Reading from ServiceNow entities - Amazon Glue
Partitioning queries
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Reading from ServiceNow entities

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="custom.spark",
    connection_options={
        "connectionName": "servicenow-basicAuth",
        "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"
    }