690.Employee Importance

员工信息结构与总重要性计算
本文介绍了一种员工信息的数据结构,包括员工的唯一ID、重要性值及直接下属ID。通过示例说明了如何计算指定员工及其所有下属的总重要性值。并提供了一个Python类实现,用于解析员工信息并返回总重要性值。

You are given a data structure of employee information, which includes the employee's unique id, his importance value and his direct subordinates' id.

For example, employee 1 is the leader of employee 2, and employee 2 is the leader of employee 3. They have importance value 15, 10 and 5, respectively. Then employee 1 has a data structure like [1, 15, [2]], and employee 2 has [2, 10, [3]], and employee 3 has [3, 5, []]. Note that although employee 3 is also a subordinate of employee 1, the relationship is not direct.

Now given the employee information of a company, and an employee id, you need to return the total importance value of this employee and all his subordinates.

Example 1:

Input: [[1, 5, [2, 3]], [2, 3, []], [3, 3, []]], 1
Output: 11
Explanation:
Employee 1 has importance value 5, and he has two direct subordinates: employee 2 and employee 3. They both have importance value 3. So the total importance value of employee 1 is 5 + 3 + 3 = 11.

Note:

One employee has at most one direct leader and may have several subordinates.
The maximum number of employees won't exceed 2000.
# Employee info
class Employee:
    def __init__(self, id, importance, subordinates):
        # It's the unique id of each node.
        # unique id of this employee
        self.id = id
        # the importance value of this employee
        self.importance = importance
        # the id of direct subordinates
        self.subordinates = subordinates

class Solution:
    def getImportance(self, employees, id):
        """
        :type employees: Employee
        :type id: int
        :rtype: int
        """
        for staff in employees:
            if staff.id == id:
                if staff.subordinates == []:
                    return staff.importance
                return staff.importance + sum(self.getImportance(employees,sub) for sub in staff.subordinates) #pay attention to this written way.

转载于:https://www.cnblogs.com/bernieloveslife/p/9757059.html

### DIS QL Types in Database or Data Integration Systems Data Integration System Query Languages (DIS QLs) refer to specialized query languages designed specifically for handling complex queries across multiple heterogeneous databases within a unified framework. These systems aim to provide seamless access, transformation, and querying capabilities over disparate data sources. A key aspect of DIS QL is its ability to handle schema mappings, transformations, and federated queries effectively while maintaining performance and scalability[^1]. Below are some important characteristics: #### Characteristics of DIS QL - **Schema Mapping**: DIS QL supports defining relationships between schemas of different data sources through declarative mapping rules. - **Federated Queries**: It enables users to write single queries spanning multiple independent databases without needing detailed knowledge about their internal structures. - **Heterogeneity Handling**: Designed explicitly for environments where various types of structured/unstructured data coexist, such as relational tables, XML documents, JSON objects, etc., it provides mechanisms to unify these formats into one logical view[^2]. #### Example Code Snippet Demonstrating Schema Mapping Using SQL-like Syntax Here’s an example demonstrating how you might define simple schema mappings using a hypothetical DIS QL syntax inspired by standard SQL constructs: ```sql -- Define source-to-target mapping rule CREATE MAPPING EmployeeMapping AS ( SELECT e.id AS emp_id, e.name AS full_name, d.departmentName AS dept_name FROM SourceDB.EmployeeTable e JOIN TargetDB.DepartmentTable d ON e.deptId = d.id ); -- Execute cross-database query leveraging defined mapping SELECT * FROM EmployeeMapping WHERE dept_name LIKE 'Sales%'; ``` This snippet illustrates creating a reusable `EmployeeMapping` object which abstracts away complexities associated with joining two distinct yet related datasets residing on separate servers before executing further operations against this combined dataset representation. #### Importance in Modern Applications Modern applications increasingly rely upon integrating diverse information resources spread out globally via web services APIs, cloud storage solutions among others making robust support provided by advanced implementations crucial not only towards achieving desired functionality but also ensuring security compliance along privacy regulations like GDPR when dealing personally identifiable details contained therein[^3].
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