You Gotta Push If You Wanna Pull
4 days ago
- #push-pull-queries
- #materialized-views
- #data-management
- Pull queries are traditional data management systems where users query data stored in various formats.
- Challenges with pull queries include performance issues, unsuitable data formats, inefficient data shapes, and incorrect data locations.
- Materialized views can precompute query results and store them in optimized formats, shapes, and locations.
- Data duplication and denormalization are key to optimizing pull queries with materialized views.
- A canonical instance of the dataset should be maintained as the source of truth to avoid inconsistencies.
- Push queries handle incremental data changes efficiently, reducing the cost and time of processing large datasets.
- Push queries are ideal for real-time use cases like fraud detection but are less suitable for human-paced queries.
- Combining push and pull queries allows for efficient incremental updates and on-demand querying.
- Incremental View Maintenance (IVM) solutions like Flink SQL, Postgres with pg_ivm, and others support complex SQL queries and state management.
- To achieve instant pull queries, constant push queries are necessary to keep materialized views up-to-date.