Master PostgreSQL Performance – WeWard's Practices for Efficient Databases

At WeWard, where every millisecond counts to improve user experience, we embarked on an ambitious journey to transform PostgreSQL into an exceptional performance engine. This article explores the challenges faced, the solutions implemented, and the best practices that allowed us to optimize PostgreSQL significantly.

🎯 Why is Database Optimization Crucial?

In a world where users expect instant response times, slow databases can lead to frustration and missed opportunities. At WeWard, this challenge translates into millions of daily transactions and complex queries requiring flawless performance. Our challenges included slow queries, excessive load, and an architecture requiring increased scalability.

Here’s how we overcame these obstacles.

⚙ Step 1: Query Optimization

The foundation of any PostgreSQL optimization begins with query analysis. We identified inefficiencies by studying query execution plans.

Execution Plan Analysis

Using EXPLAIN and ANALYZE, we gained an X-ray view of our queries.

Problematic Query Example:

SELECT level_customer.*
FROM level_customer
JOIN level ON level.id = level_customer.level_id
WHERE level_customer.customer_id = XXXXXXX
 AND level_customer.start_date <= '2024-05-07'::date
 AND level.version = 2
ORDER BY level_customer.start_date DESC, level.value DESC
LIMIT 1;


Detected Issues:

• Lack of an index to efficiently handle sorting.
• Suboptimal join strategy chosen by the planner.

Solution:Index Creation:

CREATE INDEX idx_level_customer_customer_date_value
ON level_customer (customer_id, start_date DESC, level_id DESC);


Planner Adjustment:We forced PostgreSQL to use Nested Loop Join instead of the default Hash Join for this specific query.

Materialized Views:For complex queries, materialized views reduced calculation overhead on each execution, providing remarkably stable performance.

⚖ Step 2: Load Distribution with Replica

With millions of daily queries, we adopted a replica-based architecture to distribute the load.

Architecture Setup:

• Primary Node: Handles both read and write operations, ensuring data consistency.
• Replica Node: Dedicated to read-only operations, reducing the load on the primary node and improving response times for read-intensive queries.

Benefits:

• Improved Performance: Offloading read queries to replicas reduces latency and increases throughput.
• High Availability: In case of primary node failure, replicas can be promoted, ensuring service continuity.
• Scalability: Multiple replica nodes can be added to scale horizontally as traffic increases.

Technical Considerations:

• Replication Latency: Slight lag might occur between the primary and replica nodes.
• Eventual Consistency: Read queries on replicas may return slightly outdated data.
• Monitoring: Proactive monitoring is essential to ensure reliable replication.

🗂 Step 3: Managing Large Tables with Partitioning

Why Partitioning?

Tables containing billions of rows can significantly slow down read and write operations. Partitioning splits a table into smaller subsets, improving query efficiency.

Tool: pg_partman

Advantages:

• Automated Partition Management: Handles partition creation and deletion based on predefined criteria.
• Improved Query Performance: Queries target only relevant partitions.
• Easier Historical Data Maintenance: Simplifies archiving and purging without disrupting current data.

🚦 Step 4: Connection Stabilization

Each PostgreSQL connection consumes system resources. To prevent server overload:

• Connection Limits: max_connections was adjusted based on available hardware and application needs.
• Connection Pooling: Reusing existing connections reduced server load and improved application responsiveness.

Key PostgreSQL Memory Parameters:

• shared_buffers: Controls memory allocation for caching frequently accessed data.
• work_mem: Defines memory for sorting and hash tables during query execution.

📈 Step 5: A Scalable Architecture with Data Lake

Critical ("hot") data remains in PostgreSQL, while less frequently accessed ("cold") data is transferred to Amazon S3 or Google Cloud Storage. Analysis of cold data is performed using Google BigQuery.

Benefits:

• Scalability: Unlimited storage capacity on cloud platforms.
• Cost Efficiency: Reduced costs for cold data storage.
• Flexibility: Suitable tools for each data type.

👁 Step 6: Monitoring and Observability

WeWard uses advanced monitoring tools such as:

• AWS Performance Insights: Real-time database performance visualization.
• Application Performance Monitoring (APM): End-to-end transaction tracing.
• Database Performance Monitoring (DBM): Query performance insights.

Advantages:

• Rapid issue detection.
• Performance optimization.

🏁 Conclusion: A Solid Foundation for the Future

By combining rigorous query optimization, intelligent resource management, and modern architectures, PostgreSQL has become a key ally for WeWard. These solutions are applicable to any organization aiming to maximize database potential.

💬 What are your strategies for optimizing PostgreSQL? Share your experiences and join the conversation!

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