"Database tail latency" refers to the longer response times experienced by a small percentage of requests made to a database. These long latencies, often found in the 99th percentile or "tail" of a distribution of request times, can have a significant impact on user experience and application performance.
Understanding and addressing tail latency involves multiple strategies:
Above is an example where you might use a reverse-proxy server such as Nginx for load balancing in a Python Flask application.
Request Prioritization: Prioritize time-sensitive or interactive queries over background or batch processes to minimize user-facing latency.
Redundant Requests: In some cases, sending redundant requests and using the fastest response can reduce tail latency. However, this could increase overall system load.
Optimization of Database Schema/Indexing: This is an effective way to handle tail latency. With proper indexing, databases can quickly locate and retrieve the data.
In the SQL query above, an index
idx_column is created on
table_name for faster searches.
Remember, addressing database tail latency often involves a combination of these strategies, and the effectiveness of each will depend on the specific application and database setup.