To reset the root password of MySQL, please follow the following steps: 1. Stop the MySQL server, use sudosystemctlstopmysql or sudosystemctlstopmysqld; 2. Start MySQL in --skip-grant-tables mode, execute sudomysqld-skip-grant-tables&; 3. Log in to MySQL and execute the corresponding SQL command to modify the password according to the version, such as FLUSHPRIVILEGES;ALTERUSER'root'@'localhost'IDENTIFIEDBY'your_new

Turn on MySQL slow query logs and analyze locationable performance issues. 1. Edit the configuration file or dynamically set slow_query_log and long_query_time; 2. The log contains key fields such as Query_time, Lock_time, Rows_examined to assist in judging efficiency bottlenecks; 3. Use mysqldumpslow or pt-query-digest tools to efficiently analyze logs; 4. Optimization suggestions include adding indexes, avoiding SELECT*, splitting complex queries, etc. For example, adding an index to user_id can significantly reduce the number of scanned rows and improve query efficiency.

When handling NULL values ??in MySQL, please note: 1. When designing the table, the key fields are set to NOTNULL, and optional fields are allowed NULL; 2. ISNULL or ISNOTNULL must be used with = or !=; 3. IFNULL or COALESCE functions can be used to replace the display default values; 4. Be cautious when using NULL values ??directly when inserting or updating, and pay attention to the data source and ORM framework processing methods. NULL represents an unknown value and does not equal any value, including itself. Therefore, be careful when querying, counting, and connecting tables to avoid missing data or logical errors. Rational use of functions and constraints can effectively reduce interference caused by NULL.

mysqldump is a common tool for performing logical backups of MySQL databases. It generates SQL files containing CREATE and INSERT statements to rebuild the database. 1. It does not back up the original file, but converts the database structure and content into portable SQL commands; 2. It is suitable for small databases or selective recovery, and is not suitable for fast recovery of TB-level data; 3. Common options include --single-transaction, --databases, --all-databases, --routines, etc.; 4. Use mysql command to import during recovery, and can turn off foreign key checks to improve speed; 5. It is recommended to test backup regularly, use compression, and automatic adjustment.

TosecurelyconnecttoaremoteMySQLserver,useSSHtunneling,configureMySQLforremoteaccess,setfirewallrules,andconsiderSSLencryption.First,establishanSSHtunnelwithssh-L3307:localhost:3306user@remote-server-Nandconnectviamysql-h127.0.0.1-P3307.Second,editMyS

MySQL paging is commonly implemented using LIMIT and OFFSET, but its performance is poor under large data volume. 1. LIMIT controls the number of each page, OFFSET controls the starting position, and the syntax is LIMITNOFFSETM; 2. Performance problems are caused by excessive records and discarding OFFSET scans, resulting in low efficiency; 3. Optimization suggestions include using cursor paging, index acceleration, and lazy loading; 4. Cursor paging locates the starting point of the next page through the unique value of the last record of the previous page, avoiding OFFSET, which is suitable for "next page" operation, and is not suitable for random jumps.

MySQL transactions and lock mechanisms are key to concurrent control and performance tuning. 1. When using transactions, be sure to explicitly turn on and keep the transactions short to avoid resource occupation and undolog bloating due to long transactions; 2. Locking operations include shared locks and exclusive locks, SELECT...FORUPDATE plus X locks, SELECT...LOCKINSHAREMODE plus S locks, write operations automatically locks, and indexes should be used to reduce the lock granularity; 3. The isolation level is repetitively readable by default, suitable for most scenarios, and modifications should be cautious; 4. Deadlock inspection can analyze the details of the latest deadlock through the SHOWENGINEINNODBSTATUS command, and the optimization methods include unified execution order, increase indexes, and introduce queue systems.

GROUPBY is used to group data by field and perform aggregation operations, and HAVING is used to filter the results after grouping. For example, using GROUPBYcustomer_id can calculate the total consumption amount of each customer; using HAVING can filter out customers with a total consumption of more than 1,000. The non-aggregated fields after SELECT must appear in GROUPBY, and HAVING can be conditionally filtered using an alias or original expressions. Common techniques include counting the number of each group, grouping multiple fields, and filtering with multiple conditions.