In the bustling world of MongoDB, where data consistency reigns supreme, especially in distributed environments like replica sets and sharded clusters, the concept of "read concern" emerges as a knight in shining armor. This powerful feature empowers developers with the ability to control the consistency and availability of their read operations. Understanding read concerns and selecting the appropriate level for your application's needs is the key to maintaining data integrity while optimizing performance.

What is Read Concern?

Imagine MongoDB as a bustling marketplace filled with constantly updated information. Read concern acts as a filter, allowing you to determine the level of "freshness" and "guaranteed availability" of the data you retrieve. With newer MongoDB drivers (version 3.2 and later), you can specify read concerns, essentially fine-tuning how data is handled during reads. Even better, starting from version 4.4, you can establish a global default read concern for replica sets and sharded clusters, streamlining your data management process. It's like having a control panel to balance data reliability and accessibility within MongoDB.

Difference B/W  Read Preference Vs Read Concern

In MongoDB, two important options are read preference and read concern. The read preference determines where the query is routed (whether to a primary or a secondary node), while the read concern specifies how the query behaves in consistency.

• Read Preference (Routing): Choose where to send the query (primary or secondary).
• Read Concern (Consistency): Specify how consistent the data should be (snapshot, local, majority, etc.)

The options work together, with the read preference deciding where the query goes, and the read concern determining how the data is read. Depending on your application needs, you can adjust these options to balance performance, consistency, and availability.

Scenario Overview: Developing an E-commerce Platform

Imagine you're developing an e-commerce platform that handles orders from customers. Your application involves multiple operations such as checking product availability, updating inventory, and confirming orders. Ensuring data consistency is critical to prevent issues like overselling products or processing incorrect orders. Here's a quick rundown of how you might choose each read concern based on different application use cases:

Read Concern "local"

For non-critical data, such as displaying product information to customers browsing your website, you might opt for "local" read concern. In this scenario, immediate consistency across replica set members is not critical, and you can tolerate the possibility of reading data that might be rolled back. This allows you to provide real-time data to users with a slight lag in consistency, which is acceptable for non-essential information.

• Use Case: When immediate consistency across replica set members is not critical and you can tolerate the possibility of reading data that might be rolled back.
• Example: Non-critical, real-time data where a slight lag in consistency is acceptable.

• The application initiates a read request.
• A random replica set member (either a Primary or a Secondary) is chosen.
• If the chosen node is the Primary or an updated Secondary, the application receives the most recent data.
• If the chosen node is an outdated Secondary, the application receives older data.
• The flow concludes once the application receives the data.

Read Concern "available"

When updating shopping cart items or retrieving order history, lower latency is crucial for a seamless user experience. In such cases, "available" read concern provides lower-latency reads, but with a potential risk of returning orphaned documents, especially in sharded collections. However, for real-time interactions with customers, the benefits of reduced latency outweigh the minimal risk of occasional inconsistencies.

• Use Case: Similar to "local," but not suitable for causally consistent sessions or transactions. It provides lower-latency reads but with a potential risk of returning orphaned documents in sharded collections.
• Example: Situations where lower latency is crucial, and you can handle occasional inconsistencies.

• The application triggers a read request.
• A random replica set member (either a Primary or a Secondary) is selected.
• If the chosen node is the Primary or an updated Secondary, the application receives the most recent available data.
• If the chosen node is an outdated Secondary, the application receives older available data.
• The flow completes once the application receives the selected node's available data.

Read Concern "majority"

Processing critical transactions, such as confirming orders and deducting inventory, requires strong consistency. Using "majority" read concern ensures that the data read has been acknowledged by a majority of replica set members, making it durable. This level of consistency is vital for financial transactions or any operation where data integrity is paramount to maintain customer trust and prevent revenue loss.

• Use Case: When you need strong consistency and want to ensure that the data read has been acknowledged by a majority of replica set members, making it durable.
• Example: Financial transactions or critical operations where data integrity is paramount.

• "Read Concern majority": Balance between consistency and speed.
• The application reads with "majority".
• The system validates recent data on the chosen replica.
• Returns guaranteed consistent data or handles errors.

Read Concern "Linearizable":

In scenarios demanding the highest level of consistency, such as ensuring unique document reads or maintaining strict data integrity, "linearizable" read concern is ideal. This level reflects all successful majority-acknowledged writes prior to the read operation, guaranteeing strict consistency. However, it might involve waiting for concurrent writes to propagate, resulting in potentially higher latency.

• Use Case: When you require the highest level of consistency, reflecting all successful majority-acknowledged writes prior to the read operation. It might involve waiting for concurrent writes to propagate.
• Example: Situations demanding strict consistency, like ensuring unique document reads.

• Linearizable Reads: Strictest consistency, reflects all successful writes before the read.
• Direct Primary Contact: Ensures the latest data by contacting the primary directly.
• OpTime & Wait: Waits for acknowledgements from all secondaries for strong consistency (potential delays).
• Guaranteed Consistency: Returns data only after successful acknowledgement.
• Error Handling: Handles errors or timeouts appropriately.
• Use maxTimeMS: Prevents indefinite blocking if secondaries are unavailable.

Read Concern "snapshot":

For tasks requiring data from a specific point in time, like generating reports or auditing transactions, "snapshot" read concern is invaluable. This option retrieves data that is majority-committed across shards at a specific moment in the recent past, ensuring consistency. By committing transactions with write concern "majority," you ensure that the data snapshot reflects majority-committed data, aligning with your requirements for consistency and reliability in data-intensive operations.

• Use Case: When you need data from a specific point in time and want to ensure that the transaction commits with write concern "majority."
• Example: Multi-document transactions requiring a snapshot of majority-committed data, ensuring consistency. By choosing the appropriate read concern level, you can align MongoDB's behaviour with your application's specific requirements for consistency, balancing it with the performance and latency considerations that suit your use case best.

• Read Concern "snapshot": Strong consistency within transactions.
• Requires "majority" write concern: Ensures all writes are acknowledged before reading.
• Validates snapshot at commit: Retrieves data from a snapshot reflecting the transaction's state.
• Offers strong consistency (if available): Guarantees data reflects committed writes.
• Handles missing snapshots: May return weaker consistency data or encounter errors.
• Suited for multi-document transactions: Ensures consistency across multiple document updates.

Managing Read Concerns in MongoDB:

By default, when reading from either the primary or secondary, MongoDB utilizes the "local" read concern.

Validating the Current Read Concern:

 
db.adminCommand({"getDefaultRWConcern": 1 })
	

This command retrieves the current default read concern settings in MongoDB. It allows users to validate the existing read concern configuration before making any changes.

Setting the Global Read Concern to "majority":

 
db.adminCommand({"setDefaultRWConcern" : 1, "defaultReadConcern" : { "level" : "majority" } })
	

We can modify the global read concern values using the command provided above. As a reference, I've changed the global read concern values to "majority."

Unsetting the Global Default Read Concern:

 
db.adminCommand( {"setDefaultRWConcern" : 1, "defaultReadConcern" : {} } )
	

In situations where resetting the global default read concern is needed, this command offers a solution. By providing an empty document {}, users revert the default read concern to its initial state, ready for reconfiguration as per requirements.

Managing read concerns in MongoDB is essential for upholding data consistency and fulfilling application needs. By employing commands like getDefaultRWConcern and setDefaultRWConcern, users can effectively validate and adjust read concern settings, ensuring optimal performance and reliability.

Configuring Read Concern in MongoDB Connection Strings

Setting the read concern directly within the connection string in MongoDB offers a convenient way to ensure uniformity in read consistency across all operations performed through a specific connection. This article explores how to incorporate read-concern parameters into MongoDB connection strings for enhanced data reliability.

Setting Read Concern in Connection Strings:

Syntax:

 
mongodb://[username:password@]host1[:port1][,host2[:port2],...[,hostN[:portN]]][/[defaultauthdb][?options]&readConcernLevel=]
	

Here's an example of setting the read concern to "majority" in a connection string:

 
mongodb://username:password@localhost:27017/mydatabase?readConcernLevel=majority
	

Benefits of Connection String Read Concern:

• Consistency Across Operations: All read operations initiated through the connection maintain the specified read concern level, ensuring uniformity and reliability in data retrieval.
• Simplified Configuration: By embedding read concern settings within the connection string, users streamline the configuration process, eliminating the need for additional code modifications.
• Enhanced Data Integrity: Consistent read concern settings contribute to improved data integrity and reliability, critical for applications requiring stringent consistency guarantees.

Configuring read concerns directly within MongoDB connection strings offers a straightforward solution for enforcing consistency in data retrieval operations. By leveraging this approach, users can maintain uniformity in read behaviour across all operations performed via a specific connection, enhancing data reliability and integrity within MongoDB deployments.

Understanding Read Concern Support in MongoDB Operations

Understanding the Compatibility of Read Concerns Across Various MongoDB Operations

Understanding the Effects of Different Read Concerns in MongoDB Queries

1. Default Behavior:

• Query: db.test.find({X:100})
  • Result: Operation successfully retrieves data.

2. Specifying "majority" Read Concern:

• Query: db.test.find({X:100}).readConcern("majority")
  • Result: Returns no data due to potential consistency constraints.

3. Impact of "linearizable" Read Concern:

• Query: db.test.find({X:100}).readConcern("linearizable")
  • Result: Blocks until the last write is replicated, potentially causing indefinite wait.
  • Mitigation: Utilize the maxTimeMS() option to prevent indefinite blocking.

Read Concern Limitations

• While "linearizable" read concern offers the strongest consistency, it cannot be combined with $out and $merge stages in data pipelines.
• "Snapshot" read concern ensures consistency at a specific point but has limitations for certain operations and sharded transactions.
• Distinct commands and their helpers cannot be used with "snapshot" read concern during multi-document transactions on sharded collections.

Choosing the right read concern level in MongoDB is essential for achieving the desired balance between consistency, performance, and availability. By understanding the various read concern options and their implications, developers can design applications that meet their specific requirements without compromising data integrity. Whether optimizing for low latency or ensuring strict consistency, MongoDB's read concerns provide the flexibility to tailor data access patterns to your application's needs, ultimately enhancing the user experience and driving business success.

Are you looking to optimize your MongoDB deployments? Mydbops offers expert MongoB Managed, Consulting and Remote DBA Services. Contact Mydbops today for personalised MongoDB consultation.

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