Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Apache Cassandra and AWS DynamoDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Cassandra and AWS DynamoDB perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
Apache Cassandra vs AWS DynamoDB Breakdown
Distributed wide-column database
Key-value and document store
Apache Cassandra follows a masterless, peer-to-peer architecture, where each node in the cluster is functionally the same and communicates with other nodes using a gossip protocol. Data is distributed across nodes in the cluster using consistent hashing, and Cassandra supports tunable consistency levels for read and write operations. It can be deployed on-premises, in the cloud, or as a managed service
DynamoDB is a fully managed, serverless NoSQL database provided by Amazon Web Services (AWS). It uses a single-digit millisecond latency for high-performance use cases and supports both key-value and document data models. Data is partitioned and replicated across multiple availability zones within an AWS region, and DynamoDB supports eventual or strong consistency for read operations
High write throughput applications, time series data, messaging systems, recommendation engines, IoT
Serverless web applications, real-time bidding platforms, gaming leaderboards, IoT data management, high-velocity data processing
Horizontally scalable with support for data partitioning, replication, and linear scalability as nodes are added
Automatically scales to handle large amounts of read and write throughput, supports on-demand capacity and auto-scaling, global tables for multi-region replication
Apache Cassandra Overview
Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.
AWS DynamoDB Overview
Amazon DynamoDB is a managed NoSQL database service provided by AWS. It was first introduced in 2012, and it was designed to provide low-latency, high-throughput performance. DynamoDB is built on the principles of the Dynamo paper, which was published by Amazon engineers in 2007, and it aims to offer a highly available, scalable, and distributed key-value store.
Apache Cassandra for Time Series Data
Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.
AWS DynamoDB for Time Series Data
DynamoDB can be used with time series data, although it may not be the most optimized solution compared to specialized time series databases. To store time series data in DynamoDB, you can use a composite primary key with a partition key for the entity identifier and a sort key for the timestamp. This allows you to efficiently query data for a specific entity and time range. However, DynamoDB’s main weakness when dealing with time series data is its lack of built-in support for data aggregation and downsampling, which are common requirements for time series analysis. You may need to perform these operations in your application or use additional services like AWS Lambda to process the data.
Apache Cassandra Key Concepts
- Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
- Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
- Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
- Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.
AWS DynamoDB Key Concepts
Some of the key terms and concepts specific to DynamoDB include:
- Tables: In DynamoDB, data is stored in tables, which are containers for items. Each table has a primary key that uniquely identifies each item in the table.
- Items: Items are individual records in a DynamoDB table, and they consist of one or more attributes.
- Attributes: Attributes are key-value pairs that make up an item in a table. DynamoDB supports scalar, document, and set data types for attributes.
- Primary Key: The primary key uniquely identifies each item in a table, and it can be either a single-attribute partition key or a composite partition-sort key.
Apache Cassandra Architecture
Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.
AWS DynamoDB Architecture
DynamoDB is a NoSQL database that uses a key-value store and document data model. It is designed to provide high availability, durability, and scalability by automatically partitioning data across multiple servers and using replication to ensure fault tolerance. Some of the main components of DynamoDB include:
- Partitioning: DynamoDB automatically partitions data based on the partition key, which ensures that data is evenly distributed across multiple storage nodes.
- Replication: DynamoDB replicates data across multiple availability zones within an AWS region, providing high availability and durability.
- Consistency: DynamoDB offers two consistency models: eventual consistency and strong consistency, allowing you to choose the appropriate level of consistency for your application.
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Apache Cassandra Features
Cassandra can scale horizontally, adding nodes to the cluster to accommodate growing workloads and maintain consistent performance.
With no single point of failure and support for data replication, Cassandra ensures data is always accessible, even in the event of node failures.
Users can balance between data consistency and performance by adjusting consistency levels based on their application’s requirements.
AWS DynamoDB Features
DynamoDB can automatically scale its read and write capacity based on the workload, allowing you to maintain consistent performance without over-provisioning resources.
Backup and restore
DynamoDB provides built-in support for point-in-time recovery, enabling you to restore your table to a previous state within the last 35 days.
DynamoDB global tables enable you to replicate your table across multiple AWS regions, providing low-latency access and data redundancy for global applications.
DynamoDB Streams capture item-level modifications in your table and can be used to trigger AWS Lambda functions for real-time processing or to synchronize data with other AWS services.
Apache Cassandra Use Cases
Messaging and Social Media Platforms
Cassandra’s high availability and low-latency make it suitable for messaging and social media applications that require fast, consistent access to user data.
IoT and Distributed Systems
With its ability to handle large amounts of data across distributed nodes, Cassandra is an excellent choice for IoT applications and other distributed systems that generate massive data streams.
Cassandra is a good fit for E-commerce use cases because it has the ability to support things like real-time inventory status and it’s architecture also allows for reduced latency by allowing region specific data to be closer to users.
AWS DynamoDB Use Cases
DynamoDB can be used to store session data for web applications, providing fast and scalable access to session information.
DynamoDB can be used to store player data, game state, and other game-related information for online games, providing low-latency and high-throughput performance.
Internet of Things
DynamoDB can be used to store and process sensor data from IoT devices, enabling real-time monitoring and analysis of device data.
Apache Cassandra Pricing Model
Apache Cassandra is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Cassandra cluster. Additionally, several managed Cassandra services, such as DataStax Astra and Amazon Keyspaces, offer different pricing models based on factors like data storage, request throughput, and support.
AWS DynamoDB Pricing Model
DynamoDB offers two pricing options: provisioned capacity and on-demand capacity. With provisioned capacity, you specify the number of reads and writes per second that you expect your application to require, and you are charged based on the amount of provisioned capacity. This pricing model is suitable for applications with predictable traffic or gradually ramping traffic. You can use auto scaling to adjust your table’s capacity automatically based on the specified utilization rate, ensuring application performance while reducing costs.
On the other hand, with on-demand capacity, you pay per request for the data reads and writes your application performs on your tables. You do not need to specify how much read and write throughput you expect your application to perform, as DynamoDB instantly accommodates your workloads as they ramp up or down. This pricing model is suitable for applications with fluctuating or unpredictable traffic patterns.
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