odoo开发五大方法必须了解掌握

search、name_search、search_count、search_read、read_group方法

主要包括以下几个方法及主要用途:


search(): 搜索视图中调用

search_count(): 视图中计算记录数时调用

name_search(): many2one字段搜索时调用

search_read(): many2one点开搜索更多时调用

read_group(): 搜索视图分组时调用


search()

search方法中包含有几个子方法


根据domian取查询满组条件的数据记录

active字段的特殊用法,使用active_test=False来规避

count属性可以直接进行计数统计,而不需要search_count

_uniquify_list方法会将ids进行去重,也就是说id相同时,查到的就只有一条


search_count()

根据search的结果进行统计计数,如果要计数,可以直接在search的时候加上count=true属性,这样计算更快。


    @api.model

    def search_read(self, domain=None, fields=None, offset=0, limit=None, order=None):

        """

        Performs a ``search()`` followed by a ``read()``.  先search再read

        :param domain: Search domain, see ``args`` parameter in ``search()``. Defaults to an empty domain that will match all records.

            查询条件,条件为空默认查询全部

        :param fields: List of fields to read, see ``fields`` parameter in ``read()``. Defaults to all fields.

            查询的字段,默认全部字段

        :param offset: Number of records to skip, see ``offset`` parameter in ``search()``. Defaults to 0.

            跳过的数据量

        :param limit: Maximum number of records to return, see ``limit`` parameter in ``search()``. Defaults to no limit.

            查询的条数

        :param order: Columns to sort result, see ``order`` parameter in ``search()``. Defaults to no sort.

            排序条件

        :return: List of dictionaries containing the asked fields.

            返回字段的字典值列表

        :rtype: List of dictionaries. 包含字典的列表

        """

        records = self.search(domain or [], offset=offset, limit=limit, order=order)

        if not records:

            return []

 

        if fields and fields == ['id']:

            # shortcut read if we only want the ids

            return [{'id': record.id} for record in records]

 

        # read() ignores active_test, but it would forward it to any downstream search call

        # (e.g. for x2m or function fields), and this is not the desired behavior, the flag

        # was presumably only meant for the main search().

        # read()忽略active_test,但它会将其转发到任何下游搜索调用(例如,对于x2m或函数字,

        # 这不是期望的行为,这个标志可能只针对主搜索()。

        # TODO: Move this to read() directly?

        if 'active_test' in self._context:

            context = dict(self._context)

            del context['active_test']

            records = records.with_context(context)

 

        result = records.read(fields)

        if len(result) <= 1:

            return result

 

        # reorder read

        index = {vals['id']: vals for vals in result}

        return [index[record.id] for record in records if record.id in index]


name_search()

也是通过调用_search方法来进行查询的


    @api.model

    def name_search(self, name='', args=None, operator='ilike', limit=100):

        """ name_search(name='', args=None, operator='ilike', limit=100) -> records

        Search for records that have a display name matching the given

        ``name`` pattern when compared with the given ``operator``, while also

        matching the optional search domain (``args``).

            搜索具有与给定名称匹配的显示名称的记录“name”模式与给定的“operator”模式进行比较时,同时也是匹配可选搜索域(' ' args ' ')。

        This is used for example to provide suggestions based on a partial

        value for a relational field. Sometimes be seen as the inverse

        function of :meth:`~.name_get`, but it is not guaranteed to be.

            例如,它用于根据部分内容提供建议关系字段的值。有时被看作是相反的的函数:' ~.name_get ',但它不能保证是。

        This method is equivalent to calling :meth:`~.search` with a search

        domain based on ``display_name`` and then :meth:`~.name_get` on the

        result of the search.

            这个方法相当于调用:meth:' ~.search来搜索的域名,然后是:meth: ' ~.name_get '搜索结果。

        :param str name: the name pattern to match  用于搜索的名称

        :param list args: optional search domain (see :meth:`~.search` for

                          syntax), specifying further restrictions   搜索条件

        :param str operator: domain operator for matching ``name``, such as

                             ``'like'`` or ``'='``.  条件:like、=

        :param int limit: optional max number of records to return  查询的条数

        :rtype: list

        :return: list of pairs ``(id, text_repr)`` for all matching records.

        """

        return self._name_search(name, args, operator, limit=limit)

 

    @api.model

    def _name_search(self, name='', args=None, operator='ilike', limit=100, name_get_uid=None):

        # private implementation of name_search, allows passing a dedicated user

        # for the name_get part to solve some access rights issues

        args = list(args or [])

        # optimize out the default criterion of ``ilike ''`` that matches everything

        if not self._rec_name:

            _logger.warning("Cannot execute name_search, no _rec_name defined on %s", self._name)

        elif not (name == '' and operator == 'ilike'):

            args += [(self._rec_name, operator, name)]

        access_rights_uid = name_get_uid or self._uid

        ids = self._search(args, limit=limit, access_rights_uid=access_rights_uid)

        recs = self.browse(ids)

        return lazy_name_get(recs.sudo(access_rights_uid))


read_group()

数据分组时使用


    @api.model

    def read_group(self, domain, fields, groupby, offset=0, limit=None, orderby=False, lazy=True):

        """

        Get the list of records in list view grouped by the given ``groupby`` fields

            获取列表视图中按给定的“groupby”字段分组的记录列表

        :param domain: list specifying search criteria [['field_name', 'operator', 'value'], ...]

            domain条件

        :param list fields: list of fields present in the list view specified on the object.

                Each element is either 'field' (field name, using the default aggregation),

                or 'field:agg' (aggregate field with aggregation function 'agg'),

                or 'name:agg(field)' (aggregate field with 'agg' and return it as 'name').

                The possible aggregation functions are the ones provided by PostgreSQL

                (https://www.postgresql.org/docs/current/static/functions-aggregate.html)

                and 'count_distinct', with the expected meaning.

                分组之后显示的字段

        :param list groupby: list of groupby descriptions by which the records will be grouped.  

                A groupby description is either a field (then it will be grouped by that field)

                or a string 'field:groupby_function'.  Right now, the only functions supported

                are 'day', 'week', 'month', 'quarter' or 'year', and they only make sense for 

                date/datetime fields.

                分组条件

        :param int offset: optional number of records to skip

            跳过多少查询记录

        :param int limit: optional max number of records to return

            返回的记录数

        :param list orderby: optional ``order by`` specification, for

                             overriding the natural sort ordering of the

                             groups, see also :py:meth:`~osv.osv.osv.search`

                             (supported only for many2one fields currently)

            排序条件

        :param bool lazy: if true, the results are only grouped by the first groupby and the 

                remaining groupbys are put in the __context key.  If false, all the groupbys are

                done in one call.

                是否弃用懒加载:如果为真,则结果仅按第一个groupby和其余的组放在__context键中。如果为假,则所有组都是一次调用搞定。

        :return: list of dictionaries(one dictionary for each record) containing:

                    * the values of fields grouped by the fields in ``groupby`` argument

                    * __domain: list of tuples specifying the search criteria

                    * __context: dictionary with argument like ``groupby``

        :rtype: [{'field_name_1': value, ...]

        :raise AccessError: * if user has no read rights on the requested object

                            * if user tries to bypass access rules for read on the requested object

        """

        result = self._read_group_raw(domain, fields, groupby, offset=offset, limit=limit, orderby=orderby, lazy=lazy)

 

        groupby = [groupby] if isinstance(groupby, pycompat.string_types) else list(OrderedSet(groupby))

        dt = [

            f for f in groupby

            if self._fields[f.split(':')[0]].type in ('date', 'datetime')    # e.g. 'date:month'

        ]

 

        # iterate on all results and replace the "full" date/datetime value

        # (range, label) by just the formatted label, in-place

        for group in result:

            for df in dt:

                # could group on a date(time) field which is empty in some

                # records, in which case as with m2o the _raw value will be

                # `False` instead of a (value, label) pair. In that case,

                # leave the `False` value alone

                if group.get(df):

                    group[df] = group[df][1]

        return result

 

    @api.model

    def _read_group_raw(self, domain, fields, groupby, offset=0, limit=None, orderby=False, lazy=True):

        self.check_access_rights('read')

        # domian解析为sql查询语句

        query = self._where_calc(domain)

        # 拿出存储数据库的字段

        fields = fields or [f.name for f in self._fields.values() if f.store]

 

        groupby = [groupby] if isinstance(groupby, pycompat.string_types) else list(OrderedSet(groupby))

        groupby_list = groupby[:1] if lazy else groupby

        annotated_groupbys = [self._read_group_process_groupby(gb, query) for gb in groupby_list]

        groupby_fields = [g['field'] for g in annotated_groupbys]

        order = orderby or ','.join([g for g in groupby_list])

        groupby_dict = {gb['groupby']: gb for gb in annotated_groupbys}

 

        self._apply_ir_rules(query, 'read')

        for gb in groupby_fields:

            assert gb in self._fields, "Unknown field %r in 'groupby'" % gb

            gb_field = self._fields[gb].base_field

            assert gb_field.store and gb_field.column_type, "Fields in 'groupby' must be regular database-persisted fields (no function or related fields), or function fields with store=True"

 

        aggregated_fields = []

        select_terms = []

 

        for fspec in fields:

            if fspec == 'sequence':

                continue

 

            match = regex_field_agg.match(fspec)

            if not match:

                raise UserError(_("Invalid field specification %r.") % fspec)

 

            name, func, fname = match.groups()

            if func:

                # we have either 'name:func' or 'name:func(fname)'

                fname = fname or name

                field = self._fields[fname]

                if not (field.base_field.store and field.base_field.column_type):

                    raise UserError(_("Cannot aggregate field %r.") % fname)

                if not func.isidentifier():

                    raise UserError(_("Invalid aggregation function %r.") % func)

            else:

                # we have 'name', retrieve the aggregator on the field

                field = self._fields.get(name)

                if not (field and field.base_field.store and

                        field.base_field.column_type and field.group_operator):

                    continue

                func, fname = field.group_operator, name

 

            if fname in groupby_fields:

                continue

            if name in aggregated_fields:

                raise UserError(_("Output name %r is used twice.") % name)

            aggregated_fields.append(name)

 

            expr = self._inherits_join_calc(self._table, fname, query)

            if func.lower() == 'count_distinct':

                term = 'COUNT(DISTINCT %s) AS "%s"' % (expr, name)

            else:

                term = '%s(%s) AS "%s"' % (func, expr, name)

            select_terms.append(term)

 

        for gb in annotated_groupbys:

            select_terms.append('%s as "%s" ' % (gb['qualified_field'], gb['groupby']))

 

        groupby_terms, orderby_terms = self._read_group_prepare(order, aggregated_fields, annotated_groupbys, query)

        from_clause, where_clause, where_clause_params = query.get_sql()

        if lazy and (len(groupby_fields) >= 2 or not self._context.get('group_by_no_leaf')):

            count_field = groupby_fields[0] if len(groupby_fields) >= 1 else '_'

        else:

            count_field = '_'

        count_field += '_count'

 

        prefix_terms = lambda prefix, terms: (prefix + " " + ",".join(terms)) if terms else ''

        prefix_term = lambda prefix, term: ('%s %s' % (prefix, term)) if term else ''

 

        query = """

            SELECT min("%(table)s".id) AS id, count("%(table)s".id) AS "%(count_field)s" %(extra_fields)s

            FROM %(from)s

            %(where)s

            %(groupby)s

            %(orderby)s

            %(limit)s

            %(offset)s

        """ % {

            'table': self._table,

            'count_field': count_field,

            'extra_fields': prefix_terms(',', select_terms),

            'from': from_clause,

            'where': prefix_term('WHERE', where_clause),

            'groupby': prefix_terms('GROUP BY', groupby_terms),

            'orderby': prefix_terms('ORDER BY', orderby_terms),

            'limit': prefix_term('LIMIT', int(limit) if limit else None),

            'offset': prefix_term('OFFSET', int(offset) if limit else None),

        }

        self._cr.execute(query, where_clause_params)

        fetched_data = self._cr.dictfetchall()

 

        if not groupby_fields:

            return fetched_data

 

        self._read_group_resolve_many2one_fields(fetched_data, annotated_groupbys)

 

        data = [{k: self._read_group_prepare_data(k, v, groupby_dict) for k, v in r.items()} for r in fetched_data]

 

        if self.env.context.get('fill_temporal') and data:

            data = self._read_group_fill_temporal(data, groupby, aggregated_fields,

                                                  annotated_groupbys)

 

        result = [self._read_group_format_result(d, annotated_groupbys, groupby, domain) for d in data]

 

        if lazy:

            # Right now, read_group only fill results in lazy mode (by default).

            # If you need to have the empty groups in 'eager' mode, then the

            # method _read_group_fill_results need to be completely reimplemented

            # in a sane way 

            result = self._read_group_fill_results(

                domain, groupby_fields[0], groupby[len(annotated_groupbys):],

                aggregated_fields, count_field, result, read_group_order=order,

            )

        return result

 

原文链接:https://blog.csdn.net/tsoTeo/article/details/105728776

广州众谛信息科技有限公司, Odoo小老谛 2024年1月18日
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odoo17的(NewId)创建新记录的伪标识符对象如何实现
Pseudo-ids for new records, encapsulating an optional origin id (actual record id) and an optional reference (any value).