Troubleshoot issues writing data
Learn how to avoid unexpected results and recover from errors when writing to InfluxDB.
Handle write
and delete
responses
In InfluxDB Cloud, writes and deletes are asynchronous and eventually consistent.
Once InfluxDB validates your request and queues the write or delete, it sends a success response (HTTP 204
status code) as an acknowledgement.
To ensure that InfluxDB handles writes and deletes in the order you request them, wait for the acknowledgement before you send the next request.
Because writes are asynchronous, keep the following in mind:
- Data might not yet be queryable when you receive success (HTTP
204
status code). - InfluxDB may still reject points after you receive success (HTTP
204
status code).
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:
HTTP response code | Message | Description |
---|---|---|
204 "Success" | If InfluxDB validated the request data format and queued the data for writing to the bucket | |
400 "Bad request" | message contains the first malformed line | If data is malformed |
401 "Unauthorized" | If the Authorization: Token header is missing or malformed or if the API token doesn’t have permission to write to the bucket | |
404 "Not found" | requested resource type, e.g. “organization”, and resource name | If a requested resource (e.g. organization or bucket) wasn’t found |
413 “Request too large” | cannot read data: points in batch is too large | If a write request exceeds the maximum global limit |
429 “Too many requests” | Retry-After header: xxx (seconds to wait before retrying the request) | If a read or write request exceeds your plan’s adjustable service quotas or if a delete request exceeds the maximum global limit |
500 "Internal server error" | Default status for an error | |
503 “Service unavailable“ | Series cardinality exceeds your plan’s service quota | If series cardinality exceeds your plan’s adjustable service quotas |
The message
property of the response body may contain additional details about the error.
If some of your data did not write to the bucket, see how to troubleshoot rejected points.
Troubleshoot partial writes
Because writes are asynchronous, they may fail partially or completely even though InfluxDB returns an HTTP 2xx
status code for a valid request.
For example, a partial write may occur when InfluxDB writes all points that conform to the bucket schema, but rejects points that have the wrong data type in a field.
To check for writes that fail asynchronously, create a task to check the _monitoring bucket for rejected points.
To resolve partial writes and rejected points, see troubleshoot failures.
Troubleshoot failures
- Check the
message
property in the response body for details about the error–for example,partial write error
indicates rejected points. - Check for rejected points in your organization’s
_monitoring
bucket. - Verify all lines contain valid syntax (line protocol or CSV). See how to find parsing errors.
- Verify the data types match the series or bucket schema. See how to resolve explicit schema rejections.
- 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
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 of1
. - Entries for parsing errors contain an
error
field (and don’t contain acount
field).
rejected_points schema
Name | Value |
---|---|
_measurement | rejected_points |
_field | count or error |
_value | 1 or error details |
bucket | ID of the bucket that rejected the point |
measurement | Measurement name of the point |
field | Name of the field that caused the rejection |
reason | Brief description of the problem. See specific reasons in data type conflicts and schema rejections |
gotType | Received field type: Boolean , Float , Integer , String , or UnsignedInteger |
wantType | Expected field type: Boolean , Float , Integer , String , or UnsignedInteger |
<timestamp> | Time the rejected point was logged |
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.
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:
Reason | Meaning |
---|---|
type conflict in batch write | The batch contains another point with the same series, but one of the fields has a different value type. |
type conflict with existing data | The bucket contains another point with the same series, but one of the fields has a different value type. |
Resolve explicit schema rejections
If you write to a bucket with an explicit schema, the data must conform to the schema. Otherwise, InfluxDB rejects the data.
Do the following to interpret explicit schema rejections:
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:
Reason | Meaning |
---|---|
measurement not allowed by schema | The bucket is configured to use explicit schemas and none of the schemas matches the measurement of the point. |
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.
_measurement | _field | _value | field | measurement | reason |
---|---|---|---|---|---|
rejected_points | count | 1 | humidity | airSensors | measurement not allowed by schema |
rejected_points | count | 1 | co | airSensors | measurement not allowed by schema |
rejected_points | count | 1 | temperature | airSensors | measurement not allowed by schema |
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:
Reason | Meaning |
---|---|
field type mismatch with schema | The point has the same measurement as a configured schema and they have different field value types. |
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 above, the second point has a temperature
field value with the string data type.
Because the airSensors
schema requires temperature
to have the float data type,
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:
_measurement | _field | _value | bucket | field | gotType | measurement | reason | wantType |
---|---|---|---|---|---|---|---|---|
rejected_points | count | 1 | a7d5558b880a93da | temperature | String | airSensors | field type mismatch with schema | Float |
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