MySQL database cost optimization mainly revolves around resource utilization and performance efficiency. 1. Reasonably select cloud service packages, select appropriate configurations by monitoring load conditions, give priority to memory instances and use reserved instances to save costs; 2. Optimize query and index design, avoid SELECT *, reasonably use combined indexes, and regularly analyze slow query logs; 3. Implement data archiving and database partitioning strategies, move cold data to low-cost storage, and perform horizontal table division to reduce single node pressure if necessary; 4. Use cache to reduce database pressure, cache hotspot data through Redis, reduce the number of connections and requests, and improve system stability. Continuous observation and gradual adjustment are the key to achieving long-term cost control.
The cost optimization of MySQL database actually mainly revolves around two aspects: resource utilization and performance efficiency. Many people just start to run the database, but as the data volume grows and the frequency of access increases, the cost of servers will rise rapidly. At this time, you have to consider how to save some money without affecting the business.
Let’s talk about how to do it from a few practical perspectives.
1. Reasonably choose cloud service package
Many companies use high-end machines from the beginning, but found that the utilization rate has always been very low and the waste is serious. In fact, the load situation of MySQL often does not require too high configuration.
- Monitor CPU, memory, IO usage and select according to actual conditions. For example, use AWS or Alibaba Cloud monitoring tools to see what the daily load peak is.
- If it is a scenario where more reads and less writes, memory instances can be given priority, because MySQL is more memory-sensitive, especially InnoDB buffer pools.
- Consider using reserved instances or annual monthly plans . If you run for a long time, you can save a lot of money than paying on demand.
Sometimes, if you reduce the configuration level by one, the cost saving may be thousands or even tens of thousands of years.
2. Optimize query and index design
Slow query not only affects the user experience, but also occupies more database resources, resulting in a decrease in concurrency capability, which in turn requires expansion. Optimizing queries is actually one of the most effective ways to control costs.
- **Avoid SELECT ***, only the necessary fields
- Use indexes reasonably , not the more, the better, nor do all fields need to be indexed. Fields that are often used as query conditions are suitable for adding indexes, and pay attention to the order of combined indexes.
- Check the slow log regularly, use
EXPLAINto analyze the execution plan, and see if there are any problems with full table scanning or temporary sorting.
For example: an order table. If you often check the user's orders for the last three months, it is more efficient to create a combined index for user_id and create_time than to create two indexes separately.
3. Data Archive and Database Divide Strategy
With more and more data, storage costs will naturally increase. Especially since historical data access frequency is low, it is still placed in the main library to occupy space.
- Archive cold data regularly , such as moving data from one year ago to another archive library, or exporting it to low-cost storage such as OSS and S3.
- If the data volume of a single table has exceeded 100 million, you can consider horizontal subtables and split a large table into multiple small tables, which can improve query efficiency and reduce the pressure on a single node.
- It is not recommended to start a complex database and table structure from the beginning, unless you really encounter a performance bottleneck.
In order to "plan ahead", some teams have made it particularly complicated, but instead increased maintenance costs, which is not worth the cost.
4. Use cache to reduce database stress
Caching is the most direct way to reduce burden. Many repetitive read operations can be undertaken by cache layers like Redis, without having to check the database every time.
- Cache hot data, such as popular product information, user basic information, etc.
- Set a reasonable expiration time to prevent data inconsistency
- It can combine cache penetration, cache breakdown, and cache avalanche processing mechanisms to improve system stability
Although cache cannot completely replace the database, it can significantly reduce the number of database connections and requests, which is very helpful in saving resources.
Basically that's it. Optimizing costs does not require too complicated things to be done as soon as you start. The key is to continue to observe and gradually adjust. Some changes seem to be small, but they accumulate a lot, and the effect will be obvious in the long run.
The above is the detailed content of MySQL Database Cost Optimization Strategies. For more information, please follow other related articles on the PHP Chinese website!
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