🎯 Event Sourcing¶

At ConnectSoft, Event Sourcing is a foundational architectural pattern that ensures auditability, resilience, and evolutionary adaptability for all mission-critical systems.

Rather than persisting only the latest state of an entity, Event Sourcing records every state transition as a distinct, immutable event — enabling full system history reconstruction, real-time insights, and cloud-native scalability.

Info

Event Sourcing is deeply integrated into ConnectSoft SaaS platforms, AI orchestration engines, and microservices ecosystems — empowering secure, observable, and regulatory-compliant state management at global scale.

🧠 What is Event Sourcing?¶

Event Sourcing is a data persistence strategy where changes to an application's state are captured as a series of immutable events.

Instead of simply overwriting current values in a database, each state change is appended to an event store — creating a chronological ledger of business activity.

flowchart TD
    UserAction --> DomainEvent
    DomainEvent --> EventStore
    EventStore -->|Replay| AggregateState
    EventStore -->|Projection| ReadModel

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Key Concepts:

Concept	Description
Event	An immutable fact describing something that happened (e.g., `OrderPlaced`, `PaymentReceived`).
Event Store	A durable, append-only log of all domain events.
Aggregate	A domain object that rebuilds its current state by replaying historical events.
Projection	A read-optimized view generated from events, used to power queries and APIs.

🌍 Why Event Sourcing Matters at ConnectSoft¶

In a world of microservices, SaaS multi-tenancy, AI-driven platforms, and regulatory compliance demands, traditional CRUD-based state storage introduces major limitations:

Challenge	Traditional CRUD Systems	Event-Sourced Systems
Auditability	Only latest state visible.	Full historical record available.
Resilience	Data loss during crashes.	State rebuilt from persisted events.
Scalability	Bottlenecked writes/updates.	Append-only operations, highly scalable.
Temporal Queries	Not possible without special audit tables.	Native — state at any past point can be reconstructed.
System Evolution	Schema changes risky.	New projections/views can be built without touching original events.

Tip

At ConnectSoft, Event Sourcing is the backbone of systems requiring traceability, replayability, and real-time event-driven analytics — especially in regulated, financial, and AI/ML-powered domains.

🧩 Core Principles of Event Sourcing¶

Event Sourcing transforms how systems think about and manage state.
Instead of treating current state as the primary asset, ConnectSoft platforms embrace events as the true source of record — with state as a dynamic projection.

Let's explore the foundational principles that make Event Sourcing so powerful.

📜 1. Events as the Source of Truth¶

Definition:
In Event Sourcing, events are the canonical source of data.
The current system state is always derived from replaying these historical events.

Real Example at ConnectSoft:

OrderPlaced, OrderPaid, OrderShipped events reconstruct the full order lifecycle.

public void Apply(OrderPlaced e)
{
    Id = e.OrderId;
    Status = "Placed";
}

public void Apply(OrderPaid e)
{
    Status = "Paid";
}

public void Apply(OrderShipped e)
{
    Status = "Shipped";
}

Info

At ConnectSoft, state is a consequence of events, not a first-class citizen.
Events drive everything — projections, APIs, compliance reporting, and analytics.

🔒 2. Immutability¶

Definition:
Once an event is recorded, it is never modified.
If corrections are needed, new compensating events are appended to reflect real-world adjustments.

Example:

Incorrect payment recorded?
- Don't modify PaymentProcessed.
- Append a PaymentRefunded event.

await _eventStore.AppendAsync(new PaymentRefunded(orderId, amount));

flowchart LR
    PaymentProcessed --> PaymentRefunded

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Warning

Mutating past events corrupts the event stream’s auditability and replay integrity.
Immutability is sacred in ConnectSoft Event-Sourced systems.

🔄 3. Rebuildable State¶

Definition:
Current state is reconstructed at any time by replaying events in order.

Real Example at ConnectSoft:

Shopping cart service rebuilds its state from a sequence:
- ItemAdded, ItemAdded, ItemRemoved, DiscountApplied.

foreach (var evt in events)
{
    Apply(evt); // Rebuild aggregate step-by-step
}

Tip

ConnectSoft aggregates are designed to replay event histories efficiently, often aided by snapshotting after N events.

⏳ 4. Temporal Queries¶

Definition:
You can reconstruct the state of the system at any historical point by replaying events up to a desired timestamp.

Real Example:

Query an order’s state before it was refunded.

var events = _eventStore.ReadUpTo("Order-12345", timestamp);
var order = OrderAggregate.Rebuild(events);

Typical Use Cases:

Audit investigations.
Regulatory reports (GDPR, HIPAA).
Time-based analytics ("show me cart status before Black Friday").

sequenceDiagram
    EventStore->>StateRebuilder: Replay Events Up To T1
    StateRebuilder->>QueryEngine: Serve Historical State

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⚡ Principle Summary Table¶

Principle	Key Concept	ConnectSoft Implementation Focus
Events as Source of Truth	Events drive the system; current state is derived.	Event stores prioritized over relational DBs for domain-critical entities.
Immutability	Events are permanent and append-only.	Correction = append compensating events, never edits.
Rebuildable State	Aggregates reconstitute their state by replaying event streams.	Efficient state rebuilding with optional snapshotting.
Temporal Queries	Historical system state can be reconstructed at any point.	Event filters by timestamp + snapshot merge optimizations.

🧩 Event Sourcing: Real-World Use Cases¶

Event Sourcing empowers systems that demand auditability, real-time processing, resilience, and temporal analytics.

At ConnectSoft, we apply Event Sourcing across multiple domains, using cloud-native event stores and scalable patterns to enable next-generation capabilities.

🏦 Financial Systems: Auditable Transactions¶

Scenario:
Banking platforms must maintain a complete immutable record of all financial operations for audits and compliance.

Example:

Events like DepositMade, WithdrawalMade, InterestApplied reconstruct account balances over time.

sequenceDiagram
    Customer->>BankingAPI: Deposit $500
    BankingAPI-->>EventStore: Record DepositMade
    Customer->>BankingAPI: Withdraw $200
    BankingAPI-->>EventStore: Record WithdrawalMade

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Tip

ConnectSoft finance platforms use Event Sourcing to ensure traceability, reconciliation, and GDPR/PCI compliance.

🛒 E-Commerce Order Management¶

Scenario:
Orders in an e-commerce platform undergo multiple lifecycle transitions.

Example:

Events like OrderPlaced, PaymentProcessed, OrderShipped, OrderDelivered model the full customer journey.

await _eventStore.AppendAsync(new OrderPlaced(orderId, userId, totalAmount));
await _eventStore.AppendAsync(new PaymentProcessed(orderId, paymentId, amountPaid));
await _eventStore.AppendAsync(new OrderShipped(orderId, trackingNumber));

flowchart TD
    OrderPlaced --> PaymentProcessed --> OrderShipped --> OrderDelivered

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🌡️ IoT Data Processing: Sensor Streams¶

Scenario:
IoT devices continuously emit time-series data that must be captured immutably and analyzed asynchronously.

Example:

TemperatureRecorded, HumidityRecorded, MotionDetected events stream from smart home devices.

await _eventStore.AppendAsync(new TemperatureRecorded(sensorId, 72.5, DateTime.UtcNow));

Processing Pattern:

Store raw telemetry events in append-only logs.
Build projections for averages, alerts, or anomaly detection.

📈 Real-Time Analytics and Dashboards¶

Scenario:
Streaming platforms need to analyze user behavior and derive insights dynamically.

Example:

VideoStarted, VideoPaused, VideoCompleted events feed engagement metrics dashboards.

sequenceDiagram
    User->>VideoService: Start Watching
    VideoService-->>EventStore: VideoStarted
    User->>VideoService: Stop Watching
    VideoService-->>EventStore: VideoCompleted
    EventStore->>AnalyticsPipeline: Stream Events for KPIs

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Info

ConnectSoft real-time dashboards are powered by event replay projections —
enabling time-travel analytics and live operational metrics.

Scenario:
Organizations must track every access or modification to customer data to comply with GDPR and CCPA.

Example:

DataAccessed, DataUpdated, ConsentGranted, ConsentWithdrawn events form a secure audit trail.

await _eventStore.AppendAsync(new DataAccessed(customerId, accessedBy, DateTime.UtcNow));
await _eventStore.AppendAsync(new ConsentGranted(customerId, consentType, DateTime.UtcNow));

Benefits:

Produce detailed audit reports.
Validate compliance on-demand.

🔗 Event-Driven Architectures: Microservices Communication¶

Scenario:
Microservices notify one another about state changes through published domain events.

Example:

InventoryUpdated event notifies Order Service that stock levels have changed.

await _publishEndpoint.Publish(new InventoryUpdated(productId, quantityAvailable));

flowchart TD
    InventoryService -->|InventoryUpdated Event| EventBus --> OrderService

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Tip

ConnectSoft microservices use Event Sourcing + Pub/Sub together —
ensuring that services are both autonomous and event-aware.

🧠 Summary of Event Sourcing Use Cases¶

Domain	Example Events	Outcome
Banking	`DepositMade`, `WithdrawalMade`	Full transaction audit, state recovery.
E-Commerce	`OrderPlaced`, `OrderShipped`	Lifecycle management, customer insights.
IoT	`TemperatureRecorded`, `MotionDetected`	Time-series analytics, smart alerts.
Analytics	`VideoStarted`, `VideoStopped`	Real-time engagement KPIs.
Compliance	`DataAccessed`, `ConsentGranted`	Regulatory auditability.
Microservices	`InventoryUpdated`, `PaymentCompleted`	Asynchronous cross-service communication.

⚖️ Event Sourcing: Advantages and Trade-offs¶

Event Sourcing unlocks powerful capabilities for auditability, resilience, scalability, and temporal intelligence —
but it also introduces operational complexity that must be managed carefully.

At ConnectSoft, we apply Event Sourcing strategically based on domain criticality, system resilience needs, and future evolution flexibility.

✅ Advantages of Event Sourcing¶

Advantage	Description	Real-World Benefit
Auditability	Complete, immutable history of all changes.	Full compliance (GDPR, HIPAA, PCI-DSS) and post-mortem investigations.
Resilience	System state recoverable by event replay.	Recovery from corruption, crashes, or operational failures.
Temporal Queries	Query historical state at any point in time.	Business reporting ("How many carts abandoned before Black Friday?").
Flexibility	Build new projections or views as needs evolve.	Create new KPIs, reporting models, ML training datasets without touching production services.
Scalability	Append-only storage scales efficiently.	High event write throughput for real-time and IoT platforms.
Traceability	End-to-end tracing of domain actions.	Debugging, anomaly detection, root cause analysis.

Info

ConnectSoft platforms treat event streams as business assets —
empowering future innovations without disrupting current operations.

⚠️ Trade-offs and Challenges¶

Challenge	Description	ConnectSoft Mitigation
Increased Storage Size	Events accumulate indefinitely, growing storage needs.	Use efficient cloud-native storage, lifecycle management, archiving, and cold storage strategies.
Schema Evolution Complexity	Changes to event structure must be backward-compatible.	Apply event versioning and schema migration strategies (covered later).
Query Complexity	Queries require projections rather than direct entity reads.	Build optimized projections tailored for read models (CQRS architecture).
Replay Overhead	Rebuilding large aggregates can be slow.	Use snapshotting after N events to optimize recovery time.
Operational Complexity	Requires monitoring event stores, projection lag, and retries.	Standardize observability and health monitoring (OpenTelemetry, Grafana dashboards).

Warning

Event Sourcing is not for trivial CRUD systems.
It shines when auditability, resilience, or complex workflows are business-critical.
Always evaluate domain needs carefully before applying it.

📈 Diagram: Trade-offs Overview¶

graph TB
    EventSourcing((Event Sourcing))
    EventSourcing -->|Benefits| Auditability
    EventSourcing -->|Benefits| Resilience
    EventSourcing -->|Benefits| Scalability
    EventSourcing -->|Challenges| StorageGrowth
    EventSourcing -->|Challenges| SchemaEvolution
    EventSourcing -->|Challenges| QueryComplexity

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🛠️ ConnectSoft Guidance on When to Apply Event Sourcing¶

✅ Strong candidates for Event Sourcing:

Critical domain workflows (finance, healthcare, logistics).
Systems requiring full audit trails.
Platforms needing real-time analytic pipelines based on domain activity.
Highly evolving systems where projections need to change without downtime.

❌ Poor candidates for Event Sourcing:

Simple CRUD-based apps with no audit needs.
Systems where read performance outweighs traceability concerns and state changes are trivial.

🛠️ Event Sourcing Implementation Patterns¶

At ConnectSoft, successful Event Sourcing implementations follow a set of well-structured core patterns — ensuring systems are scalable, reliable, and operationally maintainable.

🗃️ Event Store as the Source of Truth¶

Pattern:
The Event Store becomes the only authoritative source for all domain changes.
Aggregates, projections, APIs — all derive their knowledge by replaying events.

flowchart TD
    API --> EventPublisher
    EventPublisher --> EventStore
    EventStore --> Aggregate
    EventStore --> Projections

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Real-World Example at ConnectSoft:

EventStoreDB and Kafka are used as primary event storage engines.
Microservices never update aggregate state directly — only by applying new events.

await _eventStore.AppendAsync(new OrderPlaced(orderId, userId, amount));

Info

ConnectSoft mandates that no service modifies current state directly without emitting corresponding domain events.

📸 Snapshotting¶

Pattern:
Create periodic snapshots of aggregate state to optimize replay performance when the event stream becomes too long.

Why Important:

Rebuilding aggregates from thousands of events is inefficient.
Snapshots allow fast recovery, bounded replay windows, and scaling older aggregates.

if (events.Count > SnapshotThreshold)
{
    await _snapshotStore.SaveSnapshotAsync(aggregate.Id, aggregate.Version, aggregate.GetSnapshot());
}

Typical Strategy:

Take snapshots every N events (e.g., 100 events).
Save snapshot + continue appending events.

🧩 Event Streams and Aggregates¶

Pattern:
Each Aggregate owns its event stream.
Streams are isolated by Aggregate ID, ensuring concurrency control and scalable partitioning.

Stream Naming Convention at ConnectSoft:

Entity	Stream Name Format
Order	`order-{OrderId}`
Account	`account-{AccountId}`
Cart	`cart-{CartId}`

var streamName = $"order-{orderId}";
await _eventStore.AppendToStreamAsync(streamName, events);

Tip

Stream-per-aggregate design improves concurrency, reduces lock contention, and simplifies snapshots.

🏗️ Projections¶

Pattern:
Projections are read-optimized views built by subscribing to events and materializing denormalized query models.

flowchart TD
    EventStore --> ProjectionBuilder
    ProjectionBuilder --> ReadModel
    API --> ReadModel

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Real-World Example:

Build a CustomerSummary projection aggregating total spending across all PurchaseMade events.

public void Apply(PurchaseMade e)
{
    if (!_customerSummaries.ContainsKey(e.CustomerId))
    {
        _customerSummaries[e.CustomerId] = new CustomerSummary(e.CustomerId, 0);
    }
    _customerSummaries[e.CustomerId].TotalSpent += e.Amount;
}

📈 Projection Types Used at ConnectSoft¶

Projection Type	Use Case	Example
Real-time projection	User-facing dashboards, instant queries.	Active order status per customer.
Historical view	Audits, compliance reports.	Financial transaction history per account.
Materialized read model	Optimized API queries.	Cart item summaries, appointment schedules.

📜 Event Versioning¶

Pattern:
Introduce version numbers for events when the schema evolves.

V1: OrderPlaced with OrderId, Amount.
V2: OrderPlaced with OrderId, Amount, Currency.

Handling Old Events:

switch (eventMetadata.Version)
{
    case 1:
        var oldEvent = JsonSerializer.Deserialize<OrderPlacedV1>(eventData);
        break;
    case 2:
        var newEvent = JsonSerializer.Deserialize<OrderPlacedV2>(eventData);
        break;
}

Warning

At ConnectSoft, schema evolution is designed explicitly —
backward compatibility is always preserved until old consumers are fully retired.

📸 Snapshotting, Stream Management, and Projection Strategies¶

As Event-Sourced systems grow, managing long event streams, optimizing replay efficiency, and serving fast queries becomes crucial.

At ConnectSoft, we use proven patterns for snapshotting, stream partitioning, and projections to ensure cloud-native scalability and resilience.

📸 Snapshotting Strategy¶

Why Snapshot?

To optimize aggregate loading.
To reduce replay time during service startups and recoveries.
To stabilize latency for aggregates with thousands of events.

✍️ How Snapshotting Works¶

flowchart TD
    EventStream --> SnapshotBuilder
    SnapshotBuilder --> SnapshotStore
    SnapshotStore --> StateRebuilder
    EventStream --> StateRebuilder
    StateRebuilder --> Aggregate

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Aggregate emits domain events.
After N events, a Snapshot of current state is captured.
Aggregate rebuild starts from latest Snapshot + subsequent events.

📏 Snapshotting Guidelines at ConnectSoft¶

Strategy	Recommendation
Threshold-based	Take snapshots after every N events (e.g., every 100 events).
Periodic Snapshots	Optionally snapshot aggregates periodically (e.g., daily).
Selective Snapshots	Prioritize snapshotting long-lived or high-traffic aggregates.
Snapshot Compression	Optionally compress snapshot payloads for storage efficiency.

Example: Snapshotting Implementation¶

public async Task MaybeTakeSnapshot(Aggregate aggregate)
{
    if (aggregate.Version % 100 == 0) // Every 100 events
    {
        var snapshot = aggregate.ToSnapshot();
        await _snapshotStore.SaveSnapshotAsync(aggregate.Id, aggregate.Version, snapshot);
    }
}

🗃️ Stream Management¶

How We Manage Streams:

One Stream per Aggregate:
Every domain entity (Order, Account, Appointment) has its own isolated event stream.
Stream Naming Convention: Use consistent and predictable stream names for easier replay, sharding, and debugging.

Aggregate	Stream Naming Pattern
Order	`order-{OrderId}`
Customer	`customer-{CustomerId}`
Appointment	`appointment-{AppointmentId}`

🎯 Stream Partitioning and Sharding¶

At ConnectSoft scale, we sometimes partition event streams:

Strategy	Use Case
Hash-based partitioning	Distribute aggregates evenly across partitions.
Tenant-based partitioning	Multi-tenant SaaS systems. Keep each tenant's streams separate.
Domain-boundaries partitioning	Isolate domains (e.g., Payments vs. Logistics) for reliability and scaling.

flowchart TD
    EventPublisher --> PartitionRouter
    PartitionRouter --> Partition1
    PartitionRouter --> Partition2
    PartitionRouter --> Partition3

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🏗️ Projection Patterns (Deep Dive)¶

Projections transform event streams into read-optimized data models, powering APIs, dashboards, and analytics.

🔹 Types of Projections Used at ConnectSoft¶

Projection Type	Example
Real-Time Projection	User’s active cart, updated with `ItemAdded` and `ItemRemoved` events.
Aggregated Projection	Total revenue per customer, built from `PurchaseMade` events.
Temporal Projection	Order status at a specific point in time for audits or analytics.

🔄 Real-Time Projection Example¶

public class CartProjection
{
    private readonly Dictionary<Guid, CartSummary> _carts = new();

    public void Apply(ItemAdded evt)
    {
        if (!_carts.ContainsKey(evt.CartId))
            _carts[evt.CartId] = new CartSummary(evt.CartId);

        _carts[evt.CartId].AddItem(evt.ProductId, evt.Quantity);
    }

    public CartSummary GetCart(Guid cartId) => _carts[cartId];
}

Highlights:

Immediate update when event received.
Denormalized structure optimized for fast API reads.

📚 Aggregated Projection Example¶

public class CustomerRevenueProjection
{
    private readonly Dictionary<Guid, decimal> _revenues = new();

    public void Apply(PurchaseMade evt)
    {
        if (!_revenues.ContainsKey(evt.CustomerId))
            _revenues[evt.CustomerId] = 0;

        _revenues[evt.CustomerId] += evt.Amount;
    }

    public decimal GetTotalRevenue(Guid customerId) => _revenues[customerId];
}

📊 ConnectSoft Best Practices for Projections¶

Practice	Benefit
Keep projections lightweight	Faster rebuilds and updates.
Denormalize where needed	Optimize for real query patterns.
Separate read concerns	Never mix write and read models.
Use batch processing for massive projections	Replay large event sets efficiently.
Version projections	Allow side-by-side deployment of old and new read models.

🧬 Event Versioning and Schema Evolution¶

In long-lived systems, business requirements and data models inevitably change.
At ConnectSoft, evolution without breaking the past is a foundational discipline — especially critical in event-sourced systems where historical events are immutable.

🧠 Why Version Events?¶

Reason	Explanation
Schema Changes	New business fields, renamed properties, deprecated attributes.
Backward Compatibility	New services must understand old events; old consumers must ignore unknown fields.
Longevity	Systems must survive organizational, regulatory, or technology shifts over years.
Zero Downtime Evolution	Allow introducing new features without interrupting existing event processing.

📋 Strategies for Versioning Events¶

At ConnectSoft, we apply multiple strategies depending on the criticality, consistency needs, and platform constraints.

🏷️ 1. Schema Evolution Without Breaking Changes¶

Whenever possible, evolve schemas additively:

Approach	Example	Notes
Add new optional fields	Add `Currency` to `OrderPlaced` event.	Safe; old consumers ignore unknown fields.
Default values	Provide defaults for new fields.	Avoids null issues.
Field deprecation	Keep old fields until all consumers are updated.	Sunset fields gradually.

Example:

public record OrderPlacedV2
{
    public Guid OrderId { get; init; }
    public decimal Amount { get; init; }
    public string? Currency { get; init; } // Newly added optional field
}

🔖 2. Explicit Event Versioning¶

Introduce separate event types or versioned metadata:

Technique	Example
Separate event classes	`OrderPlacedV1`, `OrderPlacedV2`.
Metadata versioning	Add `version` field in event metadata/header.

Versioned Event Header Example:

{
    "eventType": "OrderPlaced",
    "version": 2,
    "timestamp": "2025-04-26T10:00:00Z"
}

🧬 3. Upcasting Older Events¶

Introduce a translator (upcaster) that migrates old events on-the-fly into new structures during replay.

Upcaster Example:

public object Upcast(EventData eventData)
{
    if (eventData.Version == 1)
    {
        var old = JsonSerializer.Deserialize<OrderPlacedV1>(eventData.Payload);
        return new OrderPlacedV2
        {
            OrderId = old.OrderId,
            Amount = old.Amount,
            Currency = "USD" // Default for legacy
        };
    }
    return JsonSerializer.Deserialize<OrderPlacedV2>(eventData.Payload);
}

Info

ConnectSoft uses dynamic upcasting inside services where backward compatibility must be maintained without rewriting event stores.

🔀 Event Versioning Diagram¶

flowchart TD
    EventV1[OrderPlaced V1]
    EventV2[OrderPlaced V2]
    EventV1 --> Upcaster --> EventV2
    EventV2 --> AggregateLoader

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🛡️ Best Practices for Schema Evolution at ConnectSoft¶

Practice	Benefit
Prefer additive changes	Maintain compatibility without upcasting if possible.
Version critical domain events	When contracts change in a breaking way, version explicitly.
Use metadata versioning	Lightweight way to indicate event structure changes.
Implement upcasters when needed	Translate old events cleanly at load time.
Maintain full documentation of event schemas	Critical for audits, debugging, and cross-team clarity.

⚠️ Common Mistakes to Avoid¶

Mistake	Why It's Risky
Overwriting existing events	Corrupts historical accuracy; breaks auditability.
Removing old fields immediately	Older consumers may still depend on them.
Ignoring event metadata	Leads to parsing failures and backward-incompatibility bugs.

Warning

In ConnectSoft systems, history is sacred —
events are immutable and trustworthy forever.
Versioning must be planned carefully, documented thoroughly, and tested rigorously.

🧪 Testing Strategies for Event-Sourced Systems¶

At ConnectSoft, rigorous automated testing of event-sourced systems is mandatory —
ensuring data integrity, correct behavior reconstruction, and projection consistency at every release.

Testing is layered: from unit tests of individual event handlers, through integration tests of event flows, to full replay and projection validation.

🛠️ Testing Layers Overview¶

Testing Layer	Purpose
Unit Testing	Validate aggregate/event logic in isolation.
Contract Testing	Ensure event schemas remain compatible.
Integration Testing	Validate append/replay across the event store.
Projection Testing	Validate correct projection building from event streams.
End-to-End Replay Testing	Rebuild full state from persisted event history.

🔹 1. Unit Testing Event Application to Aggregates¶

Ensure aggregates correctly apply each event.

[Fact]
public void ApplyOrderPlacedEvent_ChangesState()
{
    var order = new Order();
    order.Apply(new OrderPlaced { OrderId = Guid.NewGuid(), Amount = 150 });

    Assert.Equal("Placed", order.Status);
}

Tip

ConnectSoft mandates that every event handler in aggregates must be unit-tested individually.

📜 2. Contract Testing (Schema Validation)¶

Ensure all event producers and consumers agree on event structure.

Use Pact, Protobuf schema validation, or strict JSON Schema validators.
Version schema changes intentionally.

Example JSON Schema Validation:

var schema = await JsonSchema.FromJsonAsync(jsonSchemaContent);
var errors = schema.Validate(eventPayloadJson);
Assert.Empty(errors);

🔄 3. Integration Testing: Event Store Append and Replay¶

Spin up a real or in-memory event store for integration tests:

[Fact]
public async Task Should_StoreAndReplayEvents()
{
    var client = new EventStoreClient(EventStoreClientSettings.Create("esdb://localhost:2113?tls=false"));

    var orderPlaced = new OrderPlaced { OrderId = Guid.NewGuid(), Amount = 99.99m };

    await client.AppendToStreamAsync($"order-{orderPlaced.OrderId}", StreamState.Any,
        new[] { new EventData(Uuid.NewUuid(), "OrderPlaced", JsonSerializer.SerializeToUtf8Bytes(orderPlaced)) });

    var events = client.ReadStreamAsync(Direction.Forwards, $"order-{orderPlaced.OrderId}", StreamPosition.Start);

    await foreach (var resolvedEvent in events)
    {
        Assert.Equal("OrderPlaced", resolvedEvent.Event.EventType);
    }
}

🏗️ 4. Projection Testing¶

Validate projections update accurately from incoming events.

[Fact]
public void ShouldUpdateCustomerSpendingProjection()
{
    var projection = new CustomerSpendingProjection();

    projection.Apply(new PurchaseMade { CustomerId = Guid.NewGuid(), Amount = 120 });
    projection.Apply(new PurchaseMade { CustomerId = Guid.NewGuid(), Amount = 80 });

    Assert.Equal(200, projection.TotalSpending);
}

🔄 5. End-to-End Replay Testing¶

Ensure full aggregate state rebuilds correctly from historical events.

[Fact]
public async Task Should_ReplayEventsAndRebuildState()
{
    var events = new List<object>
    {
        new ItemAdded { ProductId = Guid.NewGuid(), Quantity = 2 },
        new ItemAdded { ProductId = Guid.NewGuid(), Quantity = 3 },
        new ItemRemoved { ProductId = Guid.NewGuid(), Quantity = 1 }
    };

    var cart = new CartAggregate();
    foreach (var evt in events)
    {
        cart.Apply(evt);
    }

    Assert.Equal(4, cart.TotalItemCount);
}

Info

Replay testing is critical at ConnectSoft —
every aggregate must guarantee state consistency from event history alone, even after years of operation.

🎯 Testing Best Practices at ConnectSoft¶

Practice	Benefit
Always test idempotency	Ensure repeated events don't corrupt state.
Validate all event handlers	Events must be applied correctly in aggregates.
Use real brokers or event stores in integration tests	In-memory mocks miss critical serialization and ordering edge cases.
Test snapshot creation and recovery separately	Validate correct snapshot triggers and restoration logic.
Replay historical production events periodically in non-prod	Catch latent bugs early.

🔎 Monitoring, Observability, and Debugging in Event-Sourced Systems¶

In Event-Sourced architectures, visibility into event flows, projection health, and replay performance is critical.
At ConnectSoft, monitoring and observability are built-in — not retrofitted — ensuring operational excellence and resilience at scale.

📊 Monitoring Focus Areas¶

Component	What to Monitor	Why Important
Event Store	Write/read throughput, storage usage, latency, failed writes.	Early detection of store overload or failures.
Projections	Processing lag, error rates, replay speed.	Ensure query models are up-to-date and accurate.
Consumers	Event processing latency, retry rates, dead-letter queues.	Detect bottlenecks, failures, and retry storms.
Snapshots	Snapshot creation frequency, restoration performance.	Validate optimization strategies.

🧠 Key Metrics and Alerts¶

Metric	Alert Threshold	Purpose
Event Write Latency	> 500ms average	Indicates storage performance degradation.
Projection Lag	> 30 seconds behind latest event	Signals potential staleness in read models.
Consumer Retry Count	> 5 retries per minute per consumer	Early warning of instability or bad payloads.
Dead-Letter Queue Growth	> 10 unprocessed messages	Critical event handling failures accumulating.
Snapshot Failure Rate	> 5% snapshot writes failing	Risk of slow aggregate rebuilds.

📈 Sample Grafana Dashboard Components¶

Event throughput (written/read per second).
Projection update lag by event type.
Consumer retry graphs.
Dead-lettered message tracking.
Snapshot operation durations.

Example: Event Store Metrics Visualization:

flowchart TD
    EventStore --> MetricsExporter
    MetricsExporter --> Prometheus
    Prometheus --> Grafana
    Grafana --> Dashboard

Hold "Alt" / "Option" to enable pan & zoom

🔍 Observability: Tracing Event Flows End-to-End¶

Use distributed tracing to monitor:

Which service produced an event.
Where the event traveled (brokers, partitions).
How the event was processed (consumers, projections).
Event latency across services.

🧵 OpenTelemetry for Event Sourcing at ConnectSoft¶

Trace an Event Lifecycle:

using var activity = _tracer.StartActivity("OrderPlaced", ActivityKind.Producer);
activity?.SetTag("messaging.system", "AzureServiceBus");
activity?.SetTag("messaging.destination", "order-topic");
activity?.SetTag("order.id", orderId);

Correlate Events:

Always pass Correlation IDs through headers.
Extract Correlation IDs in consumers and continue traces.

🛠️ Debugging Techniques¶

Strategy	Purpose
Structured Logging	Include event type, correlation ID, aggregate ID, event version in every log entry.
Dead-Letter Replay	Enable dead-lettered events to be manually retried after fixing consumer issues.
Event Replay Engines	Replay historical streams against newer codebases to detect replay-breaking bugs.
Projection Consistency Checkers	Validate projections match event history periodically.
Snapshot Verification	Regularly validate that snapshots match replays from raw events.

🔥 Sample Structured Logging for Events¶

_logger.LogInformation("Handled event {EventType} for aggregate {AggregateId} (Version: {Version})", 
    evt.GetType().Name, aggregate.Id, evt.Version);

🚨 Common Production Failures and How to Detect Early¶

Issue	Detection Strategy	Mitigation
Projection Lag	Monitor event version vs projection version gap.	Scale projection workers horizontally.
Consumer Poison Messages	Dead-letter queue growth spikes.	Improve validation, add pre-consumption checks.
Slow Snapshotting	Snapshot operation durations increase.	Optimize snapshot sizes, storage I/O.
Broker Overload	Throughput drops, event write latencies grow.	Auto-scale partitions or broker instances.

Warning

ConnectSoft systems alert on anomalies automatically —
because delayed detection = data loss, SLA violations, and user trust erosion.

🏁 Conclusion¶

Event Sourcing is not just a technical pattern —
at ConnectSoft, it is a strategic foundation that enables:

⚡ Auditability without compromise
🔄 Resilient state recovery under failure conditions
🧠 Real-time, temporal, and historical insights
☁️ Cloud-native scaling and storage optimization
🚀 Agile system evolution without disrupting operational continuity

By persisting every state transition as an immutable event, ConnectSoft platforms can replay, reconstruct, trace, and adapt in ways that traditional CRUD-based systems cannot match.

🎯 Event Sourcing empowers ConnectSoft systems to think in time —
not just in current state snapshots.

📚 References¶

Reference	Link
EventStoreDB Documentation	eventstore.com/docs
Kafka Documentation	kafka.apache.org/documentation
Microsoft .NET Microservices - Event Sourcing Guidance	docs.microsoft.com
Martin Fowler - Event Sourcing	martinfowler.com
CNCF Cloud Native Event Sourcing	cncf.io
Building Event-Driven Microservices (Adam Bellemare)	Book Link

🎯 Event Sourcing¶

🧠 What is Event Sourcing?¶

🌍 Why Event Sourcing Matters at ConnectSoft¶

🧩 Core Principles of Event Sourcing¶

📜 1. Events as the Source of Truth¶

🔒 2. Immutability¶

🔄 3. Rebuildable State¶

⏳ 4. Temporal Queries¶

⚡ Principle Summary Table¶

🧩 Event Sourcing: Real-World Use Cases¶

🏦 Financial Systems: Auditable Transactions¶

🛒 E-Commerce Order Management¶

🌡️ IoT Data Processing: Sensor Streams¶

📈 Real-Time Analytics and Dashboards¶

🛡️ GDPR and Compliance Event Logging¶

🔗 Event-Driven Architectures: Microservices Communication¶

🧠 Summary of Event Sourcing Use Cases¶

⚖️ Event Sourcing: Advantages and Trade-offs¶

✅ Advantages of Event Sourcing¶

⚠️ Trade-offs and Challenges¶

📈 Diagram: Trade-offs Overview¶

🛠️ ConnectSoft Guidance on When to Apply Event Sourcing¶

🛠️ Event Sourcing Implementation Patterns¶

🗃️ Event Store as the Source of Truth¶

📸 Snapshotting¶

🧩 Event Streams and Aggregates¶

🏗️ Projections¶

📈 Projection Types Used at ConnectSoft¶

📜 Event Versioning¶

📸 Snapshotting, Stream Management, and Projection Strategies¶

📸 Snapshotting Strategy¶

✍️ How Snapshotting Works¶

📏 Snapshotting Guidelines at ConnectSoft¶

Example: Snapshotting Implementation¶

🗃️ Stream Management¶

🎯 Stream Partitioning and Sharding¶

🏗️ Projection Patterns (Deep Dive)¶

🔹 Types of Projections Used at ConnectSoft¶

🔄 Real-Time Projection Example¶

📚 Aggregated Projection Example¶

📊 ConnectSoft Best Practices for Projections¶

🧬 Event Versioning and Schema Evolution¶

🧠 Why Version Events?¶

📋 Strategies for Versioning Events¶

🏷️ 1. Schema Evolution Without Breaking Changes¶

🔖 2. Explicit Event Versioning¶

🧬 3. Upcasting Older Events¶

🔀 Event Versioning Diagram¶

🛡️ Best Practices for Schema Evolution at ConnectSoft¶

⚠️ Common Mistakes to Avoid¶

🧪 Testing Strategies for Event-Sourced Systems¶

🛠️ Testing Layers Overview¶

🔹 1. Unit Testing Event Application to Aggregates¶

📜 2. Contract Testing (Schema Validation)¶

🔄 3. Integration Testing: Event Store Append and Replay¶

🏗️ 4. Projection Testing¶

🔄 5. End-to-End Replay Testing¶

🎯 Testing Best Practices at ConnectSoft¶

🔎 Monitoring, Observability, and Debugging in Event-Sourced Systems¶

📊 Monitoring Focus Areas¶

🧠 Key Metrics and Alerts¶

📈 Sample Grafana Dashboard Components¶

🔍 Observability: Tracing Event Flows End-to-End¶

🧵 OpenTelemetry for Event Sourcing at ConnectSoft¶

🛠️ Debugging Techniques¶

🔥 Sample Structured Logging for Events¶

🚨 Common Production Failures and How to Detect Early¶

🏁 Conclusion¶

📚 References¶

🔗 Related ConnectSoft Documents¶