Summary
The question of whether global consistency should be enabled by default in PolarDB for production systems is complex and depends on several factors, including latency, throughput, and read/write splitting behavior. Enabling global consistency can provide strong consistency across all nodes, but it may come at the cost of increased latency and decreased throughput.
Root Cause
The root cause of the trade-offs associated with global consistency in PolarDB is the need to balance consistency, availability, and partition tolerance. The main causes of these trade-offs are:
- Network latency: The time it takes for data to be replicated across nodes
- Disk I/O: The time it takes for data to be written to disk
- Lock contention: The time it takes for nodes to acquire locks on shared resources
Why This Happens in Real Systems
In real systems, global consistency is often required to ensure data integrity and accuracy. However, achieving global consistency can be challenging due to:
- Distributed architecture: Data is spread across multiple nodes, making it difficult to maintain consistency
- Network partitions: Nodes may become disconnected, making it difficult to achieve consistency
- Concurrent updates: Multiple nodes may try to update the same data simultaneously, leading to inconsistencies
Real-World Impact
The impact of enabling or disabling global consistency in PolarDB can be significant, with effects on:
- Latency: Increased latency can lead to poor user experience and decreased system responsiveness
- Throughput: Decreased throughput can lead to reduced system capacity and increased costs
- Data integrity: Inconsistent data can lead to errors, inconsistencies, and data loss
Example or Code (if necessary and relevant)
-- Example of a query that may be affected by global consistency
SELECT * FROM table WHERE id = 1;
-- Example of a configuration that enables global consistency
SET GLOBAL consistency_level = 'strong';How Senior Engineers Fix It
Senior engineers can fix the trade-offs associated with global consistency in PolarDB by:
- Tuning configuration parameters: Adjusting parameters such as consistency level, replication factor, and timeout values
- Optimizing database design: Designing databases to minimize lock contention and network latency
- Implementing caching mechanisms: Implementing caching mechanisms to reduce disk I/O and improve throughput
Why Juniors Miss It
Junior engineers may miss the trade-offs associated with global consistency in PolarDB because they:
- Lack experience: Lack experience with distributed systems and the challenges of achieving global consistency
- Overlook configuration parameters: Fail to consider the impact of configuration parameters on system behavior
- Underestimate the importance of testing: Fail to thoroughly test systems to identify potential issues with global consistency