docs-v2/content/shared/influxdb3-query-guides/sql/compare-values.md

Use SQL window functions to compare values across different rows in your time series data. Window functions like LAG and LEAD let you access values from previous or subsequent rows without using self-joins, making it easy to calculate changes over time.

Common use cases for comparing values include:

• Calculating the difference between the current value and a previous value
• Computing rate of change or percentage change
• Detecting significant changes or anomalies
• Comparing values at specific time intervals
• Handling counter metrics that reset to zero

To compare values across rows:

1. Use a window function such as LAG or LEAD with an OVER clause.
2. Include a PARTITION BY clause to group data by tags (like room or sensor_id).
3. Include an ORDER BY clause to define the order for comparisons (typically by time).
4. Use arithmetic operators to calculate differences, ratios, or percentage changes.

Examples of comparing values

[!Note]

Sample data

The following examples use the {{% influxdb3/home-sample-link %}}. To run the example queries and return results, write the sample data to your {{% product-name %}} database before running the example queries.

• Calculate the difference from the previous value
• Calculate the percentage change
• Compare values at regular intervals
• Compare values with exact time offsets
• Handle counter metrics and resets
  • Calculate non-negative differences (counter rate)
  • Calculate cumulative counter increase
  • Aggregate counter increases by time interval

Calculate the difference from the previous value

Use the LAG function to access the value from the previous row and calculate the difference. This is useful for detecting changes over time.

{{% influxdb/custom-timestamps %}}

SELECT
  time,
  room,
  temp,
  temp - LAG(temp, 1) OVER (
    PARTITION BY room
    ORDER BY time
  ) AS temp_change
FROM home
WHERE
  time >= '2022-01-01T08:00:00Z'
  AND time < '2022-01-01T11:00:00Z'
ORDER BY room, time

time	room	temp	temp_change
2022-01-01T08:00:00	Kitchen	21.0	NULL
2022-01-01T09:00:00	Kitchen	23.0	2.0
2022-01-01T10:00:00	Kitchen	22.7	-0.3
2022-01-01T08:00:00	Living Room	21.1	NULL
2022-01-01T09:00:00	Living Room	21.4	0.3
2022-01-01T10:00:00	Living Room	21.8	0.4

{{% /influxdb/custom-timestamps %}}

The first row in each partition returns NULL for temp_change because there's no previous value. To use a default value instead of NULL, provide a third argument to LAG:

LAG(temp, 1, 0) -- Returns 0 if no previous value exists

Calculate the percentage change

Calculate the percentage change between the current value and a previous value by dividing the difference by the previous value.

{{% influxdb/custom-timestamps %}}

SELECT
  time,
  room,
  temp,
  ROUND(
    ((temp - LAG(temp, 1) OVER (PARTITION BY room ORDER BY time)) /
     LAG(temp, 1) OVER (PARTITION BY room ORDER BY time)) * 100,
    2
  ) AS percent_change
FROM home
WHERE
  time >= '2022-01-01T08:00:00Z'
  AND time < '2022-01-01T11:00:00Z'
ORDER BY room, time

time	room	temp	percent_change
2022-01-01T08:00:00	Kitchen	21.0	NULL
2022-01-01T09:00:00	Kitchen	23.0	9.52
2022-01-01T10:00:00	Kitchen	22.7	-1.30
2022-01-01T08:00:00	Living Room	21.1	NULL
2022-01-01T09:00:00	Living Room	21.4	1.42
2022-01-01T10:00:00	Living Room	21.8	1.87

{{% /influxdb/custom-timestamps %}}

Compare values at regular intervals

For regularly spaced time series data (like hourly readings), use LAG with an offset parameter to compare values from a specific number of rows back.

The following query compares each temperature reading with the reading from one hour earlier (assuming hourly data):

{{% influxdb/custom-timestamps %}}

SELECT
  time,
  room,
  temp,
  LAG(temp, 1) OVER (
    PARTITION BY room
    ORDER BY time
  ) AS temp_1h_ago,
  temp - LAG(temp, 1) OVER (
    PARTITION BY room
    ORDER BY time
  ) AS hourly_change
FROM home
WHERE
  time >= '2022-01-01T08:00:00Z'
  AND time < '2022-01-01T12:00:00Z'
ORDER BY room, time

time	room	temp	temp_1h_ago	hourly_change
2022-01-01T08:00:00	Kitchen	21.0	NULL	NULL
2022-01-01T09:00:00	Kitchen	23.0	21.0	2.0
2022-01-01T10:00:00	Kitchen	22.7	23.0	-0.3
2022-01-01T11:00:00	Kitchen	22.4	22.7	-0.3
2022-01-01T08:00:00	Living Room	21.1	NULL	NULL
2022-01-01T09:00:00	Living Room	21.4	21.1	0.3
2022-01-01T10:00:00	Living Room	21.8	21.4	0.4
2022-01-01T11:00:00	Living Room	22.2	21.8	0.4

{{% /influxdb/custom-timestamps %}}

Compare values with exact time offsets

For irregularly spaced time series data or when you need to compare values from an exact time offset (like exactly 1 hour ago, not just the previous row), use a self-join with interval arithmetic.

{{% influxdb/custom-timestamps %}}

SELECT
  current.time,
  current.room,
  current.temp AS current_temp,
  previous.temp AS temp_1h_ago,
  current.temp - previous.temp AS hourly_diff
FROM home AS current
LEFT JOIN home AS previous
  ON current.room = previous.room
  AND previous.time = current.time - INTERVAL '1 hour'
WHERE
  current.time >= '2022-01-01T08:00:00Z'
  AND current.time < '2022-01-01T12:00:00Z'
ORDER BY current.room, current.time

time	room	current_temp	temp_1h_ago	hourly_diff
2022-01-01T08:00:00	Kitchen	21.0	NULL	NULL
2022-01-01T09:00:00	Kitchen	23.0	21.0	2.0
2022-01-01T10:00:00	Kitchen	22.7	23.0	-0.3
2022-01-01T11:00:00	Kitchen	22.4	22.7	-0.3
2022-01-01T08:00:00	Living Room	21.1	NULL	NULL
2022-01-01T09:00:00	Living Room	21.4	21.1	0.3
2022-01-01T10:00:00	Living Room	21.8	21.4	0.4
2022-01-01T11:00:00	Living Room	22.2	21.8	0.4

{{% /influxdb/custom-timestamps %}}

This self-join approach works when:

• Your data points don't fall at regular intervals
• You need to compare against a specific time offset regardless of when the previous data point occurred
• You want to ensure the comparison is against a value from exactly 1 hour ago (or any other specific interval)

Handle counter metrics and resets

Counter metrics track cumulative values that increase over time, such as total requests, bytes transferred, or errors. Unlike gauge metrics (which can go up or down), counters typically only increase, though they may reset to zero when a service restarts.

Use GREATEST with LAG to handle counter resets by treating negative differences as zero.

[!Note]

InfluxDB 3 SQL and counter metrics

InfluxDB 3 SQL doesn't provide built-in equivalents to Flux's increase() or InfluxQL's NON_NEGATIVE_DIFFERENCE() functions. Use the patterns shown below to achieve similar results.

Calculate non-negative differences (counter rate)

Calculate the increase between consecutive counter readings, treating negative differences (counter resets) as zero.

{{% influxdb/custom-timestamps %}}

SELECT
  time,
  host,
  requests,
  LAG(requests) OVER (PARTITION BY host ORDER BY time) AS prev_requests,
  GREATEST(
    requests - LAG(requests) OVER (PARTITION BY host ORDER BY time),
    0
  ) AS requests_increase
FROM metrics
WHERE host = 'server1'
ORDER BY time

time	host	requests	prev_requests	requests_increase
2024-01-01T00:00:00	server1	1000	NULL	0
2024-01-01T01:00:00	server1	1250	1000	250
2024-01-01T02:00:00	server1	1600	1250	350
2024-01-01T03:00:00	server1	50	1600	0
2024-01-01T04:00:00	server1	300	50	250

{{% /influxdb/custom-timestamps %}}

LAG(requests) retrieves the previous counter value, requests - LAG(requests) calculates the difference, and GREATEST(..., 0) returns 0 for negative differences (counter resets). PARTITION BY host ensures comparisons are only within the same host.

Calculate cumulative counter increase

Calculate the total increase in a counter over time, handling resets. Use a Common Table Expression (CTE) to first calculate the differences, then sum them.

{{% influxdb/custom-timestamps %}}

WITH counter_diffs AS (
  SELECT
    time,
    host,
    requests,
    GREATEST(
      requests - LAG(requests) OVER (PARTITION BY host ORDER BY time),
      0
    ) AS requests_increase
  FROM metrics
  WHERE host = 'server1'
)
SELECT
  time,
  host,
  requests,
  SUM(requests_increase) OVER (PARTITION BY host ORDER BY time) AS cumulative_increase
FROM counter_diffs
ORDER BY time

time	host	requests	cumulative_increase
2024-01-01T00:00:00	server1	1000	0
2024-01-01T01:00:00	server1	1250	250
2024-01-01T02:00:00	server1	1600	600
2024-01-01T03:00:00	server1	50	600
2024-01-01T04:00:00	server1	300	850

{{% /influxdb/custom-timestamps %}}

The CTE computes non-negative differences for each row, then SUM(requests_increase) OVER (...) creates a running total. The cumulative increase continues to grow despite the counter reset at 03:00.

Aggregate counter increases by time interval

Calculate the total increase in a counter for each time interval (for example, hourly totals).

{{% influxdb/custom-timestamps %}}

WITH counter_diffs AS (
  SELECT
    DATE_BIN(INTERVAL '1 hour', time) AS time_bucket,
    host,
    requests,
    GREATEST(
      requests - LAG(requests) OVER (PARTITION BY host ORDER BY time),
      0
    ) AS requests_increase
  FROM metrics
)
SELECT
  time_bucket,
  host,
  SUM(requests_increase) AS total_increase
FROM counter_diffs
WHERE requests_increase > 0
GROUP BY time_bucket, host
ORDER BY host, time_bucket

time_bucket	host	total_increase
2024-01-01T01:00:00	server1	250
2024-01-01T02:00:00	server1	350
2024-01-01T04:00:00	server1	250
2024-01-01T01:00:00	server2	400
2024-01-01T02:00:00	server2	500
2024-01-01T03:00:00	server2	300
2024-01-01T04:00:00	server2	400

{{% /influxdb/custom-timestamps %}}

The CTE calculates differences for each row. DATE_BIN() assigns each timestamp to a 1-hour interval, SUM(requests_increase) aggregates all increases within each interval, and WHERE requests_increase > 0 filters out zero increases (first row and counter resets).