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Project Design Document: Cortex - Horizontally Scalable Prometheus as a Service

Document Version: 1.1 Date: 2023-10-27 Author: Gemini (AI Expert in Software, Cloud, and Cybersecurity Architecture)

1. Project Overview

Project Name: Cortex

Project Description: Cortex is a cloud-native, horizontally scalable, multi-tenant, and durable monitoring system. It provides long-term storage and query capabilities for Prometheus, effectively functioning as a scalable Prometheus-as-a-Service platform. Cortex overcomes the limitations of single Prometheus servers by enabling the ingestion and querying of metrics at massive scale, with built-in multi-tenancy and long-term data retention. It is designed for deployment in distributed environments, particularly Kubernetes.

Project Repository: https://github.com/cortexproject/cortex

2. Goals and Objectives

  • Scalability & Performance: Achieve horizontal scalability to handle petabytes of time-series data and high-concurrency query loads with low latency.
  • Multi-tenancy & Isolation: Provide secure and logically isolated environments for numerous independent tenants within a shared Cortex cluster, ensuring data privacy and preventing cross-tenant interference.
  • Long-Term Storage & Durability: Offer reliable and cost-efficient long-term storage for Prometheus metrics, extending beyond the ephemeral storage of individual Prometheus instances and ensuring data persistence.
  • High Availability & Fault Tolerance: Design for continuous operation and data availability, even in the face of individual component failures or infrastructure disruptions, through redundancy and fault tolerance mechanisms.
  • Prometheus Compatibility & Interoperability: Maintain full compatibility with the Prometheus ecosystem, including PromQL, recording rules, alerting rules, and client libraries, enabling seamless migration and integration.
  • Cost Efficiency & Resource Optimization: Optimize resource utilization across compute, storage, and network to minimize operational costs for large-scale metric ingestion and storage.
  • Observability & Monitoring: Provide comprehensive internal observability into the Cortex system itself, exposing metrics, logs, and traces for performance monitoring, debugging, and capacity planning.
  • Security & Compliance: Implement robust security measures to protect data confidentiality, integrity, and availability, addressing common security threats and facilitating compliance requirements.

3. Target Audience

This design document is intended for:

  • Security Architects & Engineers: To perform threat modeling, security assessments, and design security controls for Cortex deployments.
  • Software Developers: To understand the system architecture for development, debugging, and contributing to the Cortex project.
  • Cloud & DevOps Engineers: To plan, deploy, operate, and maintain Cortex clusters in production environments.
  • Technical Leadership & Project Stakeholders: To gain a high-level understanding of the system's architecture, capabilities, and security considerations for strategic decision-making.

4. System Architecture

4.1. High-Level Architecture Diagram

graph LR
    subgraph "Client (Prometheus/Agent)"
        A["Prometheus/Agent"]
    end
    subgraph "Cortex Cluster"
        subgraph "Ingestion Path"
            B["Distributor"] --> C["Ingester"]
        end
        subgraph "Query Path"
            D["Query Frontend"] --> E["Querier"]
        end>
        subgraph "Storage Layer"
            F["Store Gateway"] --> G["Object Storage (e.g., S3, GCS, Azure Blob Storage)"]
            H["Compactor"] --> G
        end
        subgraph "Ruler & Alerter"
            I["Ruler"] --> J["Alerter"]
        end
        K["Gossip Ring"]
    end

    A --> B
    E --> F
    E --> C
    D --> E
    I --> E
    J --> External_Alert_Manager["External Alert Manager (Optional)"]

    B -- "Gossip Ring" --> K
    C -- "Gossip Ring" --> K
    D -- "Gossip Ring" --> K
    E -- "Gossip Ring" --> K
    F -- "Gossip Ring" --> K
    H -- "Gossip Ring" --> K
    I -- "Gossip Ring" --> K
    J -- "Gossip Ring" --> K

    classDef client fill:#f9f,stroke:#333,stroke-width:2px
    classDef storage fill:#ccf,stroke:#333,stroke-width:2px
    class A,External_Alert_Manager client
    class G storage
Loading

4.2. Component-Level Architecture

Cortex is built as a set of microservices, each responsible for a specific function within the system. These components are designed to be independently scalable and fault-tolerant.

4.2.1. Ingestion Path Components:

  • Distributor:

    • Function: The first point of contact for incoming metrics. It receives remote_write requests from Prometheus agents or other compatible sources.
    • Responsibilities:
      • Tenant Authentication & Authorization: Verifies tenant identity and permissions before accepting write requests, enforcing multi-tenancy.
      • Request Sharding & Load Balancing: Distributes incoming metric samples across multiple Ingester instances based on consistent hashing of the metric series, ensuring even load distribution.
      • Write Path Rate Limiting & Admission Control: Implements rate limiting and admission control to protect downstream components from overload and manage resource consumption.
      • Input Validation: Validates incoming metric data to prevent malformed or malicious data from entering the system.
      • Gossip Ring Membership & Service Discovery: Uses the Gossip Ring to discover available Ingester instances and monitor their health.
    • Security Focus: Tenant authentication, input validation, DoS prevention, secure gossip communication.
    • Potential Threats: Tenant ID spoofing, injection attacks via metric data, DoS attacks, unauthorized access to gossip information.

  • Ingester:

    • Function: Handles the temporary storage of recently ingested time-series data in memory and periodically persists it to long-term storage.
    • Responsibilities:
      • In-Memory Time-Series Storage: Stores recent metric samples in memory for fast querying and aggregation of real-time data.
      • Chunk Encoding & Compression: Encodes and compresses time-series data into efficient chunks for storage and retrieval, optimizing storage space and query performance.
      • Flushing to Storage (via Store Gateway): Periodically flushes in-memory chunks to durable object storage through the Store Gateway component.
      • Query Serving (Recent Data): Serves queries for recently ingested data directly from its in-memory storage, providing low-latency access to current metrics.
      • Gossip Ring Membership & Health Reporting: Participates in the Gossip Ring to advertise its availability, capacity, and health status to other components.
    • Security Focus: Memory management, data integrity, access control for recent data queries, secure gossip communication.
    • Potential Threats: Memory exhaustion attacks, data corruption in memory or during flushing, unauthorized access to in-memory data, vulnerabilities in chunk encoding/decoding, resource exhaustion.

4.2.2. Query Path Components:

  • Query Frontend:

    • Function: An optional but highly recommended layer that sits in front of Queriers to enhance query performance, reliability, and scalability.
    • Responsibilities:
      • Query Caching & Result Reuse: Caches query results to reduce redundant queries to Queriers and improve response times for repeated queries.
      • Query Splitting & Parallelization: Splits large, complex queries into smaller sub-queries and parallelizes their execution across multiple Queriers for faster processing.
      • Query Fan-out & Aggregation: Fans out queries to multiple Queriers and aggregates the results to provide a unified view of the data.
      • Request Deduplication & Optimization: Deduplicates identical queries and optimizes query execution plans to minimize resource consumption.
      • Query Path Rate Limiting & Concurrency Control: Implements rate limiting and concurrency control for query requests to protect Queriers from overload.
    • Security Focus: Cache security, input validation (PromQL), DoS prevention, access control to cached data.
    • Potential Threats: Cache poisoning, information leakage via cached data, PromQL injection attacks, DoS attacks via excessive queries, unauthorized access to cached query results.

  • Querier:

    • Function: The core query engine of Cortex. It retrieves time-series data from both Ingesters (for recent data) and long-term storage (via Store Gateway) to satisfy PromQL queries.
    • Responsibilities:
      • PromQL Query Execution & Evaluation: Parses and executes PromQL queries, evaluating expressions and functions against time-series data.
      • Query Planning & Data Source Selection: Determines which Ingesters and Store Gateways to query based on the query time range and data availability.
      • Data Merging & Aggregation (Across Sources): Merges and aggregates data retrieved from different Ingesters and Store Gateways to provide a complete query result.
      • Tenant Data Isolation & Access Control: Enforces tenant isolation by ensuring that Queriers only access data belonging to the requesting tenant.
      • Resource Management & Query Optimization: Manages resources efficiently and optimizes query execution to minimize latency and resource consumption.
      • Gossip Ring Membership & Service Discovery: Uses the Gossip Ring to discover available Ingester and Store Gateway instances.
    • Security Focus: PromQL engine security, data access control, resource management, secure communication with Ingesters/Store Gateways, secure gossip communication.
    • Potential Threats: PromQL engine vulnerabilities, unauthorized data access, resource exhaustion due to complex queries, insecure communication channels.

4.2.3. Storage Layer Components:

  • Store Gateway:

    • Function: Acts as a bridge between Queriers and Compactors and the long-term object storage system. It provides an abstraction layer for accessing and managing chunks in object storage.
    • Responsibilities:
      • Chunk Retrieval from Object Storage: Retrieves time-series data chunks from object storage based on query requests from Queriers and compaction requests from Compactors.
      • Index Management & Chunk Lookup: Manages indexes to efficiently locate and retrieve chunks in object storage based on time range and series identifiers.
      • Caching (Chunk Cache, Index Cache): Implements caching mechanisms for chunks and indexes to improve query performance and reduce object storage access latency.
      • Tenant Isolation (Storage Access Level): Enforces tenant isolation at the storage level, ensuring that each tenant's data is stored and accessed separately within object storage.
      • Gossip Ring Membership & Health Reporting: Participates in the Gossip Ring to advertise its availability and health status.
    • Security Focus: Object storage access control, tenant data isolation in storage, data encryption at rest (object storage), cache security, secure gossip communication.
    • Potential Threats: Object storage credential compromise, cross-tenant data access in object storage, data breaches due to lack of encryption, cache-based information leakage, unauthorized access to gossip information.

  • Compactor:

    • Function: Optimizes data organization and storage efficiency in the long-term object storage.
    • Responsibilities:
      • Chunk Compaction & Merging: Merges and compacts smaller chunks into larger, more efficient chunks to reduce storage overhead, improve query performance, and optimize storage costs.
      • Downsampling (Optional): Optionally downsamples high-resolution data to lower resolutions for long-term storage of historical data, reducing storage footprint and query costs for older data.
      • Retention Policy Enforcement & Data Deletion: Enforces configured data retention policies by deleting old data from object storage, managing storage space and complying with data retention requirements.
      • Gossip Ring Membership & Task Coordination: Participates in the Gossip Ring to coordinate compaction tasks and ensure consistent data optimization across the cluster.
    • Security Focus: Data integrity during compaction, secure object storage access, data retention policy enforcement accuracy, secure gossip communication.
    • Potential Threats: Data corruption during compaction, unauthorized access to object storage, incorrect data deletion due to retention policy errors, resource exhaustion during compaction, insecure gossip communication.

4.2.4. Operational Components:

  • Ruler:

    • Function: Periodically evaluates Prometheus recording rules and alerting rules defined by users.
    • Responsibilities:
      • Rule Evaluation (PromQL): Executes configured recording and alerting rules using the PromQL query language against data retrieved from Queriers.
      • Recording Rule Execution & Metric Backfill: Stores the results of recording rules back into Cortex as new derived time series, effectively pre-computing frequently used queries.
      • Alerting Rule Evaluation & Alert State Management: Evaluates alerting rule conditions and manages the state of alerts, triggering alerts when conditions are met.
      • Gossip Ring Membership & Querier Discovery: Uses the Gossip Ring to discover available Querier instances to query for rule evaluation.
    • Security Focus: Secure access to Queriers, input validation of rules (PromQL), secure rule configuration storage, secure gossip communication.
    • Potential Threats: Unauthorized access to metric data via Queriers, PromQL injection in rules, rule configuration tampering, DoS via complex rules, insecure gossip communication.

  • Alerter:

    • Function: Receives alerts triggered by the Ruler and sends notifications to external alert management systems or notification channels.
    • Responsibilities:
      • Alert Notification & Routing: Sends alert notifications to configured notification channels (e.g., email, Slack, PagerDuty, Alertmanager) based on alert routing rules.
      • Alert Deduplication & Grouping: Deduplicates and groups similar alerts to reduce notification noise and improve alert management efficiency.
      • Alert Silencing & Inhibition: Allows silencing of alerts based on defined criteria and inhibiting alerts based on other active alerts.
      • Secure Communication with External Systems: Ensures secure communication with external alert managers and notification channels, protecting sensitive alert information.
    • Security Focus: Secure communication with external systems, secure alert notification configuration storage, alert data confidentiality.
    • Potential Threats: Alert spoofing, unauthorized access to alert notification configurations, insecure communication with external alert managers, information leakage via alert notifications.

  • Gossip Ring:

    • Function: A distributed membership and failure detection system that enables Cortex components to discover each other, maintain cluster membership, and propagate cluster state information.
    • Responsibilities:
      • Service Discovery & Membership Management: Enables components to dynamically discover the locations and health status of other components in the cluster.
      • Failure Detection & Health Monitoring: Detects component failures and disseminates failure information across the cluster, enabling fault tolerance and self-healing.
      • Consistent Hashing & Data Sharding: Used by the Distributor for consistent sharding of incoming data across Ingesters, ensuring data locality and efficient query routing.
      • Distributed Consensus & Coordination: Facilitates distributed consensus and coordination among components for tasks like compaction scheduling and rule distribution.
    • Security Focus: Membership authentication and authorization, confidentiality and integrity of gossip messages, resistance to Sybil attacks, DoS prevention on gossip protocol.
    • Potential Threats: Gossip ring poisoning, unauthorized membership, information leakage via gossip messages, Sybil attacks disrupting cluster operation, DoS attacks on gossip protocol, eavesdropping on gossip communication.

5. Data Flow

The data flow within Cortex is primarily divided into two distinct paths: Ingestion Path (metrics coming in) and Query Path (metrics being queried).

5.1. Ingestion Path Data Flow (Simplified):

  1. Prometheus/Agent scrapes metrics from monitored targets.
  2. Prometheus/Agent sends remote_write requests containing metric samples to the Distributor.
  3. Distributor authenticates the tenant, validates the request, and shards the samples based on series hash.
  4. Distributor forwards sharded samples to the appropriate Ingesters.
  5. Ingesters store samples in memory, encode them into chunks, and periodically flush chunks to Object Storage via Store Gateway.

5.2. Query Path Data Flow (Simplified):

  1. User/Dashboard sends a PromQL query to the Query Frontend (recommended).
  2. Query Frontend caches, optimizes, and forwards the query to Queriers.
  3. Queriers identify relevant Ingesters (for recent data) and Store Gateways (for historical data).
  4. Queriers query Ingesters and Store Gateways to retrieve necessary data.
  5. Store Gateways fetch chunks from Object Storage and return them to Queriers.
  6. Queriers merge, aggregate, and process data from all sources.
  7. Queriers execute the PromQL query and return results to Query Frontend (or directly to the user).
  8. Query Frontend caches results and returns them to the User/Dashboard.

6. Key Components Description (Detailed - Security Perspective)

(This section expands on component descriptions, emphasizing security aspects and potential threats for threat modeling purposes. It implicitly considers STRIDE categories where applicable.)

  • Distributor:

    • Security Focus: Authentication (A), Authorization (AuthZ), Input Validation (I), Availability (A), Non-Repudiation (NR - Tenant ID logging). Crucial for tenant isolation and preventing unauthorized data ingestion.
    • Threats (STRIDE):
      • Spoofing (S): Tenant ID spoofing, impersonating legitimate tenants.
      • Tampering (T): Metric data manipulation during transit (less likely if TLS is used for remote_write).
      • Repudiation (R): Lack of audit logs for tenant activity (addressable with logging).
      • Information Disclosure (ID): Exposure of internal routing or sharding information (less likely).
      • Denial of Service (DoS): Overwhelming the Distributor with write requests, resource exhaustion.
      • Elevation of Privilege (EoP): Gaining access to other tenants' data (mitigated by tenant isolation).

  • Ingester:

    • Security Focus: Confidentiality (C - of recent data), Integrity (I - of in-memory and flushed data), Availability (A), Resource Management (RM). Protects recent data and ensures data consistency.
    • Threats (STRIDE):
      • Spoofing (S): Ingester impersonation in the gossip ring (mitigated by gossip security).
      • Tampering (T): Data corruption in memory or during flushing, chunk manipulation.
      • Repudiation (R): Lack of audit logs for data modifications (less relevant for transient in-memory data).
      • Information Disclosure (ID): Unauthorized access to in-memory data, memory leaks.
      • Denial of Service (DoS): Memory exhaustion, CPU overload, network flooding.
      • Elevation of Privilege (EoP): Gaining access to other tenants' recent data (mitigated by tenant isolation).

  • Query Frontend:

    • Security Focus: Confidentiality (C - of cached data), Integrity (I - of query results), Availability (A), Input Validation (I - PromQL), Access Control (AC - to cache). Enhances query performance securely.
    • Threats (STRIDE):
      • Spoofing (S): Query Frontend impersonation (less likely in internal communication).
      • Tampering (T): Cache poisoning, manipulation of query results.
      • Repudiation (R): Lack of audit logs for query activity (addressable with logging).
      • Information Disclosure (ID): Cache-based information leakage between tenants, exposure of query patterns.
      • Denial of Service (DoS): Cache exhaustion, overwhelming the Query Frontend with queries.
      • Elevation of Privilege (EoP): Gaining access to other tenants' cached query results (mitigated by tenant isolation in caching).

  • Querier:

    • Security Focus: Confidentiality (C - of queried data), Integrity (I - of query results), Availability (A), Access Control (AC - tenant data), Input Validation (I - PromQL), Secure Communication (SC - with Ingesters/Store Gateways). Core component for secure and reliable querying.
    • Threats (STRIDE):
      • Spoofing (S): Querier impersonation (less likely in internal communication).
      • Tampering (T): Manipulation of query results, data injection via PromQL vulnerabilities.
      • Repudiation (R): Lack of audit logs for query activity (addressable with logging).
      • Information Disclosure (ID): Unauthorized access to tenant data, PromQL injection leading to data extraction.
      • Denial of Service (DoS): Resource exhaustion due to complex queries, PromQL vulnerabilities leading to DoS.
      • Elevation of Privilege (EoP): Cross-tenant data access due to query vulnerabilities or access control bypass.

  • Store Gateway:

    • Security Focus: Confidentiality (C - of stored data), Integrity (I - of stored data), Availability (A), Access Control (AC - object storage), Secure Communication (SC - with Object Storage), Data Encryption (DE - at rest in object storage). Securely manages long-term storage access.
    • Threats (STRIDE):
      • Spoofing (S): Store Gateway impersonation (less likely in internal communication).
      • Tampering (T): Data corruption in object storage, unauthorized data modification.
      • Repudiation (R): Lack of audit logs for storage access (dependent on object storage logging).
      • Information Disclosure (ID): Object storage credential compromise, unauthorized access to object storage, data breaches due to lack of encryption.
      • Denial of Service (DoS): Object storage unavailability, overwhelming Store Gateway with requests.
      • Elevation of Privilege (EoP): Cross-tenant data access in object storage due to misconfiguration.

  • Compactor:

    • Security Focus: Integrity (I - of compacted data), Availability (A), Access Control (AC - object storage), Data Retention (DR - policy enforcement). Ensures data integrity and proper lifecycle management.
    • Threats (STRIDE):
      • Spoofing (S): Compactor impersonation (less likely in internal communication).
      • Tampering (T): Data corruption during compaction, unauthorized data modification.
      • Repudiation (R): Lack of audit logs for compaction activity (addressable with logging).
      • Information Disclosure (ID): Accidental exposure of data during compaction process (less likely).
      • Denial of Service (DoS): Resource exhaustion during compaction, impacting query performance.
      • Elevation of Privilege (EoP): Accidental cross-tenant data merging during compaction (mitigated by tenant isolation).

  • Ruler:

    • Security Focus: Integrity (I - of rules), Availability (A), Access Control (AC - Queriers), Input Validation (I - PromQL rules). Securely manages rule evaluation and recording.
    • Threats (STRIDE):
      • Spoofing (S): Ruler impersonation (less likely in internal communication).
      • Tampering (T): Rule configuration tampering, malicious rule injection.
      • Repudiation (R): Lack of audit logs for rule modifications (addressable with logging).
      • Information Disclosure (ID): Exposure of rule configurations (potentially sensitive).
      • Denial of Service (DoS): Resource exhaustion due to complex rules, rule evaluation loops.
      • Elevation of Privilege (EoP): Gaining access to other tenants' data via rules (mitigated by tenant isolation in Queriers).

  • Alerter:

    • Security Focus: Confidentiality (C - of alert data), Integrity (I - of alert notifications), Availability (A), Secure Communication (SC - with external systems), Access Control (AC - alert configurations). Securely manages alert notifications.
    • Threats (STRIDE):
      • Spoofing (S): Alert spoofing, sending fake alerts.
      • Tampering (T): Modification of alert notifications, alert suppression.
      • Repudiation (R): Lack of audit logs for alert notifications (dependent on external system logging).
      • Information Disclosure (ID): Exposure of alert data in transit or at rest, insecure notification channels.
      • Denial of Service (DoS): Overwhelming alert notification systems, alert flooding.
      • Elevation of Privilege (EoP): Gaining access to other tenants' alert information (mitigated by tenant isolation in Ruler/Alerter).

  • Gossip Ring:

    • Security Focus: Authentication (A - membership), Integrity (I - of gossip messages), Availability (A), Confidentiality (C - of gossip messages - if sensitive info is gossiped). Critical for cluster stability and secure communication.
    • Threats (STRIDE):
      • Spoofing (S): Gossip ring poisoning, node impersonation, Sybil attacks.
      • Tampering (T): Gossip message manipulation, injecting false information.
      • Repudiation (R): Lack of audit logs for gossip activity (less relevant for transient gossip data).
      • Information Disclosure (ID): Exposure of gossip messages (if they contain sensitive information).
      • Denial of Service (DoS): Gossip storm, network flooding, disrupting gossip protocol.
      • Elevation of Privilege (EoP): Gaining control of the gossip ring to manipulate cluster behavior.

7. Security Considerations (Detailed)

  • Authentication and Authorization:

    • Tenant Authentication: Implement strong tenant authentication at the Distributor to verify the identity of metric senders. Use API keys, OAuth 2.0, or mutual TLS.
    • Component Authentication: Secure internal communication between Cortex components using mutual TLS or gRPC authentication.
    • Authorization Policies: Enforce fine-grained authorization policies to control access to data and operations based on tenant and user roles.

  • Tenant Isolation:

    • Logical Isolation: Ensure logical separation of tenant data at all layers (Distributor, Ingester, Querier, Store Gateway, Object Storage) using tenant IDs and namespaces.
    • Resource Quotas & Limits: Implement resource quotas and limits per tenant to prevent resource exhaustion and ensure fair resource sharing.
    • Network Segmentation: Isolate tenant networks using network policies or VLANs to prevent cross-tenant network access.

  • Input Validation & Sanitization:

    • Write Request Validation: Thoroughly validate incoming remote_write requests at the Distributor, including metric names, labels, and values, to prevent injection attacks and data corruption.
    • PromQL Query Validation: Implement PromQL query parsing and validation in Query Frontend and Queriers to prevent PromQL injection vulnerabilities and resource-intensive queries.
    • Rule Validation: Validate recording and alerting rules in the Ruler to prevent malicious or malformed rules.

  • Rate Limiting & DoS Protection:

    • Write Path Rate Limiting: Implement rate limiting at the Distributor to control the rate of incoming write requests and prevent DoS attacks.
    • Query Path Rate Limiting: Implement rate limiting at the Query Frontend to control the rate of incoming query requests and protect Queriers from overload.
    • Connection Limits: Set connection limits for each component to prevent connection exhaustion attacks.

  • Data Encryption:

    • Encryption at Rest: Enable encryption at rest for object storage to protect stored metric data. Utilize object storage encryption features (e.g., AWS KMS, Google Cloud KMS, Azure Key Vault).
    • Encryption in Transit: Enforce TLS encryption for all external and internal communication channels, including remote_write requests, PromQL queries, and inter-component communication.

  • Network Security:

    • Network Policies: Implement Kubernetes Network Policies or firewall rules to restrict network access between Cortex components and external networks, enforcing least privilege network access.
    • Service Mesh (Optional): Consider using a service mesh (e.g., Istio, Linkerd) to enhance network security, observability, and traffic management within the Cortex cluster.

  • Access Control to Object Storage:

    • Principle of Least Privilege: Grant Store Gateways and Compactors only the necessary permissions to access object storage, following the principle of least privilege.
    • IAM Roles & Policies: Utilize IAM roles and policies provided by cloud providers to manage access to object storage securely.

  • Secrets Management:

    • Secure Secret Storage: Use secure secret management solutions (e.g., HashiCorp Vault, Kubernetes Secrets, cloud provider secret managers) to store and manage sensitive credentials, such as object storage keys and API keys.
    • Secret Rotation: Implement regular secret rotation to minimize the impact of potential secret compromise.

  • Regular Security Audits, Penetration Testing, and Vulnerability Scanning:

    • Security Audits: Conduct regular security audits of the Cortex codebase, configuration, and deployment to identify potential security weaknesses.
    • Penetration Testing: Perform penetration testing to simulate real-world attacks and assess the effectiveness of security controls.
    • Vulnerability Scanning: Implement automated vulnerability scanning for container images and dependencies to identify and remediate known vulnerabilities.

  • Monitoring, Logging, and Security Information and Event Management (SIEM):

    • Security Monitoring: Monitor Cortex components for suspicious activity, security events, and performance anomalies.
    • Centralized Logging: Aggregate logs from all Cortex components into a centralized logging system for security analysis and incident response.
    • SIEM Integration: Integrate Cortex logs and security events with a SIEM system for real-time threat detection and incident correlation.

8. Deployment Model

(No changes from previous version, section remains valid)

Cortex is typically deployed in a distributed environment, often on Kubernetes, for scalability and high availability. Common deployment models include:

  • Kubernetes Deployment: Utilizing Kubernetes for container orchestration, service discovery, scaling, and rolling updates. This is the recommended and most common deployment model.
  • On-Premise Deployment (Bare Metal/VMs): Deployment on physical servers or virtual machines, requiring manual configuration for clustering, service discovery, and scaling.
  • Cloud Provider Managed Kubernetes (EKS, GKE, AKS): Leveraging managed Kubernetes services offered by cloud providers for simplified deployment and management.

For production deployments, it is highly recommended to deploy Cortex in a highly available and fault-tolerant manner, with multiple replicas of each component and distributed storage.

9. Technologies Used

(No changes from previous version, section remains valid)

  • Programming Language: Go (primarily)
  • Time-Series Database: Custom chunk-based storage format optimized for time-series data.
  • Object Storage: Integration with various object storage systems like AWS S3, Google Cloud Storage, Azure Blob Storage, OpenStack Swift.
  • Communication Protocol: gRPC (for internal component communication), HTTP/2 (for external API access).
  • Query Language: PromQL (Prometheus Query Language)
  • Configuration Management: YAML based configuration files.
  • Service Discovery & Clustering: Gossip Ring (memberlist library).
  • Containerization & Orchestration: Docker, Kubernetes (primarily).

This improved design document provides a more detailed and security-focused overview of the Cortex project architecture. It is specifically designed to be a valuable resource for threat modeling activities, providing insights into component responsibilities, data flow, potential threats (categorized using STRIDE implicitly), and comprehensive security considerations. This document serves as a strong foundation for security teams, development teams, and operations teams to understand and secure Cortex deployments effectively.