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Nov 4, 2016 · The OrderByDescending(...) will sort items by your date/time property (or w/e logic you want to use to get most recent) and Take(...) will limit to first x items (first being most recent, thanks to the ordering). Edit: To return some rows not starting at the first row, use Skip(): results = results.OrderByDescending(x=>x.Date).Skip(50).Take(10);
- Overview
- Use indexes properly
- Project only properties you need
- Limit the resultset size
- Efficient pagination
- Avoid cartesian explosion when loading related entities
- Load related entities eagerly when possible
- Buffering and streaming
- Tracking, no-tracking and identity resolution
- Using SQL queries
Querying efficiently is a vast subject, that covers subjects as wide-ranging as indexes, related entity loading strategies, and many others. This section details some common themes for making your queries faster, and pitfalls users typically encounter.
The main deciding factor in whether a query runs fast or not is whether it will properly utilize indexes where appropriate: databases are typically used to hold large amounts of data, and queries which traverse entire tables are typically sources of serious performance issues. Indexing issues aren't easy to spot, because it isn't immediately obvious whether a given query will use an index or not. For example:
A good way to spot indexing issues is to first pinpoint a slow query, and then examine its query plan via your database's favorite tool; see the performance diagnosis page for more information on how to do that. The query plan displays whether the query traverses the entire table, or uses an index.
As a general rule, there isn't any special EF knowledge to using indexes or diagnosing performance issues related to them; general database knowledge related to indexes is just as relevant to EF applications as to applications not using EF. The following lists some general guidelines to keep in mind when using indexes:
•While indexes speed up queries, they also slow down updates since they need to be kept up-to-date. Avoid defining indexes which aren't needed, and consider using index filters to limit the index to a subset of the rows, thereby reducing this overhead.
•Composite indexes can speed up queries which filter on multiple columns, but they can also speed up queries which don't filter on all the index's columns - depending on ordering. For example, an index on columns A and B speeds up queries filtering by A and B as well as queries filtering only by A, but it does not speed up queries only filtering over B.
•If a query filters by an expression over a column (e.g. price / 2), a simple index cannot be used. However, you can define a stored persisted column for your expression, and create an index over that. Some databases also support expression indexes, which can be directly used to speed up queries filtering by any expression.
EF Core makes it very easy to query out entity instances, and then use those instances in code. However, querying entity instances can frequently pull back more data than necessary from your database. Consider the following:
Although this code only actually needs each Blog's Url property, the entire Blog entity is fetched, and unneeded columns are transferred from the database:
This can be optimized by using Select to tell EF which columns to project out:
The resulting SQL pulls back only the needed columns:
If you need to project out more than one column, project out to a C# anonymous type with the properties you want.
Note that this technique is very useful for read-only queries, but things get more complicated if you need to update the fetched blogs, since EF's change tracking only works with entity instances. It's possible to perform updates without loading entire entities by attaching a modified Blog instance and telling EF which properties have changed, but that is a more advanced technique that may not be worth it.
By default, a query returns all rows that matches its filters:
Since the number of rows returned depends on actual data in your database, it's impossible to know how much data will be loaded from the database, how much memory will be taken up by the results, and how much additional load will be generated when processing these results (e.g. by sending them to a user browser over the network). Crucially, test databases frequently contain little data, so that everything works well while testing, but performance problems suddenly appear when the query starts running on real-world data and many rows are returned.
As a result, it's usually worth giving thought to limiting the number of results:
At a minimum, your UI could show a message indicating that more rows may exist in the database (and allow retrieving them in some other manner). A full-blown solution would implement pagination, where your UI only shows a certain number of rows at a time, and allow users to advance to the next page as needed; see the next section for more details on how to implement this efficiently.
Pagination refers to retrieving results in pages, rather than all at once; this is typically done for large resultsets, where a user interface is shown that allows the user to navigate to the next or previous page of the results. A common way to implement pagination with databases is to use the Skip and Take operators (OFFSET and LIMIT in SQL); while this is an intuitive implementation, it's also quite inefficient. For pagination that allows moving one page at a time (as opposed to jumping to arbitrary pages), consider using keyset pagination instead.
For more information, see the documentation page on pagination.
In relational databases, all related entities are loaded by introducing JOINs in single query.
If a typical blog has multiple related posts, rows for these posts will duplicate the blog's information. This duplication leads to the so-called "cartesian explosion" problem. As more one-to-many relationships are loaded, the amount of duplicated data may grow and adversely affect the performance of your application.
It's recommended to read the dedicated page on related entities before continuing with this section.
When dealing with related entities, we usually know in advance what we need to load: a typical example would be loading a certain set of Blogs, along with all their Posts. In these scenarios, it is always better to use eager loading, so that EF can fetch all the required data in one roundtrip. The filtered include feature also allows you to limit which related entities you'd like to load, while keeping the loading process eager and therefore doable in a single roundtrip:
Buffering refers to loading all your query results into memory, whereas streaming means that EF hands the application a single result each time, never containing the entire resultset in memory. In principle, the memory requirements of a streaming query are fixed - they are the same whether the query returns 1 row or 1000; a buffering query, on the other hand, requires more memory the more rows are returned. For queries that result large resultsets, this can be an important performance factor.
Whether a query buffers or streams depends on how it is evaluated:
It's recommended to read the dedicated page on tracking and no-tracking before continuing with this section.
EF tracks entity instances by default, so that changes on them are detected and persisted when SaveChanges is called. Another effect of tracking queries is that EF detects if an instance has already been loaded for your data, and will automatically return that tracked instance rather than returning a new one; this is called identity resolution. From a performance perspective, change tracking means the following:
•EF internally maintains a dictionary of tracked instances. When new data is loaded, EF checks the dictionary to see if an instance is already tracked for that entity's key (identity resolution). The dictionary maintenance and lookups take up some time when loading the query's results.
•Before handing a loaded instance to the application, EF snapshots that instance and keeps the snapshot internally. When SaveChanges is called, the application's instance is compared with the snapshot to discover the changes to be persisted. The snapshot takes up more memory, and the snapshotting process itself takes time; it's sometimes possible to specify different, possibly more efficient snapshotting behavior via value comparers, or to use change-tracking proxies to bypass the snapshotting process altogether (though that comes with its own set of disadvantages).
In read-only scenarios where changes aren't saved back to the database, the above overheads can be avoided by using no-tracking queries. However, since no-tracking queries do not perform identity resolution, a database row which is referenced by multiple other loaded rows will be materialized as different instances.
To illustrate, assume we are loading a large number of Posts from the database, as well as the Blog referenced by each Post. If 100 Posts happen to reference the same Blog, a tracking query detects this via identity resolution, and all Post instances will refer the same de-duplicated Blog instance. A no-tracking query, in contrast, duplicates the same Blog 100 times - and application code must be written accordingly.
In some cases, more optimized SQL exists for your query, which EF does not generate. This can happen when the SQL construct is an extension specific to your database that's unsupported, or simply because EF does not translate to it yet. In these cases, writing SQL by hand can provide a substantial performance boost, and EF supports several ways to do this.
•Use SQL queries directly in your query, e.g. via FromSqlRaw. EF even lets you compose over the SQL with regular LINQ queries, allowing you to express only a part of the query in SQL. This is a good technique when the SQL only needs to be used in a single query in your codebase.
•Define a user-defined function (UDF), and then call that from your queries. Note that EF allows UDFs to return full resultsets - these are known as table-valued functions (TVFs) - and also allows mapping a DbSet to a function, making it look just like just another table.
•Define a database view and query from it in your queries. Note that unlike functions, views cannot accept parameters.
For the meanwhile, there is no solution to overcome this limitation other than decreasing the number of link entities in your request and it is not obvious if Microsoft is willing to allow adding more than 10 related entities in a query.
Sep 21, 2016 · Even though this statement looks more complex it’s only three lines and looks somewhat like a SQL statement. But it’s really a LINQ (Language Integrated Query) statement, specifically a LINQ to Entities statement. This LINQ statement will be translated into this SQL statement: SELECT [Extent1]. [Id] AS [Id], [Extent1].
Oct 28, 2023 · Spring Data JPA version 3.2 provides a new type named Limit to limit the number of results returned from a query. It has two static methods – of () and unlimited (). The of () method accepts an integer as an argument. It limits query results to the given maximum size.
Mar 12, 2023 · Kindly follow the steps below: Select the column where you want to limit the number of entries. Go to the "Data" tab in the ribbon and select "Data Validation". In the "Settings" tab, choose "Whole number" under "Allow". In the "Data" tab, choose "less than or equal to" under "Validation criteria". Enter "5" in the "Maximum" field.
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A value of 1 means there is 1 entity in the section. Advanced Topics Limit X Number of Entities for a Section. Click on the first route and goto action logic. Change the wait until logic from 1 to the number desired. Example: If you need 2 entities to enter the section at a time, change to 2. Replace 1 to the number desired
