Troubleshooting the multivariate API

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This article provides guidance on how to troubleshoot and remediate common error messages when using the multivariate API.

Multivariate error codes

Common Errors

Error Code HTTP Error Code Error Message Comment
SubscriptionNotInHeaders 400 apim-subscription-id is not found in headers Please add your APIM subscription ID in the header. Example header: {"apim-subscription-id": <Your Subscription ID>}
FileNotExist 400 File <source> does not exist. Please check the validity of your blob shared access signature (SAS). Make sure that it has not expired.
InvalidBlobURL 400 Your blob shared access signature (SAS) is not a valid SAS.
StorageWriteError 403 This error is possibly caused by permission issues. Our service is not allowed to write the data to the blob encrypted by a Customer Managed Key (CMK). Either remove CMK or grant access to our service again. Please refer to this page for more details.
StorageReadError 403 Same as StorageWriteError.
UnexpectedError 500 Please contact us with detailed error information. You could take the support options from this document or email us at AnomalyDetector@microsoft.com

Train a Multivariate Anomaly Detection Model

Error Code HTTP Error Code Error Message Comment
TooManyModels 400 This subscription has reached the maximum number of models. Each APIM subscription ID is allowed to have 300 active models. Please delete unused models before training a new model
TooManyRunningModels 400 This subscription has reached the maximum number of running models. Each APIM subscription ID is allowed to train 5 models concurrently. Please train a new model after previous models have completed their training process.
InvalidJsonFormat 400 Invalid json format. Training request is not a valid JSON.
InvalidAlignMode 400 The 'alignMode' field must be one of the following: 'Inner' or 'Outer' . Please check the value of 'alignMode' which should be either 'Inner' or 'Outer' (case sensitive).
InvalidFillNAMethod 400 The 'fillNAMethod' field must be one of the following: 'Previous', 'Subsequent', 'Linear', 'Zero', 'Fixed', 'NotFill' and it cannot be 'NotFill' when 'alignMode' is 'Outer'. Please check the value of 'fillNAMethod'. You may refer to this section for more details.
RequiredPaddingValue 400 The 'paddingValue' field is required in the request when 'fillNAMethod' is 'Fixed'. You need to provide a valid padding value when 'fillNAMethod' is 'Fixed'. You may refer to this section for more details.
RequiredSource 400 The 'source' field is required in the request. Your training request has not specified a value for the 'source' field. Example: {"source": <Your Blob SAS>}.
RequiredStartTime 400 The 'startTime' field is required in the request. Your training request has not specified a value for the 'startTime' field. Example: {"startTime": "2021-01-01T00:00:00Z"}.
InvalidTimestampFormat 400 Invalid Timestamp format. <timestamp> is not a valid format. The format of timestamp in the request body is not correct. You may try import pandas as pd; pd.to_datetime(timestamp) to verify.
RequiredEndTime 400 The 'endTime' field is required in the request. Your training request has not specified a value for the 'startTime' field. Example: {"endTime": "2021-01-01T00:00:00Z"}.
InvalidSlidingWindow 400 The 'slidingWindow' field must be an integer between 28 and 2880. 'slidingWindow' must be an integer between 28 and 2880 (inclusive).

Get Multivariate Model with Model ID

Error Code HTTP Error Code Error Message Comment
ModelNotExist 404 The model does not exist. The model with corresponding model ID does not exist. Please check the model ID in the request URL.

List Multivariate Models

Error Code HTTP Error Code Error Message Comment
InvalidRequestParameterError 400 Invalid values for $skip or $top … Please check whether the values for the two parameters are numerical. $skip and $top are used to list the models with pagination. Because the API only returns 10 most recently updated models, you could use $skip and $top to get models updated earlier.

Anomaly Detection with a Trained Model

Error Code HTTP Error Code Error Message Comment
ModelNotExist 404 The model does not exist. The model used for inference does not exist. Please check the model ID in the request URL.
ModelFailed 400 Model failed to be trained. The model is not successfully trained. Please get detailed information by getting the model with model ID.
ModelNotReady 400 The model is not ready yet. The model is not ready yet. Please wait for a while until the training process completes.
InvalidFileSize 413 File <file> exceeds the file size limit (<size limit> bytes). The size of inference data exceeds the upper limit (2GB currently). Please use less data for inference.

Get Detection Results

Error Code HTTP Error Code Error Message Comment
ResultNotExist 404 The result does not exist. The result per request does not exist. Either inference has not completed or result has expired (7 days).

Data Processing Errors

The following error codes do not have associated HTTP Error codes.

Error Code Error Message Comment
NoVariablesFound No variables found. Please check that your files are organized as per instruction. No csv files could be found from the data source. This is typically caused by wrong organization of files. Please refer to the sample data for the desired structure.
DuplicatedVariables There are multiple variables with the same name. There are duplicated variable names.
FileNotExist File <filename> does not exist. This error usually happens during inference. The variable has appeared in the training data but is missing in the inference data.
RedundantFile File <filename> is redundant. This error usually happens during inference. The variable was not in the training data but appeared in the inference data.
FileSizeTooLarge The size of file <filename> is too large. The size of the single csv file <filename> exceeds the limit. Please train with less data.
ReadingFileError Errors occurred when reading <filename>. <error messages> Failed to read the file <filename>. You may refer to <error messages> for more details or verify with pd.read_csv(filename) in a local environment.
FileColumnsNotExist Columns timestamp or value in file <filename> do not exist. Each csv file must have two columns with names timestamp and value (case sensitive).
VariableParseError Variable <variable> parse <error message> error. Cannot process the <variable> due to runtime errors. Please refer to the <error message> for more details or contact us with the <error message>.
MergeDataFailed Failed to merge data. Please check data format. Data merge failed. This is possibly due to wrong data format, organization of files, etc. Please refer to the sample data for the current file structure.
ColumnNotFound Column <column> cannot be found in the merged data. A column is missing after merge. Please verify the data.
NumColumnsMismatch Number of columns of merged data does not match the number of variables. Please verify the data.
TooManyData Too many data points. Maximum number is 1000000 per variable. Please reduce the size of input data.
NoData There is no effective data There is no data to train/inference after processing. Please check the start time and end time.
DataExceedsLimit The length of data whose timestamp is between startTime and endTime exceeds limit(<limit>). The size of data after processing exceeds the limit. (Currently no limit on processed data.)
NotEnoughInput Not enough data. The length of data is <data length>, but the minimum length should be larger than sliding window which is <sliding window size>. The minimum number of data points for inference is the size of sliding window. Try to provide more data for inference.