Skip to content

Latest commit

 

History

History
329 lines (271 loc) · 31.8 KB

sec-design.md

File metadata and controls

329 lines (271 loc) · 31.8 KB

Project Design Document: Grafana Loki - Improved

Project Name: Grafana Loki

Project Repository: https://github.com/grafana/loki

Document Version: 1.1 Date: 2023-10-27 Author: AI Expert

1. Project Overview

Grafana Loki is a horizontally scalable, highly available, and multi-tenant log aggregation system inspired by Prometheus. It is designed for cost-effectiveness and operational simplicity, especially at scale. Loki achieves this by indexing only metadata (labels) about log streams, rather than the full log messages. This approach, combined with efficient log chunk compression, significantly reduces storage and processing overhead compared to traditional log aggregation systems.

Key Features:

  • Cost-Optimized Storage: Indexes metadata only, storing compressed log chunks in object storage.
  • Horizontal Scalability: All components are designed to scale horizontally to handle increasing ingestion and query loads.
  • High Availability: Built for resilience with redundancy and fault tolerance in key components.
  • Multi-Tenancy: Native multi-tenancy with robust data isolation and resource management.
  • Grafana Integration: Deep integration with Grafana for seamless log exploration, visualization, and alerting.
  • LogQL Query Language: Uses LogQL, a powerful query language inspired by PromQL, for efficient log querying and filtering.
  • Push-Based Ingestion with Promtail: Employs Promtail agents for efficient and reliable log shipping to Loki.
  • Simplified Operations: Designed for ease of deployment, configuration, and maintenance.

Purpose of this Document:

This document provides a comprehensive design overview of Grafana Loki, detailing its architecture, components, data flow, technology stack, and operational considerations. It serves as a foundational resource for threat modeling, enabling security professionals to understand the system's intricacies and identify potential security vulnerabilities. This document will be used to guide security assessments and the development of appropriate security controls.

2. System Architecture

The following diagram illustrates the high-level architecture of Grafana Loki using Mermaid syntax. It highlights the key components and their interactions.

graph LR
    subgraph "Data Source (e.g., Applications, Servers)"
        "Log Sources" --> "Promtail Agent"
    end

    subgraph "Loki Client (Promtail)"
        "Promtail Agent" --> "Distributor"
    end

    subgraph "Loki Server"
        subgraph "Ingestion Tier"
            "Distributor" --> "Ingester"
        end

        subgraph "Query Tier"
            "Querier" --> "Ingester & Compactor & Object Storage (Chunks) & Index Store"
            "Compactor" --> "Index Store"
        end

        subgraph "Storage Tier"
            "Ingester" --> "Object Storage (Chunks) & Index Store"
        end
    end

    subgraph "Grafana"
        "Grafana UI" --> "Querier"
    end

    classDef component fill:#f9f,stroke:#333,stroke-width:2px
    class "Log Sources","Promtail Agent","Distributor","Ingester","Querier","Compactor","Object Storage (Chunks)","Index Store","Grafana UI" component
Loading

3. Component Description

This section provides a detailed description of each component within the Loki architecture, outlining their functions, responsibilities, and key technologies.

3.1. Promtail Agent

  • Function: Promtail is a lightweight, resource-efficient agent deployed on log-generating systems. It discovers log targets, reads log streams, processes them, and forwards them to the Loki Distributor.
  • Key Responsibilities:
    • Target Discovery: Dynamically discovers log sources based on configured service discovery mechanisms (e.g., static files, systemd journal, Kubernetes service discovery).
    • Log Tailing & Reading: Efficiently tails and reads log files or streams, handling log rotation and truncation.
    • Log Processing & Enrichment: Performs optional log processing, including:
      • Relabeling: Modifying or adding labels to log streams for better organization and querying.
      • Filtering: Dropping unwanted log lines based on configured criteria.
      • Timestamp Extraction: Ensuring accurate timestamps are associated with log entries.
      • Adding Metadata: Enriching logs with contextual information (e.g., hostname, pod name, container ID).
    • Secure Forwarding: Pushes processed logs to the Loki Distributor over gRPC, supporting TLS for encryption and authentication.
    • Buffering & Retry: Implements buffering and retry mechanisms to ensure reliable log delivery even during temporary network disruptions.
  • Technology: Primarily written in Go, designed for minimal resource footprint.
  • Deployment: Typically deployed as a daemon set in Kubernetes, a systemd service, or a sidecar container alongside applications.

3.2. Distributor

  • Function: The Distributor is the entry point for log ingestion into Loki. It receives log streams from Promtail agents, enforces tenant isolation, validates streams, and distributes them across Ingesters.
  • Key Responsibilities:
    • gRPC Endpoint: Exposes a gRPC endpoint for Promtail agents to push logs.
    • Authentication & Authorization: Authenticates Promtail agents and authorizes them to ingest logs for specific tenants.
    • Tenant Identification & Isolation: Identifies tenants based on request headers and ensures data isolation between tenants throughout the ingestion pipeline.
    • Stream Validation & Rate Limiting: Validates incoming log streams against configured limits (e.g., label cardinality, log line size, ingestion rate) to prevent abuse and ensure system stability.
    • Hashing & Consistent Distribution: Hashes log streams based on their labels to ensure that logs with the same labels are consistently routed to the same set of Ingesters. This is crucial for maintaining stream order within an Ingester and efficient querying.
    • Ingester Discovery & Load Balancing: Discovers available Ingesters using a service discovery mechanism (e.g., Consul, Kubernetes API) and distributes the load across them, ensuring even resource utilization.
    • Replication (Optional): Can optionally replicate incoming log streams to multiple Ingesters for increased fault tolerance and data durability during ingestion.
  • Technology: Primarily written in Go, designed for high throughput and low latency.
  • Deployment: Deployed as a horizontally scalable, stateless service, typically behind a load balancer to distribute traffic from Promtail agents.

3.3. Ingester

  • Function: The Ingester is responsible for the core ingestion and short-term storage of log data. It receives log streams from Distributors, builds in-memory indexes, creates chunks, and periodically flushes these chunks and indexes to long-term storage.
  • Key Responsibilities:
    • Stream Reception & Buffering: Receives log streams from Distributors and buffers incoming log entries in memory.
    • Chunk Creation & Management: Groups log entries into chunks based on time and size thresholds. Chunks are compressed in memory to optimize resource usage.
    • In-Memory Indexing: Builds an in-memory index for each chunk, mapping labels to the log entries within the chunk. This index is crucial for efficient querying of recent data.
    • Flushing to Storage (Object Storage & Index Store): Periodically flushes completed chunks to Object Storage (e.g., S3, GCS, Azure Blob Storage) and their corresponding indexes to the Index Store (e.g., Cassandra, DynamoDB, Bigtable).
    • Replication (Optional): Ingesters can be configured for replication to ensure data durability and high availability. Replication can be synchronous or asynchronous.
    • Query Serving (Recent Data): Serves queries for recent data that is still in memory or recently flushed but not yet compacted. This provides low-latency access to newly ingested logs.
    • Data Retention (Short-Term): Manages short-term data retention in memory and during the flushing process. Long-term retention is handled by the Compactor and storage backends.
  • Technology: Primarily written in Go, designed for efficient memory management and I/O operations.
  • Deployment: Deployed as a horizontally scalable, stateful service. Ingesters require persistent storage for their in-memory buffers and metadata, typically using local disks or persistent volumes in Kubernetes. Replication is crucial for HA deployments.

3.4. Querier

  • Function: The Querier is the query engine of Loki. It receives LogQL queries from Grafana or other clients, retrieves relevant log data from Ingesters (for recent data) and storage (for historical data), merges the results, and returns them to the client.
  • Key Responsibilities:
    • LogQL Query Endpoint: Exposes an HTTP API endpoint for receiving LogQL queries.
    • Authentication & Authorization: Authenticates and authorizes query requests, ensuring tenant isolation and access control.
    • Query Parsing & Planning: Parses LogQL queries and develops an efficient query execution plan.
    • Querying Ingesters (Recent Data): Queries Ingesters for recent data that might still be in memory, leveraging the in-memory indexes for fast retrieval.
    • Querying Compactor/Storage (Historical Data): Queries the Compactor and Storage backends (Object Storage and Index Store) for historical data based on the query time range and filters.
    • Result Merging & Deduplication: Merges results from Ingesters and storage backends, handling potential data duplication and ensuring consistent query results.
    • Query Optimization & Caching: Optimizes query execution for performance, potentially using caching mechanisms to improve response times for frequently executed queries.
    • Tenant Isolation Enforcement: Enforces multi-tenancy by ensuring queries are scoped to the correct tenant and preventing cross-tenant data access.
  • Technology: Primarily written in Go, designed for efficient query processing and data retrieval.
  • Deployment: Deployed as a horizontally scalable, stateless service, typically behind a load balancer to handle query traffic.

3.5. Compactor

  • Function: The Compactor is responsible for optimizing the index in the Index Store over time. It compacts fragmented index data, enforces retention policies, and improves overall query performance and storage efficiency.
  • Key Responsibilities:
    • Index Compaction: Periodically reads fragmented index data from the Index Store and rewrites it in a more efficient, consolidated format. This reduces index size and improves query lookup speed.
    • Retention Policy Enforcement (Index & Chunks): Enforces configured log retention policies by deleting old index data from the Index Store and triggering the deletion of corresponding log chunks from Object Storage.
    • Index Optimization: Optimizes the index structure for faster queries, potentially by reorganizing data and removing redundant entries.
    • Chunk Deletion (Orchestration): While not directly deleting chunks, the Compactor often orchestrates or triggers the deletion of old chunks from Object Storage based on retention policies, working in conjunction with Object Storage lifecycle management features.
  • Technology: Primarily written in Go, designed for efficient index processing and storage management.
  • Deployment: Typically deployed as a single instance or a small, highly available cluster. Compaction is generally not a high-throughput operation but needs to run reliably and periodically.

3.6. Object Storage (Chunks)

  • Function: Object Storage serves as the long-term, durable storage for compressed log data chunks. Loki is designed to be agnostic to the specific object storage provider.
  • Examples:
    • Cloud Providers: Amazon S3, Google Cloud Storage, Azure Blob Storage, DigitalOcean Spaces.
    • Self-Hosted: MinIO (S3-compatible), Ceph RADOS, OpenStack Swift, Filesystem (for non-production).
  • Key Characteristics:
    • Scalability & Durability: Object storage is inherently scalable and provides high data durability and availability.
    • Cost-Effectiveness: Generally cost-effective for storing large volumes of data, especially for long-term retention.
    • Versioning & Lifecycle Management: Often supports features like versioning for data protection and lifecycle management policies for automated data deletion based on age.
  • Security Considerations: Access to Object Storage must be strictly controlled using IAM roles, access keys, and network policies to prevent unauthorized access and data breaches. Encryption at rest should be enabled.

3.7. Index Store

  • Function: The Index Store is responsible for storing the index that maps labels to log chunks in Object Storage. Loki supports pluggable index store backends to accommodate different performance and scalability requirements.
  • Examples:
    • Embedded: BoltDB (embedded key-value store, suitable for development and small-scale deployments).
    • Scalable Key-Value Stores: Cassandra, Bigtable, DynamoDB, TiDB, Amazon Keyspaces.
    • Raft-based KV Stores: etcd, Consul (can be used for smaller deployments or specific use cases).
  • Key Characteristics:
    • Fast Reads & Lookups: Optimized for fast lookups based on labels to quickly identify relevant log chunks for queries.
    • Scalability & High Availability: Should be scalable to handle a large number of log streams and concurrent queries. High availability is crucial for production deployments.
    • Consistency: Requires strong consistency to ensure accurate index data and query results.
  • Security Considerations: Access to the Index Store must be secured using database authentication, network policies, and encryption in transit and at rest, depending on the chosen backend.

4. Data Flow - Detailed

This section provides a more granular description of the data flow within Loki for ingestion, querying, and index compaction.

4.1. Log Ingestion Flow (Detailed)

  1. Log Generation & Collection: Applications and systems generate log data. Promtail agents, deployed near these sources, discover and collect logs based on their configuration.
  2. Promtail Processing & Batching: Promtail processes logs (relabeling, filtering, etc.), batches them for efficiency, and prepares them for transmission.
  3. gRPC Push to Distributor: Promtail establishes a gRPC connection to the Distributor and pushes batches of log entries. The request includes tenant ID and log stream data.
  4. Distributor Authentication & Validation: The Distributor authenticates the Promtail agent and validates the incoming request, including tenant ID, stream limits, and data format.
  5. Tenant Routing & Hashing: The Distributor identifies the tenant and hashes the log stream based on its labels. This hash determines the target Ingester(s).
  6. Ingester Selection & Forwarding: The Distributor uses a consistent hashing mechanism and service discovery to select the appropriate Ingester instances and forwards the log stream to them. Replication may occur at this stage if configured.
  7. Ingester Buffering & Chunking (In-Memory): Ingesters receive log streams, buffer them in memory, and start creating chunks. Chunks are compressed in memory.
  8. In-Memory Index Update: As new log entries are added to chunks, the Ingester updates the in-memory index for those chunks, associating labels with log entry positions.
  9. Chunk Flushing to Object Storage (Periodic): Periodically (based on time or size thresholds), Ingesters flush completed chunks to Object Storage.
  10. Index Flushing to Index Store (Periodic): Concurrently or shortly after chunk flushing, Ingesters flush the corresponding in-memory index data to the Index Store.
  11. Acknowledgement to Distributor (gRPC): Ingesters acknowledge successful ingestion to the Distributor, which in turn acknowledges to Promtail.

4.2. Log Query Flow (Detailed)

  1. LogQL Query Submission: Grafana UI or a user submits a LogQL query to the Querier via its HTTP API. The request includes the query string, time range, and tenant ID.
  2. Querier Authentication & Parsing: The Querier authenticates the request and parses the LogQL query.
  3. Query Planning & Splitting: The Querier analyzes the query and time range, splitting it into sub-queries targeting different data sources: Ingesters (for recent data) and Compactor/Storage (for historical data).
  4. Ingester Query Execution (gRPC): For the recent data sub-query, the Querier sends gRPC requests to relevant Ingesters. Ingesters use their in-memory indexes to efficiently retrieve matching log entries from their active chunks.
  5. Storage Query Execution (Index Store & Object Storage): For the historical data sub-query, the Querier interacts with the Index Store to identify relevant chunks in Object Storage based on labels and time range. It then retrieves the necessary chunks from Object Storage.
  6. Result Merging & Ordering: The Querier receives results from Ingesters and storage backends. It merges these results, deduplicates entries if necessary, and orders them chronologically.
  7. Result Processing & Filtering: The Querier applies any remaining filters or aggregations specified in the LogQL query to the merged results.
  8. Response to Client (HTTP): The Querier formats the processed query results and sends them back to Grafana or the user via the HTTP API.

4.3. Index Compaction Flow (Detailed)

  1. Compaction Scheduling & Triggering: The Compactor periodically schedules compaction jobs based on time intervals or index size thresholds.
  2. Index Data Retrieval from Index Store: The Compactor reads fragmented index data from the Index Store for a specific time range or tenant.
  3. Index Compaction & Optimization (In-Memory): The Compactor loads the index data into memory, performs compaction and optimization operations, such as merging fragmented entries, removing duplicates, and optimizing data structures.
  4. Compacted Index Data Write Back to Index Store: The Compactor writes the compacted and optimized index data back to the Index Store, replacing the older, fragmented data.
  5. Retention Policy Enforcement (Index Deletion): During compaction, the Compactor enforces retention policies by identifying and deleting index entries that are older than the configured retention period.
  6. Chunk Deletion Orchestration (Object Storage): Based on the deleted index entries and retention policies, the Compactor may trigger or orchestrate the deletion of corresponding log chunks from Object Storage. This might involve using Object Storage lifecycle policies or direct deletion API calls.
  7. Monitoring & Reporting: The Compactor reports metrics on compaction progress, index size reduction, and retention policy enforcement.

5. Technology Stack - Expanded

  • Core Programming Language: Go (for all major components: Promtail, Distributor, Ingester, Querier, Compactor)
  • Inter-Component Communication: gRPC (high-performance, efficient communication between Loki components) with Protocol Buffers (protobuf) for data serialization.
  • Query Language: LogQL (Prometheus-inspired Log Query Language)
  • API & Client Communication: HTTP/HTTPS (for API access by Grafana and other clients), gRPC (for Promtail to Distributor communication).
  • Configuration Management: YAML files (primary configuration format), command-line flags (for basic settings), environment variables.
  • Service Discovery: Consul, Kubernetes API (for Ingester discovery by Distributor and Querier).
  • Metrics & Monitoring: Prometheus (for internal metrics exposition), Grafana (for visualization and alerting).
  • Tracing: Jaeger, OpenTelemetry (for distributed tracing and performance analysis).
  • Object Storage Backends (Examples): AWS S3 (and S3-compatible stores like MinIO), Google Cloud Storage, Azure Blob Storage, Filesystem (local, for development).
  • Index Store Backends (Examples): Cassandra, Bigtable, DynamoDB, TiDB, BoltDB (embedded), Amazon Keyspaces.
  • Deployment & Orchestration: Kubernetes (recommended for production), Docker, systemd (for simpler deployments).

6. Deployment Model - Detailed Options

Loki offers flexible deployment models to suit various scales and requirements:

  • Single Binary (Monolithic Mode):
    • Description: All Loki components (Distributor, Ingester, Querier, Compactor) are packaged and run within a single process.
    • Use Cases: Development, testing, small-scale deployments, personal projects, resource-constrained environments.
    • Pros: Simplest to deploy and manage, minimal resource overhead for small workloads.
    • Cons: Limited scalability, no component-level scaling, single point of failure for all components.
  • Microservices (Distributed Mode):
    • Description: Each Loki component (Distributor, Ingester, Querier, Compactor) is deployed as a separate, independent service.
    • Use Cases: Production environments, medium to large-scale deployments, high availability requirements, independent scaling of components.
    • Pros: Horizontal scalability for each component, fault isolation, independent scaling, improved resilience.
    • Cons: More complex to deploy and manage, increased resource overhead compared to monolithic mode. Requires service discovery and load balancing.
  • Kubernetes Deployment (Recommended for Production):
    • Description: Loki components are deployed as containers within a Kubernetes cluster, leveraging Kubernetes for orchestration, scaling, and management. Helm charts and Operators simplify deployment.
    • Use Cases: Production environments, cloud-native deployments, large-scale deployments, high availability, automated scaling and management.
    • Pros: Excellent scalability, high availability, automated deployment and management, integration with Kubernetes ecosystem, robust monitoring and logging.
    • Cons: Requires Kubernetes expertise, increased complexity compared to simpler deployments.
  • Cloud-Managed Loki Services:
    • Description: Cloud providers offer managed Loki services (e.g., Grafana Cloud Logs, cloud provider-specific offerings), abstracting away the operational complexity of running Loki.
    • Use Cases: Organizations wanting to offload Loki operations, rapid deployment, pay-as-you-go pricing.
    • Pros: Simplified operations, reduced management overhead, automatic scaling and maintenance, potentially faster deployment.
    • Cons: Vendor lock-in, potentially higher cost compared to self-managed deployments, less control over infrastructure.

7. Security Considerations for Threat Modeling - Expanded

This section expands on security considerations for threat modeling, providing more specific examples of potential threats and mitigation strategies.

  • Authentication and Authorization:

    • Threats:
      • Unauthorized Access: Unauthenticated or unauthorized access to Loki APIs (ingestion, query, management) could lead to data breaches, data manipulation, or denial of service.
      • Tenant Impersonation: Attackers could attempt to impersonate tenants to ingest malicious logs or access sensitive data from other tenants.
    • Mitigations:
      • Mutual TLS (mTLS) for gRPC: Enforce mTLS for communication between Promtail and Distributor, and between Loki components, to ensure strong authentication and encryption.
      • API Keys/Tokens: Implement API key or token-based authentication for HTTP API access (query, management). Rotate keys regularly.
      • OAuth 2.0/OIDC Integration: Integrate with OAuth 2.0 or OpenID Connect for centralized authentication and authorization, especially for user access via Grafana.
      • Role-Based Access Control (RBAC): Implement RBAC to control access to Loki resources based on user roles and tenant affiliations.
      • Tenant-Specific API Keys: Issue separate API keys per tenant to enforce tenant isolation and track usage.

  • Data Encryption:

    • Threats:
      • Data Breach in Transit: Interception of log data during transmission between components or clients could expose sensitive information.
      • Data Breach at Rest: Unauthorized access to Object Storage or Index Store could lead to exposure of stored log data.
    • Mitigations:
      • TLS Encryption for all HTTP/gRPC: Enforce TLS encryption for all HTTP and gRPC communication channels to protect data in transit.
      • Server-Side Encryption (SSE) for Object Storage: Enable server-side encryption for Object Storage (e.g., SSE-S3, SSE-GCS, SSE-AzureBlob) to encrypt data at rest.
      • Client-Side Encryption (CSE) (Optional): Consider client-side encryption for sensitive log data before ingestion, providing end-to-end encryption.
      • Encryption at Rest for Index Store: Choose Index Store backends that support encryption at rest and enable this feature.

  • Input Validation and Sanitization:

    • Threats:
      • Log Injection Attacks: Malicious actors could inject crafted log entries to manipulate logs, bypass security controls, or exploit vulnerabilities in log processing or querying.
      • LogQL Injection Attacks: Attackers could craft malicious LogQL queries to bypass authorization, extract sensitive data, or cause denial of service.
    • Mitigations:
      • Strict Input Validation in Promtail & Distributor: Implement robust input validation in Promtail and Distributor to sanitize log entries and reject malformed or suspicious data.
      • LogQL Query Parameterization: Use parameterized queries or prepared statements where possible to prevent LogQL injection vulnerabilities.
      • Least Privilege for Query Execution: Run Querier processes with minimal privileges to limit the impact of potential query injection vulnerabilities.
      • Regular Security Audits of Log Processing Logic: Conduct regular security audits of log processing and querying logic to identify and mitigate potential injection vulnerabilities.

  • Access Control to Storage Backends:

    • Threats:
      • Unauthorized Access to Object Storage: If Object Storage is not properly secured, attackers could gain unauthorized access to stored log chunks, leading to data breaches.
      • Unauthorized Access to Index Store: Similarly, unauthorized access to the Index Store could allow attackers to manipulate or exfiltrate index data.
    • Mitigations:
      • IAM Roles and Policies: Use IAM roles and policies to restrict access to Object Storage and Index Store to only authorized Loki components (Ingesters, Queriers, Compactor).
      • Principle of Least Privilege: Grant Loki components only the minimum necessary permissions to access storage backends.
      • Network Policies: Implement network policies to restrict network access to storage backends from only authorized Loki components.
      • Regularly Review Storage Access Controls: Periodically review and audit access controls for Object Storage and Index Store to ensure they are properly configured and enforced.

  • Tenant Isolation:

    • Threats:
      • Cross-Tenant Data Access: Vulnerabilities in tenant isolation mechanisms could allow attackers to access data belonging to other tenants, violating confidentiality and compliance requirements.
      • Resource Starvation (Noisy Neighbor): One tenant could consume excessive resources, impacting the performance and availability of Loki for other tenants.
    • Mitigations:
      • Strict Tenant ID Enforcement: Enforce tenant IDs throughout the ingestion and query pipelines to ensure data isolation.
      • Namespaces/Prefixes in Storage Backends: Use namespaces or prefixes in Object Storage and Index Store to further isolate tenant data at the storage level.
      • Resource Quotas and Limits: Implement resource quotas and limits per tenant to prevent resource starvation and ensure fair resource allocation.
      • Regular Security Testing of Tenant Isolation: Conduct regular security testing and penetration testing to verify the effectiveness of tenant isolation mechanisms.

  • Monitoring and Logging (Security Auditing):

    • Threats:
      • Lack of Audit Trails: Insufficient logging of security-related events could hinder incident response and forensic investigations.
      • Missed Security Incidents: Inadequate monitoring could lead to delayed detection of security breaches or malicious activities.
    • Mitigations:
      • Comprehensive Security Event Logging: Log all security-relevant events, including authentication attempts, authorization failures, API access, configuration changes, and security policy violations.
      • Centralized Logging & SIEM Integration: Centralize Loki security logs and integrate them with a Security Information and Event Management (SIEM) system for real-time monitoring and alerting.
      • Alerting on Security Anomalies: Configure alerts for suspicious activities and security anomalies detected in Loki logs and metrics.
      • Regular Security Log Reviews: Periodically review security logs to identify potential security incidents and improve security posture.

  • Dependency Management & Supply Chain Security:

    • Threats:
      • Vulnerable Dependencies: Using vulnerable dependencies could introduce security vulnerabilities into Loki components.
      • Compromised Software Supply Chain: Attackers could compromise the software supply chain to inject malicious code into Loki binaries or dependencies.
    • Mitigations:
      • Dependency Scanning & Management: Regularly scan Loki dependencies for known vulnerabilities using vulnerability scanning tools. Implement a robust dependency management process.
      • Software Bill of Materials (SBOM): Generate and maintain an SBOM for Loki components to track dependencies and facilitate vulnerability management.
      • Secure Build Pipeline: Implement a secure build pipeline with code signing and verification to ensure the integrity of Loki binaries.
      • Regular Security Updates: Apply security updates and patches to Loki components and dependencies promptly.

  • Denial of Service (DoS) Protection:

    • Threats:
      • Ingestion DoS: Attackers could flood Loki with excessive log data to overwhelm ingestion pipelines and cause denial of service.
      • Query DoS: Attackers could send resource-intensive or malicious queries to overload Queriers and cause denial of service.
    • Mitigations:
      • Rate Limiting on Ingestion Endpoints: Implement rate limiting on Distributor ingestion endpoints to prevent ingestion DoS attacks.
      • Query Cost Limits & Throttling: Implement query cost limits and throttling in Queriers to prevent resource-intensive queries from causing DoS.
      • Resource Limits for Components: Configure resource limits (CPU, memory) for Loki components to prevent resource exhaustion.
      • Load Balancing & Horizontal Scaling: Deploy Loki components behind load balancers and scale them horizontally to handle increased load and improve resilience to DoS attacks.

This improved design document provides a more detailed and comprehensive overview of Grafana Loki, with expanded sections on data flow, technology stack, deployment models, and security considerations. It should serve as a valuable resource for threat modeling and security assessments of Loki deployments.