Python Query.perform方法代码示例

# 需要导入模块: from pyignite.queries import Query [as 别名]
# 或者: from pyignite.queries.Query import perform [as 别名]
def cache_create(
    connection: 'Connection', name: str, query_id=None,
) -> 'APIResult':
    """
    Creates a cache with a given name. Returns error if a cache with specified
    name already exists.

    :param connection: connection to Ignite server,
    :param name: cache name,
    :param query_id: (optional) a value generated by client and returned as-is
     in response.query_id. When the parameter is omitted, a random value
     is generated,
    :return: API result data object. Contains zero status if a cache is
     created successfully, non-zero status and an error description otherwise.
    """

    query_struct = Query(
        OP_CACHE_CREATE_WITH_NAME,
        [
            ('cache_name', String),
        ],
        query_id=query_id,
    )
    return query_struct.perform(
        connection,
        query_params={
            'cache_name': name,
        },
    )

# 需要导入模块: from pyignite.queries import Query [as 别名]
# 或者: from pyignite.queries.Query import perform [as 别名]
def cache_destroy(
    connection: 'Connection', cache: Union[str, int], query_id=None,
) -> 'APIResult':
    """
    Destroys cache with a given name.

    :param connection: connection to Ignite server,
    :param cache: name or ID of the cache,
    :param query_id: (optional) a value generated by client and returned as-is
     in response.query_id. When the parameter is omitted, a random value
     is generated,
    :return: API result data object.
    """

    query_struct = Query(
        OP_CACHE_DESTROY,[
            ('hash_code', Int),
        ],
        query_id=query_id,
    )
    return query_struct.perform(
        connection,
        query_params={
            'hash_code': cache_id(cache),
        },
    )

# 需要导入模块: from pyignite.queries import Query [as 别名]
# 或者: from pyignite.queries.Query import perform [as 别名]
def cache_get_names(connection: 'Connection', query_id=None) -> 'APIResult':
    """
    Gets existing cache names.

    :param connection: connection to Ignite server,
    :param query_id: (optional) a value generated by client and returned as-is
     in response.query_id. When the parameter is omitted, a random value
     is generated,
    :return: API result data object. Contains zero status and a list of cache
     names, non-zero status and an error description otherwise.
    """

    query_struct = Query(OP_CACHE_GET_NAMES, query_id=query_id)
    result = query_struct.perform(
        connection,
        response_config=[
            ('cache_names', StringArray),
        ],
    )
    if result.status == 0:
        result.value = result.value['cache_names']
    return result

# 需要导入模块: from pyignite.queries import Query [as 别名]
# 或者: from pyignite.queries.Query import perform [as 别名]
def cache_get_configuration(
    connection: 'Connection', cache: Union[str, int], flags: int=0, query_id=None,
) -> 'APIResult':
    """
    Gets configuration for the given cache.

    :param connection: connection to Ignite server,
    :param cache: name or ID of the cache,
    :param flags: Ignite documentation is unclear on this subject,
    :param query_id: (optional) a value generated by client and returned as-is
     in response.query_id. When the parameter is omitted, a random value
     is generated,
    :return: API result data object. Result value is OrderedDict with
     the cache configuration parameters.
    """

    query_struct = Query(
        OP_CACHE_GET_CONFIGURATION,
        [
            ('hash_code', Int),
            ('flags', Byte),
        ],
        query_id=query_id,
    )
    result = query_struct.perform(
        connection,
        query_params={
            'hash_code': cache_id(cache),
            'flags': flags,
        },
        response_config=[
            ('cache_config', cache_config_struct),
        ],
    )
    if result.status == 0:
        result.value = compact_cache_config(result.value['cache_config'])
    return result

# 需要导入模块: from pyignite.queries import Query [as 别名]
# 或者: from pyignite.queries.Query import perform [as 别名]
def put_binary_type(
    connection: 'Connection', type_name: str, affinity_key_field: str=None,
    is_enum=False, schema: dict=None, query_id=None,
) -> APIResult:
    """
    Registers binary type information in cluster.

    :param connection: connection to Ignite server,
    :param type_name: name of the data type being registered,
    :param affinity_key_field: (optional) name of the affinity key field,
    :param is_enum: (optional) register enum if True, binary object otherwise.
     Defaults to False,
    :param schema: (optional) when register enum, pass a dict of enumerated
     parameter names as keys and an integers as values. When register binary
     type, pass a dict of field names: field types. Binary type with no fields
     is OK,
    :param query_id: (optional) a value generated by client and returned as-is
     in response.query_id. When the parameter is omitted, a random value
     is generated,
    :return: API result data object.
    """
    # prepare data
    if schema is None:
        schema = {}
    type_id = entity_id(type_name)
    data = {
        'type_name': type_name,
        'type_id': type_id,
        'affinity_key_field': affinity_key_field,
        'binary_fields': [],
        'is_enum': is_enum,
        'schema': [],
    }
    schema_id = None
    if is_enum:
        data['enums'] = []
        for literal, ordinal in schema.items():
            data['enums'].append({
                'literal': literal,
                'type_id': ordinal,
            })
    else:
        # assemble schema and calculate schema ID in one go
        schema_id = FNV1_OFFSET_BASIS if schema else 0
        for field_name, data_type in schema.items():
            # TODO: check for allowed data types
            field_id = entity_id(field_name)
            data['binary_fields'].append({
                'field_name': field_name,
                'type_id': int.from_bytes(
                    data_type.type_code,
                    byteorder=PROTOCOL_BYTE_ORDER
                ),
                'field_id': field_id,
            })
            schema_id ^= (field_id & 0xff)
            schema_id = int_overflow(schema_id * FNV1_PRIME)
            schema_id ^= ((field_id >> 8) & 0xff)
            schema_id = int_overflow(schema_id * FNV1_PRIME)
            schema_id ^= ((field_id >> 16) & 0xff)
            schema_id = int_overflow(schema_id * FNV1_PRIME)
            schema_id ^= ((field_id >> 24) & 0xff)
            schema_id = int_overflow(schema_id * FNV1_PRIME)

    data['schema'].append({
        'schema_id': schema_id,
        'schema_fields': [
            {'schema_field_id': entity_id(x)} for x in schema
        ],
    })

    # do query
    if is_enum:
        query_struct = Query(
            OP_PUT_BINARY_TYPE,
            [
                ('type_id', Int),
                ('type_name', String),
                ('affinity_key_field', String),
                ('binary_fields', binary_fields_struct),
                ('is_enum', Bool),
                ('enums', enum_struct),
                ('schema', schema_struct),
            ],
            query_id=query_id,
        )
    else:
        query_struct = Query(
            OP_PUT_BINARY_TYPE,
            [
                ('type_id', Int),
                ('type_name', String),
                ('affinity_key_field', String),
                ('binary_fields', binary_fields_struct),
                ('is_enum', Bool),
                ('schema', schema_struct),
            ],
            query_id=query_id,
        )
    result = query_struct.perform(connection, query_params=data)
#.........这里部分代码省略.........