influxdata · sanderson · Nov 28, 2023 · Nov 27, 2023 · Nov 28, 2023
@@ -120,7 +120,7 @@ send = (
 // ```
 //
 // ## Metadata
-// tags: notifcation endpoints, transformations
+// tags: notification endpoints, transformations
 //
 endpoint = (webhookToken, webhookID, username, avatar_url="") =>
     (mapFn) =>

@@ -58,7 +58,7 @@ from = array.from
 
 // concat appends two arrays and returns a new array.
 //
-// **Deprecated**: Experimetnal `array.concat()` is deprecated in favor of
+// **Deprecated**: Experimental `array.concat()` is deprecated in favor of
 // [`array.concat()`](https://docs.influxdata.com/flux/v0.x/stdlib/array/concat).
 //
 // ## Parameters

@@ -412,7 +412,7 @@ builtin join : (left: stream[A], right: stream[B], fn: (left: A, right: B) => C)
 // ##### Applicable use cases
 // - Write to an InfluxDB bucket and query the written data in a single Flux script.
 //
-//   _**Note:** `experimental.chain()` does not gaurantee that data written to
+//   _**Note:** `experimental.chain()` does not guarantee that data written to
 //   InfluxDB is immediately queryable. A delay between when data is written and
 //   when it is queryable may cause a query using `experimental.chain()` to fail.
 //
@@ -938,7 +938,7 @@ builtin spread : (<-tables: stream[{T with _value: A}]) => stream[{T with _value
 // column for each input table.
 //
 // ## Standard deviation modes
-// The following modes are avaialable when calculating the standard deviation of data.
+// The following modes are available when calculating the standard deviation of data.
 //
 // ##### sample
 // Calculate the sample standard deviation where the data is considered to be
@@ -1198,7 +1198,7 @@ builtin min : (<-tables: stream[{T with _value: A}]) => stream[{T with _value: A
 // - Outputs a single table for each input table.
 // - Outputs a single record for each unique value in an input table.
 // - Leaves group keys, columns, and values unmodified.
-// - Drops emtpy tables.
+// - Drops empty tables.
 //
 // ## Parameters
 // - tables: Input data. Default is piped-forward data (`<-`).
@@ -1254,7 +1254,7 @@ builtin unique : (<-tables: stream[{T with _value: A}]) => stream[{T with _value
 // - tables: Input data. Default is piped-forward data (`<-`).
 //
 // ## Examples
-// ### Create a histgram from input data
+// ### Create a histogram from input data
 // ```
 // import "experimental"
 // import "sampledata"

@@ -179,7 +179,7 @@ package geo
 import "experimental"
 import "influxdata/influxdb/v1"
 
-// units defines the unit of measurment used in geotemporal operations.
+// units defines the unit of measurement used in geotemporal operations.
 //
 // ## Metadata
 // introduced: 0.78.0

@@ -2,7 +2,7 @@
 //
 // **Deprecated**: This package is deprecated in favor of [`requests`](https://docs.influxdata.com/flux/v0.x/stdlib/http/requests/).
 // Do not mix usage of this experimental package with the `requests` package as the `defaultConfig` is not shared between the two packages.
-// This experimental package is completely superceded by the `requests` package so there should be no need to mix them.
+// This experimental package is completely superseded by the `requests` package so there should be no need to mix them.
 //
 // ## Metadata
 // introduced: 0.152.0

@@ -14,7 +14,7 @@ package secrets
 //
 // ## Examples
 //
-// ### Retrive a key from the InfluxDB secret store
+// ### Retrieve a key from the InfluxDB secret store
 // ```no_run
 // import "influxdata/influxdb/secrets"
 //

@@ -25,7 +25,7 @@ builtin _lastSuccess : (orTime: T, lastSuccessTime: time) => time where T: Timea
 //
 // ## Examples
 //
-// ### Return the time an InfluxDB task last succesfully ran
+// ### Return the time an InfluxDB task last successfully ran
 // ```no_run
 // import "influxdata/influxdb/tasks"
 //

@@ -537,7 +537,7 @@ builtin cosh : (x: float) => float
 //
 // ## Examples
 //
-// ### Return the maximum difference betwee two values
+// ### Return the maximum difference between two values
 // ```no_run
 // import "math"
 //
@@ -1191,7 +1191,7 @@ builtin isNaN : (f: float) => bool
 //
 builtin j0 : (x: float) => float
 
-// j1 is a funciton that returns the order-one Bessel function for the first kind.
+// j1 is a function that returns the order-one Bessel function for the first kind.
 //
 // ## Parameters
 // - x: Value to operate on.
@@ -1223,7 +1223,7 @@ builtin j0 : (x: float) => float
 //
 builtin j1 : (x: float) => float
 
-// jn returns the order-n Bessel funciton of the first kind.
+// jn returns the order-n Bessel function of the first kind.
 //
 // ## Parameters
 // - n: Order number.
@@ -1566,7 +1566,7 @@ builtin logb : (x: float) => float
 //
 builtin mMax : (x: float, y: float) => float
 
-// mMin is a function taht returns the lessser of `x` or `y`.
+// mMin is a function that returns the lesser of `x` or `y`.
 //
 // ## Parameters
 // - x: x-value to use in the operation.

@@ -54,7 +54,7 @@ option defaultURL = "https://events.pagerduty.com/v2/enqueue"
 //
 // ## Examples
 //
-// ### Convert a status level to a PagerDuty serverity
+// ### Convert a status level to a PagerDuty severity
 // ```no_run
 // import "pagerduty"
 //

@@ -60,7 +60,7 @@
 // authentication credentials using one of the following methods:
 //
 // - The `GOOGLE_APPLICATION_CREDENTIALS` environment variable that identifies the
-//   location of yur credential JSON file.
+//   location of your credential JSON file.
 // - Provide your BigQuery credentials using the `credentials` URL parameters in your BigQuery DSN.
 //
 // #### BigQuery credential URL parameter

@@ -391,7 +391,7 @@ builtin compare : (v: string, t: string) => int
 // ## Parameters
 //
 // - v: String value to search.
-// - substr: Substring to count occurences of.
+// - substr: Substring to count occurrences of.
 //
 //   The function counts only non-overlapping instances of `substr`.
 //

@@ -130,7 +130,7 @@ builtin isType : (v: A, type: string) => bool where A: Basic
 // isNumeric tests if a value is a numeric type (int, uint, or float).
 //
 // This is a helper function to test or filter for values that can be used in
-// arithmatic operations or aggregations.
+// arithmetic operations or aggregations.
 //
 // ## Parameters
 // - v: Value to test.

@@ -700,7 +700,7 @@ builtin first : (<-tables: stream[A], ?column: string) => stream[A] where A: Rec
 
 // group regroups input data by modifying group key of input tables.
 //
-// **Note**: Group does not gaurantee sort order.
+// **Note**: Group does not guarantee sort order.
 // To ensure data is sorted correctly, use `sort()` after `group()`.
 //
 // ## Parameters
@@ -711,7 +711,7 @@ builtin first : (<-tables: stream[A], ?column: string) => stream[A] where A: Rec
 //
 // - mode: Grouping mode. Default is `by`.
 //
-//   **Avaliable modes**:
+//   **Available modes**:
 //   - **by**: Group by columns defined in the `columns` parameter.
 //   - **except**: Group by all columns _except_ those in defined in the
 //     `columns` parameter.
@@ -1196,7 +1196,7 @@ builtin join : (<-tables: A, ?method: string, ?on: [string]) => stream[B] where
 //
 // ## Examples
 //
-// ### Caclulate Kaufman's Adaptive Moving Average for input data
+// ### Calculate Kaufman's Adaptive Moving Average for input data
 // ```
 // import "sampledata"
 //
@@ -1215,7 +1215,7 @@ builtin kaufmansAMA : (<-tables: stream[A], n: int, ?column: string) => stream[B
 
 // keep returns a stream of tables containing only the specified columns.
 //
-// Columns in the group key that are not specifed in the `columns` parameter or
+// Columns in the group key that are not specified in the `columns` parameter or
 // identified by the `fn` parameter are removed from the group key and dropped
 // from output tables. `keep()` is the inverse of `drop()`.
 //
@@ -1696,7 +1696,7 @@ builtin limit : (<-tables: stream[A], n: int, ?offset: int) => stream[A]
 // the group key.
 //
 // #### Preserve columns
-// `map()` drops any columns that are not mapped explictly by column label or
+// `map()` drops any columns that are not mapped explicitly by column label or
 // implicitly using the `with` operator in the `fn` function.
 // The `with` operator updates a record property if it already exists, creates
 // a new record property if it doesn’t exist, and includes all existing
@@ -1917,7 +1917,7 @@ builtin movingAverage : (
 // - q: Quantile to compute. Must be between `0.0` and `1.0`.
 // - method: Computation method. Default is `estimate_tdigest`.
 //
-//     **Avaialable methods**:
+//     **Available methods**:
 //
 //     - **estimate_tdigest**: Aggregate method that uses a
 //       [t-digest data structure](https://github.com/tdunning/t-digest) to
@@ -2465,7 +2465,7 @@ builtin skew : (<-tables: stream[A], ?column: string) => stream[B] where A: Reco
 //
 builtin spread : (<-tables: stream[A], ?column: string) => stream[B] where A: Record, B: Record
 
-// sort orders rows in each intput table based on values in specified columns.
+// sort orders rows in each input table based on values in specified columns.
 //
 // #### Output data
 // One output table is produced for each input table.
@@ -2585,7 +2585,7 @@ builtin stateTracking : (
 // - mode: Standard deviation mode or type of standard deviation to calculate.
 //   Default is `sample`.
 //
-//   **Availble modes:**
+//   **Available modes:**
 //
 //   - **sample**: Calculate the sample standard deviation where the data is
 //     considered part of a larger population.
@@ -3122,7 +3122,7 @@ builtin findRecord : (<-tables: stream[A], fn: (key: B) => bool, idx: int) => A
 // ### Convert all values in a column to booleans
 // If converting the `_value` column to boolean types, use `toBool()`.
 // If converting columns other than `_value`, use `map()` to iterate over each
-// row and `bool()` to covert a column value to a boolean type.
+// row and `bool()` to convert a column value to a boolean type.
 //
 // ```
 // # import "sampledata"
@@ -3234,7 +3234,7 @@ builtin duration : (v: A) => duration
 // ### Convert all values in a column to floats
 // If converting the `_value` column to float types, use `toFloat()`.
 // If converting columns other than `_value`, use `map()` to iterate over each
-// row and `float()` to covert a column value to a float type.
+// row and `float()` to convert a column value to a float type.
 //
 // ```
 // # import "sampledata"
@@ -3294,7 +3294,7 @@ builtin _vectorizedFloat : (v: vector[A]) => vector[float]
 // ### Convert all values in a column to integers
 // If converting the `_value` column to integer types, use `toInt()`.
 // If converting columns other than `_value`, use `map()` to iterate over each
-// row and `int()` to covert a column value to a integer type.
+// row and `int()` to convert a column value to a integer type.
 //
 // ```
 // # import "sampledata"
@@ -3331,7 +3331,7 @@ builtin int : (v: A) => int
 // ### Convert all values in a column to strings
 // If converting the `_value` column to string types, use `toString()`.
 // If converting columns other than `_value`, use `map()` to iterate over each
-// row and `string()` to covert a column value to a string type.
+// row and `string()` to convert a column value to a string type.
 //
 // ```
 // # import "sampledata"
@@ -3373,7 +3373,7 @@ builtin string : (v: A) => string
 // ### Convert all values in a column to time
 // If converting the `_value` column to time types, use `toTime()`.
 // If converting columns other than `_value`, use `map()` to iterate over each
-// row and `time()` to covert a column value to a time type.
+// row and `time()` to convert a column value to a time type.
 //
 // ```
 // # import "sampledata"
@@ -3422,7 +3422,7 @@ builtin time : (v: A) => time
 // ### Convert all values in a column to unsigned integers
 // If converting the `_value` column to uint types, use `toUInt()`.
 // If converting columns other than `_value`, use `map()` to iterate over each
-// row and `uint()` to covert a column value to a uint type.
+// row and `uint()` to convert a column value to a uint type.
 //
 // ```
 // # import "sampledata"
@@ -3951,7 +3951,7 @@ increase = (tables=<-, columns=["_value"]) =>
 // - column: Column to use to compute the median. Default is `_value`.
 // - method: Computation method. Default is `estimate_tdigest`.
 //
-//     **Avaialable methods**:
+//     **Available methods**:
 //
 //     - **estimate_tdigest**: Aggregate method that uses a
 //       [t-digest data structure](https://github.com/tdunning/t-digest) to