up (and reset, such as when a process restarts). They are
 useful for accumulating the number of events, or the amount of something at
@@ -206,11 +210,8 @@ Gauges can be set, go up, and go down. They are useful for snapshots of state,
 such as in-progress requests, free/total memory, or temperature. You should
 never take a `rate()` of a gauge.
 
-Summaries are similar to having two counters. They track the number of events
-*and* the amount of something for each event, allowing you to calculate the
-average amount per event (useful for latency, for example). In addition,
-summaries can also export quantiles of the amounts, but note that [quantiles are not
-aggregatable](http://latencytipoftheday.blogspot.de/2014/06/latencytipoftheday-you-cant-average.html).
+Summaries and histograms are more complex metric types discussed in
+[their own section](/docs/practices/histograms/).
 
 ### Timestamps, not time since
 
@@ -244,9 +245,11 @@ benchmarks are the best way to determine the impact of any given change.
 
 ### Avoid missing metrics
 
-Time series that are not present until something happens are difficult to deal with,
-as the usual simple operations are no longer sufficient to correctly handle
-them. To avoid this, export a `0` for any time series you know may exist in advance.
+Time series that are not present until something happens are difficult
+to deal with, as the usual simple operations are no longer sufficient
+to correctly handle them. To avoid this, export `0` (or `NaN`, if `0`
+would be misleading) for any time series you know may exist in
+advance.
 
 Most Prometheus client libraries (including Go and Java Simpleclient) will
 automatically export a `0` for you for metrics with no labels.

content/docs/practices/rules.md

 ---
 title: Recording rules
-sort_rank: 5
+sort_rank: 6
 ---
 
 # Recording rules

content/docs/querying/basics.md

@@ -107,6 +107,31 @@ a `job` label set to `prometheus`:
 
     http_requests_total{job="prometheus"}[5m]
 
+### Offset modifier
+
+The `offset` modifier allows changing the time offset for individual
+instant and range vectors in a query.
+
+For example, the following expression returns the value of
+`http_requests_total` 5 minutes in the past relative to the current
+query evaluation time:
+
+    http_requests_total offset 5m
+
+Note that the `offset` modifier always needs to follow the selector
+immediately, i.e. the following would be correct:
+
+    sum(http_requests_total{method="GET"} offset 5m) // GOOD.
+
+While the following would be *incorrect*:
+
+    sum(http_requests_total{method="GET"}) offset 5m // INVALID.
+
+The same works for range vectors. This returns the 5-minutes rate that
+`http_requests_total` had a week ago:
+
+    rate(http_requests_total[5m] offset 1w)
+    
 ## Operators
 
 Prometheus supports many binary and aggregation operators. These are described

content/docs/querying/functions.md

@@ -28,6 +28,12 @@ the 1-element output vector from the input vector:
 This is useful for alerting on when no time series
 exist for a given metric name and label combination.
 
+## `bottomk()`
+
+`bottomk(k integer, v instant-vector)` returns the `k` smallest elements of `v`
+by sample value.
+
+
 ## `ceil()`
 
 `ceil(v instant-vector)` rounds the sample values of all elements in `v` up to
@@ -80,6 +86,54 @@ and value across all series in the input vector.
 `floor(v instant-vector)` rounds the sample values of all elements in `v` down
 to the nearest integer.
 
+## `histogram_quantile()`
+
+`histogram_quantile(φ float, b instant-vector)` calculates the
+φ-quantile (0 ≤ φ ≤ 1) from the buckets `b` of a
+[histogram](/docs/concepts/metric_types/#histogram). (See [histograms
+and summaries](/docs/practices/histograms) for a detailed explanation
+of φ-quantiles and the usage of the histogram metric type in general.)
+The samples in `b` are the counts of observations in each bucket. Each
+sample must have a label `le` where the label value denotes the
+inclusive upper bound of the bucket. (Samples without such a label are
+silently ignored.) The [histogram metric
+type](/docs/concepts/metric_types/#histogram) automatically provides
+time series with the `_bucket` suffix and the appropriate labels.
+
+Use the `rate()` function to specify the time window for the quantile
+calculation.
+
+Example: A histogram metric is called `http_request_duration_seconds`. To
+calculate the 90th percentile of request durations over the last 10m, use the
+following expression:
+
+    histogram_quantile(0.9, rate(http_request_duration_seconds_bucket[10m]))
+
+The quantile is calculated for each label combination in
+`http_request_duration_seconds`. To aggregate, use the `sum()` aggregator
+around the `rate()` function. Since the `le` label is required by
+`histogram_quantile()`, it has to be included in the `by` clause. The following
+expression aggregates the 90th percentile by `job`:
+
+    histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[10m])) by (job, le))
+
+To aggregate everything, specify only the `le` label:
+
+    histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[10m])) by (le))
+
+The `histogram_quantile()` function interpolates quantile values by
+assuming a linear distribution within a bucket. The highest bucket
+must have an upper bound of `+Inf`. (Otherwise, `NaN` is returned.) If
+a quantile is located in the highest bucket, the upper bound of the
+second highest bucket is returned. A lower limit of the lowest bucket
+is assumed to be 0 if the upper bound of that bucket is greater than
+0. In that case, the usual linear interpolation is applied within that
+bucket. Otherwise, the upper bound of the lowest bucket is returned
+for quantiles located in the lowest bucket.
+
+If `b` contains fewer than two buckets, `NaN` is returned. For φ < 0, `-Inf` is
+returned. For φ > 1, `+Inf` is returned.
+
 ## `rate()`
 
 `rate(v range-vector)` calculate the per-second average rate of increase of the
@@ -123,6 +177,11 @@ Same as `sort`, but sorts in descending order.
 this does not actually return the current time, but the time at which the
 expression is to be evaluated.
 
+## `topk()`
+
+`topk(k integer, v instant-vector)` returns the `k` largest elements of `v` by
+sample value.
+
 ## `<aggregation>_over_time()`: Aggregating values over time:
 
 The following functions allow aggregating each series of a given range vector
@@ -133,11 +192,3 @@ over time and return an instant vector with per-series aggregation results:
 * `max_over_time(range-vector)`: the maximum value of all points under the specified interval.
 * `sum_over_time(range-vector)`: the sum of all values under the specified interval.
 * `count_over_time(range-vector)`: the count of all values under the specified interval.
-
-## `topk()` and `bottomk()`
-
-`topk(k integer, v instant-vector)` returns the `k` largest elements of `v` by
-sample value.
-
-`bottomk(k integer, v instant-vector` returns the `k` smallest elements of `v`
-by sample value.