Indexes are essential for improving the performance of database queries, but how can you determine the best index for a query? In this article, you will learn some basic concepts and steps to help you choose the right index for your database needs.
An index is a data structure that stores a subset of the columns and rows of a table, organized in a way that makes it easier and faster to find the relevant data. For example, an index can sort the data by a specific column, or create a hash table that maps the values of a column to their locations in the table. Indexes can reduce the amount of disk I/O and memory usage required to execute a query, as well as the number of comparisons and calculations needed.
Index is fuel for queries. We have to use index wisely because good for less data but once data size increased sometimes index not helpful so if possible use table partitions for large tables as index partitions.
Automatic Indexing (DBMS_AUTO_INDEX) in Oracle Database 19c The automatic indexing feature does the following. Identify potential automatic indexes based on the table column usage. The documentation calls these "candidate indexes". Create automatic indexes as invisible indexes, so they are not used in execution plans. Test the invisible automatic indexes against SQL statements to make sure they give improved performance. If they result in improved performance they are made visible. If performance is not improved, the relevant automatic index is marked as unusuable and later removed. The SQL statements tested against failed automatic indexes are blocklisted, so they will not be considered for automatic indexing in future.
índices ajudam de forma muita efetiva na melhor utiliza??o dos recursos computacionais em base de dados, principalmente aquelas que detém um alto volume de dados. Além disso, a execu??o de queries em tabelas com índices, bem aplicados, s?o em sua maioria, muito mais velozes se comparados com a mesma volumetria de dados e colunas. Faz parte das atividades de manuten??o executadas por um DBA periodicamente.
indexes are used to rearrange the table as per the index key id there are mainly two type of indexes clustered and non clustered indexes If the Table in OLTP then it should have indexes where as if the table is OLAP then avoid creating more indexes
Consider driving around town to find an address without a map. You would go down each street until you found the correct name, then you would find the correct number. This is slow and inefficient. Now consider having the street written down and driving directly to that street instead. Of course, you have to write down the location and store it somewhere, but if the city is large, it will usually be much faster to navigate directly to the street. This "map" you write down is a basic index, and works very similarly to a database index.
Before you create an index, you need to analyze the query that you want to optimize. You can use tools like EXPLAIN or SHOW PLAN to see how the database engine executes the query, and what kind of operations and resources it uses. You can also use statistics and metrics to measure the execution time, the number of rows scanned, the number of disk reads and writes, and the CPU usage of the query. By analyzing the query, you can identify the bottlenecks and the opportunities for improvement.
Los planes de ejecución hacen lo que pueden, no lo mejor. Para saber qué es lo mejor hay que analizar la query y sus joins, ver cual es el primer dataset por el que conviene empezar según la cardinalidad y asegurarte que hay índices para todas las tablas según el camino que queramos recorrer. A veces el resultado es frustrante, pero en la mayoría de las ocasiones es espectacular.
For a brand new table with a single row/no rows, it can be hard to choose an index as the execution plan may not reveal bottlenecks. Rather than wait for things to slow down, look at your queries and see what you are going to be joining on. For example, a transaction table; you may know in advance that it will be searching for records based on customer ID and date range. So build that as an index in advance before sifting through execution plans. You can always look for unused indexes afterwards.
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Before acting on optimizing your query focusing on index/statistics, check its end result and that of the sub-queries first to see if you need: 1. All those tens of columns 2. All those zillion of rows On DBA consulting, It's not rare that we stumble upon queries pulling out way too much data than needed by current business logic, which can be even harmful for your costs if you're on the cloud. Just by applying this simple and easy technique, you can save a lot on CPU, memory and I/O, thus ultimately speeding up your query to better satisfy your users!
Start from top of the query, minimize the function calls and extra links with other tables. Try to bring maximum columns and details from main table.
We can analyse a query in many ways. Like in SQL Server we have an option to see the Estimated and Actual plan to see where the query is lacking in performance. There are also many DMVs and DMFs to analyze the same. There is a build in application named perfmon that can record the data to troubleshoot and analyze. There is a process named Query Analyzer in SQL Server to analyze the query. And many more.
There are different types of indexes, such as clustered, non-clustered, unique, primary, secondary, bitmap, full-text, spatial, and so on. Each type has its own advantages and disadvantages, depending on the characteristics of the data and the query. For example, clustered indexes are good for range queries and sorting, but they can increase the overhead of insertions and updates. Non-clustered indexes are good for selective queries and joins, but they can require more disk space and memory. You need to understand the trade-offs and the compatibility of each index type with your database engine and your query.
Les index facilitent certes une localisation rapide des informations spécifiques ce qui nous permet d’optimiser nos requêtes. Cependant leurs utilisations quelque soit le type d’index entra?ne une charge supplémentaire surtout lors de la mise à jour des données. Voilà pourquoi il est important de judicieusement choisir les colonnes à indexer en fonction des types de requêtes fréquemment utilisées.
Leverage Partial Indexes: - Target Specific Data Subsets: Optimize queries that focus on a portion of table data by creating partial indexes that encompass only rows meeting certain conditions. - Example: A partial index on active_users where status = 'active' can speed up queries targeting those active users.
Choosing index types involves considering: Query patterns: B-trees for range queries, hashes for exact matches. Column cardinality: High for B-trees, low for Bitmaps. Data access patterns: Balance read and write efficiency. System specifics: Hardware and database support. Experiment, test, and monitor for optimal performance.
There are many factors we have to consider before choosing the index. Like Query patters, Data Characteristics, write operations etc. There are many types of indexes available. But the most commonly used are Clustered and Non-Clustered Index. Other indexes are Unique, Composite etc.
Clustered indexes and non clustered indexes are the most commonly used. The main difference is that clustered indexes affect how the data is physically stored, so these are good for incremental fields. E.g. ID field that is incremented with each insert, or a date field like 'transaction date' which is never inserted historically. Non clustered are best when they can be sequential, but handle non sequential data much more efficiently than clustered index. An example of non sequential data could be customer surname, as customers don't use your system in alphabetical order
The columns that you include in the index can have a significant impact on the query performance. You want to choose the columns that are most frequently used in the query conditions, such as the WHERE, JOIN, ORDER BY, and GROUP BY clauses. You also want to choose the columns that have high cardinality, meaning that they have many distinct values and low repetition. This can increase the selectivity and the efficiency of the index. You can also consider the data type and the size of the columns, as some types are more suitable for indexing than others.
Analyze the queries and understand the columns that are frequently used in the WHERE, JOIN, ORDER BY, and GROUP BY clauses to consider for indexing. Also, columns with high selectivity and columns that are included in joins between multiple tables are also consider for indexing.
Seek to use columns more selective because selectivity is a key point to develop proper indexes where data from large tables may be easily returned once that you are using a selective column and no table scan is executed by the database to return the desired data.
Se deben elegir en función de los reportes y análisis que se realice de la información contenida en la tabla, suele pasar que se toman como índices algunas llaves primarias que no necesariamente se usan en los reportes.
The key is to understand well the datamodel such that a proper tables/views can be joined as intended otherwise the result is likely wrong. Typically key fields for indexing will be join fields - primary/composite and foreign keys. As the model is used more note key required queries and extending indexes to cover any field used in where statements, case statements, sorting etc. As the model is better used it may be required to refactor to improve queryability. If indexing is not improving overall performance.
Surely indexing every column would mean that you could run fast queries against any column...? True! you would have a great deal of flexibility. But your indexes would probably take up more room on disk than the table that they are describing. Each index needs to be maintained which takes time and CPU and memory. So indexes which are not used create a performance deficit. Design your indexes to use a the minimum amount of columns for the maximum amount of business logic.
After you create an index, you need to test and monitor its effect on the query performance. You can use the same tools and metrics that you used to analyze the query, and compare the results before and after the index creation. You can also use tools like INDEX TUNING WIZARD or DATABASE TUNING ADVISOR to get recommendations and feedback on your index design. You should also monitor the impact of the index on the other aspects of the database, such as the storage space, the maintenance cost, the concurrency, and the workload.
Continuously Evaluate and Adapt: - As data and query patterns evolve, periodically review index effectiveness and make adjustments or additions as needed. - Regularly monitor query performance and resource utilization to identify potential index-related improvements.
If you are able to display an estimated execution plan you can clearly see where the majority of the execution load falls. This will help re-writing or discussing with a DBA key indexing missing or observing indexing being leveraged for effective execution. It is important to not simply add a new index given a query as it may be optimal to restructure the query, add an execution hint, or even create a temporary table/table variable as an intermediate data state to improve overall execution time. I've been able to improve queries time to subsecond from many minutes by taking this approach.
Make sure to review the execution plan before and after as well as overall impact as it might come with unexpected plan changes in other queries as well.
Il faut considérer qu'un nombre trop élevé d'index sur les même tables peuvent dégrader considérablement la performance des requètes de modification sur la base de donnée. Ceci parceque tous ces indexes doivent aussi être mits à jour lors de ces requêtes.
Profiler and filter queries that have a high execution time Use most expensive queries list in management studio Get the execution plan
Creating an index is not a one-time task. You need to update and maintain the index as the data and the query change over time. You can use tools like REINDEX or REBUILD to refresh the index and remove any fragmentation or corruption. You can also use tools like DROP or DISABLE to remove or deactivate the index if it becomes obsolete or redundant. You should also review and revise your index design periodically, and follow the best practices and guidelines of your database engine and your query.
We have to perform the regular maintenance of indexes. Like in SQL server we have DMVs to check the index fragmentation and based on the same we can perform the index reorganize & index rebuild. As per the best practice, if the fragmentation is between 5% to 30%, we can do the reorganize and if the fragmentation is more than 30%, we should do the index rebuild.
Search for Ola Hallengren. Brent Ozar is a good resource as well. Microsoft is a good place for Azure SQL and Azure Synapse. These people have created really effective index management processes which you can customise but work well out of the box. These processes will look for indexes which are fragmented and working inefficiently and take steps to fix them.
1. Estadísticas y Monitoreo 2. Programación de Mantenimiento 3. Reorganización de índices 4. Reconstrucción de índices 5. Fragmentación de índices 6. Eliminación de índices No Utilizados 7. Particionamiento de índices 8. Estudio de Planes de Ejecución de Consultas 9. Pruebas y Monitoreo Continuo 10. Realización de Operaciones en Períodos de Baja Carga
Rebuilding the index may degrade the performance of the OLTP. Do it during idle period to degrading the database performance.
Always fascinates me to see the percentages used in isolation. The way the indexes are used (mostly seek vs scan) determines s great deal about what type of maintenance is needed.
Consider Covering Indexes. Covering indexes encompass all columns required by a query, eliminating the need to access the underlying table data, significantly boosting performance. e.g., for a query that frequently retrieves only name and email from a users table, a covering index on those two columns can dramatically speed up results. Explore Function-Based Indexes. Function-based indexes create indexes on expressions or functions applied to columns, enabling efficient retrieval of data based on computed values. e.g., for frequent queries involving a upper(name) comparison, a function-based index on upper(name) can accelerate such searches.
It is always worth considering rewriting queries to make use of temporary tables to improve performance, reduce complexity and readability. Also consider artificial keys/surrogates where otherwise a very large text field needs to be included.
For many databases, there are statistics tables that track database activity, including index usage. If you find unused indexes, you should consider dropping them if they are not enforcing uniqueness. For Postgres, look at the last_idx_scan column in the pg_stat_user_indexes catalog view. With all of these sources, make sure to: - Read the manual to know any exceptions to how statistics are collected. - Keep in mind, these statistics may be reset either intentionally by DBAs, certain commands, or maintenance utilities. - Some indexes are only used infrequently. - Unique Indexes may be in place to enforce uniqueness even if they are not used in selects.
la gestión de índices es un proceso continuo que requiere atención y ajustes periódicos para garantizar un rendimiento óptimo de las consultas. La variedad de opiniones refleja la complejidad de este proceso y destaca la importancia de adaptarse a las necesidades específicas de la base de datos y las consultas.
Always remember the best queries are selective, so it is very important to develop selective queries because you will get to improve the query performance and save money once that queries with good performance take less time to complete and consume less resources what means a low cost query.