In the Strong Consistency model, all replicas are in sync, which means that any read operation following a write operation will return the latest written data. It guarantees that as soon as the write is completed, all subsequent accesses will witness the effect of that write operation.
AWS DynamoDB, for example, supports ConditionCheck operations that provide a means of managing the consistency of data items.
Here’s an example of how to utilize strong consistency when reading an item in DynamoDB:
response = dynamodb.get_item(
TableName= ‘your_table_name’,
Key={
‘your_partition_key_name’: {
‘N’: ‘your_partition_key_value’
}
},
ConsistentRead=True
)
In this script, the ‘ConsistentRead’ parameter set to ‘True’ permits you to perform a strongly consistent read.
Eventually Consistent Model
Unlike the Strong model, the Eventually Consistent model is comparatively lenient and believes that allowing the data to take a while (typically a very short period) to propagate to all the nodes is acceptable. This model helps us understand that all changes propagate through the system sooner or later and the replicas will be consistent eventually.
A good instance of AWS service that uses the eventual consistency model is Amazon S3. When a new object is included in a S3 bucket, for a brief period, if we try to read the object or find its presence, we might get a 404 error, indicating that S3 hasn’t finished applying the change.
Here’s a sample Python script illustrating how to implement Eventually Consistent model:
response = dynamodb.get_item(
TableName= ‘your_table_name’,
Key={
‘your_partition_key_name’: {
‘N’: ‘your_partition_key_value’
}
},
ConsistentRead=False
)
By setting ‘ConsistentRead’ to ‘False’, we allow for an eventually consistent read. In most cases, an eventually consistent read might suffice, and it costs half the price of a strongly consistent read.
The following table provides a comparison between the two consistency models:
| Strongly Consistent | Eventually Consistent | |
|---|---|---|
| Consistency | Immediate consistency across all nodes | Delayed consistency; changes will eventually reach all nodes |
| Performance | High with respect to data accuracy | High with respect to latency and throughput |
| Cost | More expensive (in DynamoDB) | Less expensive (in DynamoDB) |
In conclusion, understanding consistency models is vital for AWS certified developers. The choice between Strongly Consistent and Eventually Consistent comes down to the particular needs of your application. A trade-off always exists between consistency, performance, and cost, which developers should balance based on their application’s requirement.
Practice Test
True or False: In the context of Amazon S3, Read After Write consistency applies to both PUTS of new objects and to subsequent PUTS or DELETES.
- True
- False
Answer: True.
Explanation: Amazon S3 provides read-after-write consistency for PUTS of new objects and eventual consistency for overwrite PUTS and DELETES.
Which of the following AWS services is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability and supports eventual consistency?
- A. Relational Database Service (RDS)
- B. DynamoDB
- C. Redshift
- D. ElastiCache
Answer: B. DynamoDB
Explanation: DynamoDB supports both eventually consistent and strongly consistent reads. It provides fast, predictable performance at any scale.
Eventual consistency in AWS means:
- A. All changes to data will propagate to all replicas at some time, but clients may see outdated data.
- B. Data is immediately consistent across all replicas.
- C. Data is consistent only when read operations.
- D. None of the above.
Answer: A. All changes to data will propagate to all replicas at some time, but clients may see outdated data.
Explanation: In eventual consistency, every HTTP GET returns the last written data eventually.
In Amazon DynamoDB, is it possible to choose between strong and eventual consistency on a per-request basis?
- A. Yes
- B. No
Answer: A. Yes
Explanation: Both the GetItem and BatchGetItem operations have ConsistentRead parameters, which you can use to opt in to a strongly consistent read if needed.
The consistency model Amazon S3 uses is:
- A. Strong consistency
- B. Eventual consistency
- C. Acid consistency
- D. None of the above.
Answer: B. Eventual consistency.
Explanation: Amazon S3’s consistency model is eventual consistency, although it does offer read-after-write consistency for puts of new objects.
True or False: Strong consistency guarantees the return of the most recent write when reading the data.
- True
- False
Answer: True.
Explanation: Strong consistency models guarantee that all accesses return the most recent write.
A strongly consistent read returns the result of all writes that received a successful response prior to the read. Is this statement true for Amazon DynamoDB?
- A. True
- B. False
Answer: A. True
Explanation: Amazon DynamoDB provides eventual consistency by default, but you can optionally request a strongly consistent read.
True or False: The Amazon Simple Queue Service (SQS) supports only strong consistency.
- True
- False
Answer: False.
Explanation: AWS SQS uses eventual consistency, not strong consistency.
Which of the following AWS services guarantee eventual consistency?
- A. Amazon SQS
- B. Amazon S3
- C. Amazon DynamoDB
- D. All of the above
Answer: D. All of the above.
Explanation: All of these AWS services, SQS, S3, and DynamoDB, provide eventual consistency.
Strongly consistent reads in DynamoDB are generally more expensive than eventually consistent reads.
- A. True.
- B. False.
Answer: A. True.
Explanation: Strongly consistent reads consume twice as much capacity units as an eventually consistent read, hence they’re more expensive.
True or False: Eventual consistency provides a consistency model that helps distribute data at any scale and replicating data across multiple data centers located in different regions.
- True
- False
Answer: True.
Explanation: Eventual consistency model is beneficial in distributed systems where data is replicated across multiple regions for availability, latency, and fault tolerance.
Which of the following AWS services uses Strongly consistent reads?
- A. Amazon S3
- B. Amazon RDS
- C. Amazon DynamoDB
- D. Both B and C
Answer: D. Both B and C
Explanation: Amazon RDS and DynamoDB both support strongly consistent reads.
True or False: Strongly Consistent Read always returns the last updated value.
- True
- False
Answer: True.
Explanation: Strongly Consistent Read guarantees that any successful write operation will always be reflected in any subsequent read operation.
Which consistency model does Amazon Aurora use?
- A. Eventual consistency
- B. Acid consistency
- C. Strong consistency
- D. None of the above
Answer: C. Strong consistency
Explanation: Amazon Aurora uses a strong consistency model for database cluster volumes. It replicates each chunk of your database volume six ways, across three Availability Zones.
Interview Questions
What is a database consistency model?
A database consistency model is a framework that guarantees the consistency of data after a completed transaction. It controls how changes in a database are propagated and when they become visible to other users and systems.
What is strong consistency in the context of databases?
Strong consistency is a guarantee that once a write is confirmed as successful, all subsequent reads will return the updated value. It ensures a high level of consistency and is primarily used in environments where data accuracy is critical.
What is eventual consistency and when is it useful?
Eventual consistency is a model that guarantees that, if no new updates are made to a particular item, eventually all reads to that item will return the same data, although not immediately. This model is useful in scenarios where high availability and partition tolerance are more important than immediate consistency, such as in distributed databases.
How does Amazon DynamoDB maintain consistency?
Amazon DynamoDB provides two types of reads: eventually consistent reads, which are the default and provide the best performance, and strongly consistent reads, which provide more current data at the cost of increased latency.
What is the difference between a strongly consistent read and an eventually consistent read in AWS?
A strongly consistent read returns a most recent data, but it may have higher latency and consume more throughput capacity. An eventually consistent read might sometimes return stale data, but it has lower latency and consumes less throughput capacity.
How are write consistency models different than read consistency models?
Write consistency models pertain to the integrity of data being written or updated, whereas read consistency models involve the way in which updated data becomes accessible to other users or systems.
Are there any limitations of strong consistency databases?
Strong consistency often requires relatively higher latency and lower scalability as compared to eventual consistency. It’s typically not suitable for geographically distributed databases as the requirement for immediate consistency may cause delays when synchronizing between regions.
How do consistency models impact the performance of a database?
Strong consistency ensures immediate consistency across all nodes, but it can impact performance and availability. On the other hand, eventual consistency allows for higher availability and performance but at the risk of temporary inconsistencies.
Which AWS services provide strongly consistent reads?
Amazon DynamoDB and Amazon S3 use strongly consistent reads.
What is the consistency model used in Amazon S3?
Amazon S3 uses strong read-after-write consistency model for PUTS of new objects and eventual consistency for overwrite PUTS and DELETES.
How does AWS maintain transactional integrity?
AWS offers various mechanisms to maintain transactional integrity, such as DynamoDB transactions, which provide atomicity, consistency, isolation, and durability (ACID) across one or more tables within a single AWS account.
What is a monotonic read consistency?
Monotonic read consistency guarantees that if a process reads the value of a data item, any subsequent read operation on the same data item by that process will always return the same or a more recent value.
Can eventual consistency and strong consistency models coexist in the same system?
Yes, services such as Amazon DynamoDB offer the option to choose between eventual consistency and strong consistency reads on a per-request basis.
How can you control the consistency of the Amazon DynamoDB?
The consistency of the Amazon DynamoDB can be controlled by choosing between eventual consistency and strong consistency at the time of issuing read requests.
When should you use strong consistency in AWS services?
You should consider using strong consistency when you cannot tolerate stale or inconsistent data, but this may mean a trade-off with performance and potential increased costs due to higher resource usage.
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