When the CPU needs more data than can fit in the CPU cache, main memory bandwidth becomes a bottleneck. But with small tables, speed is usually not the problem. Having large tables compared to the amount of memory is the most common limiting factor. When the data is in main memory, we must process it to get our result. This is easier to optimize than seeks because you can read in parallel from multiple disks. With modern disks, one disk delivers at least 10–20MB/s throughput. When the disk is at the correct position, we need to read or write the data. The way to optimize seek time is to distribute the data onto more than one disk. This time improves slowly with new disks and is very hard to optimize for a single table.
With modern disks, the mean time for this is usually lower than 10ms, so we can in theory do about 100 seeks a second. It takes time for the disk to find a piece of data. System bottlenecks typically arise from these sources: A DBA must evaluate whether it is possible to tune the application or reconfigure the server to avoid these bottlenecks, or whether more hardware resources are required. Optimizing at the Hardware LevelAny database application eventually hits hardware limits as the database becomes more and more busy. The main memory areas to configure are the buffer pool, the key cache, and the MySQL query cache.
Are all memory areas used for caching sized correctly? That is, large enough to hold frequently accessed data, but not so large that they overload physical memory and cause paging. The storage engine handles most locking issues without involvement from you, allowing for better concurrency in the database and reducing the amount of experimentation and tuning for your code. Again, the choice of storage engine is significant. Does the application use an appropriate locking strategy? For example, by allowing shared access when possible so that database operations can run concurrently, and requesting exclusive access when appropriate so that critical operations get top priority. Compression is available for all kinds of workloads with tables, and for read-only tables. In particular, compressed tables use less disk space and so require less disk I/O to read and write the data. Does each table use an appropriate row format? This choice also depends on the storage engine used for the table. Are you using the appropriate storage engine for each table, and taking advantage of the strengths and features of each storage engine you use? In particular, the choice of a transactional storage engine such as or a nontransactional one such as can be very important for performance and scalability. Are the right indexes in place to make queries efficient?. Are the tables structured properly? In particular, do the columns have the right data types, and does each table have the appropriate columns for the type of work? For example, applications that perform frequent updates often have many tables with few columns, while applications that analyze large amounts of data often have few tables with many columns. Optimizing at the Database LevelThe most important factor in making a database application fast is its basic design: 
I always thought of maintaining the checklist for MySQL Performance Optimization, This actually helps me to draw the first line of action items and developing scope at advanced stages.
0 kommentar(er)
