Are you frustrated by slow queries that take too long to execute and consume too much resources? If so, you are not alone. Many database administrators face this challenge on a regular basis. Fortunately, there are some effective strategies that can help you speed up a slow query and improve your database performance. In this article, we will cover six of them.
The first step to speed up a slow query is to identify the bottleneck that is causing the delay. You can use various tools and methods to analyze your query and find out where it is spending most of the time and resources. For example, you can use the EXPLAIN or EXPLAIN ANALYZE commands in SQL to see the execution plan and the cost estimates of your query. You can also use the SHOW PROCESSLIST command to see the current activity and status of your connections and queries.
To speed up a identified slow query, check for the objects used (tables, views and procedures) in a query. When that has been noted, follow the below checks: 1. Check if the table stats where up-do-date. If not, query optimizer may not select optimal query plan. 2. Check if there is any calculative field being used in a query. If yes, it should be parameterized to avoid high disk IO. 3. Check if * being used. Replace this with only the fields required in output. 4. Check if filter is used, if not use where clause to filter the data to return only the desired data. 5. Run the workload/ your query by selecting estimated execution plan and see if it reports any missing indexes. 5. Finally, you can use Index tuning wizard to tune the query.
Some quick and dirty things you can consider - Use indexes on frequently used columns in the WHERE, JOIN, and ORDER BY clauses. But don’t overdo it as they can slow down data modification operations. - Minimise the use of wildcard characters, such as % and _, like when filtering entries on strings. Wildcard characters force the database to scan the entire table, which can be inefficient and time-consuming. Use them only when necessary and with an index on the column. - Use JOIN instead of subqueries when possible. SubQs can be slow and complex, especially if they are executed multiple times for each row of the outer query. JOINs can be faster and simpler than subqueries in many cases.
Pour Accélérer une requête : - Il faut privilégier l'indexation des tables et le partitionnement des tables trop volumineuses. - limiter les résultats retourner. - Utiliser les joints des sous requêtes.
To speed up a slow query just dont run it thats the most basic part. Whats the best way to avoid waiting? Dont do it ... Cary Millsap 101
To speed up a identified slow query, Check the Selected tables, views and Procedure used in query. After that follow the below checks. 1. Check the table stats and update it, If it is not Up to dated. 2. Check Where Clause, joins, group by and order by in query. 3. Zoom Out to Consider All Data Requests 4. Instead of Using SELECT DISTINCT Just SELECT. 5. Check the Table Indexes and if needed created new one or rebuild.
One of the most common reasons for a slow query is a lack of proper indexes. Indexes are data structures that help your database find and retrieve data faster. However, not all indexes are created equal. You need to choose the right type, size, and order of your indexes based on your query and data characteristics. For example, you can use a primary key index for unique and frequently accessed columns, a composite index for multiple columns that are often queried together, and a partial index for columns that have a low cardinality or a high selectivity.
The best way would be to modify your SELECT so that the engine can accomplish the task by an index only scan. And modify the WHERE clause to avoid scanning the whole index and finally the SORT clause should match the index sorting that way you ensure the system doesn't need to do any extra processing, in memory sorting or fetching tuples.
1. Avoid Over-Indexing Too many indexes on a table can lead to increased storage requirements and decreased performance during data modifications. 2. Use the Database Engine Tuning Advisor (DTA): DTA can analyze a workload and recommend indexes, statistics, and partitions to improve performance. 3. Index Statistics Keep statistics up-to-date using the UPDATE STATISTICS command or enable the auto-update statistics option.
Do not forget about functional indexes which can help you not only pre-compute some of data, but also reduce some of indexes by use of hash functions. Imagine have b-tree index on large set of long strings, or have b-tree index on large set of relatively short hashes. (Tradeoff is that you will need to "recheck" rows returned by such index, but as index is much smaller, and therefore works faster this is usually good comporomise)
Sometimes including columns (nonkey columns) in an index might come in really handy. If you need to define two or more indexes with some common columns, you can use INCLUDED COLUMNs to create only one index. You can thus consider common columns to be key columns, and other columns to be included columns. Another use case is when in some queries, certain columns appear in a different order or direction (often in ORDER BY clause). Thus, those columns are moved to included columns to avoid adding further indexes.
Los índices son fundamentales para la optimización de consultas ya que la vuelven más concreta, lo que debemos tener en cuenta es también una buena administración de estos, ya que existen varios factores que tenemos que cuidar, como su fragmentación, el factor de relleno, el espacio que ocupan en la base de datos, si es ascendente o descendente, tener actualizadas sus estadísticas y el no tener índices redundantes o en desuso. En mi experiencia he visto empresas las cuales no cuentan con un dba o un desarrollador con conocimiento en estos temas en las cuales la velocidad y el performance de sus procesos son un antes y un después de la implementación de los índices adecuados y su correcta administración.
Another way to speed up a slow query is to refine your filters and conditions. Filters and conditions are clauses that limit the scope and results of your query. However, if they are too vague, complex, or inefficient, they can slow down your query and increase the workload of your database. For example, you can avoid using wildcard characters, LIKE operators, OR operators, and subqueries in your filters and conditions. Instead, you can use exact matches, IN operators, AND operators, and joins to make your filters and conditions more precise and faster.
Depending on your database, don't forget to check for implicit conversions between datatypes in selection criteria. If specified improperly, your query could be doing such a conversion for every row retrieved!
Check your query and narrow down result sets by optimizing WHERE clauses, avoiding unnecessary conditions, and using appropriate operators for comparisons. Check your plan for FTS or cartesian and take action.
Hay ocasiones en que aplicar las premisas de "divide y vencerás" y/o "el camino largo resulta ser el más optimo" son perfectamente aplicables a la hora de crear consultas: recuperando los campos llave [primero] y posteriormente los campos que complementan. ??
Optimize Joins: Use INNER JOIN instead of OUTER JOIN if possible, as INNER JOIN is usually faster. Consider using appropriate join algorithms (e.g., hash joins, nested loop joins) based on the size of the tables and the available indexes.
A simple but effective strategy to speed up a slow query is to reduce the amount of data that your query has to process and return. You can do this by using various techniques to limit, aggregate, or compress your data. For example, you can use the LIMIT clause to specify the maximum number of rows that your query should return. You can also use the GROUP BY clause and aggregate functions to summarize your data and reduce the number of rows. Moreover, you can use compression algorithms or tools to shrink the size of your data and save storage space and bandwidth.
Remove unnecessary columns, rows, or use summary tables to decrease the volume of data processed, enhancing query performance. Avoid using select * which can cause lot of data retrieval. Limit to your requirement .
- Don't be afraid to push processing from application into database, while you will be stil reading same amount of data from storage (and cache) you will save cpu on networking. And if primary instance gets too busy, read replicas wtih "fresh enough" data will be of help. - avoid to use OFFSET, as it loads data that will be discarded, prefer use index and where condition to skip first rows/pages of data without loading them first.
Oftentimes you can reduce your data by joining better. Instead of joining entire tables, limit the joins further based on a date range or other factors. Doing this has sped up queries by several orders of magnitude, from taking several minutes to sub seconds for the same data.
Sometimes, the problem is not with your data or your database, but with your query itself. You may have written a query that is too complicated, redundant, or inefficient for your database to handle. In that case, you can try to rewrite your query and make it simpler, clearer, or more optimized. For example, you can use aliases, comments, and formatting to make your query more readable and maintainable. You can also use common table expressions, views, or stored procedures to break down your query into smaller and reusable parts. Furthermore, you can use best practices and standards to write your query and avoid errors and bugs.
Another crucial thing: Make sure you haven't inadvertently introduced a MERGE JOIN CARTESIAN operation into a query by accidentally commenting out a filter, or having that filter ignored. That said, remember that in certain rare cases, a Cartesian product is actually desirable.
It is important to perform below checks to improve query performance. 1. Check query execution plan. 2. Check if indexes are applied, if indexes are already applied then perform re-indexing or create indexes. 3. Check joins 4. Use count(column name) instead of count(*).
Based on the explain plan, optimize SQL syntax, leverage JOINs effectively, and consider alternative query structures to improve overall efficiency. Some of the rdbms have the advisory options, make use of them effectively and apply,
The last resort to speed up a slow query is to upgrade your hardware and infrastructure. Sometimes, your query is slow because your database server is overloaded, outdated, or insufficient for your needs. In that case, you can try to upgrade your hardware and infrastructure and provide more resources and capacity for your database. For example, you can increase your memory, CPU, disk, or network speed and performance. You can also migrate your database to a cloud service or a distributed system and leverage the scalability and availability of the cloud.
There can be various reasons for a query running slow and there can be various methods you can try to find the cause. Assuming all looks normal at server level and there is no blocking, pull the query execution plan from cache and look for - Key lookups and Index scans. There is a possibility that Indexes are missing or are not optimised. - Difference in actual row count and estimated row count.This could be due to outdated Statistics on mid or large size tables. Also look for - - Wait type and their duration during the query execution. Wait types are extremely helpful in troubleshooting issues. - Use of Non-Sargable predicates in query leading to unnecessary scans. It will start appearing as a problem when the data starts growing.
I think and the most obvious solution to find out is to identify why it is slow. There could be 100 reason why query responds slow. Change in data, wrong access method selected, difference in cardinality feedback, index issues, fragmentation, etc etc . After server, select the execution plan from memory and identify where it is spending more time and see if it make sense .
Most of all: Get yourself some training! You need to understand /why/ your database works the way it does. You also need to understand how that database version's query optimizer works in detail so that you don't go running off chasing at shiny objects down rabbit holes. And by "training" I don't mean "Read a few blog posts." You need to actually READ THE DOCUMENTATION and understand implicitly how it works. There are plenty of good training offerings by reputable companies out there specific to your database and version; go get you some.
Aplicando algo de lo que he descrito en este espacio, he tenido del gusto de observar como se ilumina el rostro del usuario... como si el tiempo y la estadística pasara a un segundo plano ??
More often than one can imagine, query is waiting for obtaining locks taken by other queries. it will not show in CPU, or disk IO metrics, but will be clearly visible in locks statistics view of database. Situation worsens when query that took some locks already, is then waiting for some other extra lock before can complete own transaction. 1) remember that in ACID database single statement is it's own transaction, it is not very efficient to have BEGIN; <one statement>; COMMIT; even if your ORM "insists". 2) if you need to have transaction, try to get all locks before doing operations on data. 3) embrace lock_wait_timeout setting and keep it LOW 4) teach application to retry statemnet, if lock was not obtained.