docs/sources/datasources/influxdb/query-editor/index.md
The InfluxDB query editor helps you build and run queries against your InfluxDB data source. You can access it from the Explore page or from any dashboard panel by clicking the ellipsis in the upper right of the panel and selecting Edit.
The editor supports three query languages — SQL, InfluxQL, and Flux — each with its own editing experience and macro system. SQL and InfluxQL provide both a visual builder mode and a code editing mode, while Flux provides a code editor only. You can also use the query editor to retrieve log data.
For general information about Grafana query editors, refer to Query and transform data.
If you're new to InfluxDB, these terms are used throughout the query editor:
| Term | Description |
|---|---|
| Measurement | A logical grouping of fields, tags, and timestamps, similar to a table in a relational database. |
| Field | A column in a measurement that stores the actual data values (numbers, strings, Boolean values). |
| Tag | A column used for metadata and indexing. Tags are indexed and optimized for filtering. |
| Retention policy | An InfluxDB 1.x setting that controls how long data is stored before automatic deletion. |
| Bucket | An InfluxDB 2.x and 3.x storage location that combines a database and retention policy. |
The InfluxDB data source has three query editors, each corresponding to the query language selected in the data source configuration:
Editor options vary based on query language.
The InfluxQL query editor helps you select metrics and tags to create InfluxQL queries. There are two modes: visual editor mode and raw query mode. To switch between the two modes click the pencil icon in the upper right.
Visual query editor mode contains the following components:
You can write raw InfluxQL queries by switching to raw query mode. Click the pencil in the upper right of the query editor to switch modes. When you switch to visual editor mode, any changes made in raw query mode are lost.
If you use raw query mode, your query must include WHERE $timeFilter. You should also provide a group by time and an aggregation function. Otherwise, InfluxDB may return hundreds of thousands of data points, potentially causing your browser to hang.
You can enter regular expressions for metric names or tag filter values.
Wrap the regular expression pattern in forward slashes (/), as shown in this example: /measurement/.
Grafana automatically adjusts the filter tag condition to use the InfluxDB regular expression match condition operator (=~).
In the SELECT row, you can specify which fields and functions to use.
If you group by time you must use an aggregation function. Certain functions such as derivative also require an aggregation function.
The query editor input generates an InfluxDB SELECT clause. For example:
SELECT derivative(mean("value"), 10s) / 10 AS "REQ/s"
FROM....
You can also use a * in a SELECT statement to select all fields.
SELECT * FROM <measurement_name>
To group results by a tag, specify the tag in the GROUP BY row:
You can GROUP BY multiple options.
To remove a GROUP BY option click the X icon next to the option.
| Alias pattern | Replaced with |
|---|---|
$m | Measurement name. |
$measurement | Measurement name. |
$1 - $9 | Part of measurement name (if you separate your measurement name with dots). |
$col | Column name. |
$tag_exampletag | The value of the exampletag tag. The syntax is $tag_yourTagName and must start with $tag_. To use your tag as an alias in the ALIAS BY field, you must use the tag to group by in the query. |
You can also use the [[tag_hostname]] pattern replacement syntax.
For example, entering the value Host: [[tag_hostname]] in the ALIAS BY field replaces it with the hostname tag value for each legend value.
An example legend value is Host: server1.
Grafana supports the SQL query language for InfluxDB 3.x and newer cloud products (Cloud Serverless, Cloud Dedicated, Clustered). SQL is InfluxData's recommended query language for new deployments.
The SQL query editor provides two modes:
SQL queries connect to InfluxDB using the FlightSQL (gRPC) protocol, which provides high-performance data transfer for large result sets.
The SQL query editor supports two output formats:
Select the format from the Format drop-down in the query editor.
You can use macros in your query to automatically substitute them with values from the Grafana context.
| Macro example | Replaced with |
|---|---|
$__timeFrom | The start of the currently active time selection, such as cast('2020-06-11T13:31:00Z' as timestamp). |
$__timeTo | The end of the currently active time selection, such as cast('2020-06-11T14:31:00Z' as timestamp). |
$__timeFilter(<column>) | A time range filter applied to the specified column. Expands to <column> >= $__timeFrom AND <column> <= $__timeTo. |
$__interval | An interval string that corresponds to the Grafana calculated interval based on the time range of the active time selection, such as interval '5 second'. |
$__dateBin(<column>) | Applies the date_bin function using $__interval. Column must be a timestamp. |
$__dateBinAlias(<column>) | Applies the date_bin function with a _binned suffix alias. Column must be a timestamp. |
$__timeGroup(<column>) | Groups results by a time interval using date_bin() with $__interval. Column must be a timestamp. |
$__timeGroupAlias(<column>) | Groups results by a time interval using date_bin() with $__interval and adds a _binned suffix alias. Column must be a timestamp. |
Basic time-series query:
SELECT time, usage_system, usage_user
FROM cpu
WHERE $__timeFilter(time)
ORDER BY time
Aggregated time-series query with date_bin:
SELECT
$__dateBin(time) AS time,
mean(usage_system) AS avg_system,
mean(usage_user) AS avg_user
FROM cpu
WHERE $__timeFilter(time)
GROUP BY $__dateBin(time)
ORDER BY time
Using JOINs across tables:
SQL supports JOINs, which aren't available in InfluxQL or Flux:
SELECT
c.time,
c.usage_system,
m.used_percent AS mem_used
FROM cpu c
INNER JOIN mem m ON c.time = m.time AND c.host = m.host
WHERE $__timeFilter(c.time)
ORDER BY c.time
Filtering with WHERE clauses:
SELECT $__dateBin(time) AS time, mean(usage_system) AS avg_system
FROM cpu
WHERE $__timeFilter(time)
AND host = 'server01'
AND cpu = 'cpu-total'
GROUP BY $__dateBin(time)
ORDER BY time
For the full list of supported SQL statements, operators, and functions, refer to InfluxData's SQL reference documentation.
Grafana supports Flux when running InfluxDB v1.8 and higher. If your data source is configured for Flux, you can use the Flux in the query editor, which serves as a text editor for raw Flux queries with macro support.
For more information and connection details, refer to InfluxDB 1.8 API compatibility.
You can enter macros in the query to replace them with values from the Grafana context.
<!-- vale Grafana.Spelling = NO -->Macros support copying and pasting from InfluxData Chronograf.
<!-- vale Grafana.Spelling = YES -->| Macro example | Replaced with |
|---|---|
v.timeRangeStart | The start of the currently active time selection, such as 2020-06-11T13:31:00Z. |
v.timeRangeStop | The end of the currently active time selection, such as 2020-06-11T14:31:00Z. |
v.windowPeriod | An interval string compatible with Flux that corresponds to the Grafana calculated interval based on the time range of the active time selection, such as 5s. |
v.defaultBucket | The Default Bucket value from the data source configuration. |
v.organization | The data source configuration's Organization setting. |
For example, consider the following Flux query:
from(bucket: v.defaultBucket)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "cpu" or r["_measurement"] == "swap")
|> filter(fn: (r) => r["_field"] == "usage_system" or r["_field"] == "free")
|> aggregateWindow(every: v.windowPeriod, fn: mean)
|> yield(name: "mean")
Grafana interpolates this Flux query into the following and sends it to InfluxDB, with the interval and time period values changing according to the active time selection:
from(bucket: "grafana")
|> range(start: 2020-06-11T13:59:07Z, stop: 2020-06-11T14:59:07Z)
|> filter(fn: (r) => r["_measurement"] == "cpu" or r["_measurement"] == "swap")
|> filter(fn: (r) => r["_field"] == "usage_system" or r["_field"] == "free")
|> aggregateWindow(every: 2s, fn: mean)
|> yield(name: "mean")
To view the interpolated version of a query with the Query inspector, refer to Panel Inspector.
You can query and display log data from InfluxDB in Explore and in the dashboard Logs panel.
InfluxQL: Select an InfluxDB data source in the query editor. Under the Select measurement field next to the FROM section, choose a measurement containing your log data, then choose the appropriate fields that will display the log message. Add any additional filters by clicking the + sign next to the WHERE field. Set FORMAT AS to Logs.
SQL: Write a SQL query that returns a timestamp column and a text column containing the log message. Set the Format to Table. For example:
SELECT time, message, level, host
FROM syslog
WHERE $__timeFilter(time)
ORDER BY time DESC
Flux: Use a Flux query with the _value field containing the log message.
After InfluxDB returns the results, the log panel displays log rows along with a bar chart. The x-axis represents time, while the y-axis shows the frequency or count.