xia_engine.document.DeleteEngine

class xia_engine.document.DeleteEngine

Bases: BatchEngine

For batch delete usage

Comments:

Only usable when batch is given. Table name is like batch_id-table_name

__init__()

Methods

__init__()
analyze(document_class, analytic_model)	Run the analytic model
backup(document_class[, location, ...])	Backup data of a model.
batch(operations, originals)	Data Batch Modification
compile(document_class, analytic_request[, ...])	Compile the analysis request
connect([document_class])	Connect to the engine
create(document_class, db_content[, doc_id, ...])	Create a document
create_collection(document_class)	Create Collection if needed
db_to_display(document_class, db_content[, ...])	Convert data from database form to display form
delete(document_class, doc_id[, batch_id])	Delete a document by using id
drop(document_class[, batch_id])	Drop the given collection
drop_batch([batch_id])	Drop the database which holds batch id
fetch(document_class[, batch_id])	Get document one by one from a list of document id
get(document_class, doc_id[, batch_id])	Get Document
get_connection([document_class])	Get engine connection。 Always using existed one when it is possible
get_decoder(field[, inner_field])	Get Decoder for a field
get_encoder(field[, inner_field])	Get Encoder for a field
lock(document_class, doc_id[, timeout])	Lock entries for write
merge(document_class[, start, end, purge, ...])	Merge data from log section into main table
parse_search_option(key)	Reference to search method for the specifications
parse_update_option(key)	Reference to update method for the specifications
replicate(document_class, task_list)	Data replication
restore(document_class[, location, ...])	Restore data of a model
scan(_document_class[, _batch_id, ...])	Scan the document class and get the document id list
search(_document_class, *args[, _batch_id, ...])	Searching and yield document by document
set(document_class, doc_id, db_content[, ...])	Overwrite whole document
timed_release(class_name, doc_id)
truncate(document_class)	Remove all data from the given collection
unlock(document_class, doc_id)	Release the for write
update(_document_class, _doc_id[, batch_id])	Update a document
update_doc_id(document_class, db_content, ...)	Update document id to new value

Attributes

MATCH_OPERATIONS
OPERATORS	search options
ORDER_TYPES	order options
UPDATE_TYPES	update options
analyzer	Analytic Compiler
backup_coder	Coder to be used to backup / restore
backup_storer	Storer to be used to backup / restore
decoders	Engine specific value decoder
encoders	Engine specific value encoder
engine_connector	Connector function
engine_connector_class	Connector Class use an object to handler parameters
engine_db_shared	if the value is False, each data model will have its own connector by default
engine_default_connector_param	default connection parameter
engine_foreign_key_check	Dependency check will be performed at engine level
engine_param	Engine parameter name
engine_scope_check	Scope check will be performed at engine level (pass document level check)
engine_unique_check	Unique check will be performed at engine level (pass document level check)
key_required
scan_and_fetch	Possible to separate the search operation into scan and fetch
store_embedded_as_table	Store embedded document as separate tables
support_unknown

OPERATORS = {'__eq__': '==', '__ge__': '>=', '__gt__': '>', '__le__': '<=', '__like__': 'LIKE', '__lt__': '<', '__ne__': '!='}

search options

ORDER_TYPES = {'__asc__': 'asc', '__desc__': 'desc'}

order options

UPDATE_TYPES = {'__append__': 'append', '__delete__': 'delete', '__remove__': 'remove'}

update options

classmethod analyze(document_class: Type[BaseDocument], analytic_model: dict)

Run the analytic model

Parameters

• analytic_model – Analyze model

• document_class – (subclass of BaseDocument): Document definition

analyzer = None

Analytic Compiler

classmethod backup(document_class: Type[BaseDocument], location: Optional[str] = None, data_encode: Optional[str] = None, data_format: Optional[str] = None, data_store: Optional[str] = None, **kwargs)

Backup data of a model. The real implementation must use kwargs to distribute loads

Parameters

• document_class (subclass of BaseDocument) – Document definition4

• data_encode (str) – Backup Data Code

• data_format (str) – Backup Data Format

• data_store (str) – Backup Data Store location

• location (str) – Data location to e used by data store

• **kwargs – parameter to be passed at engine level

backup_coder = None

Coder to be used to backup / restore

backup_storer = None

Storer to be used to backup / restore

classmethod batch(operations: list, originals: dict)

Data Batch Modification

The data will be updated at once or rolled back

Parameters

• operations – List of operations to be done * op: Operation type. “S” = set, “I” = create, “D” = delete, “U” = update * cls: Document Class * doc_id: Document ID * content: Document Content in Database form

• originals – Dictionary (Help to roll back) * class: document class name * id: document id * content: document db form

Returns

return True amd empty message if batch is successful, else False with error message

classmethod compile(document_class: Type[BaseDocument], analytic_request: dict, acl_condition=None)

Compile the analysis request

Parameters

• document_class (subclass of BaseDocument) – Document definition

• analytic_request – analytic request

• acl_condition – User Access List transformed to where conditions

Returns

Model}

Return type

A analytic model ready to be executed represented by as dict {Engine

classmethod connect(document_class: Optional[Type[BaseDocument]] = None)

Connect to the engine

Parameters

document_class – (subclass of BaseDocument): Document definition

Returns

Connection

classmethod create(document_class: Type[BaseDocument], db_content: dict, doc_id: Optional[str] = None, batch_id: str = '')

Create a document

Parameters

• document_class (subclass of BaseDocument) – Document definition

• db_content (dict) – content to be put to engine

• doc_id (str) – Having predefined doc id, None means could be generated by engine

Returns

Document ID

classmethod create_collection(document_class: Type[BaseDocument])

Create Collection if needed

Parameters

document_class – document_class

classmethod db_to_display(document_class: Type[BaseDocument], db_content: dict, lazy: bool = True, catalog: Optional[dict] = None, show_hidden: bool = False)

Convert data from database form to display form

Parameters

• document_class – Document class

• db_content – Database Content

• lazy – Lazy Mode

• catalog – Data Catalog

• show_hidden – Show hidden member or not

Returns

document in display form

decoders = {}

Engine specific value decoder

classmethod delete(document_class: Type[BaseDocument], doc_id: str, batch_id: str = '')

Delete a document by using id

Parameters

• document_class (subclass of BaseDocument) – Document definition

• doc_id – Document ID

classmethod drop(document_class: Type[BaseDocument], batch_id: str = '')

Drop the given collection

Parameters

document_class (subclass of BaseDocument) – Document definition

classmethod drop_batch(batch_id: str = '')

Drop the database which holds batch id

Parameters

batch_id – Batch Identity

encoders = {}

Engine specific value encoder

engine_connector: callable = None

Connector function

engine_connector_class = None

Connector Class use an object to handler parameters

engine_db_shared = True

if the value is False, each data model will have its own connector by default

engine_default_connector_param: dict = {}

default connection parameter

engine_foreign_key_check: bool = False

Dependency check will be performed at engine level

engine_param: str = 'batch'

Engine parameter name

engine_scope_check: bool = False

Scope check will be performed at engine level (pass document level check)

engine_unique_check: bool = False

Unique check will be performed at engine level (pass document level check)

classmethod fetch(document_class: Type[BaseDocument], batch_id: str = '', *args)

Get document one by one from a list of document id

Returns

An iterator for id, document dictionary pair

Comments:

when doc id is empty, it is probably because that the user only has partial read authorizations

classmethod get(document_class: Type[BaseDocument], doc_id: str, batch_id: str = '') → dict

Get Document

Parameters

• document_class (subclass of BaseDocument) – Document definition

• doc_id – Document ID

Returns

Document content on python dict

classmethod get_connection(document_class: Optional[Type[BaseDocument]] = None)

Get engine connection。 Always using existed one when it is possible

Parameters

document_class – (subclass of BaseDocument): Document definition

Returns

Connection

classmethod get_decoder(field: type, inner_field: Optional[type] = None) → callable

Get Decoder for a field

Parameters

• field (type) – class type of field class

• inner_field (type) – class type of inner field (Such qs ListField)

Returns

Decoder function

classmethod get_encoder(field: type, inner_field: Optional[type] = None) → callable

Get Encoder for a field

Parameters

• field (type) – class type of field class

• inner_field (type) – class type of inner field (Such qs ListField)

Returns

Encoder function

classmethod lock(document_class: Type[BaseDocument], doc_id: str, timeout: Optional[int] = None)

Lock entries for write

Parameters

• document_class (subclass of BaseDocument) – Document definition

• doc_id (str) – Having predefined doc id, None means could be generated by engine

• timeout – Timeout for lock

Returns

return True amd empty message if lock is successful, else false with error message

Comments:

Lock need based engine implementation

classmethod merge(document_class: Type[BaseDocument], start: Optional[float] = None, end: Optional[float] = None, purge: bool = False, criteria: Optional[dict] = None)

Merge data from log section into main table

Parameters

• document_class – (subclass of BaseDocument): Document definition

• start (timestamp) – Starting time point

• end (timestamp) – Ending time point

• purge – will remove the entries from log table after execution

• criteria – only merge the given criteria

Comments:

This method is designed to keep a high consistency data. All replicated data is kept on the log table. Only merge the data into main table when passed the consistency check

classmethod parse_search_option(key: str)

Reference to search method for the specifications

Parameters

key (str) –

Returns

key, operator, order

classmethod parse_update_option(key: str)

Reference to update method for the specifications

Parameters

key (str) –

Returns

key, update

classmethod replicate(document_class: Type[BaseDocument], task_list: list)

Data replication

Parameters

• document_class – Python class of document

• task_list – List of dictionary with the following keys: * id: document id * content: document db form * op: operation type: “I” for insert, “D” for delete, “U” for update, “L” for load

Returns

List of dictionary with the following keys:

• id: document id

• op: operation type: “I” for insert, “D” for delete, “U” for update, “L” for load

• time: time when data is replicated

• status: status code of HTTP protocol

Return type

task_results

classmethod restore(document_class: Type[BaseDocument], location: Optional[str] = None, data_encode: Optional[str] = None, data_format: Optional[str] = None, data_store: Optional[str] = None, **kwargs)

Restore data of a model

Parameters

• document_class (subclass of BaseDocument) – Document definition

• data_encode (str) – Backup Data Code

• data_format (str) – Backup Data Format

• data_store (str) – Backup Data Store location

• location (str) – Data location to e used by data store

• **kwargs – parameter to be passed at engine level

classmethod scan(_document_class: Type[BaseDocument], _batch_id: str = '', _acl_queries: Optional[list] = None, _limit: int = 1000, **kwargs)

Scan the document class and get the document id list

Parameters

• _document_class (subclass of BaseDocument) – Document definition

• _acl_queries (list) – Extra queries calculated from user’s access control list

• _limit (int) – Limited the scan results

• **kwargs – Named arguments are search string

Notes for search string:

• key, str pair: single value search

• key, list pair: array_contains_any search

• embedded search: a__b means b component of a. a.b means the key’s name is a.b

• operators: key is end with __op__. The following op are supported:

  • __eq__: Could ignore because it is a by default behavior

  • __lt__, __le__, __gt__, __ge__, __ne__: as is supposed by the name

  • __asc__, __desc__: the result will be ordered by the fields

Attentions:

• The complex query might raise compatible issues

scan_and_fetch: bool = False

Possible to separate the search operation into scan and fetch

classmethod search(_document_class: Type[BaseDocument], *args, _batch_id: str = '', _acl_queries: Optional[list] = None, _limit: int = 50, **kwargs)

Searching and yield document by document

Parameters

• _document_class (subclass of BaseDocument) – Document definition

• *args – Unnamed arguments are document id

• _acl_queries – Extra queries calculated from user’s Access Control List

• _limit – Search result is limited

• **kwargs – Named arguments are search string

Notes for search string:

• key, str pair: single value search

• key, list pair: array_contains_any search

• embedded search: a__b means b component of a. a.b means the key’s name is a.b

• operators: key is end with __op__. The following op are supported:

  • __eq__: Could ignore because it is a by default behavior

  • __lt__, __le__, __gt__, __ge__, __ne__: as is supposed by the name

  • __asc__, __desc__: the result will be ordered by the fields

Attentions:

• The complex query might raise compatible issues

classmethod set(document_class: Type[BaseDocument], doc_id: str, db_content: dict, batch_id: str = '') → str

Overwrite whole document

Parameters

• document_class (subclass of BaseDocument) – Document definition

• doc_id – Document ID

• db_content – content to be put to engine

Returns

Document ID

store_embedded_as_table: bool = False

Store embedded document as separate tables

classmethod truncate(document_class: Type[BaseDocument])

Remove all data from the given collection

Parameters

document_class (subclass of BaseDocument) – Document definition

classmethod unlock(document_class: Type[BaseDocument], doc_id: str)

Release the for write

Parameters

• document_class (subclass of BaseDocument) – Document definition

• doc_id (str) – Having predefined doc id, None means could be generated by engine

Returns

return True amd empty message if lock is successful, else false with error message

Comments:

Unlock need based engine implementation

classmethod update(_document_class: Type[BaseDocument], _doc_id: str, batch_id: str = '', **kwargs) → dict

Update a document

Parameters

• _document_class (subclass of BaseDocument) – Document definition

• _doc_id (str) – Document ID

• **kwargs – Named keyword for update

Returns

Updated data

Notes for delete string:

• embedded update: a__b means b component of a. a.b means the key’s name is a.b

• operators: key is end with __op__. The following op are supported:

  • __append__: Append an item to array

  • __remove__: Remove an item

  • __delete__: Delete the field

classmethod update_doc_id(document_class: Type[BaseDocument], db_content: dict, old_id: str, new_id: str)

Update document id to new value

Parameters

• document_class (subclass of BaseDocument) – Document definition

• db_content – content to be put to new engine

• old_id – old document id

• new_id – new document id

Returns

new_document_id if the process is successful

Comments:

By default, we return old id(not implemented). When it is implemented in the Engine, will return new document id