Partitioning in Oracle Database: Because Who Wants to Search an Endless Table?

As data volumes continue to grow, managing large database tables and indexes can become a challenge. This is where partitioning comes in. Partitioning is a feature of database systems that allows you to divide large tables and indexes into smaller, more manageable parts, known as partitions. This can improve the performance and manageability of your database. Aside from performance considerations, maintenance operations, such as backups and index rebuilds, can become easier by allowing them to be performed on smaller subsets of data.

This is achieved by reducing the amount of data that needs to be scanned during query execution. When a query is executed, the database can use the partitioning information to skip over partitions that do not contain the relevant data, instead of having to scan the entire table. This reduces the amount of I/O required to execute the query, which can result in significant performance gains, especially for large tables.

There are several types of partitioning available in Oracle Database, including range partitioning, hash partitioning, list partitioning, and composite partitioning. Each type of partitioning is suited to different use cases and can be used to optimize the performance of your database in different ways. In this blog post we will look range partitioning.

Range partitioning

Here is an example of range-based partitioning in Oracle:

CREATE TABLE books (
  id NUMBER,
  title VARCHAR2(200),
  publication_year NUMBER
)

PARTITION BY RANGE (publication_year) (
  PARTITION p_before_2000 VALUES LESS THAN (2000),
  PARTITION p_2000s VALUES LESS THAN (2010),
  PARTITION p_2010s VALUES LESS THAN (2020),
  PARTITION p_after_2020 VALUES LESS THAN (MAXVALUE)
);

In this example, we have created a table called books that stores book titles, partitioned by the year of publication. We have defined four partitions, p_before_2000, p_2000s, p_2010s, and p_after_2020.

Now, when we insert data into the books table, it will automatically be placed in the appropriate partition based on the year of publication:

INSERT INTO books (id, title, publication_year)
  VALUES (1, 'Nineteen Eighty-Four', 1949);

This book will be inserted into partition p_before_2000, as the year of publication is before 2000. The following book will be placed into partition p_2000s:

INSERT INTO books (id, title, publication_year)
  VALUES (2, 'The Hunger Games', 2008);

When we query the books table, the database will only access the partitions that contain the data we need. For example, if we want to retrieve data for books published in 2015 and 2016, the database will only access partition p_2010s.

SELECT * FROM books WHERE publication_year>=2015 AND publication_year<=2016:

However, you should be aware that while partitioning can improve query performance for some types of queries, it can also negatively impact query performance for others, especially if the partitioning scheme does not align well with the query patterns. Therefore, you should tailor the partitioning to your needs and check if it brings the desired effect.

Understanding, identifying and fixing the N+1 query problem

One of the most common performance pitfalls for applications accessing data from databases is the so-called “N+1 query problem”, or sometimes also called the “N+1 selects problem”. It is the first thing you should look for when an application has performance issues related to database access. It is especially easy to run into with object-relational mappers (ORMs).

The problem

The problem typically arises when your entity-relationship model has a 1:n or n:m association. It exists when application code executes one query to get objects of one entity and then executes another query for each of these objects to get the objects of an associated entity. An example would be a blog application that executes one query to fetch all authors whose names start with the letter ‘B’, and then another query for each of these authors to fetch their articles. In pseudocode:

# The 1 query
authors = sql("SELECT * FROM author WHERE name LIKE 'B%'");

# The N queries
articles = []
FOR EACH author IN authors:
    articles += sql("SELECT * FROM article WHERE author_id=:aid", aid: author.id)

The first query is the “1” in “N+1”, the following queries in the loop are the “N”.

Of course, to anybody who knows SQL this is a very naive way to get the desired result. However, OR mappers often seduce their users into writing inefficient database access code by hiding the SQL queries and allowing their users to reach for the normal tools of their favorite programming language like loops or collection operations such as map. A lot of popular web application frameworks come along with OR mappers: Rails with Active Record, Grails with GORM (Hibernate based), Laravel with Eloquent.

How to detect

The easiest way to detect the problem in an application is to log the database queries. Virtually all ORMs have a configuration option to enable query logging.

For Grails/GORM the logging can be enabled per data source in the application.yml config file:

dataSource:
    logSql: true
    formatSql: true

For Rails/ActiveRecord query logging is automatically enabled in the development environment. Since Grails 5.2 the Verbose Query Logs format is enabled by default, which you had to explicitly enable in earlier versions.

For Laravel/Eloquent you can enable and access the query log with these two methods/functions:

DB::connection()->enableQueryLog();
DB::getQueryLog();

Once query logging is enabled you will quickly see if the same query is executed over and over again, usually indicating the presence of the N+1 problem.

How to fix

The goal is to replace the N+1 queries with a single query. In SQL this means joining. The example above would be written as a single query:

SELECT article.*
FROM article
JOIN author
  ON article.author_id=author.id
WHERE author.name LIKE 'B%'

The query interface of ORMs usually allows you to write joins as well. Here the example in ActiveRecord:

Article.joins(:authors).where("authors.name LIKE ?", "B%")

Another option when using ORMs is to enable eager loading for associations. In GORM this can be enabled via the fetchMode static property:

class Author {
    static hasMany = [articles: Article]
    static fetchMode = [articles: 'eager']
}

REST APIs

The problem isn’t limited to SQL databases and SQL queries. For REST APIs it’s the “N+1 requests problem”, describing the situation where a client application has to call the server N+1 times to fetch one collection resource + N child resources. Here the REST-API has to be extended or modified to serve the client’s use cases with a single request. Another option is to offer a GraphQL API instead of a REST API. GraphQL is a query language for HTTP APIs that allows complex queries, so the client application can specify exactly what resources it needs with in a single request.

Pagination in SQL

Pagination is the task of dividing a data set into subsequent parts of the whole data set. For example, a search engine initially only shows the first 15 results for a search query. The user can then step through the rest of the results the by clicking a “Next” button.

Ideally this feature is also supported by the underlying database system. Otherwise, the application would have to load all matching data records from the database, just to filter out the major part of of them, because the user only wanted to see page 3 of 50. A pagination request has two components: a limit and an offset. If a page contains a maximum of 15 items and page 3 is requested, then the limit would be 15 and the offset would be 30 = (page-1) × limit.

PostgreSQL, MySQL, MariaDB

The database systems PostgreSQL, MySQL and MariaDB have a straight forward syntax for pagination: LIMIT {number} OFFSET {number} . So a simple SQL query with pagination might look like this:

SELECT * FROM users ORDER BY name LIMIT 15 OFFSET 30;

Oracle DB

Oracle DB didn’t have a dedicated syntax for pagination before Oracle 12c, but it was still possible to achieve the same result with other means. With Oracle 12c a new syntax for pagination was introduced under the name “Row limiting clause”. First I’ll show the old method, then the new syntax.

The old method is based on ROWNUM . If you wanted to specify both an offset and a limit, you had to nest multiple queries:

SELECT *
FROM (SELECT *, rownum AS rnum
      FROM (SELECT *
            FROM users
            ORDER BY name)
      WHERE rownum < 45)
WHERE rnum >= 30;

The newer row limiting clause syntax is shorter and looks as follows:

SELECT * FROM users ORDER BY name
  OFFSET 30 ROWS FETCH NEXT 15 ROWS ONLY;

This syntax also allows the option to specify a percentage of rows instead of a fixed number of rows:

SELECT * FROM users ORDER BY name
  FETCH FIRST 20 PERCENT ROWS ONLY;

MS SQL Server

Microsoft’s SQL Server also supports the Oracle-like syntax with OFFSET and FETCH clauses and recommends the usage of this syntax for pagination.