Troubleshoot issues writing data

Learn how to handle and recover from errors when writing to InfluxDB.

• Discover common failure scenarios
• Review HTTP status codes
• Troubleshoot failures
• Troubleshoot rejected points

Discover common failure scenarios

Write requests made to InfluxDB may fail for a number of reasons. Common failure scenarios that return an HTTP 4xx or 5xx error status code include the following:

• API token was invalid. See how to manage API tokens.
• Requests exceeded service quotas.
• Payload size exceeded global limits.
• Client or server reached a timeout threshold.
• Data was not formatted correctly. See how to find parsing errors
• Data did not conform to the explicit bucket schema. See how to resolve explicit schema rejections.

To find the causes of a specific error, review HTTP status codes.

Troubleshoot partial writes

Writes may fail partially or completely even though InfluxDB returns an HTTP 2xx status code for a valid request. To resolve partial writes and rejected points, see troubleshoot failures.

Review HTTP status codes

InfluxDB uses conventional HTTP status codes to indicate the success or failure of a request.

Write requests return the following status codes:

The message property of the response body may contain additional details about the error.

Troubleshoot failures

If you notice data is missing in your bucket, do the following:

• Check the message property in the response body for details about the error, e.g. partial write error indicates rejected points.
• Check for rejected points in your organization’s _monitoring bucket.
• Verify all lines contain valid syntax, e.g. line protocol or CSV.
• Verify the data types match the series or bucket schema.
• Verify the timestamps match the precision parameter.
• Minimize payload size and network errors by optimizing writes.

Troubleshoot rejected points

InfluxDB may have rejected points even if the HTTP request returned “Success”. InfluxDB logs rejected data points and associated errors to your organization’s _monitoring bucket.

• Review rejected points
  • Find parsing errors
  • Find data type conflicts and schema rejections
• Resolve data type conflicts
• Resolve explicit schema rejections

Review rejected points

To get a log of rejected points, query the rejected_points measurement in your organization’s _monitoring bucket. To more quickly locate rejected_points, keep the following in mind:

• If your line protocol batch contains single lines with multiple fields, InfluxDB logs an entry for each point (each unique field) that is rejected.
• Each entry contains a reason tag that describes why the point was rejected.
• Entries for data type conflicts and schema rejections have a count field value of 1.
• Entries for parsing errors contain an error field (and don’t contain a count field).

rejected_points schema

Find parsing errors

If InfluxDB can’t parse a line (e.g. due to syntax problems), the response message might not provide details. To find parsing error details, query rejected_points entries that contain the error field (instead of the count field).

from(bucket: "_monitoring")
    |> range(start: -1h)
    |> filter(fn: (r) => r._measurement == "rejected_points")
    |> filter(fn: (r) => r._field == "error")

Find data type conflicts and schema rejections

To find rejected_points caused by data type conflicts or schema rejections, query for the count field.

from(bucket: "_monitoring")
    |> range(start: -1h)
    |> filter(fn: (r) => r._measurement == "rejected_points")
    |> filter(fn: (r) => r._field == "count")

Resolve data type conflicts

When you write to a bucket that has the implicit schema type, InfluxDB compares new points to points that have the same series. If a point has a field with a different data type than the series, InfluxDB rejects the point and logs a rejected_points entry. The rejected_points entry contains one of the following reasons:

Resolve explicit schema rejections

Buckets with the [explicit schema type] use explicit bucket schemas. When you write to a bucket that uses explicit bucket schemas, InfluxDB rejects data that don’t conform to one of the configured schemas.

Learn how to interpret rejected_points logging for explicit bucket schemas.

• Detect a measurement mismatch
• Detect a field type mismatch

Detect a measurement mismatch

InfluxDB rejects a point if the measurement doesn’t match the name of a bucket schema. The rejected_points entry contains the following reason tag value:

Consider the following line protocol data.

airSensors,sensorId=TLM0201 temperature=73.97,humidity=35.23,co=0.48 1637014074

The line has an airSensors measurement and three fields (temperature, humidity, and co). If you try to write this data to a bucket that has the explicit schema type and doesn’t have an airSensors schema, the /api/v2/write InfluxDB API returns an error and the following data:

{
  "code": "invalid",
  "message": "3 out of 3 points rejected (check rejected_points in your _monitoring bucket for further information)"
}

InfluxDB logs three rejected_points entries, one for each field.

Detect a field type mismatch

InfluxDB rejects a point if the measurement matches the name of a bucket schema and the field data types don’t match. The rejected_points entry contains the following reason:

Consider a bucket that has the following airSensors explicit bucket schema:

{
 "name": "airSensors",
 "columns": [
   {
     "name": "time",
     "type": "timestamp"
   },
   {
     "name": "sensorId",
     "type": "tag"
   },
   {
     "name": "temperature",
     "type": "field",
     "dataType": "float"
   },
   {
     "name": "humidity",
     "type": "field",
     "dataType": "float"
   },
   {
     "name": "co",
     "type": "field",
     "dataType": "float"
   }
 ]
}

The following line protocol data has an airSensors measurement, a sensorId tag, and three fields (temperature, humidity, and co).

airSensors,sensorId=L1 temperature=90.5,humidity=70.0,co=0.2 1637014074
airSensors,sensorId=L1 temperature="90.5",humidity=70.0,co=0.2 1637014074

In the example data, one of the points has a temperature field value with the string data type. However, the airSensors schema requires temperature to have the float data type. If you try to write the example data to the airSensors bucket schema, InfluxDB returns a 400 error and a message that describes the result:

{
  "code": "invalid",
  "message": "partial write error (5 accepted): 1 out of 6 points rejected (check rejected_points in your _monitoring bucket for further information)"
}

InfluxDB logs the following rejected_points entry to the _monitoring bucket: