AzureTableStor (version 1.0.0)

insert_table_entity: Operations on table entities (rows)

Description

Operations on table entities (rows)

Usage

insert_table_entity(table, entity)
update_table_entity(
  table,
  entity,
  row_key = NULL,
  partition_key = NULL,
  etag = NULL
)
delete_table_entity(table, row_key, partition_key, etag = NULL)
list_table_entities(table, filter = NULL, select = NULL, as_data_frame = TRUE)
get_table_entity(table, row_key, partition_key, select = NULL)
import_table_entities(
  table,
  data,
  row_key = NULL,
  partition_key = NULL,
  batch_status_handler = c("warn", "stop", "message", "pass"),
  ...
)

Arguments

table

A table object, of class storage_table.

entity

For insert_table_entity and update_table_entity, a named list giving the properties (columns) of the entity. See 'Details' below.

row_key, partition_key

For get_table_entity, update_table_entity and delete_table_entity, the row and partition key values that identify the entity to get, update or delete. For import_table_entities, the columns in the imported data to treat as the row and partition keys. The default is to use columns named 'RowKey' and 'PartitionKey' respectively.

etag

For update_table_entity and delete_table_entity, an optional Etag value. If this is supplied, the update or delete operation will proceed only if the target entity's Etag matches this value. This ensures that an entity is only updated/deleted if it has not been modified since it was last retrieved.

filter, select

For list_table_entities, optional row filter and column select expressions to subset the result with. If omitted, list_table_entities will return all entities in the table.

as_data_frame

For list_table_entities, whether to return the results as a data frame, rather than a list of table rows.

data

For import_table_entities, a data frame. See 'Details' below.

batch_status_handler

For import_table_entities, what to do if one or more of the batch operations fails. The default is to signal a warning and return a list of response objects, from which the details of the failure(s) can be determined. Set this to "pass" to ignore the failure.

...

For import_table_entities, further named arguments passed to do_batch_transaction.

Value

insert_table_entity and update_table_entity return the Etag of the inserted/updated entity, invisibly.

get_table_entity returns a named list of properties for the given entity.

list_table_entities returns a data frame if as_data_frame=TRUE, and a list of entities (rows) otherwise.

import_table_entities invisibly returns a named list, with one component for each value of the PartitionKey column. Each component contains the results of the individual operations to insert each row into the table.

Details

These functions operate on rows of a table, also known as entities. insert, get, update and delete_table_entity operate on an individual row. import_table_entities bulk-inserts multiple rows of data into the table, using batch transactions. list_table_entities queries the table and returns multiple rows, subsetted on the filter and select arguments.

Table storage imposes the following requirements for properties (columns) of an entity:

• There must be properties named RowKey and PartitionKey, which together form the entity's unique identifier. These properties must be of type character.

• The property Timestamp cannot be used (strictly speaking, it is reserved by the system).

• There can be at most 255 properties per entity, although different entities can have different properties.

• Table properties must be atomic. In particular, they cannot be nested lists.

Note that table storage does not require that all entities in a table must have the same properties.

For insert_table_entity, update_table_entity and import_table_entities, you can also specify JSON text representing the data to insert/update/import, instead of a list or data frame.

list_table_entities(as_data_frame=TRUE) for a large table may be slow. If this is a problem, and you know that all entities in the table have the same schema, try setting as_data_frame=FALSE and converting to a data frame manually.

See Also

storage_table, do_batch_transaction

Understanding the table service data model

Examples

# NOT RUN {
endp <- table_endpoint("https://mycosmosdb.table.cosmos.azure.com:443", key="mykey")
tab <- create_storage_table(endp, "mytable")

insert_table_entity(tab, list(
    RowKey="row1",
    PartitionKey="partition1",
    firstname="Bill",
    lastname="Gates"
))

get_table_entity(tab, "row1", "partition1")

# specifying the entity as JSON text instead of a list
update_table_entity(tab,
'{
    "RowKey": "row1",
    "PartitionKey": "partition1",
    "firstname": "Bill",
    "lastname": "Gates"
}')

# we can import to the same table as above: table storage doesn't enforce a schema
import_table_entities(tab, mtcars,
    row_key=row.names(mtcars),
    partition_key=as.character(mtcars$cyl))

list_table_entities(tab)
list_table_entities(tab, filter="firstname eq 'Satya'")
list_table_entities(tab, filter="RowKey eq 'Toyota Corolla'")

delete_table_entity(tab, "row1", "partition1")

# }