The core of capacity planning is to predict data growth in advance and avoid performance bottlenecks and storage crises. To do a good job in capacity planning for MySQL database, we need to start from the following points: 1. The data volume estimate should be combined with business growth expectations, and estimate the total data volume and storage requirements in the next 6 months to 1 year; 2. The disk space should consider data files, indexes, logs, backups and at least 20% redundant space, and regularly monitor the fragmentation rate; 3. In terms of performance bottlenecks, pay attention to single table size, index maintenance, full table scanning and other issues, and optimize performance by dividing tables, partitioning, archive cold data, etc.; 4. Establish an automated monitoring and expansion mechanism, continuously evaluate the matching degree of capacity planning and business development, prepare expansion plans in advance, and ensure that resources always meet business needs.

The capacity planning of MySQL database is actually the core sentence: predict data growth in advance and avoid performance bottlenecks and storage crises . Many people only start to consider expanding capacity until the server can't hold on. At this time, the problem has affected the business. Therefore, capacity planning is not a technical problem, but requires "operation and maintenance homework" to be done in advance.

The following is based on several actual scenarios and talk about how to plan the capacity of MySQL database.

1. Data volume estimate: Don’t just look at the present, but also the future

Many projects have little data volume in the early stages of online launch, but once they start running, the data growth may be exponential. For example, user registry, logging, and order flow, these tables often grow faster than expected.

Suggested practices:

• Understand the business data model and estimate the amount of new data added per core table every day
• Based on business growth expectations (such as number of users and transaction volume), calculate the total data volume in the next 6 months to one year
• Each table estimates the average row size, and combines the total number of rows to obtain the approximate storage requirements

For example, an order table has an average of 1KB per record, with 100,000 new items added every day, which is 365 million items a year, which is about 365GB of data. This has not counted index, backup, temporary space.

2. Disk space: Don’t just look at data files, but also leave enough margin

Many people only look at the current .ibd file size when calculating capacity, but disk space is far more than the data file itself.

Space overhead that is easy to ignore:

• Index occupancy: primary key and secondary index will take up extra space, especially large field indexes
• Temporary space: For example, sorting and join operations will use the tmp table, occupying disk
• Log files: binlog, undo log, and redo log will take up space, especially if the binlog is retained for a long period of time, it will be scary to accumulate.
• Backup space: Local backup and logical backup require additional disks
• Space fragmentation: frequently delete and update tables will produce fragmentation. Although InnoDB is automatically sorted, it will not automatically release disk space.

Suggested practices:

• Total disk capacity = Data file index log backup at least 20% redundant space
• Use information_schema or SHOW TABLE STATUS to view the actual size of tables and indexes
• Regularly analyze table space usage and monitor fragmentation rates

3. Performance bottleneck: Capacity does not equal performance, but capacity affects performance

Many people think that as long as the disk is large enough, it can hold on, but it is not. When the amount of data becomes larger, query performance, write performance, lock competition, backup and recovery time will all become worse.

Frequently Asked Questions:

• The single table is too large (such as more than 100 million rows) causes the query to slow down
• Index maintenance costs become higher and write speed decreases
• Full table scanning and large-scale query slow down the overall performance of the database
• Backup and recovery take a long time, affecting operation and maintenance efficiency

Coping strategies:

• Appropriately divided (horizontal or vertical) and do not put all data in one table
• Design indexes rationally to avoid redundant indexes and inefficient indexes
• Consider partition tables (for certain specific scenarios)
• Regularly archive cold data to reduce the amount of active data
• Use caching, read-write separation and other means to alleviate database pressure

4. Automatic monitoring and capacity expansion mechanism: Don’t wait until the alarm is called in the middle of the night before starting

Capacity planning is not a one-time job, but an ongoing process. As the business develops, the original design may no longer be applicable.

Suggested practices:

• Deploy monitoring systems (such as Prometheus Grafana) to view disk, table size, and index growth trends in real time
• Set capacity warning threshold (for example, disk usage exceeds 70%)
• Prepare capacity expansion plans in advance: such as adding disks, replacing instances, and migrating library
• Regularly evaluate whether capacity planning matches the pace of business development

Capacity planning is ultimately about using current data and business expectations to deduce future resource requirements . It is not a one-time transaction, but a continuous iteration process. Do it in advance and achieve twice the result with half the effort; cramming at the moment is often the rhythm of going online in the middle of the night.

Basically that's it.

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