A MongoDB replica set is a group of mongod processes that maintain the same dataset to provide data redundancy and high availability. It consists of a primary node and multiple secondary nodes. The primary node receives all write operations, while the secondary nodes stay synchronized by replicating the primary's oplog. The minimum recommended configuration includes three members: one primary and two secondaries, supporting automatic failover. Replica set members can have different roles, including regular secondaries, hidden nodes, delayed nodes, and arbiters. The election mechanism is based on a variant of the Raft algorithm and requires majority support from voting members. Data synchronization is achieved through the oplog, and different levels of read/write concern control consistency are provided. Deployment considerations include hardware configuration and network topology. Maintenance tasks involve adding or removing members and monitoring health status. Fault handling addresses scenarios like network partitions and primary node failures. Replica sets serve as the foundational component of sharded clusters. Performance optimization involves oplog configuration and network latency management. Security configurations include authentication and network encryption.
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MongoDB cross-shard transactions implement ACID properties across multiple shards through a two-phase commit protocol to ensure atomicity. Support for transactions within replica sets was introduced in version 4.0, and cross-shard transactions were extended in version 4.2. The workflow includes a prepare phase and a commit/abort phase, along with a recovery mechanism. An example demonstrates executing cross-shard transactions using the Node.js driver. While providing strong consistency guarantees, it introduces performance issues such as increased latency, lock contention, and resource consumption. Optimization strategies include shortening transaction duration, reducing the number of involved shards, and designing shard keys appropriately. Transactions default to a 60-second timeout, with configurable retry mechanisms. E-commerce order processing is a typical use case. Monitoring metrics include transaction statistics, active transactions, and log analysis. Best practices emphasize shard key design and error handling. Limitations include the inability to create or delete collections or modify shard key values. Compared to traditional RDBMS, MongoDB adopts optimistic concurrency control and snapshot isolation. Compared to NewSQL, it offers eventual consistency as the default mode. Compared to NoSQL, it provides stronger guarantees and multi-document ACID transactions.
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MongoDB's transaction mechanism provides ACID guarantees for multi-document operations, but in distributed environments, issues like network latency, node failures, or resource contention can lead to timeouts or failures. Properly configuring timeout parameters and implementing effective retry strategies are crucial. Transaction timeout control includes two approaches: overall transaction timeout and operation-level timeout. Typical timeout scenarios involve long-running transactions, cross-shard transaction coordination, and network partitions. The basic retry pattern handles transient errors through error classification and exponential backoff algorithms. Advanced strategies include cross-service transaction coordination and large transaction splitting. Performance optimization involves setting monitoring metrics and handling write conflicts. Special scenarios, such as deadlock detection, require specific handling. Different client drivers vary in automatic retry and session management. Best practice recommendations include setting appropriate transaction lifetimes and read/write concern levels.
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MongoDB introduced multi-document transaction support in version 4.0, providing ACID properties to ensure atomicity across document operations. Transactions must be executed within a session and use snapshot isolation to maintain data consistency. Typical use cases include bank transfers, order processing, inventory management, and other operations requiring atomicity. Transactions incur performance overhead, such as lock contention, memory pressure, and network round trips, especially in sharded clusters. Usage is subject to various limitations, such as the inability to create or drop collections, modify shard keys, size and time constraints, etc. In replica set environments, it is recommended to configure a sufficiently large oplog. Cross-shard transactions in sharded clusters are more complex and require two-phase commit. Applications should implement error handling and retry mechanisms to handle potential transaction failures. MongoDB provides special error labels to help identify retryable errors.
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Causal consistency in MongoDB is a distributed database consistency model that ensures the correct maintenance of causal relationships between operations. Starting from version 3.6, it is supported through mechanisms such as operation timestamps, cluster time, and causality identifiers. It is suitable for scenarios like social networks and e-commerce systems and is closely related to read and write concern levels. While it incurs some performance overhead, optimizations like batch processing of metadata are employed. Monitoring tools are provided, but limitations such as the lack of guaranteed global real-time consistency exist. Compared to other models, it strikes a balance between ensuring causality and performance. Best practices include the rational use of sessions and setting timeouts. Future development directions include cross-shard transaction support and more efficient tracking mechanisms.
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In MongoDB, write concern and read preference are core mechanisms that control data consistency and availability. Write concern defines the conditions that must be met for a write operation to be considered successful, including configurations such as the `w` value, `j` option, and `wtimeout`. Read preference determines how query requests are routed to replica set members, with five primary modes: `primary`, `primaryPreferred`, `secondary`, `secondaryPreferred`, and `nearest`. When combining these, potential read-write inconsistencies must be considered. Stricter write concerns increase write latency but enhance data safety. Reading from secondary nodes can distribute load but may result in stale data. Transactions have special restrictions, and implementation may vary across driver versions. Best practices suggest using high write concern for critical data and relaxing requirements for non-critical data. For read scaling, analytical queries can be routed to secondary nodes. Geographically distributed deployments can optimize regional reads using tag sets. Troubleshooting includes addressing write timeouts, stale read data, and driver compatibility issues.
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Starting from version 4.0, MongoDB supports multi-document transactions, offering four standard isolation levels: read uncommitted, read committed, repeatable read, and serializable. By default, it uses read committed, but in sharded clusters, this is downgraded to read uncommitted. MongoDB employs a Multi-Version Concurrency Control (MVCC) mechanism to achieve transaction isolation, where read operations do not block write operations and write operations do not block read operations. Conflict detection is handled through optimistic concurrency control. In sharded clusters, a two-phase commit protocol ensures atomicity for cross-shard transactions. Different isolation levels impact applications differently: read committed may expose modifications from other committed transactions, while repeatable read ensures consistent reads within a transaction through snapshot isolation. Write conflicts are typically detected at commit time, and retry mechanisms are recommended for handling them. For performance, it is advisable to keep transactions short, limit the number of operations, avoid processing large documents, and optimize configurations. Sharded transactions require stricter settings. Change streams can be combined with transactions to implement reliable event-driven architectures.
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MongoDB's ACID transaction support has evolved from single-document to multi-document operations. Early versions only guaranteed atomicity, consistency, isolation, and durability at the single-document level. Starting with version 4.0, MongoDB introduced support for multi-document transactions, enabling operations across collections and documents. Single-document transactions ensure atomicity through document-level locking, suitable for scenarios like updating multiple fields or nested documents. Multi-document transactions support replica sets and sharded clusters, offering snapshot isolation but incurring performance overhead. It's essential to set reasonable timeout limits and keep transactions brief. In sharded clusters, transactions have several limitations, such as not spanning multiple shard key ranges. Error handling requires distinguishing between transient and permanent errors. Monitoring tools can analyze transaction performance. Best practices include using embedded documents to minimize cross-document operations, implementing retry logic, and considering the two-phase commit pattern.
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MongoDB achieves data redundancy and ensures high availability through replica sets, which consist of a primary node and multiple secondary nodes using an asynchronous replication mechanism. The primary node receives write operations and records them in the Oplog, while secondary nodes synchronize data by replaying the Oplog. Read and write concern levels allow a trade-off between performance and consistency. In a sharded cluster, each shard is also a replica set, providing dual redundancy. During failover, secondary nodes automatically elect a new primary node. The majority principle in network partitions prevents split-brain scenarios. Client drivers automatically handle connection changes. Monitoring replication lag and node status is critical. Regular backups and cross-data-center deployments enhance disaster recovery capabilities. The design must balance performance and redundancy, while the application layer should implement retry and fallback mechanisms to ensure system reliability.
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MongoDB read-write load balancing enhances database performance by rationally distributing requests, primarily employing methods such as read-write separation, sharded clusters, and client-side routing. The replica set architecture inherently supports read-write separation by configuring read preferences to control read request routing strategies. Sharded clusters horizontally partition data, with mongos directing requests based on shard keys. Intelligent clients can monitor node status and dynamically adjust request distribution. An effective monitoring system is the foundation for maintaining load balancing, including replica set status, sharded cluster balancing, and performance counters. Advanced strategies involve tag-based sharding, read-write weight adjustment, and hotspot data caching. Performance tuning requires attention to connection pool configuration, batch operations, and indexing strategies. Fault handling mechanisms include retry policies, automatic failure detection, and write concern configuration to ensure system stability.
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