Welkom bij Deel 2 van onze uitgebreide gids voor toegang tot gegevens in .NET! Deel 1, we onderzochten Entity Framework Core in detail, waaronder SQL generatie, gemeenschappelijke valkuilen, en die kritische waarschuwing over proxies en caching.
In dit artikel verkennen we de lichtergewicht alternatieven en hoe meerdere benaderingen te combineren voor optimale prestaties:
Dapper is een lichtgewicht, high-performance micro-ORM gemaakt door Stack Overflow. Het biedt een dunne laag over ADO.NET, het omgaan met het vervelende werk van het in kaart brengen van query resultaten naar objecten terwijl u volledige SQL controle.
Dapper werd geboren uit de behoefte van Stack Overflow aan high-performance data-toegang. Het team vond dat volledige ORM's zoals Entity Framework (pre-Core) te veel overhead toegevoegd voor hun high-traffic scenario's. Dapper geeft u 95% van het gemak met slechts 5-15% overhead over rauwe ADO.NET.
using Npgsql;
using Dapper;
public class DapperBlogRepository
{
private readonly string _connectionString;
public DapperBlogRepository(string connectionString)
{
_connectionString = connectionString;
// Configure Dapper to work with PostgreSQL naming conventions
DefaultTypeMap.MatchNamesWithUnderscores = true;
}
// Simple query
public async Task<IEnumerable<BlogPost>> GetRecentPostsAsync(int count)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
SELECT id, title, content, tags, published_date, category_id
FROM blog_posts
ORDER BY published_date DESC
LIMIT @Count";
return await connection.QueryAsync<BlogPost>(sql, new { Count = count });
}
// Query with WHERE clause
public async Task<BlogPost> GetPostByIdAsync(int id)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
SELECT id, title, content, published_date
FROM blog_posts
WHERE id = @Id";
return await connection.QueryFirstOrDefaultAsync<BlogPost>(sql, new { Id = id });
}
// Insert with returning ID
public async Task<int> CreatePostAsync(BlogPost post)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
INSERT INTO blog_posts (title, content, published_date, category_id)
VALUES (@Title, @Content, @PublishedDate, @CategoryId)
RETURNING id";
return await connection.ExecuteScalarAsync<int>(sql, post);
}
// Update
public async Task UpdatePostAsync(BlogPost post)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
UPDATE blog_posts
SET title = @Title,
content = @Content,
published_date = @PublishedDate
WHERE id = @Id";
await connection.ExecuteAsync(sql, post);
}
// Delete
public async Task DeletePostAsync(int id)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = "DELETE FROM blog_posts WHERE id = @Id";
await connection.ExecuteAsync(sql, new { Id = id });
}
}
Een van Dapper's meest krachtige functies is multi-mapping - efficiënt omgaan met joins en mapping naar meerdere gerelateerde objecten:
public async Task<IEnumerable<BlogPost>> GetPostsWithCategoryAsync()
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
SELECT
p.id, p.title, p.content, p.published_date,
c.id, c.name, c.description
FROM blog_posts p
INNER JOIN categories c ON p.category_id = c.id
ORDER BY p.published_date DESC";
return await connection.QueryAsync<BlogPost, Category, BlogPost>(
sql,
(post, category) =>
{
post.Category = category;
return post;
},
splitOn: "id" // Split at the second "id" column
);
}
// More complex: Posts with comments
public async Task<IEnumerable<BlogPost>> GetPostsWithCommentsAsync()
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
SELECT
p.id, p.title, p.content,
c.id, c.author, c.content, c.created_at
FROM blog_posts p
LEFT JOIN comments c ON p.id = c.blog_post_id
ORDER BY p.published_date DESC, c.created_at";
var postDict = new Dictionary<int, BlogPost>();
await connection.QueryAsync<BlogPost, Comment, BlogPost>(
sql,
(post, comment) =>
{
if (!postDict.TryGetValue(post.Id, out var existingPost))
{
existingPost = post;
existingPost.Comments = new List<Comment>();
postDict.Add(post.Id, existingPost);
}
if (comment != null)
{
existingPost.Comments.Add(comment);
}
return existingPost;
},
splitOn: "id"
);
return postDict.Values;
}
Dynamische parameters voor complexe zoekopdrachten:
public async Task<IEnumerable<BlogPost>> SearchWithDynamicFiltersAsync(SearchCriteria criteria)
{
using var connection = new NpgsqlConnection(_connectionString);
var parameters = new DynamicParameters();
var conditions = new List<string>();
var sql = new StringBuilder("SELECT * FROM blog_posts");
if (!string.IsNullOrEmpty(criteria.SearchTerm))
{
conditions.Add("search_vector @@ to_tsquery('english', @SearchTerm)");
parameters.Add("SearchTerm", criteria.SearchTerm);
}
if (criteria.CategoryIds?.Any() == true)
{
conditions.Add("category_id = ANY(@CategoryIds)");
parameters.Add("CategoryIds", criteria.CategoryIds);
}
if (criteria.FromDate.HasValue)
{
conditions.Add("published_date >= @FromDate");
parameters.Add("FromDate", criteria.FromDate.Value);
}
if (criteria.Tags?.Any() == true)
{
conditions.Add("tags && @Tags"); // PostgreSQL array overlap
parameters.Add("Tags", criteria.Tags);
}
if (conditions.Any())
{
sql.Append(" WHERE ");
sql.Append(string.Join(" AND ", conditions));
}
sql.Append(" ORDER BY published_date DESC LIMIT @Limit");
parameters.Add("Limit", criteria.Limit);
return await connection.QueryAsync<BlogPost>(sql.ToString(), parameters);
}
Aangepaste type handlers voor PostgreSQL types:
// Handle PostgreSQL arrays
public class PostgresArrayTypeHandler : SqlMapper.TypeHandler<string[]>
{
public override void SetValue(IDbDataParameter parameter, string[] value)
{
parameter.Value = value;
((NpgsqlParameter)parameter).NpgsqlDbType = NpgsqlDbType.Array | NpgsqlDbType.Text;
}
public override string[] Parse(object value)
{
return (string[])value;
}
}
// Handle PostgreSQL JSONB
public class JsonTypeHandler<T> : SqlMapper.TypeHandler<T>
{
public override void SetValue(IDbDataParameter parameter, T value)
{
parameter.Value = JsonSerializer.Serialize(value);
((NpgsqlParameter)parameter).NpgsqlDbType = NpgsqlDbType.Jsonb;
}
public override T Parse(object value)
{
return JsonSerializer.Deserialize<T>(value.ToString());
}
}
// Register handlers (in startup)
SqlMapper.AddTypeHandler(new PostgresArrayTypeHandler());
SqlMapper.AddTypeHandler(new JsonTypeHandler<Dictionary<string, object>>());
Bulk Operations met PostgreSQL COPY:
public async Task BulkInsertPostsAsync(IEnumerable<BlogPost> posts)
{
using var connection = new NpgsqlConnection(_connectionString);
await connection.OpenAsync();
using var writer = await connection.BeginBinaryImportAsync(
"COPY blog_posts (title, content, tags, published_date) FROM STDIN (FORMAT BINARY)"
);
foreach (var post in posts)
{
await writer.StartRowAsync();
await writer.WriteAsync(post.Title);
await writer.WriteAsync(post.Content);
await writer.WriteAsync(post.Tags, NpgsqlDbType.Array | NpgsqlDbType.Text);
await writer.WriteAsync(post.PublishedDate);
}
await writer.CompleteAsync();
}
Transactieondersteuning:
public async Task TransferPostToCategoryAsync(int postId, int newCategoryId)
{
using var connection = new NpgsqlConnection(_connectionString);
await connection.OpenAsync();
using var transaction = await connection.BeginTransactionAsync();
try
{
// Update the post
await connection.ExecuteAsync(
"UPDATE blog_posts SET category_id = @CategoryId WHERE id = @PostId",
new { CategoryId = newCategoryId, PostId = postId },
transaction
);
// Log the change
await connection.ExecuteAsync(
@"INSERT INTO category_history (post_id, category_id, changed_at)
VALUES (@PostId, @CategoryId, @ChangedAt)",
new { PostId = postId, CategoryId = newCategoryId, ChangedAt = DateTime.UtcNow },
transaction
);
await transaction.CommitAsync();
}
catch
{
await transaction.RollbackAsync();
throw;
}
}
Gebruik Dapper wanneer:
Vermijd dapperheid wanneer:
Voor absolute maximale prestaties en controle, kunt u gebruiken Npgsql direct zonder ORM-laag.
Raw ADO.NET is geschikt wanneer:
public class NpgsqlBlogRepository
{
private readonly string _connectionString;
public async Task<List<BlogPost>> GetRecentPostsAsync(int count)
{
var posts = new List<BlogPost>();
using var connection = new NpgsqlConnection(_connectionString);
await connection.OpenAsync();
using var command = new NpgsqlCommand(
"SELECT id, title, content, tags, published_date FROM blog_posts ORDER BY published_date DESC LIMIT @count",
connection
);
command.Parameters.AddWithValue("count", count);
using var reader = await command.ExecuteReaderAsync();
while (await reader.ReadAsync())
{
posts.Add(new BlogPost
{
Id = reader.GetInt32(0),
Title = reader.GetString(1),
Content = reader.GetString(2),
Tags = reader.GetFieldValue<string[]>(3),
PublishedDate = reader.GetDateTime(4)
});
}
return posts;
}
// Using prepared statements for repeated queries
public async Task<BlogPost> GetPostByIdAsync(int id)
{
using var connection = new NpgsqlConnection(_connectionString);
await connection.OpenAsync();
using var command = new NpgsqlCommand(
"SELECT id, title, content FROM blog_posts WHERE id = $1",
connection
);
command.Parameters.AddWithValue(id);
await command.PrepareAsync(); // Prepared statement for performance
using var reader = await command.ExecuteReaderAsync();
if (await reader.ReadAsync())
{
return new BlogPost
{
Id = reader.GetInt32(0),
Title = reader.GetString(1),
Content = reader.GetString(2)
};
}
return null;
}
// Working with PostgreSQL JSONB
public async Task<Dictionary<string, object>> GetPostMetadataAsync(int id)
{
using var connection = new NpgsqlConnection(_connectionString);
await connection.OpenAsync();
using var command = new NpgsqlCommand(
"SELECT metadata FROM blog_posts WHERE id = $1",
connection
);
command.Parameters.AddWithValue(id);
var json = await command.ExecuteScalarAsync() as string;
return JsonSerializer.Deserialize<Dictionary<string, object>>(json);
}
// Streaming large result sets
public async IAsyncEnumerable<BlogPost> StreamAllPostsAsync()
{
using var connection = new NpgsqlConnection(_connectionString);
await connection.OpenAsync();
using var command = new NpgsqlCommand(
"SELECT id, title, content FROM blog_posts ORDER BY id",
connection
);
using var reader = await command.ExecuteReaderAsync();
while (await reader.ReadAsync())
{
yield return new BlogPost
{
Id = reader.GetInt32(0),
Title = reader.GetString(1),
Content = reader.GetString(2)
};
}
}
}
Bij het werken met Dapper of rauw ADO.NET moet je vaak in kaart brengen tussen verschillende objectrepresentaties (DTO's, entiteiten, weergavemodellen). Verschillende bibliotheken kunnen dit automatiseren.
Mapster is een snelle, op conventies gebaseerde objectmapper die brongeneratie gebruikt voor optimale prestaties.
// Install: Mapster and Mapster.Tool
using Mapster;
public class BlogPostDto
{
public int Id { get; set; }
public string Title { get; set; }
public string Summary { get; set; }
public List<string> CategoryNames { get; set; }
}
public class BlogPost
{
public int Id { get; set; }
public string Title { get; set; }
public string Content { get; set; }
public List<Category> Categories { get; set; }
}
// Configuration
public class MappingConfig : IRegister
{
public void Register(TypeAdapterConfig config)
{
config.NewConfig<BlogPost, BlogPostDto>()
.Map(dest => dest.Summary, src => src.Content.Substring(0, Math.Min(200, src.Content.Length)))
.Map(dest => dest.CategoryNames, src => src.Categories.Select(c => c.Name).ToList());
// Reverse map with ignore
config.NewConfig<BlogPostDto, BlogPost>()
.Ignore(dest => dest.Content);
}
}
// Registration in Program.cs
TypeAdapterConfig.GlobalSettings.Scan(Assembly.GetExecutingAssembly());
// Usage with Dapper
public class BlogService
{
private readonly string _connectionString;
public async Task<List<BlogPostDto>> GetPostsAsync()
{
using var connection = new NpgsqlConnection(_connectionString);
var posts = await connection.QueryAsync<BlogPost>(@"
SELECT p.id, p.title, p.content
FROM blog_posts p
");
// Map to DTOs - very fast with Mapster
return posts.Adapt<List<BlogPostDto>>();
}
// Projection mapping (compile-time)
public async Task<List<BlogPostDto>> GetPostsDtosDirectlyAsync()
{
using var connection = new NpgsqlConnection(_connectionString);
// Query directly to DTO shape
return (await connection.QueryAsync<BlogPostDto>(@"
SELECT
id,
title,
SUBSTRING(content, 1, 200) as summary
FROM blog_posts
")).ToList();
}
}
Automapper is de meest populaire mapping library, hoewel langzamer dan Mapster.
// Install: AutoMapper and AutoMapper.Extensions.Microsoft.DependencyInjection
using AutoMapper;
public class MappingProfile : Profile
{
public MappingProfile()
{
CreateMap<BlogPost, BlogPostDto>()
.ForMember(d => d.Summary, opt => opt.MapFrom(s =>
s.Content.Length > 200 ? s.Content.Substring(0, 200) : s.Content))
.ForMember(d => d.CategoryNames, opt => opt.MapFrom(s =>
s.Categories.Select(c => c.Name)));
// Reverse map
CreateMap<BlogPostDto, BlogPost>()
.ForMember(d => d.Content, opt => opt.Ignore());
}
}
// Registration in Program.cs
services.AddAutoMapper(typeof(MappingProfile));
// Usage
public class BlogService
{
private readonly IMapper _mapper;
private readonly string _connectionString;
public BlogService(IMapper mapper, IConfiguration configuration)
{
_mapper = mapper;
_connectionString = configuration.GetConnectionString("DefaultConnection");
}
public async Task<List<BlogPostDto>> GetPostsAsync()
{
using var connection = new NpgsqlConnection(_connectionString);
var posts = await connection.QueryAsync<BlogPost>(@"
SELECT id, title, content FROM blog_posts
");
return _mapper.Map<List<BlogPostDto>>(posts.ToList());
}
}
Soms is de beste aanpak een expliciete handmatige mapping:
public static class BlogPostMapper
{
public static BlogPostDto ToDto(this BlogPost post)
{
return new BlogPostDto
{
Id = post.Id,
Title = post.Title,
Summary = post.Content.Length > 200
? post.Content.Substring(0, 200) + "..."
: post.Content,
CategoryNames = post.Categories?.Select(c => c.Name).ToList() ?? new List<string>()
};
}
public static List<BlogPostDto> ToDtoList(this IEnumerable<BlogPost> posts)
{
return posts.Select(p => p.ToDto()).ToList();
}
// Inline mapping for simple cases
public static BlogPostDto MapToDto(BlogPost post) => new()
{
Id = post.Id,
Title = post.Title,
Summary = post.Content[..Math.Min(200, post.Content.Length)]
};
}
// Usage
var posts = await _repository.GetAllPostsAsync();
var dtos = posts.ToDtoList();
BenchmarkDotNet Results (mapping 1000 objects):
Method | Mean | Allocated
--------------------|-----------|----------
Manual Mapping | 45.2 μs | 78 KB
Mapster | 52.1 μs | 79 KB
AutoMapper | 184.3 μs | 156 KB
Key Takeaways:
In echte toepassingen wil je vaak verschillende benaderingen gebruiken voor verschillende scenario's binnen dezelfde toepassing. de aanbevolen aanpak voor de meeste productiesystemen.
Het CQRS (Command Query Responsibility Segregation) patroon is een natuurlijke pasvorm voor hybride data access benaderingen. Voor een diepere duik in CQRS en event sourcing met Marten, zie mijn artikel over Moderne CQRS en Event Sourcing.
Hoe Marten zich verhoudt tot deze discussie:
Marten is een document database en event store gebouwd op PostgreSQL die hybride data toegang neemt tot een ander niveau. Het combineert:
Terwijl dit artikel zich richt op traditionele relationele datatoegang (EF Core, Dapper), laat Marten zien hoe je de geavanceerde functies van PostgreSQL (JSONB, eventstreams) kunt benutten om geavanceerde architecturen te implementeren.
graph TB
Client[Client Application]
subgraph "Write Side - Commands"
WriteAPI[Write API / Commands]
EFCore[EF Core Context]
WriteDB[(PostgreSQL<br/>Write Operations)]
end
subgraph "Read Side - Queries"
ReadAPI[Read API / Queries]
Dapper[Dapper Repository]
ReadDB[(PostgreSQL<br/>Read Operations)]
end
Client -->|Create/Update/Delete| WriteAPI
WriteAPI --> EFCore
EFCore -->|Change Tracking<br/>Validation<br/>Business Logic| WriteDB
Client -->|Query/Search| ReadAPI
ReadAPI --> Dapper
Dapper -->|Optimized SQL<br/>DTOs<br/>No Tracking| ReadDB
WriteDB -.->|Same Database| ReadDB
style Client stroke:#6366f1,stroke-width:2px
style WriteAPI stroke:#2563eb,stroke-width:2px
style EFCore stroke:#2563eb,stroke-width:2px
style WriteDB stroke:#2563eb,stroke-width:2px
style ReadAPI stroke:#059669,stroke-width:2px
style Dapper stroke:#059669,stroke-width:2px
style ReadDB stroke:#059669,stroke-width:2px
Dit patroon maakt gebruik van:
// Commands: Use EF Core for change tracking and validation
public class BlogCommandService
{
private readonly BlogDbContext _context;
private readonly ILogger<BlogCommandService> _logger;
public BlogCommandService(BlogDbContext context, ILogger<BlogCommandService> logger)
{
_context = context;
_logger = logger;
}
public async Task<int> CreatePostAsync(CreatePostCommand command)
{
// Business logic and validation
var post = new BlogPost
{
Title = command.Title,
Content = command.Content,
CategoryId = command.CategoryId,
PublishedDate = DateTime.UtcNow
};
_context.BlogPosts.Add(post);
await _context.SaveChangesAsync();
_logger.LogInformation("Created blog post {PostId}", post.Id);
return post.Id;
}
public async Task UpdatePostAsync(UpdatePostCommand command)
{
var post = await _context.BlogPosts.FindAsync(command.Id);
if (post == null)
throw new InvalidOperationException($"Post {command.Id} not found");
post.Title = command.Title;
post.Content = command.Content;
post.UpdatedAt = DateTime.UtcNow;
await _context.SaveChangesAsync();
_logger.LogInformation("Updated blog post {PostId}", post.Id);
}
public async Task DeletePostAsync(int id)
{
var post = await _context.BlogPosts.FindAsync(id);
if (post != null)
{
_context.BlogPosts.Remove(post);
await _context.SaveChangesAsync();
_logger.LogInformation("Deleted blog post {PostId}", id);
}
}
}
// Queries: Use Dapper for read performance
public class BlogQueryService
{
private readonly string _connectionString;
private readonly ILogger<BlogQueryService> _logger;
public BlogQueryService(IConfiguration configuration, ILogger<BlogQueryService> logger)
{
_connectionString = configuration.GetConnectionString("DefaultConnection");
_logger = logger;
}
public async Task<BlogPostDto> GetPostBySlugAsync(string slug)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
SELECT
p.id,
p.title,
p.slug,
p.content,
p.published_date,
c.id as category_id,
c.name as category_name,
(SELECT COUNT(*) FROM comments WHERE blog_post_id = p.id) as comment_count
FROM blog_posts p
INNER JOIN categories c ON p.category_id = c.id
WHERE p.slug = @Slug";
var post = await connection.QueryFirstOrDefaultAsync<BlogPostDto>(sql, new { Slug = slug });
if (post != null)
{
_logger.LogInformation("Retrieved blog post by slug {Slug}", slug);
}
return post;
}
public async Task<PagedResult<BlogPostSummaryDto>> GetRecentPostsAsync(int page, int pageSize)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
SELECT
p.id,
p.title,
p.slug,
LEFT(p.content, 200) as summary,
p.published_date,
c.name as category_name
FROM blog_posts p
INNER JOIN categories c ON p.category_id = c.id
ORDER BY p.published_date DESC
LIMIT @PageSize OFFSET @Offset";
const string countSql = "SELECT COUNT(*) FROM blog_posts";
var posts = await connection.QueryAsync<BlogPostSummaryDto>(
sql,
new { PageSize = pageSize, Offset = (page - 1) * pageSize }
);
var totalCount = await connection.ExecuteScalarAsync<int>(countSql);
return new PagedResult<BlogPostSummaryDto>
{
Items = posts.ToList(),
TotalCount = totalCount,
Page = page,
PageSize = pageSize
};
}
public async Task<List<BlogPostDto>> SearchPostsAsync(string searchTerm)
{
using var connection = new NpgsqlConnection(_connectionString);
const string sql = @"
SELECT
p.id,
p.title,
p.slug,
p.content,
p.published_date,
c.name as category_name,
ts_rank(p.search_vector, query) as relevance_score
FROM blog_posts p
INNER JOIN categories c ON p.category_id = c.id,
to_tsquery('english', @SearchTerm) query
WHERE p.search_vector @@ query
ORDER BY relevance_score DESC
LIMIT 50";
var posts = await connection.QueryAsync<BlogPostDto>(sql, new { SearchTerm = searchTerm });
_logger.LogInformation(
"Searched posts with term {SearchTerm}, found {Count} results",
searchTerm,
posts.Count()
);
return posts.ToList();
}
}
// Service layer orchestrating commands and queries
public class BlogService
{
private readonly BlogCommandService _commands;
private readonly BlogQueryService _queries;
public BlogService(BlogCommandService commands, BlogQueryService queries)
{
_commands = commands;
_queries = queries;
}
// Write operations delegate to command service
public Task<int> CreatePostAsync(CreatePostCommand command) => _commands.CreatePostAsync(command);
public Task UpdatePostAsync(UpdatePostCommand command) => _commands.UpdatePostAsync(command);
public Task DeletePostAsync(int id) => _commands.DeletePostAsync(id);
// Read operations delegate to query service
public Task<BlogPostDto> GetPostBySlugAsync(string slug) => _queries.GetPostBySlugAsync(slug);
public Task<PagedResult<BlogPostSummaryDto>> GetRecentPostsAsync(int page, int pageSize)
=> _queries.GetRecentPostsAsync(page, pageSize);
public Task<List<BlogPostDto>> SearchPostsAsync(string searchTerm)
=> _queries.SearchPostsAsync(searchTerm);
}
Voor toepassingen die voornamelijk EF Core zijn maar incidentele prestatieoptimalisatie nodig hebben:
public class BlogService
{
private readonly BlogDbContext _context;
// 95% of queries: Use EF Core LINQ
public async Task<List<BlogPost>> GetPostsByCategoryAsync(int categoryId)
{
return await _context.BlogPosts
.Where(p => p.CategoryId == categoryId)
.Include(p => p.Comments)
.ToListAsync();
}
// 5% of queries: Use raw SQL for complex analytics
public async Task<List<PostAnalytics>> GetPostAnalyticsAsync()
{
using var connection = _context.Database.GetDbConnection();
await _context.Database.OpenConnectionAsync();
using var command = connection.CreateCommand();
command.CommandText = @"
WITH post_metrics AS (
SELECT
p.id,
p.title,
COUNT(DISTINCT c.id) as comment_count,
COUNT(DISTINCT v.id) as view_count,
AVG(c.sentiment_score) as avg_sentiment
FROM blog_posts p
LEFT JOIN comments c ON p.id = c.post_id
LEFT JOIN post_views v ON p.id = v.post_id
WHERE p.published_date >= NOW() - INTERVAL '30 days'
GROUP BY p.id, p.title
)
SELECT * FROM post_metrics
ORDER BY view_count DESC";
var analytics = new List<PostAnalytics>();
using var reader = await command.ExecuteReaderAsync();
while (await reader.ReadAsync())
{
analytics.Add(new PostAnalytics
{
PostId = reader.GetInt32(0),
Title = reader.GetString(1),
CommentCount = reader.GetInt64(2),
ViewCount = reader.GetInt64(3),
AverageSentiment = reader.IsDBNull(4) ? 0 : reader.GetDouble(4)
});
}
return analytics;
}
}
Laten we kijken naar real-world prestatie benchmarks voor gemeenschappelijke operaties met PostgreSQL:
BenchmarkDotNet Results (Lower is Better):
Method | Mean | Allocated
------------------------- |----------- |-----------
EF Core (No Tracking) | 12.34 ms | 2.4 MB
EF Core (With Tracking) | 15.67 ms | 4.8 MB
Dapper | 8.21 ms | 1.8 MB
Raw Npgsql | 7.45 ms | 1.2 MB
Method | Mean | Allocated
------------------------- |----------- |-----------
EF Core (SaveChanges) | 245.3 ms | 15.2 MB
EF Core (BulkInsert) | 42.1 ms | 8.4 MB
Dapper (Loop) | 189.7 ms | 2.1 MB
Npgsql COPY | 18.3 ms | 0.8 MB
Method | Mean | Allocated
------------------------- |----------- |-----------
EF Core (Include) | 28.5 ms | 5.2 MB
EF Core (Split Query) | 24.1 ms | 4.8 MB
Dapper (Multi-Map) | 16.8 ms | 3.1 MB
Raw Npgsql | 15.2 ms | 2.4 MB
Zie Deel 1 voor uitgebreide EF Core guidelance.
Het kiezen van de juiste data access benadering voor uw .NET applicatie met PostgreSQL gaat niet over het vinden van de "beste" tool - het gaat over het aanpassen van de juiste tool aan uw specifieke behoeften:
In de praktijk maken de meest succesvolle toepassingen vaak gebruik van een hybride benadering, gebruik makend van de sterke punten van elk instrument, in voorkomend geval:
De sleutel is om:
Vergeet niet: vroegtijdige optimalisatie is de wortel van alle kwaad, maar ook het bouwen van een systeem dat niet kan schalen wanneer dat nodig is. Start eenvoudig, meet prestaties, en optimaliseer waar het belangrijk is.
Deel 1 van deze serie:
Officiële documentatie:
Gerelateerde artikelen op dit blog:
Dat sluit onze tweedelige serie over datatoegang in .NET af! We hebben alles behandeld van EF Core's krachtige abstracties tot rauwe SQL's maximale prestaties, met praktische begeleiding over het combineren van benaderingen voor optimale resultaten.
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