Since we are not modelling the effect of multiple simultaneously running queries, the parameters which might make a material impact on the query execution time are shared_buffers (the change requires restart) and work_mem (can be modified in session). The total execution time is a little but over one second. We can also notice that the number of shared buffers is insufficient (to achieve consistent results, run the same query or EXPLAIN ANALYZE more than once) and that PostgreSQL chooses to run two parallel workers to speed up the process. Looking at this query plan, we can see that the optimizer uses two indexes on table flight,Īnd then scans all the blocks checking for the actual departure. Execution plan with default memory allocations The query plan will show up in text as a result set in whatever tool you are using.įigure 1. Your results may differ a bit, especially in some of the actual numbers, but should be very similar if you restored a copy of the Air database and added the indexes that were indicated.
The execution plan for this query is presented in Figure 1. To be able to replicate the examples, you will need to create a couple of additional indexes: The restore will create a schema postgres_air populated with data but without any indexes except for the ones which support primary/unique constraints. We suggest that you do it on your personal device or any other instance where you have a complete control of what’s going on, since you will need to restart the instance a couple of times during these experiments. We ran the examples on version 15.2, however, they will work the same at a minimum on versions 13 and 14.
Postgresql count download#
If you want to repeat the experiments described in this article, download the latest version of this database (file postges_air_2023.backup) and restore it on a Postgres instance where you are going to run these experiments. To run our experiments, we will use a database that I have created for performance tuning examples: postgres_air database. With this in mind, let’s examine the impact of parameters modifications and alternative ways of performance tuning. Moreover, we will demonstrate that essential database performance tuning goes beyond choosing the appropriate parameters settings. In this blog, we will segue from discussing PostgreSQL system parameters best practices to other ways of performance tuning. Indeed, it is challenging to model such an impact on the training database.
In this first blog, we didn’t provide any examples of the practical impact of parameters tuning on performance. That might sound like impressive numbers, but individual query optimization routinely makes queries run several times faster, in some cases, ten or more times faster, and restructuring applications can improve overall system performance by hundreds of times! However, the very first paragraph of the very first blog on this topic stated that:Īlthough some parameter tuning can indeed improve the database performance, we are usually talking about 10%, 20%, and in rare cases, up to 50% performance improvement if some parameters are set poorly. In two previous blogs, we discussed how tuning PostgreSQL parameters could help improve overall system performance. Welcome to the third and final blog of the “magic of parameters” series. Result := db.Where( "processed = ?", false).FindInBatches(&results, 100, func (tx *gorm.So much about parameters tuning, but does it always help? Query and process records in batch // batch size 100 ScanRows is a method of `gorm.DB`, it can be used to scan a row into a struct GORM allows selecting specific fields with Select, if you often use this in your application, maybe you want to define a smaller struct for API usage which can select specific fields automatically, for example: type User struct ).Where( "name = ?", "jinzhu").Rows()
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