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Amazon DynamoDB vs RocksDB: What are the differences?
Introduction
In this article, we will compare Amazon DynamoDB and RocksDB, two popular database systems. We will highlight the key differences between them and provide a brief description of each difference.
Data Model: Amazon DynamoDB is a NoSQL database that supports document, key-value, and wide-column data models. It allows flexible schema design and is suitable for applications that require high scalability. On the other hand, RocksDB is a key-value store database that follows a simple key-value data model. It is optimized for read-heavy workloads and offers high-performance data storage and retrieval.
Data Distribution and Scalability: DynamoDB is a fully managed distributed database that automatically partitions data across multiple servers for high availability and scalability. It transparently handles the distribution of data and workload across these servers. In contrast, RocksDB operates as a single-node database and does not provide built-in mechanisms for data distribution or scaling. It can be used as a component within a larger distributed system.
Consistency Models: DynamoDB offers both strong consistency and eventual consistency models. Strong consistency ensures that all reads reflect the latest write, while eventual consistency allows for faster and more scalable operations at the cost of potential data inconsistency. RocksDB, on the other hand, does not provide built-in support for consistency models. Consistency needs to be implemented at the application level when using RocksDB.
Data Persistence: DynamoDB automatically replicates data across multiple data centers for durability and fault tolerance. It provides a managed service that takes care of data persistence. In contrast, RocksDB is an in-memory database and requires additional measures like write-ahead logs or persistent storage engines to ensure data persistence and durability.
Indexing and Querying: DynamoDB supports secondary indexes, allowing efficient querying of data based on different attributes. It also provides a query language for filtering and sorting data. RocksDB, being a key-value store, does not provide native support for secondary indexes or complex querying. Querying in RocksDB is primarily based on key lookups or range scans.
Transaction Support: DynamoDB supports atomic transactions that provide consistency and isolation guarantees. It allows developers to group multiple operations into a transaction and ensures that they are either all executed or none are. RocksDB, being an embedded database, does not offer built-in transaction support. Transactional behavior needs to be implemented manually by the application when using RocksDB.
In summary, Amazon DynamoDB and RocksDB differ in terms of their data models, data distribution and scalability, consistency models, data persistence, indexing and querying capabilities, and transaction support. DynamoDB provides a managed, scalable, and highly available distributed database service, while RocksDB is optimized for high-performance key-value storage in single-node environments.
We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?
Hi, Akash,
I wouldn't make this decision without lots more information. Cloud Firestore has a much richer metamodel (document-oriented) than Dynamo (key-value), and Dynamo seems to be particularly restrictive. That is why it is so fast. There are many needs in most applications to get lightning access to the members of a set, one set at a time. Dynamo DB is a great choice. But, social media applications generally need to be able to make long traverses across a graph. While you can make almost any metamodel act like another one, with your own custom layers on top of it, or just by writing a lot more code, it's a long way around to do that with simple key-value sets. It's hard enough to traverse across networks of collections in a document-oriented database. So, if you are moving, I think a graph-oriented database like Amazon Neptune, or, if you might want built-in reasoning, Allegro or Ontotext, would take the least programming, which is where the most cost and bugs can be avoided. Also, managed systems are also less costly in terms of people's time and system errors. It's easier to measure the costs of managed systems, so they are often seen as more costly.
Pros of Amazon DynamoDB
- Predictable performance and cost62
- Scalable56
- Native JSON Support35
- AWS Free Tier21
- Fast7
- No sql3
- To store data3
- Serverless2
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
Pros of RocksDB
- Very fast5
- Made by Facebook3
- Consistent performance2
- Ability to add logic to the database layer where needed1
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of RocksDB
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What is Amazon DynamoDB?
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