Reading from Zoho CRM entities - Amazon Glue
Partitioning queries
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Reading from Zoho CRM entities

Prerequisite

Zoho CRM objects you would like to read from. You will need the object name.

Supported entities for source:

Entity Can be filtered Supports limit Supports Order by Supports Select * Supports partitioning
Product Yes Yes Yes Yes Yes
Quote Yes Yes Yes Yes Yes
Purchase Order Yes Yes Yes Yes Yes
Solution Yes Yes Yes Yes Yes
Call Yes Yes Yes Yes Yes
Task Yes Yes Yes Yes Yes
Event Yes Yes Yes Yes Yes
Invoice Yes Yes Yes Yes Yes
Account Yes Yes Yes Yes Yes
Contact Yes Yes Yes Yes Yes
Vendor Yes Yes Yes Yes Yes
Campaign Yes Yes Yes Yes Yes
Deal Yes Yes Yes Yes Yes
Lead Yes Yes Yes Yes Yes
Custom Module Yes Yes Yes Yes Yes
Sales Order Yes Yes Yes Yes Yes
Price Books Yes Yes Yes Yes Yes
Case Yes Yes Yes Yes Yes

Example:

zoho_read = glueContext.create_dynamic_frame.from_options(
    connection_type="ZOHO",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "entityName",
        "API_VERSION": "v7",
        "INSTANCE_URL": "https://www.zohoapis.in/"
    }

Zoho CRM field details:

Zoho CRM provides endpoints to fetch metadata dynamically for supported entities. Therefore, operator support is captured at the datatype level.

Entity Data type Supported operators
Zoho entities (all entities) Integer !=, =, <, <=, >, >=, BETWEEN
String Like, =, !=
BigInteger !=, =, <, <=, >, >=, BETWEEN
Boolean =
Double !=, =, <, <=, >, >=, BETWEEN
BigDecimal !=, =, <, <=, >, >=, BETWEEN
Date !=, =, <, <=, >, >=, BETWEEN
DateTime !=, =, <, <=, >, >=, BETWEEN
Struct N/A
List N/A

Partitioning queries

Filter-based 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.

  • 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:

    "2024-09-30T01:01:01.000Z"

  • UPPER_BOUND: an exclusive upper bound value of the chosen partition field.

  • NUM_PARTITIONS: the number of partitions.

Example:

zoho_read = glueContext.create_dynamic_frame.from_options(
    connection_type="ZOHO",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "entityName",
        "API_VERSION": "v7",
        "PARTITION_FIELD": "Created_Time"
        "LOWER_BOUND": "2022-01-01T01:01:01.000Z"
        "UPPER_BOUND": "2024-01-01T01:01:01.000Z"
        "NUM_PARTITIONS": "10"
    }