What are the best ways to index a large table?

When deciding between clustered or non-clustered indexes, remember: clustered defines the physical order of rows in the table, while non-clustered creates a separate reference structure. A table supports only one clustered index but multiple non-clustered ones. Choose based on operations and queries. The key lies in understanding your needs and patterns to optimize the database performance.

If the table is oltp then create cluster index and multiple non clustered indexes based on requirements If table is OLAP then don’t create more indexes

Optimizing large tables is crucial for efficient database performance. Proper indexing is key, encompassing primary keys, unique constraints, clustered/non-clustered indexes, and covering indexes. Indexing foreign keys enhances JOIN operations, while avoiding over-indexing maintains a balance between query speed and data modification costs. Regularly updating index statistics is vital, and partitioning can significantly boost performance. Ongoing maintenance involves reviewing and optimizing indexes. Leverage database-specific tools to identify opportunities for optimization. Effective indexing requires strategic planning and continuous adaptation to data and usage changes, ensuring a responsive and scalable database system.

For large tables, optimise efficiency by giving priority to indexing on key columns, using both clustered and non-clustered indexes selectively, and periodically reviewing and fine-tuning.

Both has specific usecases and can be incoorporated based on desired application design and logic.Most important thing to considered is how database design has been done and whether it is normalised as per requirement neither less nor too much.Indexes are used as a general rule of thumb to improve your performance speed but if your overall design and compute resources are not inline then indexes will not provide greater performance benefits.

I like to keep it simple. I usually put a clustered index on an identity column unless I know another column that will be unique. It depends on the purpose of the table. If the table is recording transaction type data, where duplicates are possible, identity columns would be easiest to use, unless you create a composite key which is made up of multiple columns.

Traditional IDs, while common, pose a security risk due to web crawler spoofing. UUIDs offer uniqueness but lack sequentiality, potentially impacting search efficiency. In contrast, ULIDs strike a balance, providing the best of both worlds. Ideal for large datasets and distributed systems, ULIDs ensure sequentiality, enhancing search methods. Well-suited for scenarios requiring precise sequential accuracy, ULIDs may be less suitable for projects needing millisecond precision. The choice between ID, UUID, and ULID depends on project specifics, with ULID emerging as a compelling option for those seeking a blend of uniqueness, sequentiality, and improved search capabilities.

Apart from choosing the right column you have to keep in mind if you are using any inbuilt database function in that case it is better to use function based indexes.There are many type of indexes provided by different database vendor and you can easily design your application logic to use those indexes.Also it is best to have a performance benchmarking done in test environment so that you shouldn't get any issue in production environment

Checking which column to add index sometimes is very tricky but first thing that we can consider is by examining which column is frequently being used on a query.

Ten presente que dependiendo el motor, existen algunos tipos de índices mas adecuados para determinados tipos de datos. Como ejemplo existen índices específicos para valores numéricos únicos (ID) o para valores booleanos.

In general, the choice of index type depends on the specific needs of your application. You should consider the performance and queries that have the greatest impact on the application and then choose the appropriate indexing type.

Es clave entender el propósito de la tabla, y el modelo en el que se trabajará. Es muy distinta la indexación de un modelo objeto-relacional o un datawerehouse.

Proper maintenance of indexes need to be performed and has to be incorporate in your database housekeeping scripts.There are various scripts through which you can confirm whether desired indexes are fragmented or not.Sometime you can also work on changing your indexing strategies as your table size grows.Also do log all the activities that your are going to perform in database as a maintaninence activity so that in future you can easily revisit those logs for any auditing purposes.

Adding indexes is definitely a game changer for tuning up the performance of your queries but you also have to understand that great power comes great responsibility. I mean, once we add these indexes on our table we also need to in-place a proper maintenance like having a separate job that REORGANIZE or REBUILD your indexes to avoid fragmented indexes.

Indexes are not created on the basis of the size of the table but based on the queries you would run on the table. A clustered index is put in place to sort the data of the table in a certain order based on the column values. You can create a CI on a non-primary key as well. Large Size of the table comes in picture when you have large and frequent DMLs. It is impacted by indexes since the large size indexes would also get updated. Over indexing on tables is far more troubling than under indexing since it also impacts the index maintenance along with slow performing DML queries. So, it actually depends on the nature of the table how it is being used by the applications and jobs whether it is a read intensive or write intensive.

Too many index will be troublesome. Be careful in making the index. Rebuild the indexes from time to time and remove the indexes that are not used.

Extreme Normalization is needed to maintain sorted organization of data. You need to ask yourself how the data is related. Is it 1-to-1? Is it 1-to-many? Or is it Many-to-Many? Breakup your large data table into smaller tables of unique fields out of the large table. Then rebuild your master table using record identifiers that link the fields to the smaller tables using SQL queries. Breaking up your large table into a master table with links into multiple tables with underlying queries reduces the size of your large table and hence increases the speed of sorting due to the reduced record size of your table. More advice on 1-to-1-to-many-to-many to follow.

2 Corolarios importantes. "conoce a tus datos" si son muy dispersos o consecutivos, si se insertan secuencialemte, si se borran con asiduidad, etc. "No por crear mas indices la agilidad aumentará", llegado el extramo puede ser lo contrario.