Merge pull request #53 from influxdata/update-flux-links

Fixed all Flux links and other broken links

content/v2.0/collect-data/scraper-quickstart.md

@@ -13,7 +13,7 @@ menu:
 {{% note %}}
 The steps below are available on a page that appears after you complete the initial configuration described in [Set up InfluxDB](/v2.0/get-started/#setup-influxdb). After clicking one of the three options, the page is no longer available.
 
-If you missed the change to select Quick Start or you want to learn how to configure a scraper yourself, see [Scrape data using the /metrics endpoint](influxdb/v2.0/collect-data/scraper-endpoint/).
+If you missed the change to select Quick Start or you want to learn how to configure a scraper yourself, see [Scrape data using the /metrics endpoint](/v2.0/collect-data/scraper-metrics-endpoint/).
 {{% /note %}}
 
 ## Use Quick Start to collect InfluxDB metrics

content/v2.0/process-data/common-tasks/downsample-data.md

@@ -31,7 +31,7 @@ A separate bucket where aggregated, downsampled data is stored.
 To downsample data, it must be aggregated in some way.
 What specific method of aggregation you use depends on your specific use case,
 but examples include mean, median, top, bottom, etc.
-View [Flux's aggregate functions](/v2.0/reference/flux/functions/transformations/aggregates/)
+View [Flux's aggregate functions](/v2.0/reference/flux/functions/built-in/transformations/aggregates/)
 for more information and ideas.
 
 ## Create a destination bucket
@@ -46,7 +46,7 @@ The example task script below is a very basic form of data downsampling that doe
 1. Defines a task named "cq-mem-data-1w" that runs once a week.
 2. Defines a `data` variable that represents all data from the last 2 weeks in the
    `mem` measurement of the `system-data` bucket.
-3. Uses the [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow/)
+3. Uses the [`aggregateWindow()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow/)
    to window the data into 1 hour intervals and calculate the average of each interval.
 4. Stores the aggregated data in the `system-data-downsampled` bucket under the
    `my-org` organization.

content/v2.0/process-data/write-a-task.md

@@ -51,8 +51,8 @@ in form fields when creating the task.
 {{% /note %}}
 
 ## Define a data source
-Define a data source using Flux's [`from()` function](/v2.0/reference/flux/functions/inputs/from/)
-or any other [Flux input functions](/v2.0/reference/flux/functions/inputs/).
+Define a data source using Flux's [`from()` function](/v2.0/reference/flux/functions/built-in/inputs/from/)
+or any other [Flux input functions](/v2.0/reference/flux/functions/built-in/inputs/).
 
 For convenience, consider creating a variable that includes the sourced data with
 the required time range and any relevant filters.
@@ -85,7 +85,7 @@ specific use case.
 The example below illustrates a task that downsamples data by calculating the average of set intervals.
 It uses the `data` variable defined [above](#define-a-data-source) as the data source.
 It then windows the data into 5 minute intervals and calculates the average of each
-window using the [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow/).
+window using the [`aggregateWindow()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow/).
 
 ```js
 data
@@ -101,7 +101,7 @@ _See [Common tasks](/v2.0/process-data/common-tasks) for examples of tasks commo
 In the vast majority of task use cases, once data is transformed, it needs to sent and stored somewhere.
 This could be a separate bucket with a different retention policy, another measurement, or even an alert endpoint _(Coming)_.
 
-The example below uses Flux's [`to()` function](/v2.0/reference/flux/functions/outputs/to)
+The example below uses Flux's [`to()` function](/v2.0/reference/flux/functions/built-in/outputs/to)
 to send the transformed data to another bucket:
 
 ```js

content/v2.0/query-data/get-started/query-influxdb.md

@@ -17,8 +17,8 @@ Every Flux query needs the following:
 
 
 ## 1. Define your data source
-Flux's [`from()`](/v2.0/reference/flux/functions/inputs/from) function defines an InfluxDB data source.
-It requires a [`bucket`](/v2.0/reference/flux/functions/inputs/from#bucket) parameter.
+Flux's [`from()`](/v2.0/reference/flux/functions/built-in/inputs/from) function defines an InfluxDB data source.
+It requires a [`bucket`](/v2.0/reference/flux/functions/built-in/inputs/from#bucket) parameter.
 The following examples use `example-bucket` as the bucket name.
 
 ```js
@@ -30,7 +30,7 @@ Flux requires a time range when querying time series data.
 "Unbounded" queries are very resource-intensive and as a protective measure,
 Flux will not query the database without a specified range.
 
-Use the pipe-forward operator (`|>`) to pipe data from your data source into the [`range()`](/v2.0/reference/flux/functions/transformations/range)
+Use the pipe-forward operator (`|>`) to pipe data from your data source into the [`range()`](/v2.0/reference/flux/functions/built-in/transformations/range)
 function, which specifies a time range for your query.
 It accepts two properties: `start` and `stop`.
 Ranges can be **relative** using negative [durations](/v2.0/reference/flux/language/lexical-elements#duration-literals)

content/v2.0/query-data/get-started/transform-data.md

@@ -41,7 +41,7 @@ A common type of function used when transforming data queried from InfluxDB is a
 Aggregate functions take a set of `_value`s in a table, aggregate them, and transform
 them into a new value.
 
-This example uses the [`mean()` function](/v2.0/reference/flux/functions/transformations/aggregates/mean)
+This example uses the [`mean()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/mean)
 to average values within each time window.
 
 {{% note %}}
@@ -51,7 +51,7 @@ It's just good to understand the steps in the process.
 {{% /note %}}
 
 ## Window your data
-Flux's [`window()` function](/v2.0/reference/flux/functions/transformations/window) partitions records based on a time value.
+Flux's [`window()` function](/v2.0/reference/flux/functions/built-in/transformations/window) partitions records based on a time value.
 Use the `every` parameter to define a duration of each window.
 
 For this example, window data in five minute intervals (`5m`).
@@ -74,7 +74,7 @@ When visualized, each table is assigned a unique color.
 
 ## Aggregate windowed data
 Flux aggregate functions take the `_value`s in each table and aggregate them in some way.
-Use the [`mean()` function](/v2.0/reference/flux/functions/transformations/aggregates/mean) to average the `_value`s of each table.
+Use the [`mean()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/mean) to average the `_value`s of each table.
 
 ```js
 from(bucket:"example-bucket")
@@ -100,7 +100,7 @@ Aggregate functions don't infer what time should be used for the aggregate value
 Therefore the `_time` column is dropped.
 
 A `_time` column is required in the [next operation](#unwindow-aggregate-tables).
-To add one, use the [`duplicate()` function](/v2.0/reference/flux/functions/transformations/duplicate)
+To add one, use the [`duplicate()` function](/v2.0/reference/flux/functions/built-in/transformations/duplicate)
 to duplicate the `_stop` column as the `_time` column for each windowed table.
 
 ```js
@@ -145,7 +145,7 @@ process helps to understand how data changes "shape" as it is passed through eac
 
 Flux provides (and allows you to create) "helper" functions that abstract many of these steps.
 The same operation performed in this guide can be accomplished using the
-[`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow).
+[`aggregateWindow()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow).
 
 ```js
 from(bucket:"example-bucket")

content/v2.0/query-data/guides/custom-functions.md

@@ -70,7 +70,7 @@ functionName = (tables=<-) => tables |> functionOperations
 ###### Multiply row values by x
 The example below defines a `multByX` function that multiplies the `_value` column
 of each row in the input table by the `x` parameter.
-It uses the [`map()` function](/v2.0/reference/flux/functions/transformations/map)
+It uses the [`map()` function](/v2.0/reference/flux/functions/built-in/transformations/map)
 to modify each `_value`.
 
 ```js
@@ -104,9 +104,9 @@ Defaults are overridden by explicitly defining the parameter in the function cal
 ###### Get the winner or the "winner"
 The example below defines a `getWinner` function that returns the record with the highest
 or lowest `_value` (winner versus "winner") depending on the `noSarcasm` parameter which defaults to `true`.
-It uses the [`sort()` function](/v2.0/reference/flux/functions/transformations/sort)
+It uses the [`sort()` function](/v2.0/reference/flux/functions/built-in/transformations/sort)
 to sort records in either descending or ascending order.
-It then uses the [`limit()` function](/v2.0/reference/flux/functions/transformations/limit)
+It then uses the [`limit()` function](/v2.0/reference/flux/functions/built-in/transformations/limit)
 to return the first record from the sorted table.
 
 ```js

content/v2.0/query-data/guides/group-data.md

@@ -28,7 +28,7 @@ Understanding how modifying group keys shapes output data is key to successfully
 grouping and transforming data into your desired output.
 
 ## group() Function
-Flux's [`group()` function](/v2.0/reference/flux/functions/transformations/group) defines the
+Flux's [`group()` function](/v2.0/reference/flux/functions/built-in/transformations/group) defines the
 group key for output tables, i.e. grouping records based on values for specific columns.
 
 ###### group() example

content/v2.0/query-data/guides/histograms.md

@@ -14,7 +14,7 @@ Histograms provide valuable insight into the distribution of your data.
 This guide walks through using Flux's `histogram()` function to transform your data into a **cumulative histogram**.
 
 ## histgram() function
-The [`histogram()` function](/v2.0/reference/flux/functions/transformations/histogram) approximates the
+The [`histogram()` function](/v2.0/reference/flux/functions/built-in/transformations/histogram) approximates the
 cumulative distribution of a dataset by counting data frequencies for a list of "bins."
 A **bin** is simply a range in which a data point falls.
 All data points that are less than or equal to the bound are counted in the bin.
@@ -41,7 +41,7 @@ Flux provides two helper functions for generating histogram bins.
 Each generates an array of floats designed to be used in the `histogram()` function's `bins` parameter.
 
 ### linearBins()
-The [`linearBins()` function](/v2.0/reference/flux/functions/misc/linearbins) generates a list of linearly separated floats.
+The [`linearBins()` function](/v2.0/reference/flux/functions/built-in/misc/linearbins) generates a list of linearly separated floats.
 
 ```js
 linearBins(start: 0.0, width: 10.0, count: 10)
@@ -50,7 +50,7 @@ linearBins(start: 0.0, width: 10.0, count: 10)
 ```
 
 ### logarithmicBins()
-The [`logarithmicBins()` function](/v2.0/reference/flux/functions/misc/logarithmicbins) generates a list of exponentially separated floats.
+The [`logarithmicBins()` function](/v2.0/reference/flux/functions/built-in/misc/logarithmicbins) generates a list of exponentially separated floats.
 
 ```js
 logarithmicBins(start: 1.0, factor: 2.0, count: 10, infinty: true)

content/v2.0/query-data/guides/join.md

@@ -9,7 +9,7 @@ menu:
 weight: 205
 ---
 
-The [`join()` function](/v2.0/reference/flux/functions/transformations/join) merges two or more
+The [`join()` function](/v2.0/reference/flux/functions/built-in/transformations/join) merges two or more
 input streams, whose values are equal on a set of common columns, into a single output stream.
 Flux allows you to join on any columns common between two data streams and opens the door
 for operations such as cross-measurement joins and math across measurements.
@@ -204,7 +204,7 @@ These represent the columns with values unique to the two input tables.
 
 ## Calculate and create a new table
 With the two streams of data joined into a single table, use the
-[`map()` function](/v2.0/reference/flux/functions/transformations/map)
+[`map()` function](/v2.0/reference/flux/functions/built-in/transformations/map)
 to build a new table by mapping the existing `_time` column to a new `_time`
 column and dividing `_value_mem` by `_value_proc` and mapping it to a
 new `_value` column.

content/v2.0/query-data/guides/sort-limit.md

@@ -11,7 +11,7 @@ menu:
 weight: 206
 ---
 
-The [`sort()`function](/v2.0/reference/flux/functions/transformations/sort)
+The [`sort()`function](/v2.0/reference/flux/functions/built-in/transformations/sort)
 orders the records within each table.
 The following example orders system uptime first by region, then host, then value.
 
@@ -25,7 +25,7 @@ from(bucket:"telegraf/autogen")
   |> sort(columns:["region", "host", "_value"])
 ```
 
-The [`limit()` function](/v2.0/reference/flux/functions/transformations/limit)
+The [`limit()` function](/v2.0/reference/flux/functions/built-in/transformations/limit)
 limits the number of records in output tables to a fixed number, `n`.
 The following example shows up to 10 records from the past hour.
 
@@ -51,6 +51,6 @@ from(bucket:"telegraf/autogen")
 ```
 
 You now have created a Flux query that sorts and limits data.
-Flux also provides the [`top()`](/v2.0/reference/flux/functions/transformations/selectors/top)
-and [`bottom()`](/v2.0/reference/flux/functions/transformations/selectors/bottom)
+Flux also provides the [`top()`](/v2.0/reference/flux/functions/built-in/transformations/selectors/top)
+and [`bottom()`](/v2.0/reference/flux/functions/built-in/transformations/selectors/bottom)
 functions to perform both of these functions at the same time.

content/v2.0/query-data/guides/window-aggregate.md

@@ -85,7 +85,7 @@ Table: keys: [_start, _stop, _field, _measurement]
 {{% /truncate %}}
 
 ## Windowing data
-Use the [`window()` function](/v2.0/reference/flux/functions/transformations/window)
+Use the [`window()` function](/v2.0/reference/flux/functions/built-in/transformations/window)
 to group your data based on time bounds.
 The most common parameter passed with the `window()` is `every` which
 defines the duration of time between windows.
@@ -169,14 +169,14 @@ When visualized in the InfluxDB UI, each window table is displayed in a differen
 ![Windowed data](/img/simple-windowed-data.png)
 
 ## Aggregate data
-[Aggregate functions](/v2.0/reference/flux/functions/transformations/aggregates) take the values
+[Aggregate functions](/v2.0/reference/flux/functions/built-in/transformations/aggregates) take the values
 of all rows in a table and use them to perform an aggregate operation.
 The result is output as a new value in a single-row table.
 
 Since windowed data is split into separate tables, aggregate operations run against
 each table separately and output new tables containing only the aggregated value.
 
-For this example, use the [`mean()` function](/v2.0/reference/flux/functions/transformations/aggregates/mean)
+For this example, use the [`mean()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/mean)
 to output the average of each window:
 
 ```js
@@ -240,7 +240,7 @@ These represent the lower and upper bounds of the time window.
 
 Many Flux functions rely on the `_time` column.
 To further process your data after an aggregate function, you need to re-add `_time`.
-Use the [`duplicate()` function](/v2.0/reference/flux/functions/transformations/duplicate) to
+Use the [`duplicate()` function](/v2.0/reference/flux/functions/built-in/transformations/duplicate) to
 duplicate either the `_start` or `_stop` column as a new `_time` column.
 
 ```js
@@ -328,7 +328,7 @@ With the aggregate values in a single table, data points in the visualization ar
 You have now created a Flux query that windows and aggregates data.
 The data transformation process outlined in this guide should be used for all aggregation operations.
 
-Flux also provides the [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow)
+Flux also provides the [`aggregateWindow()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow)
 which performs all these separate functions for you.
 
 The following Flux query will return the same results:

content/v2.0/reference/flux/functions/built-in/misc/intervals.md

@@ -19,7 +19,7 @@ The set of intervals includes all intervals that intersect with the initial rang
 
 {{% note %}}
 The `intervals()` function is designed to be used with the intervals parameter
-of the [`window()` function](/v2.0/reference/flux/functions/transformations/window).
+of the [`window()` function](/v2.0/reference/flux/functions/built-in/transformations/window).
 {{% /note %}}
 
 By default the end boundary of an interval will align with the Unix epoch (zero time)

content/v2.0/reference/flux/functions/built-in/misc/linearbins.md

@@ -12,7 +12,7 @@ weight: 401
 
 The `linearBins()` function generates a list of linearly separated floats.
 It is a helper function meant to generate bin bounds for the
-[`histogram()` function](/v2.0/reference/flux/functions/transformations/histogram).
+[`histogram()` function](/v2.0/reference/flux/functions/built-in/transformations/histogram).
 
 _**Function type:** Miscellaneous_  
 _**Output data type:** Array of floats_

content/v2.0/reference/flux/functions/built-in/misc/logarithmicbins.md

@@ -12,7 +12,7 @@ weight: 401
 
 The `logarithmicBins()` function generates a list of exponentially separated floats.
 It is a helper function meant to generate bin bounds for the
-[`histogram()` function](/v2.0/reference/flux/functions/transformations/histogram).
+[`histogram()` function](/v2.0/reference/flux/functions/built-in/transformations/histogram).
 
 _**Function type:** Miscellaneous_  
 _**Output data type:** Array of floats_

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/_index.md

@@ -28,7 +28,7 @@ Any output table will have the following properties:
 - It will not have a `_time` column.
 
 ### aggregateWindow helper function
-The [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow)
+The [`aggregateWindow()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow)
 does most of the work needed when aggregating data.
 It windows and aggregates the data, then combines windowed tables into a single output table.
 

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/median.md

@@ -10,7 +10,7 @@ menu:
 weight: 501
 ---
 
-The `median()` function is a special application of the [`percentile()` function](/v2.0/reference/flux/functions/transformations/aggregates/percentile)
+The `median()` function is a special application of the [`percentile()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/percentile)
 that returns the median `_value` of an input table or all non-null records in the input table
 with values that fall within the 50th percentile depending on the [method](#method) used.
 
@@ -30,9 +30,9 @@ value that represents the 50th percentile.
 
 {{% note %}}
 The `median()` function can only be used with float value types.
-It is a special application of the [`percentile()` function](/v2.0/reference/flux/functions/transformations/aggregates/percentile) which
+It is a special application of the [`percentile()` function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/percentile) which
 uses an approximation implementation that requires floats.
-You can convert your value column to a float column using the [`toFloat()` function](/v2.0/reference/flux/functions/transformations/type-conversions/tofloat).
+You can convert your value column to a float column using the [`toFloat()` function](/v2.0/reference/flux/functions/built-in/transformations/type-conversions/tofloat).
 {{% /note %}}
 
 ## Parameters

content/v2.0/reference/flux/functions/built-in/transformations/histogram.md

@@ -56,8 +56,8 @@ _**Data type:** Array of floats_
 #### Bin helper functions
 The following helper functions can be used to generated bins.
 
-[linearBins()](/v2.0/reference/flux/functions/misc/linearbins)  
-[logarithmicBins()](/v2.0/reference/flux/functions/misc/logarithmicbins)
+[linearBins()](/v2.0/reference/flux/functions/built-in/misc/linearbins)  
+[logarithmicBins()](/v2.0/reference/flux/functions/built-in/misc/logarithmicbins)
 
 ### normalize
 When `true`, will convert the counts into frequency values between 0 and 1.

content/v2.0/reference/flux/functions/built-in/transformations/keep.md

@@ -12,7 +12,7 @@ weight: 401
 
 The `keep()` function returns a table containing only the specified columns, ignoring all others.
 Only columns in the group key that are also specified in the `keep()` function will be kept in the resulting group key.
-_It is the inverse of [`drop`](/v2.0/reference/flux/functions/transformations/drop)._
+_It is the inverse of [`drop`](/v2.0/reference/flux/functions/built-in/transformations/drop)._
 
 _**Function type:** Transformation_  
 _**Output data type:** Object_

content/v2.0/reference/flux/functions/built-in/transformations/selectors/_index.md

@@ -23,5 +23,5 @@ The following selector functions are available:
 The following functions can be used as both selectors or aggregates, but they are
 categorized as aggregate functions in this documentation:
 
-- [median](/v2.0/reference/flux/functions/transformations/aggregates/median)
-- [percentile](/v2.0/reference/flux/functions/transformations/aggregates/percentile)
+- [median](/v2.0/reference/flux/functions/built-in/transformations/aggregates/median)
+- [percentile](/v2.0/reference/flux/functions/built-in/transformations/aggregates/percentile)

content/v2.0/reference/flux/functions/testing/assertempty.md

@@ -24,7 +24,7 @@ _The `testing.assertEmpty()` function can be used to perform in-line tests in a
 ## Examples
 
 #### Check if there is a difference between streams
-This example uses the [`diff()` function](/flux/v0.x/functions/tests/diff)
+This example uses the [`testing.diff()` function](/v2.0/reference/flux/functions/testing/diff)
 which outputs the diff for the two streams.
 The `.testing.assertEmpty()` function checks to see if the diff is empty.
 
@@ -36,6 +36,6 @@ got = from(bucket: "telegraf/autogen")
 want = from(bucket: "backup_telegraf/autogen")
   |> range(start: -15m)
 got
-  |> diff(want: want)
+  |> testing.diff(want: want)
   |> testing.assertEmpty()
 ```

content/v2.0/users/_index.md

@@ -9,7 +9,7 @@ weight: 12
 ---
 
 Users are those with access to InfluxDB.
-In order to access any data, a user must be [added as a member](/v2.0/organizations/members/add) of an organization.
+In order to access any data, a user must be added as a member of an organization.
 All users have unique authentication tokens with specific permissions used to grant them access to data within InfluxDB.
 
 The following articles walk through managing users.

content/v2.0/users/create-user.md

@@ -20,7 +20,7 @@ While in alpha, additional users cannot be created in the InfluxDB UI.
 
 ## Create a user using the influx CLI
 
-Use the [`influx user create` command](/v2.0/reference/cli/influx/create/create)
+Use the [`influx user create` command](/v2.0/reference/cli/influx/user/create)
 to create a new user. A new user requires the following:
 
 - A username

content/v2.0/visualize-data/visualization-types.md

@@ -12,7 +12,7 @@ weight: 101
 
 The InfluxDB's user interface's (UI) dashboard views support the following visualization types,
 which can be selected in the **Visualization Type** selection view of the
-[Data Explorer](/v2.0/visualize-data/explore-metrices).
+[Data Explorer](/v2.0/visualize-data/explore-metrics).
 
 [Visualization Type selector](/img/chrono-viz-types-selector.png)
 
@@ -20,7 +20,7 @@ Each of the available visualization types and available user controls are descri
 
 * [Line Graph](#line-graph)
 * [Stacked Graph](#stacked-graph)
-* [Step-Plot Graph](#step-plot-graph)
+* [Step Graph](#step-graph)
 * [Single Stat](#single-stat)
 * [Line Graph + Single Stat](#line-graph-single-stat)
 * [Bar Graph](#bar-graph)