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Amazon RDS for Aurora vs Cassandra: What are the differences?
Introduction:
In this comparison, we will highlight the key differences between Amazon RDS for Aurora and Cassandra. Both databases are used for different purposes and have distinct features that set them apart. Below are the unique characteristics of each database.
1. Performance and Scalability:
Amazon RDS for Aurora is a relational database engine built for the cloud that offers high performance and scalability. It is designed to handle heavy workloads efficiently, utilizing a distributed architecture that allows for automatic scaling of storage and compute resources. On the other hand, Cassandra is a highly scalable and distributed NoSQL database optimized for write-heavy workloads, making it ideal for applications requiring high throughput and low latency.
2. Data Model:
Amazon RDS for Aurora supports the traditional relational data model and offers compatibility with MySQL and PostgreSQL. It provides ACID-compliant transactions and supports advanced SQL features. Cassandra, on the other hand, follows a distributed key-value data model, where data is organized into tables with a flexible schema. It does not support joins or ACID transactions but offers tunable consistency levels and automatic partitioning.
3. Architecture:
Amazon RDS for Aurora uses a cluster of replicated storage volumes for data durability and high availability. It employs a master-slave replication model with automated failover capabilities. Cassandra, on the other hand, employs a decentralized architecture where data is distributed across a cluster of nodes. It uses a peer-to-peer replication model with no single point of failure, providing high availability and fault tolerance.
4. Data Replication:
In Amazon RDS for Aurora, data replication is handled automatically through multiple redundant copies of data across different availability zones. It provides quick and automated failover to the standby replicas in the event of a failure. Cassandra, on the other hand, uses a peer-to-peer replication mechanism utilizing consistent hashing. It allows for custom replication strategies and replication factor, giving more control over data replication.
5. Consistency Model:
Amazon RDS for Aurora provides strong consistency with support for ACID transactions. It ensures that all reads see the most recent committed data. Cassandra, on the other hand, provides tunable consistency allowing developers to choose between strong consistency, eventual consistency, or a combination of both. This flexibility allows for optimizations in terms of latency and availability.
6. Query Language:
Amazon RDS for Aurora supports the SQL query language, making it easier for developers with existing SQL knowledge to work with. It can leverage the power of advanced SQL features like joins, subqueries, and complex aggregations. In contrast, Cassandra uses the CQL (Cassandra Query Language) which is similar to SQL but differs in some aspects. It does not support joins or complex aggregations, focusing more on simple key-value lookups and denormalized data models.
In Summary, Amazon RDS for Aurora is a high-performance relational database engine with ACID compliance and SQL support, while Cassandra is a highly scalable NoSQL database optimized for write-heavy workloads with a distributed key-value data model and tunable consistency.
The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.
The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.
Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring
Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.
By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.
i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra
Pros of Amazon Aurora
- MySQL compatibility14
- Better performance12
- Easy read scalability10
- Speed9
- Low latency read replica7
- High IOPS cost2
- Good cost performance1
Pros of Cassandra
- Distributed119
- High performance98
- High availability81
- Easy scalability74
- Replication53
- Reliable26
- Multi datacenter deployments26
- Schema optional10
- OLTP9
- Open source8
- Workload separation (via MDC)2
- Fast1
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Cons of Amazon Aurora
- Vendor locking2
- Rigid schema1
Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
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What is Amazon Aurora?
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