Last updated on Jul 15, 2024

You're dealing with a massive database. How can you speed up your queries with optimized indexing?

When handling a massive database, the speed of your queries can significantly impact your productivity and performance. An optimized indexing strategy is a key to ensuring that your data retrieval processes are as efficient as possible. Indexing creates a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure. Properly utilizing indexing can drastically reduce the time it takes for your database to process queries, especially when dealing with large volumes of data.

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1 Understanding Indexes

Before diving into optimization, you need to understand what indexes are and how they function. Indexes in a database are somewhat like the index in a book; they help you find information quickly without having to look through every page. A database index is a data structure that improves the speed of operations in a table. Indexes are used to quickly locate data without having to search every row in a database table every time a database table is accessed. They can be created using one or more columns of a database table, providing the basis for both rapid random lookups and efficient access of ordered records.

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Nilson Braga

Analista de dados | BI | Excel | SQL | Python
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A melhor maneira é começar a analisar as consultas mais frequentes e identificar os campos mais utilizados em filtros e junções. Criar índices apropriados nesses campos, utilizando índices compostos para cobrir múltiplas colunas quando necessário. Utilizar índices de cobertura para reduzir o número de acessos ao disco e índices particionados para gerenciar grandes volumes de dados. Com frequência, avaliar a performance dos índices e remover ou ajustar os que não estão mais sendo utilizados de forma eficaz.

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Johnatan Kayevo

International Software Engineer at @BMW | Black Power content creator | Bitcoin developer
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To understand better why the index creation improves the search in massive databases it's important to know how they do it. When you create an index for a column in a table the database manager will create a new binary three with all values in that column. This way, the database storage increases, but the query time decreases. Once the search in a binary tree has a logarithmic complexity.

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2 Primary Keys

Primary keys are a fundamental aspect of indexing. They uniquely identify each record in a database table, which means that indexing by primary keys allows for quick and direct access to data. When you define a primary key, the database automatically creates a unique index on the primary key column. This is beneficial as it prevents duplicate rows from existing in the table and ensures that the query engine can quickly find a record when the primary key is provided in a query.

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Paulo Aleo

Consultor dba cloud
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Sempre na criação de indice , verifique a cardinalidade x size . Muitas vezes o impacto na operação de insert/update divido ao size e maior que a otimização da consulta.

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3 Index Types

Different types of indexes serve various purposes. The most common type is the B-tree index, which is excellent for high-cardinality data (columns with unique or almost unique values). Bitmap indexes are efficient for low-cardinality data (columns with few unique values, like 'yes'/'no' fields). Hash indexes are ideal for equality comparisons that use the equals operator. Understanding the nature of your data and the queries you run most often will guide you in choosing the right type of index to use.

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Johnatan Kayevo

International Software Engineer at @BMW | Black Power content creator | Bitcoin developer
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We can use "query performance analyzer" tools to discover how the most run queries in a database. For example the "MySQL Performance Schema" in MySQL system and "pg_stat_statements" in PostgreSQL.

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4 Column Selection

Selecting the right columns to index is crucial. Indexing columns used frequently in WHERE clauses can significantly speed up query performance. However, indexing too many columns can slow down write operations because the indexes must be updated each time a row is inserted or updated. You should also consider indexing columns used in JOIN operations, as this can greatly speed up queries that involve multiple tables.

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5 Composite Indexes

Composite indexes are indexes on multiple columns. When you often query using several columns, a composite index can provide a performance boost. The order of columns in your composite index matters; it should match the order of columns in your query conditions. This alignment allows the database engine to efficiently navigate through the index to find the relevant rows.

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6 Maintenance Matters

Regular maintenance of your indexes is essential for preserving their performance benefits. Over time, as data is added, removed, or updated in your database, indexes can become fragmented. This fragmentation can lead to inefficient query performance. Rebuilding or reorganizing indexes can remedy this issue. Additionally, updating statistics on your indexes ensures that the query optimizer has accurate information about the distribution of data within your index, leading to more efficient query plans.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You're dealing with a massive database. How can you speed up your queries with optimized indexing?

1

2

3

4

5

6

7

1 Understanding Indexes

2 Primary Keys

3 Index Types

4 Column Selection

5 Composite Indexes

6 Maintenance Matters

7 Here’s what else to consider

Database Development

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You're dealing with a massive database. How can you speed up your queries with optimized indexing?

1

2

3

4

5

6

7

1 Understanding Indexes

2 Primary Keys

3 Index Types

4 Column Selection

5 Composite Indexes

6 Maintenance Matters

7 Here’s what else to consider

Database Development

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