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by sending them to a user browser over the network). 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. Limit the resultset sizeīy default, a query returns all rows that matches its filters: var blogsAll = context.Posts 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.

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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.

If you need to project out more than one column, project out to a C# anonymous type with the properties you want. The resulting SQL pulls back only the needed columns: SELECT. This can be optimized by using Select to tell EF which columns to project out: foreach (var blogName in (b => b.Url))

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Consider the following: foreach (var blog in context.Blogs)Īlthough this code only actually needs each Blog's Url property, the entire Blog entity is fetched, and unneeded columns are transferred from the database: SELECT. However, querying entity instances can frequently pull back more data than necessary from your database. Consult your provider's documentation for more information.ĮF Core makes it very easy to query out entity instances, and then use those instances in code. For example, the SQL Server provider allows you to configure whether an index is clustered, or set its fill factor.

Different databases allow indexes to be configured in various ways, and in many cases EF Core providers expose these via the Fluent API.

Some databases also support expression indexes, which can be directly used to speed up queries filtering by any expression. However, you can define a stored persisted column for your expression, and create an index over that. price / 2), a simple index cannot be used.

If a query filters by an expression over a column (e.g.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. 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.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. While indexes speed up queries, they also slow down updates since they need to be kept up-to-date.The following lists some general guidelines to keep in mind when using indexes: The query plan displays whether the query traverses the entire table, or uses an index.Īs 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.

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Var posts2 = (p => p.Title.EndsWith('A')).ToList() Ī 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. Matches on end, so does not use the index

Var posts1 = (p => p.Title.StartsWith('A')).ToList() For example: // Matches on start, so uses an index (on SQL Server) Indexing issues aren't easy to spot, because it isn't immediately obvious whether a given query will use an index or not. 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. This section details some common themes for making your queries faster, and pitfalls users typically encounter. Querying efficiently is a vast subject, that covers subjects as wide-ranging as indexes, related entity loading strategies, and many others.