Performance
Performance Principles of Search
Efficient Resource Utilization of Search:
- Serverless architectures to reduce the amount of resources needed to run search operations.
- Cloud computing to leverage the elasticity of the cloud to scale up or down resources as needed.
- Autoscaling to dynamically adjust resources based on demand.
- Containers to package and run applications with their own set of resources.
- Virtual machines to create virtual machines that can be used to run search operations with dedicated resources.
Efficient Logic, queries of Search:
- Use optimized algorithms to reduce the computational complexity of search queries.
- Utilize query optimization techniques to reduce the number of query executions.
- Use indexing techniques to reduce the search space for a given query.
- Utilize sharding techniques to distribute data across multiple nodes, reducing query latency.
- Caching techniques to reduce the amount of data retrieval from the server.
Efficient Storage, read/write of Search:
- Data partitioning techniques to reduce the number of disk reads/writes.
- Columnar data storage techniques to reduce the amount of data that needs to be read/written.
- Utilize compression techniques to reduce the size of data stored on disk.
- Data replication techniques to increase the availability of data.
- Utilize distributed storage techniques to enable data to be distributed across multiple nodes.
Concurrency (Hardware/Software) of Search:
- Distributed systems to enable concurrent execution of search operations.
- Threading techniques to enable concurrent execution of search operations.
- Asynchronous processing techniques to enable concurrent execution of search operations.
- Message queues to enable concurrent execution of search operations.
- Utilize task scheduling techniques to enable concurrent execution of search operations.
Caching (Hardware/Software) of Search:
- Use distributed caching systems to enable caching of data across multiple nodes.
- Utilize in-memory caching systems to enable caching of data in memory.
- Use disk-based caching systems to enable caching of data on disk.
- Content delivery networks (CDN) to enable caching of data in geographically distributed locations.
- Caching frameworks to enable caching of data in a distributed fashion.
Capacity of Search:
- Distributed systems to scale up the capacity of search operations.
- Elastic scaling techniques to increase the capacity of search operations.
- Load balancing techniques to distribute workloads across multiple nodes.
- Sharding techniques to increase the capacity of search operations.
- Partitioning techniques to increase the capacity of search operations.