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Mitigation Strategies Analysis for apache/spark

Mitigation Strategy: 1. Use Kryo Serialization

  • Mitigation Strategy: Use Kryo Serialization
  • Description:
    1. Identify Current Serializer: Check your spark-defaults.conf or Spark application configuration to determine if spark.serializer is explicitly set. If not, Java serialization is the default Spark serializer.
    2. Configure Kryo in Spark: In your spark-defaults.conf file (for cluster-wide default) or within your Spark application's SparkConf object (for application-specific setting), set the property spark.serializer to org.apache.spark.serializer.KryoSerializer. This instructs Spark to use Kryo for serialization of data and objects within Spark jobs.
    3. Register Custom Classes with Kryo (Recommended): For improved Kryo performance and handling of custom data types used in your Spark applications, register these classes with Kryo. In your Spark application code, use conf.registerKryoClasses(Array(classOf[YourCustomClass1], classOf[YourCustomClass2], ...)). This step also enhances security by explicitly controlling the classes Kryo can deserialize.
    4. Thorough Testing: After switching to Kryo, rigorously test your Spark applications to ensure compatibility and that performance remains acceptable or improves. Kryo's serialization behavior might differ from Java serialization in certain edge cases.
  • Threats Mitigated:
    • Spark Deserialization Vulnerabilities (High Severity): Exploits targeting Java serialization within Spark can lead to remote code execution if attackers can control serialized data processed by Spark. Kryo is generally considered less vulnerable to these types of exploits compared to default Java serialization in Spark.
  • Impact:
    • Spark Deserialization Vulnerabilities (High Impact): Significantly reduces the risk of remote code execution attacks that exploit deserialization flaws within the Spark framework itself by utilizing a more secure serialization library.
  • Currently Implemented: Partially implemented. spark.serializer is set to org.apache.spark.serializer.KryoSerializer in spark-defaults.conf for the development environment.
  • Missing Implementation: Kryo class registration is not consistently implemented across all Spark applications. Production environment spark-defaults.conf needs to be updated to use Kryo. Custom classes used in specific Spark applications are not explicitly registered with Kryo, potentially impacting performance and security.

Mitigation Strategy: 2. Regularly Update Spark Version

  • Mitigation Strategy: Regularly Update Spark Version
  • Description:
    1. Track Current Spark Version: Maintain a clear record of the Apache Spark version currently deployed in your environment.
    2. Monitor Spark Security Announcements: Subscribe to the Apache Spark security mailing list and regularly check official Apache Spark security advisories for your deployed version and newer releases.
    3. Plan and Schedule Updates: Establish a schedule for regularly updating to the latest stable and security-patched version of Apache Spark. Prioritize updates that specifically address reported security vulnerabilities in Spark.
    4. Staging Environment Testing: Before deploying Spark version updates to production, thoroughly test the new version in a staging environment that mirrors your production setup. This ensures compatibility with existing Spark applications and infrastructure.
    5. Automate Update Process (If Possible): Explore automating the Spark update process to streamline deployments and ensure timely patching of vulnerabilities.
  • Threats Mitigated:
    • Spark Known Vulnerabilities (High to Medium Severity): Outdated Spark versions may contain publicly disclosed security vulnerabilities within the Spark core, Spark SQL, Spark Streaming, or other Spark components. Severity depends on the specific vulnerability and affected Spark components.
  • Impact:
    • Spark Known Vulnerabilities (High to Medium Impact): Significantly reduces the risk of exploitation of known vulnerabilities within the Spark framework itself by applying security patches and fixes included in newer Spark releases.
  • Currently Implemented: Partially implemented. We have a process for tracking the Spark version and occasionally updating, but it is not on a regular, scheduled basis, and the update process is not fully automated.
  • Missing Implementation: A formal, regularly scheduled process for Spark version updates needs to be established and enforced. Automation of the Spark update process should be explored to ensure timely security patching.

Mitigation Strategy: 3. Enable Authentication for Spark UI and History Server

  • Mitigation Strategy: Enable Authentication for Spark UI and History Server
  • Description:
    1. Choose Spark Authentication Method: Select an appropriate authentication method supported by Spark for securing the UI and History Server. Options include simple ACLs, Kerberos, LDAP, or custom authentication filters. For production environments, Kerberos or LDAP integration is generally recommended for stronger authentication.
    2. Configure Spark UI Authentication: In spark-defaults.conf or SparkConf, set spark.ui.acls.enable=true. For simple ACLs, configure spark.acls.users and spark.admin.acls.groups to define authorized users and groups. For Kerberos or LDAP, configure the relevant Spark properties as detailed in the Spark documentation for your chosen method (e.g., spark.kerberos.principal, spark.kerberos.keytab).
    3. Configure History Server Authentication: Similarly, for the History Server, set spark.history.ui.acls.enable=true and configure the authentication properties consistent with the Spark UI settings to ensure unified access control.
    4. Restart Spark Services: After modifying the Spark configuration, restart the Spark Master, Workers, and History Server for the authentication settings to be applied and become active.
    5. Verify Access Control: Thoroughly test access to the Spark UI and History Server to confirm that authentication is enforced and that only authorized users can access these interfaces. Test with both authorized and unauthorized accounts to validate proper access control.
  • Threats Mitigated:
    • Unauthorized Access to Spark UI/History Server (High Severity): Without authentication, anyone with network access to the Spark UI or History Server can view sensitive details about running and past Spark applications, cluster configuration, and potentially sensitive data processed by Spark. This can lead to information disclosure and unauthorized control of Spark resources.
    • Spark Information Disclosure (High Severity): Exposes sensitive Spark application metadata, logs, environment variables, and potentially data samples visible through the UI, leading to unauthorized information disclosure about Spark workloads and data.
  • Impact:
    • Unauthorized Access to Spark UI/History Server (High Impact): Significantly reduces the risk of unauthorized access to sensitive Spark cluster and application information, preventing potential misuse and information leakage.
    • Spark Information Disclosure (High Impact): Prevents unauthorized viewing of sensitive operational and potentially data-related information exposed through the Spark monitoring UIs.
  • Currently Implemented: Partially implemented. Simple ACLs are enabled for the development Spark UI, restricting access to developers. History Server authentication is not yet enabled.
  • Missing Implementation: History Server authentication needs to be enabled. Production environment Spark UI and History Server need to be secured with a more robust authentication mechanism like LDAP or Kerberos, integrated with our organization's identity management system. Simple ACLs are insufficient for production security.

Mitigation Strategy: 4. Enable HTTPS for Spark UI and History Server

  • Mitigation Strategy: Enable HTTPS for Spark UI and History Server
  • Description:
    1. Generate or Obtain SSL Certificates: Obtain SSL/TLS certificates for your Spark UI and History Server. You can generate self-signed certificates for testing or use certificates issued by a trusted Certificate Authority (CA) for production.
    2. Configure Spark UI HTTPS: In spark-defaults.conf or SparkConf, set spark.ui.https.enabled=true. Configure the SSL keystore path and password using spark.ui.https.keyStorePath and spark.ui.https.keyStorePassword. Optionally, configure the keystore type and protocol if needed.
    3. Configure History Server HTTPS: Similarly, for the History Server, set spark.history.ui.https.enabled=true and configure the SSL keystore properties (spark.history.ui.https.keyStorePath, spark.history.ui.https.keyStorePassword, etc.) consistently with the Spark UI settings.
    4. Restart Spark Services: Restart the Spark Master, Workers, and History Server after configuration changes to enable HTTPS for the UIs.
    5. Access via HTTPS: Verify that you can now access the Spark UI and History Server using HTTPS URLs (e.g., https://<spark-ui-hostname>:<port>). Ensure that your browser recognizes the SSL certificate as valid (if using a CA-signed certificate) or accept the self-signed certificate if used for testing.
  • Threats Mitigated:
    • Spark UI/History Server Data in Transit Sniffing (Medium to High Severity): Without HTTPS, communication between users' browsers and the Spark UI/History Server is unencrypted. Attackers on the network could potentially eavesdrop and intercept sensitive information transmitted, including session cookies, application details, and potentially data samples displayed in the UI.
    • Man-in-the-Middle (MitM) Attacks on Spark UI/History Server (Medium Severity): Without HTTPS, the Spark UI and History Server are vulnerable to Man-in-the-Middle attacks where attackers can intercept and potentially manipulate communication between users and the Spark UIs.
  • Impact:
    • Spark UI/History Server Data in Transit Sniffing (Medium to High Impact): Prevents eavesdropping and interception of sensitive data transmitted between users and the Spark UIs, protecting confidentiality during monitoring and management.
    • Man-in-the-Middle (MitM) Attacks on Spark UI/History Server (Medium Impact): Mitigates the risk of Man-in-the-Middle attacks against the Spark UIs, ensuring the integrity and authenticity of communication.
  • Currently Implemented: Not implemented. Currently, the Spark UI and History Server are accessed over HTTP in both development and production environments.
  • Missing Implementation: HTTPS needs to be enabled for both the Spark UI and History Server in all environments, especially production. SSL certificates need to be obtained and properly configured for these Spark components.

Mitigation Strategy: 5. Restrict Network Access to Spark UI and History Server

  • Mitigation Strategy: Restrict Network Access to Spark UI and History Server
  • Description:
    1. Identify Necessary Access: Determine which users and networks legitimately require access to the Spark UI and History Server for monitoring and management purposes. Typically, this should be limited to development teams, operations teams, and potentially authorized monitoring systems.
    2. Configure Network Firewalls: Implement network firewalls or security groups to restrict network access to the ports used by the Spark UI (default 4040) and History Server (default 18080). Configure firewall rules to allow inbound traffic only from authorized networks or IP address ranges.
    3. Use Access Control Lists (ACLs) on Network Devices: If firewalls are not sufficient, utilize Access Control Lists (ACLs) on network switches or routers to further refine network access control to the Spark UI and History Server ports.
    4. Internal Network Deployment: Ideally, deploy the Spark cluster and its UI/History Server within a private internal network, not directly exposed to the public internet. Access should be controlled through VPNs or other secure access gateways for authorized users outside the internal network.
    5. Regularly Review Access Rules: Periodically review and update network access rules for the Spark UI and History Server to ensure they remain aligned with current access requirements and security policies.
  • Threats Mitigated:
    • Unauthorized Access to Spark UI/History Server from External Networks (High Severity): If the Spark UI and History Server are accessible from the public internet or untrusted networks, unauthorized individuals can potentially gain access, leading to information disclosure, manipulation of Spark applications, or denial of service.
    • Spark UI/History Server Exposure to Broader Attack Surface (Medium Severity): Exposing the Spark UIs to a wider network increases the attack surface and the potential for exploitation of vulnerabilities in the UI components or underlying Spark services.
  • Impact:
    • Unauthorized Access to Spark UI/History Server from External Networks (High Impact): Significantly reduces the risk of unauthorized external access by limiting network reachability to only authorized networks and users.
    • Spark UI/History Server Exposure to Broader Attack Surface (Medium Impact): Reduces the overall attack surface by limiting network exposure of the Spark monitoring interfaces.
  • Currently Implemented: Partially implemented. Network firewalls are in place, but rules might be overly permissive, allowing access from broader internal networks than strictly necessary.
  • Missing Implementation: Network access rules for Spark UI and History Server need to be tightened to restrict access to only essential networks and IP ranges. Consider deploying Spark cluster entirely within a private network with VPN access for authorized personnel.

Mitigation Strategy: 6. Disable Spark UI and History Server in Production (If Not Needed)

  • Mitigation Strategy: Disable Spark UI and History Server in Production (If Not Needed)
  • Description:
    1. Assess Production Monitoring Needs: Evaluate whether the Spark UI and History Server are actively and regularly used for monitoring and debugging in your production environment. If monitoring is primarily done through other dedicated monitoring tools and the Spark UIs are not essential for day-to-day operations, consider disabling them.
    2. Disable Spark UI: In spark-defaults.conf or SparkConf, set spark.ui.enabled=false. This will prevent the Spark UI from starting for Spark applications.
    3. Disable History Server (Effectively): To disable the History Server, ensure that spark.history.fs.logDirectory is set to an empty or non-existent path. This prevents the History Server from loading and serving application history logs. Alternatively, you can choose not to deploy or start the History Server service at all in production.
    4. Restart Spark Services: Restart the Spark Master and Workers after disabling the Spark UI. If disabling the History Server, ensure the History Server service is stopped.
    5. Monitor via Alternative Tools: If disabling the Spark UIs, ensure that you have alternative monitoring tools and mechanisms in place to track the health and performance of your Spark applications and cluster in production.
  • Threats Mitigated:
    • Spark UI/History Server Vulnerabilities (Medium to High Severity): Disabling the UI and History Server eliminates the attack surface associated with these components. If vulnerabilities are discovered in the Spark UI or History Server in the future, disabling them prevents potential exploitation.
    • Accidental Exposure of Spark Information (Medium Severity): Even with authentication and network restrictions, there's always a residual risk of misconfiguration or accidental exposure of sensitive Spark information through the UI if it is running. Disabling it removes this risk entirely.
  • Impact:
    • Spark UI/History Server Vulnerabilities (High Impact): Completely eliminates the risk of vulnerabilities within the Spark UI and History Server being exploited, as these components are no longer running and accessible.
    • Accidental Exposure of Spark Information (Medium Impact): Removes the possibility of accidental information exposure through the Spark UIs in production.
  • Currently Implemented: Not implemented. The Spark UI and History Server are enabled in both development and production environments.
  • Missing Implementation: Evaluate the necessity of Spark UI and History Server in production. If not essential for routine monitoring, disable them in the production Spark cluster configuration to reduce the attack surface. Ensure alternative monitoring solutions are in place if the Spark UIs are disabled.

Mitigation Strategy: 7. Use Spark's Security Features (ACLs)

  • Mitigation Strategy: Use Spark's Security Features (ACLs)
  • Description:
    1. Enable ACLs: Ensure that Spark's Access Control Lists (ACLs) are enabled by setting spark.acls.enable=true in spark-defaults.conf or SparkConf. This activates Spark's authorization framework.
    2. Configure User and Group ACLs: Define ACLs to control access to Spark resources and actions. Use properties like spark.acls.users, spark.admin.acls.users, spark.modify.acls.users, spark.view.acls.users, and their group counterparts (spark.acls.groups, etc.) to specify authorized users and groups for different levels of access (view, modify, admin).
    3. Apply ACLs to Spark Resources: Spark ACLs can control access to various resources, including applications, executors, storage levels, and more. Configure ACLs to restrict who can view application details, kill jobs, access data in specific storage locations, or perform administrative actions within the Spark cluster.
    4. Integrate with Authentication: Spark ACLs typically work in conjunction with authentication mechanisms (like Kerberos or simple authentication) to identify users and enforce access control based on their identities.
    5. Regularly Review and Update ACLs: Periodically review and update Spark ACL configurations to ensure they remain aligned with current access control requirements and user roles within your organization.
  • Threats Mitigated:
    • Unauthorized Actions within Spark Cluster (Medium to High Severity): Without ACLs, users with access to the Spark cluster might be able to perform unauthorized actions, such as viewing sensitive application details, killing jobs belonging to other users, or accessing data they are not authorized to see.
    • Privilege Escalation within Spark (Medium Severity): In the absence of proper authorization, users might be able to escalate their privileges within the Spark environment and gain access to resources or actions beyond their intended roles.
  • Impact:
    • Unauthorized Actions within Spark Cluster (Medium to High Impact): Reduces the risk of unauthorized actions by enforcing access control and ensuring that users can only perform actions they are explicitly authorized to perform within the Spark environment.
    • Privilege Escalation within Spark (Medium Impact): Mitigates the risk of privilege escalation by enforcing role-based access control within the Spark cluster.
  • Currently Implemented: Partially implemented. spark.acls.enable=true is set in development, but detailed ACL configurations for users and groups are not fully defined and enforced across all Spark resources and actions.
  • Missing Implementation: Need to define comprehensive ACL policies for Spark resources and actions based on user roles and responsibilities. Implement fine-grained ACL configurations to control access to applications, data, and administrative functions within the Spark cluster. Production environment needs full ACL configuration.

Mitigation Strategy: 8. Review and Harden Default Spark Configurations

  • Mitigation Strategy: Review and Harden Default Spark Configurations
  • Description:
    1. Review Default Spark Configuration: Carefully examine the default Spark configuration settings in spark-defaults.conf and any other Spark configuration files used in your environment. Understand the security implications of each configuration parameter.
    2. Disable Unnecessary Features/Services: Identify any Spark features or services that are not required for your Spark applications or environment. Disable these unnecessary components to reduce the attack surface. Examples might include disabling certain Spark UI features, unused Spark modules, or optional services.
    3. Harden Security-Related Configurations: Focus on hardening security-related Spark configuration parameters. This includes:
      • Enabling authentication and authorization (as covered in other strategies).
      • Configuring encryption for data in transit and at rest (as covered in other strategies).
      • Setting appropriate resource limits and quotas to prevent resource exhaustion.
      • Disabling insecure or deprecated features.
    4. Follow Security Best Practices: Apply general security best practices to your Spark configuration. This includes using strong passwords for any configured secrets, limiting permissions for Spark processes, and regularly auditing configuration settings.
    5. Document Configuration Changes: Document all changes made to the default Spark configuration for security hardening purposes. This helps with maintainability, auditing, and understanding the security posture of your Spark environment.
  • Threats Mitigated:
    • Insecure Default Spark Settings (Medium Severity): Default Spark configurations might not be optimally secure out-of-the-box. Leaving default settings unchanged can leave the Spark cluster vulnerable to various security issues.
    • Unnecessary Feature Exposure (Medium Severity): Enabling unnecessary Spark features or services increases the attack surface and the potential for exploitation of vulnerabilities in those components.
  • Impact:
    • Insecure Default Spark Settings (Medium Impact): Improves the overall security posture of the Spark cluster by addressing potential weaknesses in default configurations.
    • Unnecessary Feature Exposure (Medium Impact): Reduces the attack surface by disabling unnecessary features and services, minimizing the potential for exploitation of vulnerabilities in unused components.
  • Currently Implemented: Partially implemented. Some basic hardening steps have been taken, such as enabling Kryo serialization and simple ACLs in development. However, a comprehensive review and hardening of all default Spark configurations has not been performed.
  • Missing Implementation: Conduct a thorough security review of all default Spark configuration settings. Develop a hardened Spark configuration template based on security best practices and organizational security policies. Apply this hardened configuration to all Spark environments, especially production.

Mitigation Strategy: 9. Enable Encryption for Data in Transit (TLS/SSL) within Spark

  • Mitigation Strategy: Enable Encryption for Data in Transit (TLS/SSL) within Spark
  • Description:
    1. Generate or Obtain SSL Certificates for Spark: Obtain SSL/TLS certificates for your Spark cluster components (Master, Workers, Executors, etc.). You can use self-signed certificates for internal encryption or CA-signed certificates for stronger trust and external communication.
    2. Configure Spark SSL Properties: In spark-defaults.conf or SparkConf, enable SSL encryption for Spark internal communication by setting spark.ssl.enabled=true. Configure the SSL keystore path, password, and other relevant SSL properties (e.g., spark.ssl.keyStorePath, spark.ssl.keyStorePassword, spark.ssl.protocol, spark.ssl.algorithm). Configure these properties for both the driver and executor components (spark.driver.ssl.*, spark.executor.ssl.*).
    3. Configure SSL for Spark UI and History Server (Separate): As covered in a previous strategy, configure HTTPS separately for the Spark UI and History Server using spark.ui.https.* and spark.history.ui.https.* properties.
    4. Restart Spark Cluster: Restart the entire Spark cluster (Master, Workers, and applications) after configuring SSL to enable encryption for all internal Spark communication channels.
    5. Verify Encryption: Monitor network traffic within the Spark cluster to verify that communication between Spark components is now encrypted using TLS/SSL. Use network monitoring tools to inspect traffic and confirm encryption protocols are in use.
  • Threats Mitigated:
    • Spark Data in Transit Sniffing (Medium to High Severity): Without TLS/SSL encryption, data transmitted between Spark components (executors, driver, master) is unencrypted. Attackers on the internal network could potentially eavesdrop and intercept sensitive data being processed and transferred within the Spark cluster.
    • Man-in-the-Middle (MitM) Attacks within Spark Cluster (Medium Severity): Without encryption, Spark internal communication is vulnerable to Man-in-the-Middle attacks within the cluster network, where attackers could intercept and potentially manipulate data exchanged between Spark components.
  • Impact:
    • Spark Data in Transit Sniffing (Medium to High Impact): Prevents eavesdropping and interception of sensitive data transmitted within the Spark cluster, protecting data confidentiality during processing and communication.
    • Man-in-the-Middle (MitM) Attacks within Spark Cluster (Medium Impact): Mitigates the risk of Man-in-the-Middle attacks against Spark internal communication, ensuring the integrity and authenticity of data exchange between Spark components.
  • Currently Implemented: Not implemented. Data in transit within the Spark cluster is currently not encrypted using TLS/SSL in either development or production environments.
  • Missing Implementation: TLS/SSL encryption needs to be enabled for all internal Spark communication channels in both development and production environments. SSL certificates need to be obtained and properly configured for Spark Master, Workers, and Executors.

Mitigation Strategy: 10. Utilize Spark's Fair Scheduler or Capacity Scheduler for Resource Management

  • Mitigation Strategy: Utilize Spark's Fair Scheduler or Capacity Scheduler for Resource Management
  • Description:
    1. Choose a Scheduler: Select either the Fair Scheduler or Capacity Scheduler based on your resource management requirements. The Fair Scheduler provides fair sharing of resources between applications, while the Capacity Scheduler allows for hierarchical resource queues with guaranteed capacities.
    2. Configure Scheduler in Spark: In spark-defaults.conf or SparkConf, set spark.scheduler.mode to either FAIR or CAPACITY to enable the chosen scheduler.
    3. Configure Scheduler Pools/Queues (If Applicable): If using the Fair Scheduler, configure fair scheduling pools in fair-scheduler.xml (or programmatically). If using the Capacity Scheduler, configure capacity queues in capacity-scheduler.xml (or through YARN configuration if running on YARN). Define resource allocation policies, weights, minimum shares, and maximum shares for pools or queues to control resource distribution among Spark applications.
    4. Assign Applications to Pools/Queues: Configure Spark applications to be assigned to specific fair scheduler pools or capacity scheduler queues based on user roles, application priorities, or organizational units. This can be done programmatically in SparkConf using spark.scheduler.pool or through YARN queue assignment if using Capacity Scheduler on YARN.
    5. Monitor Resource Usage and Scheduler Performance: Monitor resource usage and scheduler performance to ensure that resource allocation is happening as expected and that the scheduler is effectively preventing resource starvation and ensuring fair or capacity-based resource sharing.
  • Threats Mitigated:
    • Spark Resource Exhaustion by Single Application (Medium Severity): Without proper resource management, a single poorly written or malicious Spark application could potentially consume all available cluster resources, leading to denial of service for other applications and users.
    • Spark Denial of Service (DoS) due to Resource Starvation (Medium Severity): Resource starvation can occur if resource allocation is not managed effectively, leading to some Spark applications being unable to obtain necessary resources to run, effectively causing a denial of service for those applications.
  • Impact:
    • Spark Resource Exhaustion by Single Application (Medium Impact): Prevents a single application from monopolizing cluster resources by enforcing resource sharing policies defined by the chosen scheduler.
    • Spark Denial of Service (DoS) due to Resource Starvation (Medium Impact): Mitigates the risk of resource starvation by ensuring fair or capacity-based resource allocation, allowing multiple Spark applications to run concurrently without one application starving others of resources.
  • Currently Implemented: Partially implemented. The default FIFO scheduler is currently in use. Fair Scheduler configuration is present in development but not actively enforced or finely tuned.
  • Missing Implementation: Implement either Fair Scheduler or Capacity Scheduler in production to manage Spark resource allocation effectively. Configure scheduler pools or queues and assign Spark applications to appropriate pools/queues based on resource management policies. Fine-tune scheduler configurations to optimize resource utilization and prevent resource exhaustion or starvation.

Mitigation Strategy: 11. Implement Rate Limiting for Spark Job Submissions

  • Mitigation Strategy: Implement Rate Limiting for Spark Job Submissions
  • Description:
    1. Identify Job Submission Points: Determine all entry points for submitting Spark jobs to the cluster (e.g., spark-submit, REST APIs, custom job submission interfaces).
    2. Choose Rate Limiting Mechanism: Select a rate limiting mechanism to control the frequency of Spark job submissions. This could be implemented at the application level (e.g., using a queuing system or throttling logic in your job submission service) or at the infrastructure level (e.g., using API gateways or load balancers with rate limiting capabilities).
    3. Define Rate Limits: Define appropriate rate limits for Spark job submissions based on your cluster capacity, resource availability, and expected workload. Set limits on the number of job submissions per user, per application, or per time period.
    4. Implement Rate Limiting Logic: Implement the chosen rate limiting mechanism at the job submission entry points. This might involve:
      • Adding throttling logic to your job submission service to queue or reject submissions exceeding the defined rate limits.
      • Configuring rate limiting policies in your API gateway or load balancer if used for job submission.
      • Developing a custom rate limiting component that intercepts job submission requests and enforces limits.
    5. Monitor Rate Limiting and Adjust Limits: Monitor the effectiveness of rate limiting and adjust the limits as needed based on observed job submission patterns, cluster load, and performance.
  • Threats Mitigated:
    • Spark Job Submission Flood (Medium Severity): A malicious actor or a misconfigured application could flood the Spark cluster with excessive job submission requests, potentially overwhelming the cluster's resources and leading to denial of service.
    • Spark Denial of Service (DoS) due to Job Submission Overload (Medium Severity): A flood of job submissions can overload the Spark Master and scheduler, causing performance degradation or complete failure to schedule and execute jobs, resulting in a denial of service for legitimate Spark workloads.
  • Impact:
    • Spark Job Submission Flood (Medium Impact): Prevents a flood of job submissions from overwhelming the Spark cluster by limiting the rate at which new jobs can be submitted.
    • Spark Denial of Service (DoS) due to Job Submission Overload (Medium Impact): Mitigates the risk of DoS attacks caused by excessive job submissions by ensuring that the Spark cluster is not overloaded with more job requests than it can handle.
  • Currently Implemented: Not implemented. There is currently no rate limiting in place for Spark job submissions.
  • Missing Implementation: Implement rate limiting for Spark job submissions at all job submission entry points. Define appropriate rate limits based on cluster capacity and expected workload. Choose a suitable rate limiting mechanism and integrate it into the job submission process. Monitor rate limiting effectiveness and adjust limits as needed.