Related to "Transformations"

join package

The join package provides functions that join two table streams together.

aggregateWindow() function

aggregateWindow() downsamples data by grouping data into fixed windows of time and applying an aggregate or selector function to each window.

bottom() function

bottom() sorts each input table by specified columns and keeps the bottom n records in each table.

chandeMomentumOscillator() function

chandeMomentumOscillator() applies the technical momentum indicator developed by Tushar Chande to input data.

columns() function

columns() returns the column labels in each input table.

count() function

count() returns the number of records in each input table.

cov() function

cov() computes the covariance between two streams of tables.

covariance() function

covariance() computes the covariance between two columns.

cumulativeSum() function

cumulativeSum() computes a running sum for non-null records in a table.

derivative() function

derivative() computes the rate of change per unit of time between subsequent non-null records.

difference() function

difference() returns the difference between subsequent values.

distinct() function

distinct() returns all unique values in a specified column.

doubleEMA() function

doubleEMA() returns the double exponential moving average (DEMA) of values in the _value column grouped into n number of points, giving more weight to recent data.

drop() function

drop() removes specified columns from a table.

duplicate() function

duplicate() duplicates a specified column in a table.

elapsed() function

elapsed() returns the time between subsequent records.

experimental.alignTime() function

experimental.alignTime() shifts time values in input tables to all start at a common start time.

experimental.count() function

experimental.count() returns the number of records in each input table.

experimental.distinct() function

experimental.distinct() returns unique values from the _value column.

experimental.fill() function

experimental.fill() replaces all null values in the _value column with a non-null value.

experimental.first() function

experimental.first() returns the first record with a non-null value in the _value column for each input table.

experimental.group() function

experimental.group() introduces an extend mode to the existing group() function.

experimental.histogram() function

experimental.histogram() approximates the cumulative distribution of a dataset by counting data frequencies for a list of bins.

experimental.histogramQuantile() function

experimental.histogramQuantile() approximates a quantile given a histogram with the cumulative distribution of the dataset.

experimental.integral() function

experimental.integral() computes the area under the curve per unit of time of subsequent non-null records.

experimental.join() function

experimental.join() joins two streams of tables on the group key and _time column.

experimental.kaufmansAMA() function

experimental.kaufmansAMA() calculates the Kaufman’s Adaptive Moving Average (KAMA) of input tables using the _value column in each table.

experimental.last() function

experimental.last() returns the last record with a non-null value in the _value column for each input table.

experimental.max() function

experimental.max() returns the record with the highest value in the _value column for each input table.

experimental.mean() function

experimental.mean() computes the mean or average of non-null values in the _value column of each input table.

experimental.min() function

experimental.min() returns the record with the lowest value in the _value column for each input table.

experimental.mode() function

experimental.mode() computes the mode or value that occurs most often in the _value column in each input table.

experimental.preview() function

experimental.preview() limits the number of rows and tables in the stream.

experimental.quantile() function

experimental.quantile() returns non-null records with values in the _value column that fall within the specified quantile or represent the specified quantile.

experimental.set() function

experimental.set() sets multiple static column values on all records.

experimental.skew() function

experimental.skew() returns the skew of non-null values in the _value column for each input table as a float.

experimental.spread() function

experimental.spread() returns the difference between the minimum and maximum values in the _value column for each input table.

experimental.stddev() function

experimental.stddev() returns the standard deviation of non-null values in the _value column for each input table.

experimental.sum() function

experimental.sum() returns the sum of non-null values in the _value column for each input table.

experimental.unique() function

experimental.unique() returns all records containing unique values in the _value column.

experimental.unpivot() function

experimental.unpivot() creates _field and _value columns pairs using all columns (other than _time) not in the group key. The _field column contains the original column label and the _value column contains the original column value.

experimental.window() function

experimental.window() groups records based on time.

exponentialMovingAverage() function

exponentialMovingAverage() calculates the exponential moving average of n number of values in the _value column giving more weight to more recent data.

fill() function

fill() replaces all null values in input tables with a non-null value.

filter() function

filter() filters data based on conditions defined in a predicate function (fn).

first() function

first() returns the first non-null record from each input table.

group() function

group() regroups input data by modifying group key of input tables.

highestAverage() function

highestAverage() calculates the average of each input table and returns the highest n averages.

highestCurrent() function

highestCurrent() selects the last record from each input table and returns the highest n records.

highestMax() function

highestMax() selects the record with the highest value in the specified column from each input table and returns the highest n records.

histogram() function

histogram() approximates the cumulative distribution of a dataset by counting data frequencies for a list of bins.

histogramQuantile() function

histogramQuantile() approximates a quantile given a histogram that approximates the cumulative distribution of the dataset.

holtWinters() function

holtWinters() applies the Holt-Winters forecasting method to input tables.

hourSelection() function

hourSelection() filters rows by time values in a specified hour range.

increase() function

increase() returns the cumulative sum of non-negative differences between subsequent values.

integral() function

integral() computes the area under the curve per unit of time of subsequent non-null records.

interpolate.linear() function

interpolate.linear() inserts rows at regular intervals using linear interpolation to determine values for inserted rows.

join.full() function

join.full() performs a full outer join on two table streams.

join.inner() function

join.inner() performs an inner join on two table streams.

join.left() function

join.left() performs a left outer join on two table streams.

join.right() function

join.right() performs a right outer join on two table streams.

join.tables() function

join.tables() joins two input streams together using a specified method, predicate, and a function to join two corresponding records, one from each input stream.

join.time() function

join.time() joins two table streams together exclusively on the _time column.

join() function

join() merges two streams of tables into a single output stream based on columns with equal values. Null values are not considered equal when comparing column values. The resulting schema is the union of the input schemas. The resulting group key is the union of the input group keys.

kaufmansAMA() function

kaufmansAMA() calculates the Kaufman’s Adaptive Moving Average (KAMA) using values in input tables.

kaufmansER() function

kaufmansER() computes the Kaufman’s Efficiency Ratio (KER) of values in the _value column for each input table.

keep() function

keep() returns a stream of tables containing only the specified columns.

keys() function

keys() returns the columns that are in the group key of each input table.

keyValues() function

keyValues() returns a stream of tables with each input tables’ group key and two columns, _key and _value, that correspond to unique column label and value pairs for each input table.

last() function

last() returns the last row with a non-null value from each input table.

limit() function

limit() returns the first n rows after the specified offset from each input table.

lowestAverage() function

lowestAverage() calculates the average of each input table and returns the lowest n averages.

lowestCurrent() function

lowestCurrent() selects the last record from each input table and returns the lowest n records.

lowestMin() function

lowestMin() selects the record with the lowest value in the specified column from each input table and returns the bottom n records.

map() function

map() iterates over and applies a function to input rows.

max() function

max() returns the row with the maximum value in a specified column from each input table.

mean() function

mean() returns the average of non-null values in a specified column from each input table.

median() function

median() returns the median _value of an input table or all non-null records in the input table with values that fall within the 0.5 quantile (50th percentile).

min() function

min() returns the row with the minimum value in a specified column from each input table.

movingAverage() function

movingAverage() calculates the mean of non-null values using the current value and n - 1 previous values in the _values column.

pagerduty.endpoint() function

pagerduty.endpoint() returns a function that sends a message to PagerDuty that includes output data.

pearsonr() function

pearsonr() returns the covariance of two streams of tables normalized to the Pearson R coefficient.

pivot() function

pivot() collects unique values stored vertically (column-wise) and aligns them horizontally (row-wise) into logical sets.

pushbullet.endpoint() function

pushbullet.endpoint() creates the endpoint for the Pushbullet API and sends a notification of type note.

quantile() function

quantile() returns rows from each input table with values that fall within a specified quantile or returns the row with the value that represents the specified quantile.

range() function

range() filters rows based on time bounds.

reduce() function

reduce() aggregates rows in each input table using a reducer function (fn).

relativeStrengthIndex() function

relativeStrengthIndex() measures the relative speed and change of values in input tables.

rename() function

rename() renames columns in a table.

sample() function

sample() selects a subset of the rows from each input table.

set() function

set() assigns a static column value to each row in the input tables.

skew() function

skew() returns the skew of non-null records in each input table as a float.

slack.endpoint() function

slack.endpoint() returns a function that can be used to send a message to Slack per input row.

sort() function

sort() orders rows in each input table based on values in specified columns.

spread() function

spread() returns the difference between the minimum and maximum values in a specified column.

stateCount() function

stateCount() returns the number of consecutive rows in a given state.

stateDuration() function

stateDuration() returns the cumulative duration of a given state.

stateTracking() function

stateTracking() returns the cumulative count and duration of consecutive rows that match a predicate function that defines a state.

stddev() function

stddev() returns the standard deviation of non-null values in a specified column.

sum() function

sum() returns the sum of non-null values in a specified column.

tail() function

tail() limits each output table to the last n rows.

timedMovingAverage() function

timedMovingAverage() returns the mean of values in a defined time range at a specified frequency.

timeShift() function

timeShift() adds a fixed duration to time columns.

timeWeightedAvg() function

timeWeightedAvg() returns the time-weighted average of non-null values in _value column as a float for each input table.

toBool() function

toBool() converts all values in the _value column to boolean types.

toFloat() function

toFloat() converts all values in the _value column to float types.

toInt() function

toInt() converts all values in the _value column to integer types.

top() function

top() sorts each input table by specified columns and keeps the top n records in each table.

toString() function

toString() converts all values in the _value column to string types.

toTime() function

toTime() converts all values in the _value column to time types.

toUInt() function

toUInt() converts all values in the _value column to unsigned integer types.

tripleEMA() function

tripleEMA() returns the triple exponential moving average (TEMA) of values in the _value column.

tripleExponentialDerivative() function

tripleExponentialDerivative() returns the triple exponential derivative (TRIX) values using n points.

truncateTimeColumn() function

truncateTimeColumn() truncates all input time values in the _time to a specified unit.

union() function

union() merges two or more input streams into a single output stream.

unique() function

unique() returns all records containing unique values in a specified column.

window() function

window() groups records using regular time intervals.

aggregate.rate() function

aggregate.rate() calculates the average rate of increase per window of time for each input table.

geo.asTracks() function

geo.asTracks() groups rows into tracks (sequential, related data points).

geo.filterRows() function

geo.filterRows() filters data by a specified geographic region with the option of strict filtering.

geo.gridFilter() function

geo.gridFilter() filters data by a specified geographic region.

geo.groupByArea() function

geo.groupByArea() groups rows by geographic area.

geo.ST_LineString() function

geo.ST_LineString() converts a series of geographic points into linestring.

geo.strictFilter() function

geo.strictFilter() filters data by latitude and longitude in a specified region.

geo.toRows() function

geo.toRows() pivots fields into columns based on time.

geo.totalDistance() function

geo.totalDistance() calculates the total distance covered by subsequent points in each input table.

polyline.rdp() function

polyline.rdp() applies the Ramer Douglas Peucker (RDP) algorithm to input data to downsample curves composed of line segments into visually indistinguishable curves with fewer points.

prometheus.histogramQuantile() function

prometheus.histogramQuantile() calculates a quantile on a set of Prometheus histogram values.

query.filterFields() function

query.filterFields() filters input data by field.

query.filterMeasurement() function

query.filterMeasurement() filters input data by measurement.

query.fromRange() function

query.fromRange() returns all data from a specified bucket within given time bounds.

table.fill() function

table.fill() adds a single row to empty tables in a stream of tables.

alerta.endpoint() function

alerta.endpoint() sends alerts to Alerta using data from input rows.

bigpanda.endpoint() function

bigpanda.endpoint() sends alerts to BigPanda using data from input rows.

discord.endpoint() function

discord.endpoint() sends a single message to a Discord channel using a Discord webhook and data from table rows.

events.duration() function

events.duration() calculates the duration of events.

monitor.check() function

monitor.check() checks input data and assigns a level (ok, info, warn, or crit) to each row based on predicate functions.

monitor.deadman() function

monitor.deadman() detects when a group stops reporting data. It takes a stream of tables and reports if groups have been observed since time t.

monitor.stateChanges() function

monitor.stateChanges() detects state changes in a stream of data with a _level column and outputs records that change from fromLevel to toLevel.

monitor.stateChangesOnly() function

monitor.stateChangesOnly() takes a stream of tables that contains a _level column and returns a stream of tables grouped by _level where each record represents a state change.

naiveBayesClassifier.naiveBayes() function

naiveBayesClassifier.naiveBayes() performs a naive Bayes classification.

opsgenie.endpoint() function

opsgenie.endpoint() sends an alert message to Opsgenie using data from table rows.

sample.alignToNow() function

sample.alignToNow() shifts time values in input data to align the chronological last point to now.

schema.fieldsAsCols() function

schema.fieldsAsCols() is a special application of pivot() that pivots input data on _field and _time columns to align fields within each input table that have the same timestamp.

sensu.endpoint() function

sensu.endpoint() sends an event to the Sensu Events API using data from table rows.

teams.endpoint() function

teams.endpoint() sends a message to a Microsoft Teams channel using data from table rows.

telegram.endpoint() function

telegram.endpoint() sends a message to a Telegram channel using data from table rows.

tickscript.alert() function

tickscript.alert() identifies events of varying severity levels and writes them to the statuses measurement in the InfluxDB _monitoring system bucket.

tickscript.compute() function

tickscript.compute() is an alias for tickscript.select() that changes a column’s name and optionally applies an aggregate or selector function.

tickscript.deadman() function

tickscript.deadman() detects low data throughput and writes a point with a critical status to the InfluxDB _monitoring system bucket.

tickscript.groupBy() function

tickscript.groupBy() groups results by the _measurement column and other specified columns.

tickscript.join() function

tickscript.join() merges two input streams into a single output stream based on specified columns with equal values and appends a new measurement name.

tickscript.select() function

tickscript.select() changes a column’s name and optionally applies an aggregate or selector function to values in the column.

tickscript.selectWindow() function

tickscript.selectWindow() changes a column’s name, windows rows by time, and then applies an aggregate or selector function the specified column for each window of time.

v1.fieldsAsCols() function

v1.fieldsAsCols() is a special application of pivot() that pivots input data on _field and _time columns to align fields within each input table that have the same timestamp.

victorops.endpoint() function

victorops.endpoint() sends events to VictorOps using data from input rows.

webexteams.endpoint() function

webexteams.endpoint() returns a function that sends a message that includes data from input rows to a Webex room.

zenoss.endpoint() function

zenoss.endpoint() sends events to Zenoss using data from input rows.