Audit Log

MongoDB audit logging is a critical mechanism for recording system operations, used to track data changes, user activities, and system events. Configuration methods include setting output destinations and filters in the configuration file, supporting three output methods: file, Syslog, and console. Audit events are categorized into six major types, including authentication, CRUD operations, and administrative operations. For large-scale deployments, optimization strategies such as sharded cluster configuration, log rotation, and storage separation need to be considered. Analytical practices can leverage aggregation pipelines for log analysis and security alerts. Compliance requirements like GDPR and HIPAA can be met through specific configurations. Audit logging impacts performance primarily due to I/O pressure, which can be optimized by adjusting caching and asynchronous writes.

Data encryption (field-level encryption, storage encryption)

MongoDB provides two data protection methods: field-level encryption and storage encryption. Field-level encryption targets specific sensitive fields and includes two implementation approaches: client-side field-level encryption and automatic encryption. Storage encryption, on the other hand, encrypts the entire database file, supporting WiredTiger encryption and multiple key management solutions. The article compares the characteristics and use cases of different encryption algorithms, analyzes the performance impact of encryption, and offers best practice recommendations, including data classification, key rotation, and performance testing. It also emphasizes that encryption should be combined with other security measures such as TLS, RBAC, and audit mechanisms to form a comprehensive security protection system.

Encrypted transmission (TLS/SSL)

MongoDB supports encrypting client-server communication through the TLS/SSL protocol to ensure data transmission security, preventing man-in-the-middle attacks and data leaks, making it particularly suitable for sensitive data scenarios. TLS/SSL provides authentication, encryption, and integrity protection. MongoDB supports TLS versions 1.0 to 1.3 but recommends disabling older versions. Server-side configuration requires specifying certificate files and verification modes, while client connections must provide corresponding certificate files. For development and testing environments, self-signed certificates can be generated using OpenSSL. Enabling TLS/SSL may impact performance, which can be improved by optimizing encryption algorithms and hardware acceleration. Common issues include certificate verification failures and protocol mismatches. Security best practices include disabling older protocols, using strong encryption, and regularly rotating certificates. Advanced configurations support custom cipher suites, OCSP stapling, and CRL checks. TLS should also be enabled for communication between sharded clusters and replica set members. Client certificate authentication enhances security, and certificates must include SANs to support multiple hostnames. For public certificates, enabling certificate transparency logs is recommended.

User Management and Access Control

MongoDB user management is based on Role-Based Access Control (RBAC), where each user is assigned roles defining their permissions for database resource operations. User information is stored in the admin database, and multiple roles can be specified during user creation. Built-in roles include database user, cluster administration, backup and restore, and global database types, with support for custom role creation. Permissions can be inherited and combined, and multiple authentication mechanisms such as SCRAM-SHA-1 and x.509 certificates are supported. Enabling authentication requires configuring security settings, and access control must be enabled on all instances in a sharded cluster. Auditing functionality is provided to log user actions. Best practices include the principle of least privilege, regular credential rotation, and TLS encryption. Common issues include forgotten passwords and insufficient permissions, with solutions available. Advanced scenarios involve field-level access control and time-restricted access. Performance considerations include avoiding excessive role nesting and regularly cleaning up users. Integration with systems like LDAP is supported.

Role and permission management (built-in roles, custom roles)

MongoDB's permission system is built on roles and privileges. A role is a collection of privileges that can be assigned to users, while privileges define the operations allowed on specific resources. MongoDB provides various built-in roles, including database user roles, database administration roles, cluster administration roles, backup and restoration roles, all-database roles, and superuser roles. It also supports creating custom roles to meet specific needs. Role management includes operations such as creating, viewing, updating, and deleting roles. Users can obtain corresponding permissions through role assignments. Best practices include adhering to the principle of least privilege, role hierarchy, regular audits, environment isolation, and documentation. In practical applications, permission schemes for scenarios like e-commerce platforms can be designed, and permission verification and testing can ensure correct configurations. Cross-database permission management and collection-level control offer more granular permission management capabilities, while system collections require special permission management.

Authentication mechanisms (SCRAM, x.509, LDAP, etc.)

MongoDB offers multiple authentication mechanisms to ensure database security, including SCRAM, x.509 certificates, and LDAP. SCRAM is the default authentication method, supporting SHA-1 and SHA-256 algorithms, which protect against attacks by salting and hashing passwords. x.509 certificate authentication is suitable for high-security environments, requiring clients to provide valid certificates. LDAP authentication integrates with enterprise directory services for centralized identity management. Kerberos authentication is ideal for environments already using Kerberos. MongoDB supports configuring multiple authentication mechanisms simultaneously, allowing clients to choose different methods based on their needs. Different authentication mechanisms have varying performance impacts: SCRAM has moderate computational overhead, x.509 involves TLS handshake overhead, LDAP depends on network latency, and Kerberos requires KDC interaction. Authentication performance can be evaluated using monitoring commands.

Combining sharding with replica sets

MongoDB combines sharding and replica sets to achieve both horizontal scaling and data redundancy. A sharded cluster includes shards, config servers, and query routing components. In production environments, it is recommended to use replica sets as shards to ensure high availability. Sharding strategies include range-based, hash-based, and tag-aware approaches. Read and write operations are routed to the corresponding shards via mongos. The balancer monitors data distribution and triggers chunk migrations. In case of failure, the replica set automatically elects a new primary node to restore services. Key monitoring metrics include data balance and sharding latency. Performance optimization involves hotspot shard handling and query techniques. For actual deployment, it is advised to configure multiple mongos instances and plan shard capacity considering data growth and replica count. Special scenarios include handling oversized collections and cross-shard transaction operations.

Monitoring and optimization of sharded clusters

A MongoDB sharded cluster achieves horizontal scaling through mongos routing processes, config servers, and shard replica sets, requiring attention to data balancing and performance metric monitoring. Shard key selection is critical for query performance and should have high cardinality and align with query patterns. Targeted queries using the shard key can avoid full-shard broadcasts. The balancer manages data distribution and can be configured with a window for balancing operations. Index optimization involves choosing and maintaining global and local indexes. Connection management requires optimizing mongos connection pools and read/write concern settings. Troubleshooting involves analyzing query execution plans and checking replica set status to address performance bottlenecks and cluster anomalies.

Shard balancer and data migration

The MongoDB sharding balancer ensures even data distribution across the cluster by automatically migrating chunks to enhance performance. As a default-enabled component of mongos, the balancer periodically monitors shard status and triggers migrations when the chunk count difference between shards exceeds a threshold, prioritizing well-defined chunk boundaries. Data migration occurs in three phases: 1. **Initialization**: The source shard prepares for migration, creates a temporary collection, and records the oplog position. 2. **Data Transfer**: Documents are copied in batches while synchronizing new oplog entries. 3. **Commit**: Metadata is updated, source data is cleaned up, and indexes are built. The migration strategy supports parallel transfers (default: 2 concurrent operations) and configurable migration windows. Common issues include migration stalls (requiring network latency checks or chunk size adjustments) and hot shards (recommended to use hashed shard keys). Monitoring techniques include aggregating migration history analysis and tracking key metrics such as migration queue length, duration, and disk space fluctuations.

Scaling out and scaling in a sharded cluster

MongoDB sharded clusters address single-node performance bottlenecks through horizontal scaling. During business volume fluctuations, dynamic cluster resizing is required. Expanding a sharded cluster involves adding config servers and integrating new shard replica sets, then triggering data rebalancing. Reducing a sharded cluster requires migrating data out of target shards before removing empty shards. Special scenarios include handling large chunks and maintenance window operations. Key monitoring metrics include migration queue backlog, network traffic, and disk space changes. Data consistency checks can be performed by comparing document counts across shards or using hash verification. Automated operations can utilize Ansible templates or MongoDB management API calls. Performance optimization focuses on pre-splitting collections, tag-aware sharding, and index synchronization strategies.