# Endpoint requests for time series data
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**Note**  
After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Clarify, effective 7/30/26. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Clarify, but we do not plan to introduce new features. For more information, see [Clarify availability change](clarify-availability-change.md). 

A SageMaker Clarify processing job serializes data into arbitrary JSON structures (with MIME type: `application/json`). To do this, you must provide a template string to the analysis configuration `content_template` parameter. This is used by the SageMaker Clarify processing job to construct the JSON query provided to your model. `content_template` contains a record or multiple records from your dataset. You must also provide a template string for `record_template`, which is used to construct the JSON structure of each record. These records are then inserted into `content_template`. For more information about `content_type` or `dataset_type`, see [Analysis Configuration Files](clarify-processing-job-configure-analysis.md).

**Note**  
Because `content_template` and `record_template` are string parameters, any double quote characters (") that are part of the JSON serialized structure should be noted as an escaped character in your configuration. For example, if you want to escape a double quote in Python, you could enter the following value for `content_template`:  

```
'$record'
```

The following table shows examples of serialized JSON request payloads and the corresponding `content_template` and `record_template` parameters required to construct them.


| Use case | Endpoint request payload (string representation) | content\_template | record\_template | 
| --- | --- | --- | --- | 
| Single record at a time | `{"target": [1, 2, 3],"start": "2024-01-01 01:00:00"}` | `'$record'` | `'{"start": $start_time, "target": $target_time_series}'` | 
| Single record with `$related_time_series` and `$static_covariates` | `{"target": [1, 2, 3],"start": "2024-01-01 01:00:00","dynamic_feat": [[1.0, 2.0, 3.0],[1.0, 2.0, 3.0],"cat": [0,1]}` | `'$record'` | `'{"start": $start_time, "target": $target_time_series, "dynamic_feat": $related_time_series, "cat": $static_covariates}'` | 
| Multi-records | `{"instances": [{"target": [1, 2, 3],"start": "2024-01-01 01:00:00"}, {"target": [1, 2, 3],"start": "2024-01-01 02:00:00"}]}` | `'{"instances": $records}'` | `'{"start": $start_time, "target": $target_time_series}'` | 
| Multi-records with `$related_time_series` and `$static_covariates` | `{"instances": [{"target": [1, 2, 3],"start": "2024-01-01 01:00:00","dynamic_feat": [[1.0, 2.0, 3.0],[1.0, 2.0, 3.0],"cat": [0,1]}, {"target": [1, 2, 3],"start": "2024-01-01 02:00:00","dynamic_feat": [[1.0, 2.0, 3.0],[1.0, 2.0, 3.0],"cat": [0,1]}]}` | `'{"instances": $records}'` | `''{"start": $start_time, "target": $target_time_series, "dynamic_feat": $related_time_series, "cat": $static_covariates}'` |