jaewonmoon00
commented
4 months ago We might improve the performance by having a cache or having proper indexing for models, selecting related and prefetch related to reduce the number of db queries.
more info..
Select Related and Prefetch Related: Use select_related and prefetch_related to reduce the number of database queries. select_related is used for foreign key and one-to-one relationships and performs a SQL join, thus reducing the query count. prefetch_related is used for many-to-many and reverse foreign key relationships, fetching related objects in a separate query and joining them in Python, which can significantly reduce the number of queries in list views.
Values and Values_list: When you only need a subset of fields from a model, use values() or values_list() to only fetch the fields you need, reducing memory usage and potentially speeding up your query.
Defer and Only: If there are fields in the model that are not needed, use defer() to delay loading of specific fields until they are accessed, or use only() to load only a subset of fields, minimizing the amount of data transferred.
Bulk Operations: Use bulk_create(), bulk_update(), and in_bulk() for performing operations on multiple objects in a more efficient way than processing them one by one, reducing the overall number of queries.
Database Indexes: Properly indexing your database can dramatically improve query performance. Use Django’s Meta class options indexes and index_together to add indexes to your models.
Expression Wrappers: Use F expressions to perform database operations directly in the query, and annotate() for aggregations, to minimize Python-side processing and leverage the database's power.
Caching Strategies
Per-view Cache: Use Django's per-view cache to store the response of entire views. This is useful for views that don't change often and are expensive to generate.
Template Fragment Caching: Cache parts of a template that are expensive to render using the {% cache %} template tag. This is useful for dynamic sites that have parts of pages which change infrequently.
Low-level Cache API: Use Django’s low-level cache API to cache objects or expensive calculations that are not tied to models or views directly. This allows for fine-grained control over what gets cached, how long it is cached, and when it is invalidated.
Cache Backends: Choose the right cache backend. Django supports several cache backends like Memcached or Redis. These are fast, in-memory data stores that are ideal for caching.
Query Optimization
Examine Query Performance: Use Django’s QuerySet.explain() method to get information about how the database executes a query. This can help identify inefficient queries and understand how indexes are being used.
Avoid N+1 Queries: Be vigilant about N+1 query problems, where you make one query to retrieve an object and then N additional queries to get related data. select_related and prefetch_related help mitigate this issue.
Use Aggregates Wisely: When using aggregate functions (Count, Sum, Avg, etc.), be mindful of their impact on query performance, especially on large datasets. Sometimes, it might be more efficient to calculate aggregates in Python.
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