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 Redis and Rockset so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Redis and Rockset 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.
Redis vs Rockset Breakdown
Real time database
Redis can be deployed on-premises, in the cloud, or as a managed service
Rockset is a real-time analytics database built for modern cloud applications, designed to enable developers to create real-time, event-driven applications and run complex queries on structured, semi-structured, and unstructured data with low-latency. Rockset uses a cloud-native, distributed architecture that separates storage and compute, allowing for horizontal scalability and efficient resource utilization. Data is automatically indexed and served by a distributed, auto-scaled set of query processing nodes.
Caching, message brokering, real-time analytics, session storage, geospatial data processing
Real-time analytics, event-driven applications, search and aggregations, personalized user experiences, IoT data analysis
Horizontally scalable via partitioning and clustering, supports data replication
Horizontally scalable with distributed storage and compute
Redis, which stands for Remote Dictionary Server, is an open-source, in-memory data structure store that can be used as a database, cache, and message broker. It was created by Salvatore Sanfilippo in 2009 and has since gained significant popularity due to its high performance and flexibility. Redis supports various data structures, such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, and geospatial indexes with radius queries.
Rockset is a real-time indexing database designed for fast, efficient querying of structured and semi-structured data. Founded in 2016 by former Facebook engineers, Rockset aims to provide a serverless search and analytics solution that enables users to build powerful applications and data-driven products without the complexities of traditional database management.
Redis for Time Series Data
Redis has a dedicated module for working with time series data called RedisTimeSeries. RedisTimeSeries offers functionality like downsampling, data retention policies, and specialized queries for time series data in Redis. Being an in-memory database, Redis will be very fast for reading and writing time series data, but due to the cost of RAM compared to disk using Redis could become expensive depending on the size of your dataset. If your use case doesn’t require extremely fast response times, you could save money by going with a more traditional time series database.
Rockset for Time Series Data
Rockset’s real-time indexing and low-latency querying capabilities make it an excellent choice for time series data analysis. Its schemaless ingestion and support for complex data types enable effortless handling of time series data, while its Converged Index ensures efficient querying of both historical and real-time data. Rockset is particularly suitable for applications that demand real-time analytics, such as IoT monitoring and anomaly detection.
Redis Key Concepts
- In-memory store: Redis stores data in memory, which allows for faster data access and manipulation compared to disk-based databases .
- Data structures: Redis supports a wide range of data structures, including strings, hashes, lists, sets, and more, which provide flexibility in how data is modeled and stored.
- Persistence: Redis offers optional data persistence, allowing data to be periodically saved to disk or written to a log for durability.
- Pub/Sub: Redis provides a publish/subscribe messaging system, enabling real-time communication between clients without the need for a centralized message broker.
Rockset Key Concepts
- Converged Index: Rockset uses a unique indexing approach that combines both an inverted index and a columnar index, allowing the database to optimize for both search and analytics use cases.
- Schemaless Ingestion: Rockset automatically infers schema on ingestion, making it easy to work with semi-structured data formats like JSON.
- Virtual Instances: Rockset uses the concept of virtual instances to provide isolation and resource allocation to different workloads, ensuring predictable performance.
Redis is a NoSQL database that uses a key-value data model, where each key is associated with a value stored as one of Redis’ supported data structures. The database is single-threaded, which simplifies its internal architecture and reduces contention. Redis can be deployed as a standalone server, a cluster, or a master-replica setup for scalability and high availability. The Redis Cluster mode automatically shards data across multiple nodes, providing data partitioning and fault tolerance.
Rockset uses a cloud-native, serverless architecture that is built on top of a distributed, shared-nothing system. It is a NoSQL database, which allows for greater flexibility and scalability compared to traditional relational databases. The core components of Rockset’s architecture include the Ingestion Service, Storage Service, and Query Service. The Ingestion Service is responsible for ingesting data from various sources, while the Storage Service maintains the Converged Index. The Query Service processes queries and provides APIs for developers to interact with the database.
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Redis supports atomic operations on complex data types, allowing developers to perform powerful operations without worrying about race conditions or other concurrent processing issues.
Broad data structure support
Redis supports a range of data structures such as lists, sets, sorted sets, hashes, bitmaps, hyperloglog, and geospatial indexes. This flexibility allows developers to use Redis for a wide variety of tasks by using data structures that are optimized for their data in terms of performance characteristics.
Redis provides a publish/subscribe messaging system for real-time communication between clients.
Developers can run Lua scripts in the Redis server, enabling complex operations to be executed atomically in the server itself, reducing network round trips.
Rockset automatically scales resources based on the workload, which means users don’t need to manage any infrastructure or capacity planning. ### Full-Text Search Rockset’s Converged Index supports full-text search, making it an ideal choice for applications that require advanced search capabilities. ### Integration with BI tools Rockset provides native integrations with popular business intelligence (BI) tools like Tableau, Looker, and Redash, allowing users to visualize and analyze their data without any additional setup.
Redis Use Cases
Redis is often used as a cache to store frequently accessed data and reduce the load on other databases or services, improving application performance and reducing latency.
Redis can be used to implement task queues, which are useful for managing tasks that take longer to process and should be executed asynchronously. This is particularly common in web applications, where background tasks can be processed independently of the request/response cycle
Real-time analysis and machine learning
Redis’ high performance and low-latency data access make it suitable for real-time analysis and machine learning applications, such as processing streaming data, media streaming, and handling time-series data. This can be achieved using Redis’ data structures and capabilities like sorted sets, timestamps, and pub/sub messaging.
Rockset Use Cases
Rockset’s low-latency querying and real-time ingestion capabilities make it ideal for building real-time analytics dashboards for applications like IoT monitoring, social media analysis, and log analytics.
With its Converged Index and support for advanced search features, Rockset is an excellent choice for building full-text search applications, such as product catalogs or document search systems.
Rockset’s ability to ingest and query large-scale, semi-structured data in real-time makes it a suitable choice for machine learning applications.
Redis Pricing Model
Redis is open-source software, which means it can be deployed and used freely on your own infrastructure. However, there are also managed Redis services available, such as Redis Enterprise which offer additional features, support, and ease of deployment. Pricing for these services typically depends on factors like the size of the instance, data storage, and data transfer.
Rockset Pricing Model
Rockset offers a usage-based pricing model that charges customers for the amount of data ingested, the number of virtual instances, and the volume of queries executed. The pricing model is designed to be transparent and flexible, allowing users to only pay for the resources they consume. Rockset also provides a free tier with limited resources for developers to explore the platform. Users can choose between on-demand and reserved instances, depending on their needs.
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