timestreamwrite | R Documentation |
Amazon Timestream is a fast, scalable, fully managed time-series database service that makes it easy to store and analyze trillions of time-series data points per day. With Timestream, you can easily store and analyze IoT sensor data to derive insights from your IoT applications. You can analyze industrial telemetry to streamline equipment management and maintenance. You can also store and analyze log data and metrics to improve the performance and availability of your applications.
Timestream is built from the ground up to effectively ingest, process, and store time-series data. It organizes data to optimize query processing. It automatically scales based on the volume of data ingested and on the query volume to ensure you receive optimal performance while inserting and querying data. As your data grows over time, Timestream’s adaptive query processing engine spans across storage tiers to provide fast analysis while reducing costs.
timestreamwrite(
config = list(),
credentials = list(),
endpoint = NULL,
region = NULL
)
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...)
, where svc
is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- timestreamwrite( config = list( credentials = list( creds = list( access_key_id = "string", secret_access_key = "string", session_token = "string" ), profile = "string", anonymous = "logical" ), endpoint = "string", region = "string", close_connection = "logical", timeout = "numeric", s3_force_path_style = "logical", sts_regional_endpoint = "string" ), credentials = list( creds = list( access_key_id = "string", secret_access_key = "string", session_token = "string" ), profile = "string", anonymous = "logical" ), endpoint = "string", region = "string" )
create_batch_load_task | Creates a new Timestream batch load task |
create_database | Creates a new Timestream database |
create_table | Adds a new table to an existing database in your account |
delete_database | Deletes a given Timestream database |
delete_table | Deletes a given Timestream table |
describe_batch_load_task | Returns information about the batch load task, including configurations, mappings, progress, and other details |
describe_database | Returns information about the database, including the database name, time that the database was created, and the total number of tables found within the database |
describe_endpoints | Returns a list of available endpoints to make Timestream API calls against |
describe_table | Returns information about the table, including the table name, database name, retention duration of the memory store and the magnetic store |
list_batch_load_tasks | Provides a list of batch load tasks, along with the name, status, when the task is resumable until, and other details |
list_databases | Returns a list of your Timestream databases |
list_tables | Provides a list of tables, along with the name, status, and retention properties of each table |
list_tags_for_resource | Lists all tags on a Timestream resource |
resume_batch_load_task | Resume batch load task |
tag_resource | Associates a set of tags with a Timestream resource |
untag_resource | Removes the association of tags from a Timestream resource |
update_database | Modifies the KMS key for an existing database |
update_table | Modifies the retention duration of the memory store and magnetic store for your Timestream table |
write_records | Enables you to write your time-series data into Timestream |
## Not run:
svc <- timestreamwrite()
svc$create_batch_load_task(
Foo = 123
)
## End(Not run)
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