Documentation

Handle late arriving data

In some cases, due to network latency or other issues, your time series data may arrive to InfluxDB late. To ensure that your computed aggregations are correct, you must account for data latency in your aggregation and downsampling tasks. This guides walks through a method that detects and accounts for late arriving data using InfluxDB tasks and API-invokable scripts.

Scenario

Your are collecting and storing water levels at 100 different locations. Data at each location is reported every 10 seconds. Network connectivity varies at each location, but reported data can confidently be written to InfluxDB at least every hour, but maybe more often.

Setup

In order to follow this guide you’ll need to create the following resources:

  • An All-Access token.
  • Three InfluxDB buckets:
    • water_level_raw: Stores the raw water level data.
    • water_level_mean: Stores one minute averages of water levels. Averages include late arriving data from the last hour.
    • water_level_checksum: Stores one minute counts of water levels. The count is used as a checksum for each one minute window.
  • An API-invokable script:
    • water_level_process.flux: This script computes the minute water level averages and counts the number of points that were used in water level average calculation. The average and count is written to the water_level_mean and water_level_checksum buckets respectively.
  • A Task:
    • water_level_checksum.flux: This task triggers the water_level_process.flux script. This task also recomputes a count of the number of points used to calculagte the most recent water level average value. It compares the most recent count from water_level_checksum bucket against this new count and triggers a recaclulation of the water level average to accomodate an increase in the count from late arriving data.

In this process, you compute the average water level at each location over one minute windows. It’s designed to handle data arriving up to one hour late. Data from each location is written every 10 seconds. Additionally, every 10 seconds, a late data point is written somewhere in the last one hour for each location.

Overview

Before diving into the code, take a high level look at the logic of the Flux scripts.

Late arriving data architecture

The water_level_checksum.flux is a task that runs every minute. It counts the number of points that exist in the water_level_raw bucket (new count) and compares that count against the count in the water_level_checksum bucket (old count). If the new count from the water_level_raw bucket isn’t equal to the count from the water_level_checksum bucket, then the task invokes water_level_process.flux API-invokable script which recalculates the old count and aggregation.

Flux scripts in detail

water_level_process.flux

water_level_process.flux is an invokable script that does two things:

  1. Computes the mean of values in the time range defined by the start and stop script parameters and writes the computed mean to the water_level_mean bucket.
  2. Computes the count or total number of points in the time range defined by the start and stop script parameters and writes the count to the water_level_checksum bucket.
// Compute and store the mean for the window
from(bucket: "water_level_raw")
    |> range(start: params.start, stop: params.stop)
    |> mean()
    |> to(bucket: "water_level_mean", timeColumn: "_stop")
    |> yield(name: "means")

// Compute and store the new checksum for this window
from(bucket: "water_level_raw")
    |> range(start: params.start, stop: params.stop)
    |> group(columns: ["_measurement", "_field", "_stop"])
    |> count()
    |> to(bucket: "water_level_checksum", timeColumn: "_stop")
    |> yield(name: "checksums")

Use the API or CLI to create an invokable script.

water_level_checsum.flux

water_level_process.flux is a task that does the following:

  1. Counts the number of points in the water_level_raw bucket (new count) for the last hour across one minute windows.
  2. Invokes the water_level_process.flux invokable script to calculate a new mean and a new count across one minute windows.
  3. Gathers the previous count in the water_level_checksum bucket (old count) for the last hour.
  4. Joins the old and new streams and compares the old count against a new count.
  5. Filters for counts that do not match.
  6. Invokes the water_level_process.flux invokable script to recompute the mean and count for every one minute window where the counts do not match.

Task details

  • The task option provides configuration settings for the task:
    • name: Provides a name for the task.
    • every: Defines how often the task runs (every one minute) and, in this case, the window interval used to compute means and counts.
    • offset: Defines how much time to wait before executing the task. _The offset does not change the time range queried by the task. _
  • invokeScripts() is a custom function that invokes the water_level_process.flux invokable script.
    • start and stop parameters are required.
    • scriptID is required. Find the scriptID with the API or CLI
    • Store your InfluxDB API token as an InfluxDB secret and use the secrets package to retrieve the token.
option task = {name: "water_level_checksum", every: 1m, offset: 10s}

invokeScript = (start, stop, scriptID) =>
    requests.post(
        url: "https://cloud2.influxdata.com/api/v2/scripts/${scriptID}/invoke",

        headers: ["Authorization": "Token ${token}", "Accept": "application/json", "Content-Type": "application/json"],
        body: json.encode(v: {params: {start: string(v: start), stop: string(v: stop)}}),
    )

First the new counts are calculated and stored in the variable newCounts.
newCounts =

    from(bucket: "water_level_raw")
        |> range(start: start, stop: stop)
        |> group(columns: ["_measurement", "_field"])
        |> aggregateWindow(every: every, fn: count)

Where the start and stop values for the range are defined as:
start = date.truncate(t: -late_window, unit: every)

stop = date.truncate(t: now(), unit: every. The late_window is equal to the longest amount of time you’re willing to wait for late arriving data (in this example it’s equal to ). The date.truncate() function is used to truncate the start and stop time to the latest minute to ensure that you successfully recompute values on the same timestamps. Where every = task.every. Since the task is running at 1 minute intervals every is equal to 1m. Additionally remember that the aggregateWindow function uses the _stop column as the source of the new time value for aggregate values by default.

Next, compute the current mean and count with the following code:
// Always compute the most recent interval

newCounts
    |> filter(fn: (r) => r._time == stop)
    |> map(
        fn: (r) => {
            response = invokeScript(start: date.sub(d: every, from: r._time), stop: r._time)

            return {r with code: response.statusCode}
        },
    )
    |> yield(name: "current")

filter for the last newCount value. Remember, the time value is equal to the stop value because of the default behavior of the aggregateWindow() function. Then map over that single row table to call the invokeScript function once. Here you also pass in values to the start and stop parameters of date.sub(d: every, from: r._time) and r._time, respectively. Remember that the every variable is equal to 1m. Effectively, this means that you will calculate the mean and count over 1minute intervals (with timestamps appropriately truncated to ensure overwrites of recomputed means later). This code ensures that you will always invoke the water_level_process.flux script at least once to write new means and counts to the water_level_mean and water_level_checksum buckets, respectively.

Next, query the water_level_checksum bucket for the last hour:

oldCounts =
    from(bucket: "water_level_checksum")
        |> range(start: start, stop: stop)
        |> group(columns: ["_measurement", "_field"])

Remember the start and stop times here are equal to - and now() truncated to the minute.

Now join the old counts and new counts together. You also filter for when the counts differ. If they do differ, then there will be records in the response that can be mapped over. Map over those records to recalculate the mean and count by invoking the level_water_process.flux script:

experimental.join(
    left: oldCounts,
    right: newCounts,
    fn: (left, right) => ({left with old_count: left._value, new_count: right._value}),
)
    // Recompute any windows where the checksum is different
    |> filter(fn: (r) => r.old_count != r.new_count)
    |> map(
        fn: (r) => {
            response = invokeScript(start: date.sub(d: every, from: r._time), stop: r._time)

            return {r with code: response.statusCode}
        },
    )
    |> yield(name: "diffs")

The complete water_level_checsum.flux is shown below:

import "influxdata/influxdb/secrets"
import "experimental/http/requests"
import "json"
import "date"
import "experimental"

option task = {name: "water_level_checksum", every: 1m, offset: 10s}

// Size of the window to aggregate
every = task.every

// Longest we are willing to wait for late data
late_window = 1h

token = secrets.get(key: "SELF_TOKEN")

// invokeScript calls a Flux script with the given start stop
// parameters to recompute the window.
invokeScript = (start, stop) =>
    requests.post(
        // We have hardcoded the script ID here
        url: "https://eastus-1.azure.cloud2.influxdata.com/api/v2/scripts/095fabd404108000/invoke",
        headers: ["Authorization": "Token ${token}", "Accept": "application/json", "Content-Type": "application/json"],
        body: json.encode(v: {params: {start: string(v: start), stop: string(v: stop)}}),
    )

// Only query windows that span a full minute
start = date.truncate(t: -late_window, unit: every)
stop = date.truncate(t: now(), unit: every)

newCounts =
    from(bucket: "water_level_raw")
        |> range(start: start, stop: stop)
        |> group(columns: ["_measurement", "_field"])
        |> aggregateWindow(every: every, fn: count)

// Always compute the most recent interval
newCounts
    |> filter(fn: (r) => r._time == stop)
    |> map(
        fn: (r) => {
            response = invokeScript(start: date.sub(d: every, from: r._time), stop: r._time)

            return {r with code: response.statusCode}
        },
    )
    |> yield(name: "current")

oldCounts =
    from(bucket: "water_level_checksum")
        |> range(start: start, stop: stop)
        |> group(columns: ["_measurement", "_field"])

// Compare old and new checksum
experimental.join(
    left: oldCounts,
    right: newCounts,
    fn: (left, right) => ({left with old_count: left._value, new_count: right._value}),
)
    // Recompute any windows where the checksum is different
    |> filter(fn: (r) => r.old_count != r.new_count)
    |> map(
        fn: (r) => {
            response = invokeScript(start: date.sub(d: every, from: r._time), stop: r._time)

            return {r with code: response.statusCode}
        },
    )
    |> yield(name: "diffs")

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