Prerequisite
A ServiceNow Tables object you would like to read from. You will need the object name such as pa_bucket 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.
| Entity | Data type | Supported operators |
|---|---|---|
| Tables (dynamic entities) | Integer | =, !=, <, <=, >, >=, BETWEEN |
| BigDecimal | =, !=, <, <=, >, >=, BETWEEN | |
| Float | =, !=, <, <=, >, >=, BETWEEN | |
| Long | =, !=, <, <=, >, >=, BETWEEN | |
| Date | =, !=, <, <=, >, >=, BETWEEN | |
| DateTime | =, !=, <, <=, >, >=, BETWEEN | |
| Boolean | =, != | |
| String | =, !=, <, <=, >, >=, BETWEEN, LIKE | |
| Struct | N/A |
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.
| Entity name | Partitioning fields | Data type |
|---|---|---|
| Dynamic Entity | sys_mod_count | Integer |
| sys_created_on, sys_updated_on | DateTime |
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"
}