Performance

Performance

Performance Principles for databases

Efficient Resource Utilization of Databases

• Utilize vertical and horizontal scaling to increase the performance of the database
• Leverage connection pooling to reduce the amount of resources used when connecting to a DB
• Use a cost-efficient DB service that provides the required features and resources
• Utilize indexing and query optimization techniques to reduce the amount of resources needed to process queries
• Use an optimized storage engine for the specific use-case and data type

Efficient Logic, Queries of Databases

• Utilize query optimization techniques to optimize the queries being used
• Leverage stored procedures and functions to reduce the amount of code needed to process queries
• Use the proper data types for the specific use-case and data type
• Utilize query hints to optimize query execution plans
• Implement caching techniques to reduce the amount of queries processed

Efficient Storage, Read/Write of Databases

• Utilize the appropriate storage engine for the specific use-case
• Utilize the most efficient read/write patterns for the specific use-case
• Use proper indexing techniques to reduce the amount of data being read/written
• Leverage bulk operations when possible to reduce the amount of operations being performed
• Utilize partitioning to reduce the amount of data being read/written

Concurrency (Hardware/Software) of Databases

• Utilize connection pooling to reduce the amount of resources allocated to connections
• Utilize proper locking techniques to reduce the amount of conflicts between concurrent transactions
• Implement isolation levels to reduce the amount of locks being held
• Utilize read replicas to reduce the amount of concurrent transactions on the master database
• Use proper transaction management techniques to reduce the amount of conflicts between concurrent transactions

Caching (Hardware/Software) of Databases

• Utilize caching techniques to reduce the amount of data being read/written
• Leverage in-memory databases to reduce the amount of time spent querying data
• Implement a distributed caching system to reduce the amount of data being read/written
• Utilize query hints to optimize query execution plans
• Use a cost-efficient DB service that provides the required caching features

Capacity of Databases

• Utilize vertical and horizontal scaling to increase the performance of the database
• Leverage connection pooling to reduce the amount of resources used when connecting to a DB
• Use a cost-efficient DB service that provides the required features and resources
• Utilize partitioning to reduce the amount of data being read/written
• Implement sharding techniques to increase the capacity of the database

RDBMS/SQL

• Indexing:
  • Creating indexes on frequently searched columns can greatly improve query performance.
• Normalization
  • Normalizing the database schema can improve performance by reducing data redundancy and increasing overall data integrity.
• Partitioning
  • Partitioning large tables can improve query performance by reducing the amount of data that needs to be searched.
• Caching
  • Caching frequently accessed data in memory can greatly improve read performance.
• Optimizing queries
  • Tuning SQL queries by using EXPLAIN PLAN and other query optimization tools can improve performance.
• Denormalization
  • Denormalizing the database schema can improve write performance by reducing the number of JOIN operations required for a query.
• Regular maintenance
  • Regularly performing tasks such as vacuum and analyze can improve performance by keeping the database optimizer informed about the distribution of data.
• Upgrading hardware
  • upgrading hardware to faster and more powerful components, such as faster processors and larger amounts of RAM, can improve performance.
• Scale horizontally
  • Scaling out the database by adding more machines can improve performance by distributing the load among multiple machines.
• Use connection pooling
  • Connecting to a database can be a time-consuming process, so connection pooling can improve performance by reusing existing connections.

NoSQL

• Data modeling:
  • Carefully modeling data to align with the query patterns can greatly improve performance.
• Indexing:
  • Creating indexes on frequently searched fields can improve query performance.
• Caching:
  • Caching frequently accessed data in memory can greatly improve read performance.
• Sharding: Splitting large datasets across multiple machines can improve performance by distributing the load among multiple machines.
• Tuning memory and storage:
  • Adjusting memory and storage settings can improve performance.
• Denormalization:
  • Denormalizing the data model can improve write performance by reducing the number of JOIN operations required for a query.
• Aggregation:
  • Aggregating data to a higher level can improve query performance by reducing the amount of data that needs to be searched.
• Batch Processing:
  • Batch processing large amount of data can improve performance by reducing the number of requests sent to the database.
• Using a CDN:
  • Using a CDN (Content Delivery Network) to cache data closer to the clients can improve performance by reducing latency.
• Use connection pooling:
  • Connecting to a database can be a time-consuming process, so connection pooling can improve performance by reusing existing connections.

AWS DynamoDB

• Data modeling
  • Carefully modeling data to align with the query patterns can greatly improve performance.
• Properly selecting partition key
  • Choosing the right partition key can ensure that the data is distributed evenly and improves the performance of queries and scans.
• Indexing
  • Creating indexes on frequently searched fields can improve query performance.
• Caching
  • Caching frequently accessed data in memory can greatly improve read performance.
• Provisioned throughput
  • Configuring provisioned throughput appropriately can improve performance by ensuring that sufficient resources are available to meet the traffic demands.
• Batch operations
  • Using batch operations for write operations can improve performance by reducing the number of requests sent to the database.
• Use Global Secondary Index
  • Creating Global Secondary Indexes can improve performance by allowing you to query data from multiple attributes.
• Use DynamoDB Accelerator (DAX)
  • DAX can improve performance by caching frequently accessed data in memory.
• Use Time-to-Live (TTL)
  • Using TTL allows you to automatically expire and delete old data that is no longer needed, which can improve performance and reduce costs.
• Use DynamoDB Streams
  • DynamoDB Streams allows you to capture data modification events in real-time, which can improve performance when building real-time data pipelines and applications.