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Bielu.PersistentQueues.Storage.LMDB 0.6.0-beta.639124791581654207

Bielu.PersistentQueues - Fast Persistent Queues for .NET

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👁 License: MIT

Bielu.PersistentQueues is a high-performance, lightweight, store-and-forward message queue for .NET applications with pluggable storage backends. It ensures fast and durable persistence for sending and receiving messages, making it an excellent choice for lightweight and cross-platform message queuing needs.

This project is a fork of LightningQueues, originally created by Corey Kaylor. It extends the original with additional features, bug fixes, and alignment with the bielu package ecosystem.

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Why Bielu.PersistentQueues?

• Simple API: Easily interact with the message queue through an intuitive API.
• Strongly-Typed Messages: Send, enqueue, and receive messages as C# objects — serialization is handled automatically via DI.
• Pluggable Storage: Choose from multiple storage backends (LMDB included, more coming).
• Microsoft DI Integration: First-class support for Microsoft.Extensions.DependencyInjection with a fluent builder API.
• No Administration: Unlike MSMQ or other Server / Brokers, it requires zero administrative setup.
• XCopy Deployable: No complex installation; just copy and run.
• Cross-Platform: Works on Windows, macOS, and Linux.
• Durable Storage: High-performance reliable message storage.
• TLS Encryption: Optionally secure your transport layer. You have full control.
• Batch Receive: Efficiently receive and process messages in batches.

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Packages

Package	Description	NuGet
Bielu.PersistentQueues	Core library with queue abstractions and DI support	👁 NuGet
Bielu.PersistentQueues.Storage.LMDB	LMDB storage provider (using LightningDB)	👁 NuGet
Bielu.PersistentQueues.OpenTelemetry	OpenTelemetry instrumentation for distributed tracing and metrics	👁 NuGet

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Installation

Install the core package and the storage provider you want to use:

dotnet add package Bielu.PersistentQueues
dotnet add package Bielu.PersistentQueues.Storage.LMDB

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Getting Started

Using Microsoft Dependency Injection (Recommended)

The recommended way to configure Bielu.PersistentQueues is via the fluent DI builder API:

using Bielu.PersistentQueues;
using Bielu.PersistentQueues.Storage.LMDB;

services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("C:\\path_to_your_queue_folder")
 .ListenOn(new IPEndPoint(IPAddress.Loopback, 5050))
 .CreateQueues("my-queue");
});

You can also customize the LMDB storage configuration:

services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("C:\\path_to_your_queue_folder", config =>
 {
 config.EnvironmentConfiguration = new LightningDB.EnvironmentConfiguration
 {
 MaxDatabases = 10,
 MapSize = 1024 * 1024 * 500 // 500 MB
 };
 config.StorageOptions = LmdbStorageOptions.MaxThroughput();
 })
 .AutomaticEndpoint()
 .CreateQueues("queue-a", "queue-b");
});

Then inject IQueue wherever you need it:

public class MyService(IQueue queue)
{
 public void SendMessage()
 {
 // Strongly-typed: serialization is handled automatically
 queue.Send(new OrderMessage("ORD-001", 99.99m, "USD"),
 destinationUri: "lq.tcp://localhost:5050",
 queueName: "my-queue");
 }

 public void EnqueueMessage()
 {
 // Enqueue a typed object for local processing
 queue.Enqueue(new OrderMessage("ORD-002", 49.99m, "EUR"),
 queueName: "my-queue");
 }
}

You can also send raw Message objects if you need full control:

queue.Send(new Message
{
 Data = "hello"u8.ToArray(),
 Id = MessageId.GenerateRandom(),
 Queue = "my-queue",
 Destination = new Uri("lq.tcp://localhost:5050")
});

Manual Configuration (Without DI)

You can also configure the queue manually using QueueConfiguration:

using Bielu.PersistentQueues;

var queue = new QueueConfiguration()
 .WithDefaults()
 .StoreWithLmdb("C:\\path_to_your_queue_folder")
 .BuildAndStart("queue-name");

Sending Messages

Send strongly-typed objects — the content serializer (configured via DI or defaulting to JSON) handles serialization automatically:

// Strongly-typed: serialize and send a C# object
queue.Send(new OrderMessage("ORD-001", 99.99m, "USD"),
 destinationUri: "lq.tcp://localhost:port",
 queueName: "queue-name");

// Strongly-typed: enqueue for local processing
queue.Enqueue(new OrderMessage("ORD-002", 49.99m, "EUR"),
 queueName: "queue-name");

Or send raw Message objects when you need full control over serialization:

var message = new Message
{
 Data = "hello"u8.ToArray(),
 Id = MessageId.GenerateRandom(),
 Queue = "queue-name",
 Destination = new Uri("lq.tcp://localhost:port")
};
queue.Send(message);

Receiving Messages

await foreach (var msg in queue.Receive("queue-name", token))
{
 // Deserialize the message payload to a strongly-typed object
 var order = msg.Message.GetContent<OrderMessage>();

 // Process the message and respond with one or more of the following:
 msg.QueueContext.SuccessfullyReceived(); // Done processing
 msg.QueueContext.ReceiveLater(TimeSpan.FromSeconds(1)); // Retry later
 msg.QueueContext.Enqueue(msg.Message); // Re-enqueue to same/other queue
 msg.QueueContext.Send(msg.Message); // Send to another endpoint
 msg.QueueContext.MoveTo("other-queue"); // Move to different queue

 // Strongly-typed context operations (serializer resolved from DI):
 msg.QueueContext.Enqueue(new FollowUpMessage(...), queueName: "follow-ups");
 msg.QueueContext.Send(new ResponseMessage(...), destinationUri: "lq.tcp://...");

 msg.QueueContext.CommitChanges(); // Commit all changes atomically
}

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Dead Letter Queue (DLQ)

Bielu.PersistentQueues has built-in support for a dead letter queue. When a message cannot be processed after a configurable number of attempts, it is automatically moved to the DLQ — or you can explicitly dead-letter it from your consumer code.

How It Works

• There is a single, shared dead letter queue named dead-letter.
• The DLQ is created automatically when any queue is created (and DLQ is enabled).
• Dead-lettered messages carry an original-queue header so you always know where they came from.
• A processing-attempts counter is incremented on every ReceiveLater call.

Message ID Persistence

Message IDs remain constant throughout the entire message lifecycle, including:

• Multiple ReceiveLater operations (retry/defer)
• Moving to the dead letter queue (automatic or explicit)
• Moving between queues with MoveTo
• Reading from storage multiple times

Each MessageId consists of two components:

• SourceInstanceId: Identifies the queue instance that generated the message
• MessageIdentifier: A unique GUID (using COMB algorithm for performance)

Both components are preserved across all operations, enabling reliable message tracking, deduplication, and correlation in distributed systems. This persistence is guaranteed by the immutable Message struct design.

Automatic Dead-Lettering (MaxAttempts)

Set maxAttempts when creating a message. When ReceiveLater is called and the processing attempt count reaches the limit, the message is moved to the DLQ automatically:

// Create a message that will be dead-lettered after 3 failed processing attempts
var message = Message.Create(
 data: Encoding.UTF8.GetBytes("order-data"),
 queue: "orders",
 maxAttempts: 3);
queue.Enqueue(message);

// Consumer — each ReceiveLater call increments the processing attempt counter.
// After 3 attempts, the message is auto-moved to "dead-letter".
await foreach (var ctx in queue.Receive("orders", cancellationToken: token))
{
 try
 {
 ProcessOrder(ctx.Message);
 ctx.QueueContext.SuccessfullyReceived();
 }
 catch
 {
 ctx.QueueContext.ReceiveLater(TimeSpan.FromSeconds(5));
 }
 ctx.QueueContext.CommitChanges();
}

Explicit Dead-Lettering

You can also move a message to the DLQ manually:

await foreach (var ctx in queue.Receive("orders", cancellationToken: token))
{
 if (IsPoisonMessage(ctx.Message))
 {
 ctx.QueueContext.MoveToDeadLetter(); // → "dead-letter" queue
 ctx.QueueContext.CommitChanges();
 continue;
 }
 // normal processing …
}

Batch consumers can dead-letter individual messages or the entire batch:

await foreach (var batch in queue.ReceiveBatch("orders", maxMessages: 10, cancellationToken: token))
{
 var poison = batch.Messages.Where(IsPoisonMessage).ToArray();
 var good = batch.Messages.Except(poison).ToArray();

 batch.MoveToDeadLetter(poison); // dead-letter the bad ones
 batch.SuccessfullyReceived(good); // acknowledge the good ones
 batch.CommitChanges();
}

You can also mix subset operations with batch-wide operations. Messages already processed by subset operations are automatically excluded from batch-wide calls:

await foreach (var batch in queue.ReceiveBatch("orders", maxMessages: 10, cancellationToken: token))
{
 // Process some messages individually
 batch.ReceiveLater(new[] { msg1.Id.MessageIdentifier }, TimeSpan.FromMinutes(5));
 batch.MoveToDeadLetter(new[] { msg2.Id.MessageIdentifier });

 // Then successfully receive the rest of the batch
 // This will only affect messages not already processed above
 batch.SuccessfullyReceived();
 batch.CommitChanges();
}

Inspecting DLQ Messages

foreach (var msg in queue.Store.PersistedIncoming(DeadLetterConstants.QueueName))
{
 Console.WriteLine($"Original queue: {msg.OriginalQueue}");
 Console.WriteLine($"Attempts: {msg.ProcessingAttempts}");
 Console.WriteLine($"Data: {Encoding.UTF8.GetString(msg.Data.Span)}");
}

Requeuing DLQ Messages

Once you've fixed the underlying issue, you can move all messages from the DLQ back to their original queues in a single call. Processing attempt counters are reset to zero:

int count = queue.RequeueDeadLetterMessages();
Console.WriteLine($"Requeued {count} messages back to their original queues");

Clearing DLQ Messages

If you want to permanently remove all messages from the DLQ without requeuing them:

int count = queue.ClearDeadLetterQueue();
Console.WriteLine($"Permanently deleted {count} messages from the dead letter queue");

Warning: This operation permanently deletes all messages in the DLQ and cannot be undone. Use with caution.

Enabling the DLQ

The DLQ is disabled by default. Enable it via the builder API:

Using DI:

services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("./queue_data")
 .WithDeadLetterQueue() // ← enable dead letter queue with default settings
 .CreateQueues("my-queue");
});

Manual configuration:

var queue = new QueueConfiguration()
 .WithDefaults()
 .WithDeadLetterQueue()
 .StoreWithLmdb("./queue_data")
 .BuildAndStart("my-queue");

When the DLQ is not enabled:

• ReceiveLater never auto-moves messages to the DLQ, even when MaxAttempts is exceeded — the message is retried indefinitely.
• Outgoing messages that fail all send retries are silently discarded.
• Calling MoveToDeadLetter() throws InvalidOperationException.

Global Max Attempts

In addition to per-message maxAttempts, you can configure a global maximum that applies to all messages:

services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("./queue_data")
 .WithDeadLetterQueue(options =>
 {
 options.GlobalMaxAttemptsForMessage = 5; // Default is 3
 })
 .CreateQueues("my-queue");
});

How it works:

• If a message has maxAttempts set, the lower of the two values takes precedence.
• Example: GlobalMaxAttemptsForMessage = 5, message has maxAttempts: 10 → DLQ after 5 attempts.
• Example: GlobalMaxAttemptsForMessage = 5, message has maxAttempts: 3 → DLQ after 3 attempts.
• Messages without maxAttempts are dead-lettered after reaching the global max.

DLQ Metrics (OpenTelemetry)

When OpenTelemetry is enabled, the following DLQ-specific metrics are emitted:

Metric	Type	Description
bielupersistentqueues.messages.dead_lettered	Counter	Total dead-lettered messages (tags: queue.name, reason)
bielupersistentqueues.dead_letter.queue.depth	Gauge	Current message count in the DLQ

Reason values: manual, max_processing_attempts, send_failed.

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Architecture

Pluggable Storage

The core library defines a storage-agnostic IMessageStore interface with IStoreTransaction for atomic operations. Storage providers are distributed as separate NuGet packages, each implementing these interfaces.

Currently available:

• LMDB (Bielu.PersistentQueues.Storage.LMDB) — High-performance embedded key-value store powered by LightningDB.
• ZoneTree (Bielu.PersistentQueues.Storage.ZoneTree) — Pure C# LSM-tree storage with no native dependencies.

Extensibility:

• Additional storage backends can be added by creating a package that references Bielu.PersistentQueues and implements IMessageStore and IStoreTransaction.

Storage Provider Comparison

Two storage backends are available. Choose based on your requirements:

Feature	LMDB	ZoneTree
Implementation	C wrapper (LightningDB)	Pure C# (LSM tree)
Native dependencies	Yes (lmdb)	None
Transaction model	Full ACID	Buffered commit
Memory mapping	Yes (mmap)	No
Best for	Low-latency point lookups	Write-heavy workloads

Benchmark Results

Benchmarked on Linux (Ubuntu 24.04), AMD EPYC 7763, .NET 10.0.5. Times are in microseconds (μs). Lower is better.

Small batches (100 messages)

Operation	LMDB (64B)	ZoneTree (64B)	LMDB (512B)	ZoneTree (512B)
StoreIncoming	308 μs	171 μs	404 μs	204 μs
GetMessage	655 μs	506 μs	717 μs	486 μs
PersistedIncoming	595 μs	608 μs	948 μs	837 μs
DeleteIncoming	808 μs	446 μs	762 μs	289 μs
QueueCycle	848 μs	668 μs	966 μs	656 μs
OutgoingCycle	24,987 μs	828 μs	25,402 μs	844 μs

Larger batches (1,000 messages)

Operation	LMDB (64B)	ZoneTree (64B)	LMDB (512B)	ZoneTree (512B)
StoreIncoming	1,541 μs	2,041 μs	2,117 μs	2,149 μs
GetMessage	4,406 μs	4,703 μs	4,863 μs	5,060 μs
PersistedIncoming	4,254 μs	5,161 μs	4,390 μs	5,172 μs
DeleteIncoming	2,871 μs	3,575 μs	3,664 μs	3,766 μs
QueueCycle	5,268 μs	6,719 μs	6,028 μs	6,746 μs
OutgoingCycle	261,570 μs	8,298 μs	335,853 μs	8,341 μs

Update date benchmark results: https://bielu.github.io/Bielu.PersistentQueues/benchmarks/

Summary

• ZoneTree excels at small-batch operations (100 messages) where it is consistently faster across all operations, particularly for store and delete.
• LMDB excels at larger batches (1,000+ messages) where its transactional batch commit model amortizes overhead and its B+ tree provides excellent read locality.
• LMDB has lower memory allocations due to zero-copy reads from memory-mapped files.
• In the benchmarked outgoing message workflow using PersistedOutgoing() with per-message SuccessfullySent(), ZoneTree dramatically outperforms LMDB at all scales measured (30x faster at 1K messages).
• These outgoing-cycle results do not measure the production raw/bulk send path (PersistedOutgoingRaw + SuccessfullySentByIds), so they should not be treated as a universal conclusion for all send scenarios.
• Choose LMDB for read-heavy, large-batch, low-latency workloads with available native dependencies.
• Choose ZoneTree for write-heavy, small-batch workloads or when native dependencies are not available, especially when your usage matches the benchmarked API pattern above.

Custom Storage Providers

To implement your own storage provider:

1. Create a new project referencing Bielu.PersistentQueues
2. Implement IMessageStore and IStoreTransaction
3. Create an extension method on PersistentQueuesBuilder to register your storage:

public static PersistentQueuesBuilder AddMyStorage(
 this PersistentQueuesBuilder builder,
 string connectionString)
{
 builder.UseStorage(sp =>
 {
 // Create and return your IMessageStore implementation
 return new MyMessageStore(connectionString);
 });
 return builder;
}

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Strongly-Typed Messages

Bielu.PersistentQueues supports sending, enqueuing, and receiving strongly-typed C# objects as message payloads. Serialization is handled automatically — the content serializer is resolved from DI, so you never need to manually serialize or pass a serializer instance.

Sending and Enqueuing Typed Objects

The IQueue and IQueueContext interfaces include Send<T> and Enqueue<T> methods that accept any object and serialize it using the DI-configured IContentSerializer (JSON by default):

// Send a typed object to a remote endpoint
queue.Send(new OrderMessage("ORD-001", 99.99m, "USD"),
 destinationUri: "lq.tcp://localhost:5050",
 queueName: "orders");

// Enqueue a typed object for local processing
queue.Enqueue(new OrderMessage("ORD-002", 49.99m, "EUR"),
 queueName: "orders");

Inside a message handler, the same methods are available on IQueueContext:

await foreach (var msg in queue.Receive("orders", token))
{
 var order = msg.Message.GetContent<OrderMessage>();

 // Enqueue a follow-up message (serializer from DI)
 msg.QueueContext.Enqueue(new AuditEvent(order.OrderId, "processed"),
 queueName: "audit");

 // Send a response to another endpoint
 msg.QueueContext.Send(new OrderConfirmation(order.OrderId),
 destinationUri: "lq.tcp://notifications:5050",
 queueName: "confirmations");

 msg.QueueContext.SuccessfullyReceived();
 msg.QueueContext.CommitChanges();
}

Deserializing Message Content

Use Message.GetContent<T>() to deserialize the Data payload:

var order = message.GetContent<OrderMessage>();

Custom Content Serializer

The default serializer uses System.Text.Json. You can replace it globally by registering your own IContentSerializer before calling AddPersistentQueues:

// Register a custom serializer (e.g., MessagePack, Protobuf)
services.AddSingleton<IContentSerializer, MyMessagePackSerializer>();

// Queue will automatically use it for all Send<T>/Enqueue<T>/GetContent<T> calls
services.AddPersistentQueues(builder => { ... });

For one-off overrides, extension methods accept an explicit serializer:

var msgPackSerializer = new MyMessagePackSerializer();
queue.Send(order, msgPackSerializer, destinationUri: "lq.tcp://...", queueName: "orders");
queue.Enqueue(order, msgPackSerializer, queueName: "orders");

When using manual configuration (without DI), you can configure the serializer via the builder:

var queue = new QueueConfiguration()
 .WithDefaults()
 .SerializeContentWith(new MyMessagePackSerializer())
 .StoreWithLmdb("C:\\queue_path")
 .BuildAndStart("queue-name");

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Running Tests

To ensure everything is running smoothly, clone the repository and run:

dotnet test src/Bielu.PersistentQueues.slnx

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OpenTelemetry Integration

Bielu.PersistentQueues provides built-in OpenTelemetry support for distributed tracing and metrics through the PersistentQueueOtelDecorator. This enables comprehensive observability of your queue operations.

Installation

dotnet add package Bielu.PersistentQueues.OpenTelemetry

Usage

Add instrumentation to your service collection to automatically decorate the queue with OpenTelemetry:

using Bielu.PersistentQueues.OpenTelemetry;

services.AddBieluPersistentQueueInstrumentation();

Metrics Collected

The decorator automatically collects the following metrics:

• bielupersistentqueues.messages.sent - Counter: Total number of messages sent
• bielupersistentqueues.messages.received - Counter: Total number of messages received
• bielupersistentqueues.messages.enqueued - Counter: Total number of messages enqueued
• bielupersistentqueues.operations.errors - Counter: Total number of operation errors
• bielupersistentqueues.message.processing.duration - Histogram: Duration of message processing in milliseconds
• bielupersistentqueues.batch.size - Histogram: Number of messages in batches
• bielupersistentqueues.queues.active - Gauge: Number of currently active queues
• bielupersistentqueues.storage.used_bytes - Gauge: Number of bytes currently used by the storage
• bielupersistentqueues.storage.total_bytes - Gauge: Total number of bytes allocated for the storage
• bielupersistentqueues.storage.usage_percent - Gauge: Percentage of storage currently in use (0–100%)

All metrics include relevant dimensions such as queue.name, operation, and batch.size.

Note: Storage usage metrics are automatically registered when the underlying store supports usage reporting (e.g., LMDB). No additional configuration is needed beyond enabling OpenTelemetry instrumentation.

Distributed Tracing

The decorator creates OpenTelemetry activities (spans) for all queue operations:

• Producer operations: Send, SendBatch, Enqueue
• Consumer operations: Receive, ReceiveBatch, ProcessMessage, ProcessBatch
• Internal operations: CreateQueue, Start, MoveToQueue, ReceiveLater

Each span includes tags such as:

• queue.name - The queue name
• message.id - Unique message identifier
• destination - Message destination endpoint
• batch.size - Number of messages in batch operations
• delay.seconds - Delay time for scheduled messages

Error Tracking

All operations are wrapped with exception handling that:

• Records error metrics with operation context
• Sets activity status to Error
• Adds exception details to the span
• Preserves original exception for caller handling

Example: Full Configuration with OpenTelemetry

using OpenTelemetry;
using OpenTelemetry.Metrics;
using OpenTelemetry.Trace;
using Bielu.PersistentQueues.OpenTelemetry;

// Configure OpenTelemetry
services.AddOpenTelemetry()
 .WithMetrics(metrics => metrics
 .AddBieluPersistentQueuesInstrumentation())
 .WithTracing(tracing => tracing
 .AddBieluPersistentQueuesInstrumentation());

// Configure queue
services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("C:\\queue_path")
 .ListenOn(new IPEndPoint(IPAddress.Loopback, 5050))
 .CreateQueues("my-queue");
});

// Add instrumentation
services.AddBieluPersistentQueueInstrumentation();

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Partitioned Queues (Kafka-like Partitioning)

Bielu.PersistentQueues supports Kafka-like partitioning to enable parallel consumption and message ordering guarantees within a partition key.

Concepts

• A partitioned queue divides a logical queue into N partitions (e.g., orders:partition-0, orders:partition-1, ...).
• Messages are routed to partitions using a configurable partition strategy.
• Messages with the same partition key are guaranteed to land in the same partition, preserving ordering for related messages.
• Consumers can receive from all partitions, a specific partition, or a subset of partitions.

Built-in Partition Strategies

Strategy	Description
HashPartitionStrategy (default)	Routes messages based on FNV-1a hash of the partition key. Same key → same partition. Messages without a key use the message ID for distribution.
RoundRobinPartitionStrategy	Distributes messages evenly across partitions in round-robin order, ignoring partition keys.
ExplicitPartitionStrategy	Routes messages to the partition index specified in the partition key (e.g., key "2" → partition 2).

Manual Configuration

var queue = new QueueConfiguration()
 .WithDefaults()
 .StoreWithLmdb("C:\\queue_path", StorageSize.MB(100))
 .BuildAndStartPartitioned("orders", partitionCount: 4);

// Enqueue with a partition key (routed by strategy)
var message = Message.Create(
 data: "order-data"u8.ToArray(),
 queue: "orders",
 partitionKey: "customer-123"
);
queue.EnqueueToPartition(message, "orders");

// Receive from a specific partition
await foreach (var ctx in queue.ReceiveFromPartition("orders", partition: 0, cancellationToken: token))
{
 // Process message
 ctx.QueueContext.SuccessfullyReceived();
 ctx.QueueContext.CommitChanges();
}

Using Dependency Injection

services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("C:\\queue_path", config =>
 {
 config.MapSize = StorageSize.MB(500); // Use StorageSize helper instead of raw bytes
 config.EnvironmentConfiguration.MaxDatabases = 20; // Increase for partitioned queues
 })
 .AutomaticEndpoint()
 .UsePartitioning(
 new HashPartitionStrategy(),
 ("orders", 4), // 4 partitions for "orders"
 ("events", 8) // 8 partitions for "events"
 );
});

// Inject IPartitionedQueue
public class MyService(IPartitionedQueue queue)
{
 public void ProcessOrder(string customerId, byte[] data)
 {
 var message = Message.Create(
 data: data,
 queue: "orders",
 partitionKey: customerId // Same customer → same partition → ordered
 );
 queue.EnqueueToPartition(message, "orders");
 }

 public async Task ConsumePartition(int partition, CancellationToken token)
 {
 await foreach (var ctx in queue.ReceiveFromPartition("orders", partition, cancellationToken: token))
 {
 // Deserialize to a strongly-typed object
 var order = ctx.Message.GetContent<OrderMessage>();

 // Process — messages from same partition key arrive in order
 ctx.QueueContext.SuccessfullyReceived();
 ctx.QueueContext.CommitChanges();
 }
 }
}

Custom Partition Strategy

Implement IPartitionStrategy to create your own routing logic:

public class GeoPartitionStrategy : IPartitionStrategy
{
 public int GetPartition(Message message, int partitionCount)
 {
 // Route by geographic region from partition key
 var region = message.PartitionKeyString;
 return region switch
 {
 "us-east" => 0 % partitionCount,
 "us-west" => 1 % partitionCount,
 "eu" => 2 % partitionCount,
 _ => 0
 };
 }
}

LMDB Configuration Note

When using partitioned queues with LMDB storage, ensure MaxDatabases is set high enough to accommodate all partitions. Each partition creates a separate LMDB database. For example, 4 partitions + 1 outgoing database requires at least MaxDatabases = 6.

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Storage Usage Monitoring

Bielu.PersistentQueues exposes storage usage information through the IMessageStore.GetStorageUsageInfo() API. This is useful for monitoring disk pressure and alerting when storage is nearing capacity.

Programmatic Access

You can query storage usage directly from the store:

var usageInfo = queue.Store.GetStorageUsageInfo();
if (usageInfo != null)
{
 Console.WriteLine($"Used: {usageInfo.Value.UsedBytes} bytes");
 Console.WriteLine($"Total: {usageInfo.Value.TotalBytes} bytes");
 Console.WriteLine($"Usage: {usageInfo.Value.UsagePercentage:F2}%");
}

GetStorageUsageInfo() returns null if the storage provider does not support usage reporting. The LMDB provider reports usage based on the environment's page utilization relative to the configured MapSize.

OpenTelemetry Integration

When OpenTelemetry instrumentation is enabled, storage usage metrics are automatically collected as observable gauges — no extra configuration is needed:

services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("./queue_data", config =>
 {
 config.MapSize = StorageSize.MB(100); // Use StorageSize helper
 })
 .CreateQueues("my-queue");
});

// Storage gauges are registered automatically
services.AddBieluPersistentQueueInstrumentation();

The three storage gauges (storage.used_bytes, storage.total_bytes, storage.usage_percent) are polled at each metric scrape and always reflect the current state.

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Transport Security (TLS Encryption)

Bielu.PersistentQueues supports TLS encryption to secure communication. The library provides hooks to enable custom certificate validation and encryption settings:

services.AddPersistentQueues(builder =>
{
 builder
 .AddLmdbStorage("C:\\queue_path")
 .SecureTransportWith(
 new TlsStreamSecurity(async (uri, stream) =>
 {
 var sslStream = new SslStream(stream, true, (_, _, _, _) => true, null);
 await sslStream.AuthenticateAsClientAsync(uri.Host);
 return sslStream;
 }),
 new TlsStreamSecurity(async (_, stream) =>
 {
 var sslStream = new SslStream(stream, false);
 await sslStream.AuthenticateAsServerAsync(certificate, false,
 checkCertificateRevocation: false, enabledSslProtocols: SslProtocols.Tls12);
 return sslStream;
 }));
});

---

Contributing

We welcome contributions! See our for details on:

• Setting up your development environment
• Building and testing the project
• Submitting pull requests

---

Acknowledgments

This project is based on and builds upon the excellent work of:

• LightningQueues - The original fast persistent queues library for .NET, created and maintained by Corey Kaylor and contributors. Licensed under the MIT License.

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License

This project is licensed under the MIT License - see the file for details.