Things every developer absolutely, positively needs to know about database indexing - Kai Sassnowski

Summary

In this insightful talk, the speaker, Keselowski, enlightens developers on the intricacies of database indexing, emphasizing its importance for application performance. He clarifies misconceptions, delves into data structures like B-trees, and discusses the impact of functions and inequality operators on index usability. Through a live coding session, he illustrates common pitfalls, such as the inefficiency of full table scans and the importance of index order in multi-column indexes. The talk underscores that indexing is a nuanced, developer-centric task, crucial for optimizing query performance.

Takeaways

• 🗣️ The speaker emphasizes the importance of database indexing for developers, highlighting that it's a crucial aspect of ensuring application performance.
• 🔍 Indexes are primarily used to improve read performance in databases, which can significantly enhance the speed of queries and overall application responsiveness.
• 📚 The analogy of a phone book is used to explain what an index is, illustrating how an ordered representation of data can expedite searches.
• 🌳 The B-tree is introduced as the underlying data structure for database indexes, with a focus on its balanced nature to ensure efficient searching.
• 🔑 The index only contains the values of the columns it's created on, and uses a row ID to reference the original table for data not stored in the index.
• ⚡ The benefits of indexing include fast search capabilities due to binary search-like operations, with logarithmic scalability.
• 🛑 However, indexing is not without trade-offs; it can slow down write operations such as inserts, updates, and deletes due to the need to update the index as well.
• 👀 Understanding execution plans is vital for diagnosing how a database will use an index to execute a query, with different 'access types' indicating different strategies.
• 💡 The talk demonstrates practical scenarios where mismanagement of indexes can lead to suboptimal performance, emphasizing the need for developers to have a deep understanding of how to design effective indexes.
• 🚫 Common pitfalls include the misuse of functions in WHERE clauses which can invalidate the use of indexes, and the importance of column order in multi-column indexes.
• 🔄 The order of columns in an index matters significantly, as does the presence of inequality operators which can limit the effectiveness of an index.

Q & A

What is the main topic of the speaker's presentation?

The main topic of the speaker's presentation is database indexing and why every developer should understand its importance and nuances.

Why did the speaker change the title of Joel Spolsky's blog post in their talk?

The speaker changed the title to focus on database indexing instead of Unicode because they wanted to emphasize the importance of understanding indexing in the context of their talk.

What is the speaker's profession and where is their workplace located?

The speaker works for a software development agency located in Berlin.

What is the common mistake the speaker sees in developers' understanding of database indexing?

The common mistake is that developers often think that adding an index to every column in the 'where' clause of a query will improve performance, without understanding the nuances and potential downsides of indexing.

What is the B-tree and why is it significant in the context of database indexing?

The B-tree is a balanced tree data structure used in databases to store indexes. It is significant because it allows for efficient searching, insertion, deletion, and access to data in a sorted manner.

Why is the order of data important when creating an index?

Order is important because it allows for more efficient searching, such as binary search, which is much faster on ordered data compared to unordered data.

What is the purpose of the doubly linked list in the leaf nodes of a B-tree?

The doubly linked list in the leaf nodes allows for efficient sequential scanning of data without having to go back up the tree each time, thus improving performance during searches.

What additional piece of information does the database store along with the indexed values?

The database stores a row ID, which is a database-internal identifier that points to a specific row in a table, allowing the database to retrieve the full row if needed.

What is the difference between a 'range scan' and a 'full index scan' according to the speaker?

A 'range scan' uses the index to find the starting point of a range and then scans through the leaf nodes within that range. A 'full index scan' scans through every value in the index without using it for limiting or filtering the rows.

Why did the speaker's initial attempt to optimize a query with an index not improve performance?

The initial attempt did not improve performance because the query involved a function on the indexed column (`YEAR` function on the `created_at` column), which prevented the database from using the index effectively.

What is the consequence of using functions on indexed columns in a query's WHERE clause?

Using functions on indexed columns in a WHERE clause can prevent the database from using the index, as the function's result may not correlate with the index values, thus negating the benefits of indexing.

What is the 'force index' and why should it be used with caution?

The 'force index' is a way to force the database to use a specific index for a query. It should be used with caution because it can lead to suboptimal query performance if the chosen index is not the most efficient for the query's needs.

What is the term used to describe an index that can be used to satisfy a query entirely from memory?

An 'index only scan' is used to describe an index that contains all the data needed for a query, allowing the operation to be performed entirely in memory without additional disk reads.

Why did adding the 'total' column to the index improve the performance of the query?

Adding the 'total' column to the index improved performance because it allowed the query to perform an 'index only scan', meaning all necessary data was available in the index, eliminating the need for additional disk reads.

What is the significance of the order of columns in a multi-column index?

The order of columns in a multi-column index is significant because it determines which parts of the index can be used for filtering data in a query. The database can only use the index from left to right and cannot skip columns.

What is the impact of using inequality operators on the usage of a multi-column index?

Using inequality operators on any of the columns in a multi-column index can limit the effectiveness of the index. The index can only be used up to the point where the inequality operation is applied, as if the index stops at that column.

What is the final recommendation the speaker gives regarding indexing and query optimization?

The speaker recommends that developers should consider indexing as their concern, design indexes specifically for the queries they write, and understand that the context of data access is crucial for creating efficient indexes.