Merge branch 'master' into docs

docs/sources/configuration/index.md

@@ -56,7 +56,7 @@ Direct access is still supported because in some cases it may be useful to acces
 
 Direct DB Connection allows plugin to use existing SQL data source for querying history data directly from Zabbix
 database. This way usually faster than pulling data from Zabbix API, especially on the wide time ranges, and reduces
-amount of data transfered.
+amount of data transferred.
 
 Read [how to configure](./sql_datasource) SQL data source in Grafana.
 

docs/sources/guides/gettingstarted.md

@@ -25,7 +25,7 @@ Another case to use regex is comparing the same metrics for different hosts. Use
 ![Backend system time](../img/getstarting-regex_backend_system_time.png)
 
 ## Bar Chart
-Let's create a graph wich show queries stats for MySQL database. Select Group, Host, Application (_MySQL_ in my case) and Items. I use `/MySQL .* operations/` regex for filtering different types of operations.
+Let's create a graph which show queries stats for MySQL database. Select Group, Host, Application (_MySQL_ in my case) and Items. I use `/MySQL .* operations/` regex for filtering different types of operations.
 
 ![MySQL operations 1](../img/getstarting-mysql_operations_1.png)
 

docs/sources/reference/alerting.md

@@ -14,7 +14,7 @@ consists of two main parts:
 
 - **Alerting execution engine**  
     The alert rules are evaluated in the Grafana backend in a scheduler and query execution engine that is part of core 
-    Grafana. Only some data soures are supported right now. They include Graphite, Prometheus, InfluxDB and OpenTSDB.
+    Grafana. Only some data sources are supported right now. They include Graphite, Prometheus, InfluxDB and OpenTSDB.
 - **Alerting visualisations**  
     Alerts highlight panels with problems and it can easily be found on the dashboard.
 

docs/sources/reference/functions.md

@@ -1,6 +1,23 @@
 Functions reference
 ===================
 
+## Functions Variables
+
+There are some built-in template variables available for using in functions:
+
+- `$__range_ms` - panel time range in ms
+- `$__range_s` - panel time range in seconds
+- `$__range` - panel time range, string representation (`30s`, `1m`, `1h`)
+- `$__range_series` - invoke function over all series values
+
+Examples:
+```
+groupBy($__range, avg)
+percentile($__range_series, 95) - 95th percentile over all values
+```
+
+---
+
 ## Transform
 
 
@@ -10,7 +27,7 @@ Functions reference
 groupBy(interval, function)
 ```
 
-Takes each timeseries and consolidate its points falled in given _interval_ into one point using _function_, which can be one of: _avg_, _min_, _max_, _median_.
+Takes each timeseries and consolidate its points fallen in the given _interval_ into one point using _function_, which can be one of: _avg_, _min_, _max_, _median_.
 
 Examples:
 ```
@@ -124,7 +141,7 @@ Replaces `null` values with N
 aggregateBy(interval, function)
 ```
 
-Takes all timeseries and consolidate all its points falled in given _interval_ into one point using _function_, which can be one of: _avg_, _min_, _max_, _median_.
+Takes all timeseries and consolidate all its points fallen in the given _interval_ into one point using _function_, which can be one of: _avg_, _min_, _max_, _median_.
 
 Examples:
 ```
@@ -142,6 +159,20 @@ This will add metrics together and return the sum at each datapoint. This method
 
 ---
 
+### _percentile_
+```
+percentile(interval, N)
+```
+Takes all timeseries and consolidate all its points fallen in the given _interval_ into one point by Nth percentile.
+
+Examples:
+```
+percentile(1h, 99)
+percentile($__range_series, 95) - 95th percentile over all values
+```
+
+---
+
 ### _average_
 ```
 average(interval)