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 intput 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 rate of change per windows 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.
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.