Key insights

• ⚙️ Using a second PostgreSQL server as a caching layer on top of the primary DB to offload query load
• 📊 Understanding query plans is crucial for optimizing queries; Postgres's query planner can be tricky
• 🔍 Managing big data involves careful consideration of indexing, materialized views, denormalization, and understanding data
• 📈 Scaling app with a caching layer on top of databases
• 🛠️ Using lean engineering setup and hitting the limits before seeking new solutions
• 🔍 Understanding the query planner is important for optimizing query performance
• 🔄 Continuous rewriting of apps and schemas is essential for improvement and scalability
• 🙏 Gratitude to the PostgreSQL community for support and knowledge sharing

Q&A

• How does the speaker approach engineering setup and app scalability?

  The speaker advocates using a lean engineering setup and hitting the limits before seeking new solutions. They emphasize the importance of experimenting with different databases, being open to alternatives to PostgreSQL, overcoming database limitations through server configuration, scaling the app with a caching layer, and recognizing the consequences of overloading the database. The speaker also highlights the importance of the caching layer for app scalability.

• What are the strategies for scaling the database and managing query performance?

  The strategies include using PostgreSQL and Redis for caching, sharding the database for scalability, setting hard limits for query performance, deleting unnecessary data to manage the database size and scalability, and avoiding overloading Postgres for computations.

• How is a second PostgreSQL server used in the context of caching and scalability?

  A second PostgreSQL server is used as a caching layer on top of the primary DB to offload query load. It is designed to handle around 500 GB of data per day. Data is imported from the kite platform into the console for buy average and profit and loss statements. The caching layer serves data for the entire day, while the primary DB remains free until the night. Scalability decisions are made based on usage patterns.

• Why is understanding query plans important in optimizing queries?

  Understanding query plans is crucial for optimizing queries, especially for complex joins and sorting. Postgres's query planner can be unpredictable and may require trial and error to understand. Autovacuum may not be suitable for all contexts, and tuning its parameters can be challenging. Vacuum full reclaims space, while vacuum analyze improves query plans. Additionally, a single node setup without master-slave replication, using foreign database wrapper for partitioning, and archiving backups in S3 for quick recovery are essential for effective query optimization.

• What are the key considerations for managing big data?

  Managing big data involves careful consideration of indexing, materialized views, denormalization, understanding data, and fine-tuning databases and tables. It's essential to prioritize queries, use partial indexing, opt for materialized views, denormalize data sets, understand the data before choosing a database, and tune tables based on specific needs rather than tuning the entire database.

• What is the speaker's experience of using postris in zeroda?

  The speaker shares their experience of using postris in zeroda, highlighting the importance of learning from mistakes and continuous improvement. They express gratitude to the postgress community for their support.

• 00:00 The speaker shares their experience of using postris in zeroda, highlights the importance of learning from mistakes, and expresses gratitude to the postgress community. They discuss the context of data usage in zeroda, challenges faced initially, and how they scaled their systems. The speaker emphasizes the need for continuous improvement, rewriting apps and schemas, and the resilience of postris in managing large data volumes.
• 08:11 Managing big data involves careful consideration of indexing, materialized views, denormalization, understanding data, and fine-tuning databases and tables. It's essential to prioritize queries, use partial indexing, opt for materialized views, denormalize data sets, understand the data before choosing a database, and tune tables based on specific needs rather than tuning the entire database.
• 15:33 Understanding query plans is crucial for optimizing queries; Postgres's query planner can be tricky. Autovacuum may not be the best solution for all contexts; vacuum full reclaims space, while vacuum analyze improves query plans. Single node setup without master-slave replication using foreign database wrapper for partitioning; backups are archived in S3 for quick recovery.
• 22:50 Using a second PostgreSQL server as a caching layer on top of the primary DB to offload query load, designed to handle around 500 GB of data per day. Data import from kite platform into console for buy average and profit and loss statements. Caching layer serves data for the entire day, primary DB remains free until the night. Scalability decisions made based on usage patterns.
• 30:05 Using PostgreSQL and Redis for caching, sharding database for scalability, setting hard limits for query performance, deleting unnecessary data, overloading Postgres for computations.
• 37:30 Using lean engineering setup with a focus on hitting the limits before seeking new solutions. Experimenting with different databases. Overcoming database limitations through server configuration. Scaling app with caching layer. Lessons learned from overloading the database. Importance of caching layer for app scalability.