Amazon - DynamoDB Strong consistent reads, Are they latest and how?

DynamoDB Strong Consistent Reads: Are They Always Latest and How?

Explore Amazon DynamoDB's strong consistent reads, understanding their guarantees, how they work, and when to use them for data accuracy.

Amazon DynamoDB is a fast and flexible NoSQL database service for all applications that need consistent, single-digit millisecond latency at any scale. One of its key features is the ability to choose between eventually consistent and strongly consistent reads. While eventually consistent reads offer the best performance, understanding strongly consistent reads is crucial for applications where data accuracy is paramount. This article delves into what strongly consistent reads guarantee, how they are implemented, and when you should leverage them in your DynamoDB applications.

Understanding DynamoDB Consistency Models

DynamoDB offers two consistency models for reads:

1. Eventually Consistent Reads (Default): When you request an eventually consistent read, DynamoDB returns a response immediately, but the data might not reflect the results of a recently completed write operation. This is because the write might not have propagated to all storage locations yet. If you read a few milliseconds after a write, you might get stale data. However, if you repeat your read request after a short time, the response will eventually return the latest data.

2. Strongly Consistent Reads: When you request a strongly consistent read, DynamoDB returns a response with the most up-to-date data, reflecting all successful writes that occurred before the read. This ensures that you always get the latest version of an item, but it comes with potential trade-offs in terms of latency and availability.

flowchart TD
    A[Write Request] --> B{DynamoDB Coordinator}
    B --> C[Primary Partition Replica]
    C --> D[Write to Primary]
    D --> E[Replicate to Secondary Replicas]
    E --> F{Acknowledgement to Coordinator}
    F --> G[Write Acknowledged to Client]

    H[Strongly Consistent Read Request] --> I{DynamoDB Coordinator}
    I --> J[Read from Primary Partition Replica]
    J --> K[Return Latest Data to Client]

    L[Eventually Consistent Read Request] --> M{DynamoDB Coordinator}
    M --> N[Read from Any Replica (Primary or Secondary)]
    N --> O[Return Data (Potentially Stale) to Client]

DynamoDB Consistency Models Flow

How Strongly Consistent Reads Work

When you perform a strongly consistent read, DynamoDB ensures that the data returned reflects all successful write operations that completed before the read request was initiated. This is achieved by directing the read request to the primary replica of the partition where the item resides. The primary replica is always guaranteed to have the most up-to-date data because all write operations are first applied to the primary before being asynchronously replicated to secondary replicas.

Here's a breakdown of the process:

1. Write Operation: A write request (e.g., PutItem, UpdateItem) is sent to DynamoDB. The coordinator node directs it to the primary replica for the relevant partition.
2. Primary Replica Update: The primary replica processes the write, updates its local storage, and then asynchronously replicates the change to its secondary replicas.
3. Write Acknowledgment: Once the primary replica successfully processes the write, it acknowledges the write operation back to the client.
4. Strongly Consistent Read: A subsequent strongly consistent read request for the same item is also directed to the primary replica.
5. Latest Data Retrieval: The primary replica, having already processed the write, returns the absolute latest version of the data to the client.

When to Use Strongly Consistent Reads

Choosing between consistency models depends on your application's requirements. Strongly consistent reads are essential in scenarios where reading stale data could lead to incorrect application behavior or user experience issues. Here are some common use cases:

• Financial Transactions: When dealing with money transfers, inventory updates, or any financial ledger, it's critical to ensure that a read operation reflects the most recent state to prevent double-spending or incorrect balances.
• User Registration/Authentication: After a user registers or changes their password, a subsequent login attempt should immediately reflect the updated credentials. Reading stale data could lead to failed logins or a poor user experience.
• Critical Business Logic: Any operation where subsequent actions depend on the absolute latest state of data, such as updating a user's subscription status or processing an order, should use strongly consistent reads.
• Counter Increments: If you're implementing a counter (e.g., for views or votes) and need to ensure that each increment is immediately visible to avoid overcounting or undercounting due to race conditions, strongly consistent reads are appropriate.

For most other use cases, such as displaying product catalogs, social media feeds, or analytics dashboards where a few milliseconds of staleness is acceptable, eventually consistent reads are generally preferred due to their lower latency and higher throughput.

const AWS = require('aws-sdk');

const dynamoDb = new AWS.DynamoDB.DocumentClient();

async function getItemStronglyConsistent(tableName, key) {
  const params = {
    TableName: tableName,
    Key: key,
    ConsistentRead: true // This flag enables strongly consistent read
  };

  try {
    const data = await dynamoDb.get(params).promise();
    console.log('Strongly consistent read successful:', data.Item);
    return data.Item;
  } catch (error) {
    console.error('Error during strongly consistent read:', error);
    throw error;
  }
}

// Example usage:
// getItemStronglyConsistent('YourTableName', { id: '123' });

Example of performing a strongly consistent read in Node.js using the AWS SDK.