Application performance is one of the most critical factors in software development. Applications should be designed and implemented with performance objectives in mind. When defining architecture, designing, or implementing code, several best practices should be considered to enhance performance. At the same time, bad practices or anti-patterns should be avoided to prevent performance degradation.
Some best practices and anti-patterns may be universally applicable, while others depend on the specific project context. Architects and project teams should make informed decisions about adopting or rejecting these best practices. However, teams should not dismiss best practices merely on the grounds of subjectivity without thorough evaluation, as many practices can be beneficial despite perceived constraints.
Long-Running Transactions
The throughput of a system (requests processed per second) is negatively affected by long-running transactions. These transactions create wait times, leading to congestion, delays, and locks, which can further escalate into deadlocks.
Strategies to mitigate long-running transactions:
Split large transactions into smaller, manageable ones.
Keep transactions as short as possible.
Use asynchronous processing where applicable.
Optimize and tune long-running database queries.
Increase thread pool size where necessary.
Avoid chatty transactions.
Utilize caching to reduce round trips and increase throughput.
Define a Uniform Architecture
A uniform architecture facilitates horizontal scaling. When architectural components have complex interdependencies, scaling out by adding new nodes becomes challenging. Standardized components simplify scalability and maintenance.
Use Caching
Caching improves performance by reducing expensive database queries and minimizing the number of waiting threads, thereby increasing throughput.
Partitioning
Partitioning refers to the division of data to optimize storage, manageability, availability, and performance. While sometimes confused with normalization, partitioning is a physical-level optimization rather than a conceptual one. Partitioning helps overcome database size limitations and improve read/write performance.
Vertical Splitting (Partitioning)
Vertical partitioning involves storing different tables and columns in separate databases. This approach logically splits application data and stores it across multiple databases. The application layer ensures that data is read from and written to the correct partition. Sometimes called "row splitting," this technique distributes columns across multiple databases to reduce the burden on a single database.
Horizontal Splitting (Sharding)
Horizontal partitioning, or sharding, involves distributing rows of a table across multiple database nodes. Some databases, such as Cassandra, HBase, HDFS, and MongoDB, offer native support for sharding. Sharding can be implemented at either the application or database level.
Example: A user table may be split based on geographical location, creating tables such as UsersNorth and UsersSouth to distribute data more effectively.
Challenges of Distributed Data
While partitioning and sharding provide performance benefits, they also introduce challenges:
Cross-partition searches: Querying across partitions can be inefficient and complex.
Data distribution imbalance: Uneven data distribution, or "hot spots," can limit the effectiveness of sharding.
By applying the correct strategies to address these challenges, application performance can be optimized while maintaining scalability and efficiency.
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