Reading from Slack entities

Prerequisites

A Slack object you would like to read from.

Supported entities

Entity	Can be Filtered	Supports Limit	Supports Order By	Supports Select *	Supports Partitioning
conversations	Yes	Yes	No	Yes	Yes

Example


slack_read = glueContext.create_dynamic_frame.from_options(
    connection_type="slack",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "conversations/C058W38R5J8"
    }
)

Slack entity and field details

Entity	Field	Data Type	Supported Operators
conversations	attachments	List	NA
conversations	bot_id	String	NA
conversations	blocks	List	NA
conversations	client_msg_id	String	NA
conversations	is_starred	Boolean	NA
conversations	last_read	String	NA
conversations	latest_reply	String	NA
conversations	reactions	List	NA
conversations	replies	List	NA
conversations	reply_count	Integer	NA
conversations	reply_users	List	NA
conversations	reply_users_count	Integer	NA
conversations	subscribed	Boolean	NA
conversations	subtype	String	NA
conversations	text	String	NA
conversations	team	String	NA
conversations	thread_ts	String	NA
conversations	ts	String	EQUAL_TO, BETWEEN, LESS_THAN, LESS_THAN_OR_EQUAL_TO, GREATER_THAN, GREATER_THAN_OR_EQUAL_TO
conversations	type	String	NA
conversations	user	String	NA
conversations	inviter	String	NA
conversations	root	Struct	NA
conversations	is_locked	Boolean	NA
conversations	files	List	NA
conversations	room	Struct	NA
conversations	upload	Boolean	NA
conversations	display_as_bot	Boolean	NA
conversations	channel	String	NA
conversations	no_notifications	Boolean	NA
conversations	permalink	String	NA
conversations	pinned_to	List	NA
conversations	pinned_info	Struct	NA
conversations	edited	Struct	NA
conversations	app_id	String	NA
conversations	bot_profile	Struct	NA
conversations	metadata	Struct	NA

Partitioning queries

Additional spark options PARTITION_FIELD, LOWER_BOUND, UPPER_BOUND, NUM_PARTITIONS can be provided 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 query.
LOWER_BOUND: an inclusive lower bound value of the chosen partition field.

For date, we accept the Spark date format used in Spark SQL queries. Example of valid value: "2024-07-01T00:00:00.000Z".
UPPER_BOUND: an exclusive upper bound value of the chosen partition field.
NUM_PARTITIONS: number of partitions.

Entity wise partitioning field support details are captured in below table.

Entity Name	Partitioning Field	Data Type
conversations	ts	String

Example


slack_read = glueContext.create_dynamic_frame.from_options(
    connection_type="slack",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "conversations/C058W38R5J8",
        "PARTITION_FIELD": "ts"
        "LOWER_BOUND": "2022-12-01T00:00:00.000Z"
        "UPPER_BOUND": "2024-09-23T15:00:00.000Z"
        "NUM_PARTITIONS": "2"
    }
)

Javascript is disabled or is unavailable in your browser.

To use the Amazon Web Services Documentation, Javascript must be enabled. Please refer to your browser's Help pages for instructions.

Configuring Slack connections

Slack connection options