# Reading from ServiceNow entities


**Prerequisite**

A ServiceNow Tables object you would like to read from. You will need the object name such as pa\$1bucket or incident.

**Example**:

```
servicenow_read = glueContext.create_dynamic_frame.from_options(
    connection_type="servicenow",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "pa_buckets",
        "API_VERSION": "v2"
        "instanceUrl": "https://<instance-name>.service-now.com"
    }
)
```

**ServiceNow entity and field details**:

For the following entities, ServiceNow provides endpoints to fetch metadata dynamically, so that operator support is captured at the datatype level for each entity.

[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/glue/latest/dg/servicenow-reading-from-entities.html)

**Note**  
The Struct data type is converted to a String data type in the response of the connector.

**Note**  
`DML_STATUS` is an additional user-defined attribute used for tracking CREATED/UPDATED records.

## Partitioning queries


**Field base partitioning**:

You can provide the additional Spark options `PARTITION_FIELD`, `LOWER_BOUND`, `UPPER_BOUND`, and `NUM_PARTITIONS` if you want to utilize concurrency in Spark. With these parameters, the original query would be split into `NUM_PARTITIONS` number of sub-queries that can be executed by Spark tasks concurrently.

[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/glue/latest/dg/servicenow-reading-from-entities.html)
+ `PARTITION_FIELD`: the name of the field to be used to partition the query.
+ `LOWER_BOUND`: an **inclusive** lower bound value of the chosen partition field.

  For the Datetime field, we accept the Spark timestamp format used in SPark SQL queries.

  Examples of valid value:

  ```
  "2024-01-30T06:47:51.000Z"
  ```
+ `UPPER_BOUND`: an **exclusive** upper bound value of the chosen partition field.
+ `NUM_PARTITIONS`: the number of partitions.

The following table describes the entity partitioning field support details:

Example:

```
servicenow_read = glueContext.create_dynamic_frame.from_options(
    connection_type="servicenow",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "pa_buckets",
        "API_VERSION": "v2",
        "instanceUrl": "https://<instance-name>.service-now.com"
        "PARTITION_FIELD": "sys_created_on"
        "LOWER_BOUND": "2024-01-30T06:47:51.000Z"
        "UPPER_BOUND": "2024-06-30T06:47:51.000Z"
        "NUM_PARTITIONS": "10"
    }
```

**Record-based partitioning**:

You can provide the additional Spark option `NUM_PARTITIONS` if you want to utilize concurrency in Spark. With this parameter, the original query is split into `NUM_PARTITIONS` number of sub-queries that can be executed by Spark tasks concurrently.

In record-based partitioning, the total number of records present is queried from the ServiceNow API, and it is divided by `NUM_PARTITIONS` number provided. The resulting number of records are then concurrently fetched by each sub-query.
+ `NUM_PARTITIONS`: the number of partitions.

Example:

```
servicenow_read = glueContext.create_dynamic_frame.from_options(
    connection_type="servicenow",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "pa_buckets",
        "API_VERSION": "v2",
        "instanceUrl": "https://<instance-name>.service-now.com"
        "NUM_PARTITIONS": "2"
    }
```