SDE - 1

def trapRainWater(arr):
n = len(arr)
if n <= 2:
return 0

left, right = 0, n - 1
left_max, right_max = 0, 0
result = 0

while left < right:
if arr[left] < arr[right]:
if arr[left] >= left_max:
left_max = arr[left]
else:
result += left_max - arr[left]
left += 1
else:
if arr[right] >= right_max:
right_max = arr[right]
else:
result += right_max - arr[right]
right -= 1

return result

# Example usage:
elevation_map = [0,1,0,2,1,0,1,3,2,1,2,1]
print(trapRainWater(elevation_map)) # Output: 6

bool isValid(string s) {
int n = s.length();
stackst;
for(int i=0;i char ch = s[i];
if(ch=='('||ch=='['||ch=='{'){
st.push(ch);
}
else{
if(st.empty()){
return false;
}
else{
if(ch==')'&&st.top()=='('){
st.pop();
}
else if(ch==']'&&st.top()=='['){
st.pop();
}
else if(ch=='}'&&st.top()=='{'){
st.pop();
}
else return false;
}


}

}

return st.empty()?true:false;
}

Virtual memory is a fundamental concept in operating systems that enables a computer to use more memory than it physically has by utilizing disk space as an extension of physical RAM. It enhances system performance in several ways and facilitates efficient memory management.

Key Components of Virtual Memory Systems:

Physical Memory (RAM): This is the actual, limited amount of RAM installed in the computer.

Virtual Address Space: The range of memory addresses that a process can use. It's typically larger than the physical memory size.

Page Tables: These data structures map virtual addresses to physical addresses. Each process has its page table.

Page Faults: Occur when a process tries to access a page that is not currently in physical memory. It triggers the OS to bring the required page from disk to RAM.

Disk Space: Used as secondary storage for pages that can't fit in physical memory.

How Virtual Memory Enhances Performance and Memory Management:

Process Isolation: Each process believes it has its dedicated memory space, preventing one process from accessing or modifying the memory of another.

Efficient Use of Physical Memory: The OS can use a technique called paging or segmentation to load only the necessary pages into RAM, reducing memory wastage.

Process Size Flexibility: Processes can be larger than physical memory, allowing for more extensive and resource-intensive applications.

Memory Protection: Virtual memory systems enable the OS to protect memory from unauthorized access, enhancing security.

Dynamic Allocation: Memory allocation is more flexible, and applications can allocate or release memory without worrying about physical constraints.

Handling Page Faults:

When a page fault occurs, the OS must resolve it efficiently. The steps involved include:

Identifying the page causing the fault.
Determining if the page is on disk or not.
If on disk, reading it into an available physical memory page.
Updating the page table to reflect the change.
Restarting the instruction that caused the page fault.
Practical Application and Benefits:

Consider a scenario where multiple applications are running on a computer with limited physical memory. Without virtual memory, running all these applications simultaneously could lead to frequent out-of-memory errors and sluggish performance. Virtual memory allows the OS to swap pages in and out of RAM and disk dynamically, ensuring that the currently executing processes have the required memory resources while keeping less-used portions on disk.

Here's the strategy to identify the odd ball and determine whether it's heavier or lighter:

First Weighing (Step 1): Divide the balls into three groups, with three balls in each group. Place two of these groups on the weighing device, leaving one group aside. If the two groups balance, you know the odd ball is in the group you left aside.

Second Weighing (Step 2): If the first weighing showed an imbalance, you now have two possibilities: either the left group is heavier or the right group is heavier. In this step, take the group that was identified as potentially having the odd ball and divide it into three balls: place one ball aside and weigh the other two on the device.

If the two balls balance, the one you set aside in this step is the odd ball. If they don't balance, you've identified the group (left or right) containing the odd ball, but you don't yet know if it's heavier or lighter.

Third Weighing (Step 3): In the final step, take the group from Step 2 that you suspect contains the odd ball and weigh one ball from that group against a known standard ball (a ball you are sure is of regular weight).

If they balance, the odd ball is the one you didn't weigh in this step. 

If they don't balance, you'll know whether the odd ball is heavier or lighter based on the direction of the imbalance.

By carefully following these steps, you can uniquely identify the odd ball and determine whether it is heavier or lighter in just three weighings.

Optimizing database query performance for a large e-commerce platform involves a multifaceted approach to ensure efficient product searches and handle heavy concurrent operations. 

Database Schema and Indexing: Begin by designing an efficient database schema that minimizes redundant data while accommodating complex queries. Use appropriate indexing on frequently queried columns, such as product attributes, category, and customer reviews. Composite indexes may be beneficial for specific search scenarios to reduce query execution time.

Query Optimization: Utilize query optimization techniques like query rewriting, subquery optimization, and join order optimization. Consider the use of stored procedures to encapsulate complex queries for reuse and improve maintainability.

Caching Mechanisms: Implement caching mechanisms, such as content delivery networks (CDNs) and in-memory caching (e.g., Redis or Memcached), to reduce the load on the database for frequently requested data, like product images and frequently accessed product details.

Load Balancing and Scaling: Employ load balancing across multiple database servers to distribute the query workload. Use techniques like database sharding to divide the database into smaller, more manageable parts, ensuring horizontal scalability. Additionally, vertical scaling through hardware upgrades may be necessary as data grows.

Concurrency Control: To handle heavy concurrent read and write operations, implement robust concurrency control mechanisms. Consider techniques like optimistic locking for write operations and snapshot isolation for read-heavy scenarios. Implement proper transaction management and isolation levels to maintain data integrity.

Database Maintenance: Regularly perform database maintenance tasks, such as index rebuilding, statistics updating, and query optimization. Monitor and tune database configurations to align with changing workload patterns.

Data Archiving and Purging: To handle data growth, implement data archiving and purging strategies. Move less frequently accessed data to historical archives while retaining essential data in the main database. This helps maintain efficient query performance over time.

Replication and Backups: Set up database replication for data redundancy and fault tolerance. Perform regular backups and implement disaster recovery plans to ensure data integrity and availability.

Monitoring and Analytics: Implement robust monitoring systems to track query performance, system resource usage, and database health. Utilize analytics tools to identify performance bottlenecks and plan necessary optimizations.

Query Caching and Query Rewrite: Use query caching to store and reuse frequently executed queries. Additionally, consider query rewrite techniques to transform complex queries into more efficient forms.

Tip 1 : Please give honest answers. Please don't try to lie.
Tip 2 : Try to be calm.