MySQL: 部分インデックスとBitmapインデックスを使いこなせ！低基数列のパフォーマンスを最適化する

MySQL: 低基数/低い選択性列のインデックス方法

低基数/低い選択性列に対して有効なインデックス方法としては、以下の2つが挙げられます。

部分インデックスとは、列の一部のみをインデックス化する手法です。具体的には、列の最初のN文字のみをインデックス化したり、列の特定の値のみをインデックス化したりします。

部分インデックスは、通常のインデックスよりもスペース効率が高く、インデックススキャンにかかる時間も短くなります。しかし、部分インデックスを使用できるのは、クエリの条件が列の一部のみを使用する場合に限られます。

Bitmap indexは、各値に対してビットを割り当てるインデックスです。クエリの条件に一致する値のビットが立っている行のみが検索対象となります。

Bitmap indexは、低基数/低い選択性列に対して非常に効果的なインデックスです。しかし、Bitmap indexは、通常のインデックスよりもスペース効率が低く、インデックスの作成にかかる時間も長くなります。

低基数/低い選択性列に対してどのインデックス方法を使用するのが最適かは、クエリの条件やデータ量によって異なります。以下の状況では、部分インデックスが有効です。

• 列の最初のN文字のみを条件とするクエリが多い場合

以下の状況では、Bitmap indexが有効です。

• 列の値の数が非常に少ない場合
• 列の値の分布が偏っている場合

Partial index

CREATE INDEX idx_customers_name_first_n ON customers (name(30));

This index will only index the first 30 characters of the name column. This is useful for queries that only need to match the beginning of the name, such as a search autocomplete feature.

CREATE BITMAP INDEX idx_customers_active ON customers (active);

This index will create a bitmap for the active column. The bitmap will have one bit for each possible value of the active column. For example, if the active column can be either 0 or 1, then the bitmap will have two bits. The bit for the value 0 will be set for all rows where the active column is equal to 0, and the bit for the value 1 will be set for all rows where the active column is equal to 1.

To use a bitmap index, you can use the USING BITMAP clause in your query. For example, the following query will use the idx_customers_active index to find all active customers:

SELECT * FROM customers
WHERE active = 1
USING BITMAP idx_customers_active;

Note:

• Partial indexes and bitmap indexes are not supported by all MySQL versions. Please refer to the MySQL documentation for your specific version to see if these features are available.
• Bitmap indexes can be very efficient for low-cardinality columns, but they can also be very large. You should only use bitmap indexes for columns that have a very small number of possible values.

I hope this helps!

A composite index is an index that includes multiple columns. This can be useful for low-cardinality columns that are frequently used together in queries. For example, if you have a table with a country column and an city column, and you frequently query for customers in a specific city, you could create a composite index on the country and city columns. This index would be more efficient than a separate index on each column, because it would allow MySQL to find the matching rows in a single index scan.

Avoid using indexes on columns that are frequently updated

Indexes can be expensive to maintain, especially for columns that are frequently updated. If you have a low-cardinality column that is frequently updated, it may be better to avoid indexing it altogether. Instead, you can rely on full table scans to find the matching rows.

Use a different data type

If you have a low-cardinality column that is currently stored as a string, you may want to consider changing the data type to an integer or an enumeration. This can make the column more efficient to store and query.

Denormalize the data

Denormalization is the process of storing redundant data in multiple tables. This can improve query performance for low-cardinality columns, but it can also make the data model more complex and difficult to maintain.

If you are using MySQL for a database that has a lot of low-cardinality columns, you may want to consider using a different database that is better optimized for this type of data. For example, PostgreSQL is a good choice for databases with a lot of categorical data.

Here is a table summarizing the pros and cons of each method:

The best method for dealing with low-cardinality/selectivity columns will depend on the specific situation. You should carefully consider the pros and cons of each method before making a decision.