One key to effective query tuning is understanding MongoDB's architecture. In this video, we'll discuss some aspects of MongoDB's architecture that are critical for query performance. Specifically, we'll focus on vertical scalability and horizontal scalability, the storage engine, the document model, and the query planner. By understanding how these components work together to keep things running smoothly, you'll be able to optimize your queries for maximum performance. Let's get started. We'll begin with scalability, which is the ability to efficiently handle increasing data and user loads. When we talk about scalability in MongoDB, we're referring to vertical scalability and horizontal scalability. Vertical scalability means adding more resources to a single server, firming up CPU, memory, and storage to meet demand. Horizontal scalability refers to spreading data across multiple servers. This is the essence of distributing load, handling vast data efficiency, and maintaining performance as the load increases. So how exactly does MongoDB support scalability? The answer lies in a combination of vertical scaling and sharding, a technique that enables you to distribute the data and workload across multiple nodes. We can choose vertical scaling, sharding, or both. By spreading the load across a cluster of machines, sharding eliminates limitations imposed by the resources of a single machine. This means MongoDB can manage vast datasets in high request volumes with ease. For instance, imagine a large product catalog. By partitioning it by category or product ID, each shard maintains only a portion of the load, leading to smoother operations. Replication is another pillar of MongoDB strategy to maintain high availability and scalability by copying data across multiple nodes. This helps distribute read operations across secondary nodes and reduce load on the primary. However, it's important to understand the consistency trade offs involved with reading from secondaries. Since secondary nodes may not reflect the most recent writes, these reads can result in stale data. While secondary reads can improve performance, enforcing strict consistency can lead to increased latency due to the time needed to synchronize writes across the nodes. The next aspect of MongoDB's architecture is the storage engine. Wiretiger is the default storage engine in MongoDB. It uses a variety of techniques such as compression and caching and memory to manage data storage and retrieval. This makes it a robust option for workloads that require high throughput and low latency. WiredTiger uses its own caching mechanism alongside the ones provided by the operating system to accelerate data access. This drastically reduces query response times because cached data is pulled from memory. However, when data is not present in the cache, it must be retrieved from the storage disk, which can increase latency due to the slower nature of disk IO. While no caching system can perfectly anticipate all data access patterns, Wiretiger is designed to intelligently manage its cache, prioritizing the storage of frequently accessed data. This caching strategy minimizes disk access in most scenarios. Next, let's talk about the document model. MongoDB uses a document oriented data model where data is stored in flexible JSON like documents within collections. Each document can have its own unique data structure. This feature provides a lot of flexibility but requires strategic data modeling to optimize query performance. For a deeper dive into MongoDB document structure, check out our videos on the document model. The last architectural component we'll discuss is MongoDB's query planner. You can think of the query planner as the brain behind query execution. The query planner is part of a larger query processing architecture that consists of three components. The parser, which interprets, validates, and translates the client's query into an internal format usable by MongoDB. The next part of MongoDB's query processing architecture is the query planner or query optimizer. The query planner selects the most efficient query plan to execute that query and then caches that plan for later use. The final part is the execution engine, which carries out the chosen query plan and returns the resulting documents to the client. The query planner determines the possible query plans and selects the best one. Query plans are the steps used by a database engine to use a query and return results. Plans can use different indexes leading to different resource usage levels and time required to run the query. The query planner selects and caches the most efficient query plan, that is the plan that can retrieve the data in the shortest amount of time. When evaluating plans, the query planner takes into account any indexes on the collection. Indexes support efficient execution of queries in MongoDB. Without indexes, MongoDB must scan every document in a collection to return query results. If an appropriate index exists for a query, MongoDB uses the index to limit the number of documents it must scan. This can drastically reduce query execution times and improve read performance in MongoDB. For example, let's say we want to retrieve a list of users who are over the age of thirty. The query planner will assess the possible paths based on the available indexes and then select the approximate optimal path to deliver the results. Great work. In this video, we explore the architectural elements of MongoDB that are essential for optimizing query performance. You learned how MongoDB tackles scalability by using vertical scaling to beef up server resources and horizontal scaling through sharding to distribute data across multiple servers. Then you learned about the WiredTiger storage engine and how it utilizes caching to optimize data retrieval. We also discussed the document model, which organizes data into collections and documents. Lastly, we examined the query planner, our database's navigator, which selects the most efficient path to execute queries. Understanding the architecture at a high level will be valuable to you as you learn to optimize your queries.