Optimize MySQL deduplication query techniques for tens of millions of data

When dealing with large-scale data, MySQL deduplication query is a common and important task.

This article will discuss in depth how to optimize MySQL deduplication queries with tens of millions of data, and provide practical skills and best practices from index optimization, query optimization to hardware resource utilization.

I. Understand the de-duplication query.

Delete queries are usually used DISTINCTKeyword to ensure that there are no duplicate lines in the result set.

E.g:

SELECT DISTINCT column1, column2 FROM large_table;

However, when the amount of data reaches the tens of millions level, this simple query method can become very slow because it requires scanning the entire table and checking whether each row is repeated.

II. Index optimization.

Indexes are a key tool to improve query performance.

For deduplicated queries, a reasonable index can significantly reduce the amount of data scanned.

\n#

1. Single-column index.

If the deduplication query only involves a single column, you can create an index for that column:

CREATE INDEX idx_column1 ON large_table(column1);

\n#

2. Multi-column combined index.

If the deduplication query involves multiple columns, a composite index can be created:

CREATE INDEX idx_columns ON large_table(column1, column2);

The order of the combined index is important and should be designed according to the frequency and order of use of the columns in the query.

III. Query optimization.

In addition to indexes, the way the query itself is written can also affect performance.

Here are some optimization suggestions: \n#

1. Avoid unnecessary columns.

Select only the columns you need, not use SELECT*：

SELECT DISTINCT column1, column2 FROM large_table;

\n#

2. Use an overlay index.

If all columns of the query are in the index, MySQL can get data directly from the index without having to access the table data:

CREATE INDEX idx_covering ON large_table(column1, column2);

Then execute the query:

SELECT column1, column2 FROM large_table GROUP BY column1, column2;

\n#

3. Partition table.

For very large tables, consider using partitioned tables.

Partitioning can divide tables into smaller, manageable parts to improve query performance:

ALTER TABLE large_table PARTITION BY RANGE (column1) (
    PARTITION p0 VALUES LESS THAN (1000),
    PARTITION p1 VALUES LESS THAN (2000),
    ...
);

Fourth, the utilization of hardware resources.

Hardware resources are also an important factor affecting query performance.

Here are some optimization suggestions: \n#

1. Increase memory.

More memory can reduce disk I/O operations because more data can be cached into memory.

Ensure innodb_buffer_pool_sizeLarge enough to hold most or all of the data.

\n#

2. Use SSD.

Solid state drives (SSDs) have faster read and write speeds than traditional mechanical hard drives (HDDs), which can significantly improve query performance.

\n#

3. Parallel query.

MySQL supports parallel queries, you can set innodb_read_io_threadsSuminnodb_write_io_threadsParameters to enable:

SET GLOBAL innodb_read_io_threads = 8;
SET GLOBAL innodb_write_io_threads = 8;

V. Examples and summaries.

Suppose we have a table containing user information users, which contains a large number of duplicate records.

We want to remove duplicate user records.

The following is an optimized query example:

-- 创建组合索引
CREATE INDEX idx_user_email ON users(email);

-- 使用覆盖索引进行去重查询
SELECT email FROM users GROUP BY email;

Through the above steps, we can significantly improve the de-duplication query performance of tens of millions of data.

The key is to use indexes wisely, optimize query statements, and make full use of hardware resources.

I hope this article can provide you with valuable information to help you efficiently deal with the million-level data deduplication challenge.