CachinginLaravelsignificantlyimprovesapplicationperformancebyreducingdatabasequeriesandminimizingredundantprocessing.Tousecachingeffectively,followthesesteps:1.Useroutecachingforstaticrouteswithphpartisanroute:cache,idealforpublicpageslike/aboutbutno

The main reasons for slow operation of DOM are the high cost of rearrangement and redrawing and low access efficiency. Optimization methods include: 1. Reduce the number of accesses and cache read values; 2. Batch read and write operations; 3. Merge and modify, use document fragments or hidden elements; 4. Avoid layout jitter and centrally handle read and write; 5. Use framework or requestAnimationFrame asynchronous update.

MySQL query performance optimization needs to start from the core points, including rational use of indexes, optimization of SQL statements, table structure design and partitioning strategies, and utilization of cache and monitoring tools. 1. Use indexes reasonably: Create indexes on commonly used query fields, avoid full table scanning, pay attention to the combined index order, do not add indexes in low selective fields, and avoid redundant indexes. 2. Optimize SQL queries: Avoid SELECT*, do not use functions in WHERE, reduce subquery nesting, and optimize paging query methods. 3. Table structure design and partitioning: select paradigm or anti-paradigm according to read and write scenarios, select appropriate field types, clean data regularly, and consider horizontal tables to divide tables or partition by time. 4. Utilize cache and monitoring: Use Redis cache to reduce database pressure and enable slow query

Laravel performance optimization can improve application efficiency through four core directions. 1. Use the cache mechanism to reduce duplicate queries, store infrequently changing data through Cache::remember() and other methods to reduce database access frequency; 2. Optimize database from the model to query statements, avoid N 1 queries, specifying field queries, adding indexes, paging processing and reading and writing separation, and reduce bottlenecks; 3. Use time-consuming operations such as email sending and file exporting to queue asynchronous processing, use Supervisor to manage workers and set up retry mechanisms; 4. Use middleware and service providers reasonably to avoid complex logic and unnecessary initialization code, and delay loading of services to improve startup efficiency.

In MongoDB, the documents in the collection are retrieved using the find() method, and the conditions can be filtered through query operators such as $eq, $gt, $lt, etc. 1. Use $eq or directly specify key-value pairs to match exactly, such as db.users.find({status:"active"}); 2. Use comparison operators such as $gt and $lt to define the numerical range, such as db.products.find({price:{$gt:100}}); 3. Use logical operators such as $or and $and to combine multiple conditions, such as db.users.find({$or:[{status:"inact

An effective indexing strategy needs to be combined with query patterns, data distribution and business needs, rather than blindly added. 1. Understand common query paths, prioritize the establishment of joint indexes for multi-field combination, sorting or grouping operations, and pay attention to index order; 2. Avoid excessive indexing to reduce write overhead, regularly clean redundant indexes, and view unused indexes through the system view; 3. Use overlay indexes to make the index itself contain the fields required for query, reduce table back operations, and improve reading efficiency; 4. Consider partitioning and indexing for super-large tables, select partition keys that are consistent with the query conditions, and establish a reasonable index for each partition, but the complexity and performance improvement are required.

$unwinddeconstructsanarrayfieldintomultipledocuments,eachcontainingoneelementofthearray.1.Ittransformsadocumentwithanarrayintomultipledocuments,eachhavingasingleelementfromthearray.2.Touseit,specifythearrayfieldpathwith$unwind,suchas{$unwind:"$t

MongoDBAtlas' free hierarchy has many limitations in performance, availability, usage restrictions and storage, and is not suitable for production environments. First, the M0 cluster shared CPU resources it provides, with only 512MB of memory and up to 2GB of storage, making it difficult to support real-time performance or data growth; secondly, the lack of high-availability architectures such as multi-node replica sets and automatic failover, which may lead to service interruption during maintenance or failure; further, hourly read and write operations are limited, the number of connections and bandwidth are also limited, and the current limit can be triggered; finally, the backup function is limited, and the storage limit is easily exhausted due to indexing or file storage, so it is only suitable for demonstration or small personal projects.