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 Rockset and TimescaleDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Rockset and TimescaleDB 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.
Rockset vs TimescaleDB Breakdown
Real time database
Time Series Database
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.
TimescaleDB is built on top of PostgreSQL and inherits its architecture. It extends PostgreSQL with time-series-specific optimizations and functions, allowing it to manage time series data efficiently. It can be deployed as a single node, in a multi-node setup, or in the cloud as a managed service.
Timescale License (for TimescaleDB Community Edition); Apache 2.0 (for core PostgreSQL)
Real-time analytics, event-driven applications, search and aggregations, personalized user experiences, IoT data analysis
Monitoring, observability, IoT, real-time analytics, financial market data
Horizontally scalable with distributed storage and compute
Horizontally scalable through native support for partitioning, replication, and sharding. Offers multi-node capabilities for distributing data and queries across nodes.
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.
TimescaleDB is an open source time series database built on top of PostgreSQL. It was created to address the challenges of managing time series data, such as scalability, query performance, and data retention policies. TimescaleDB was first released in 2017 and has since become a popular choice for storing and analyzing time series data due to its PostgreSQL compatibility, performance optimizations, and flexible data retention policies.
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.
TimescaleDB for Time Series Data
TimescaleDB is specifically designed for time series data, making it a natural choice for storing and querying such data. It provides several advantages for time series data management like horizontal scalability, columnar storage, and retention policy support. However, TimescaleDB may not be the best choice for all time series use cases. One example would be if an application requires very high write throughput or real-time analytics, other specialized time series databases like InfluxDB may be more suitable.
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.
TimescaleDB Key Concepts
- Hypertable: A hypertable is a distributed table that is partitioned by time and possibly other dimensions, such as device ID or location. It is the primary abstraction for storing time series data in TimescaleDB and is designed to scale horizontally across multiple nodes.
- Chunk: A chunk is a partition of a hypertable, containing a subset of the hypertable’s data. Chunks are created automatically by TimescaleDB based on a specified time interval and can be individually compressed, indexed, and backed up for better performance and data management.
- Distributed Hypertables: For large-scale deployments, TimescaleDB supports distributed hypertables, which partition data across multiple nodes for improved query performance 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.
TimescaleDB is an extension built on PostgreSQL, inheriting its relational data model and SQL support. However, TimescaleDB extends PostgreSQL with custom data structures and optimizations for time series data, such as hypertables and chunks.
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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.
TimescaleDB automatically partitions time series data tables using hypertables and chunks, which simplifies data management and improves query performance.
Time series focused SQL functions
TimescaleDB provides several specialized SQL functions and operators for time series data application scenarios, such as time_bucket, first, and last, which simplify querying and aggregating time series data.
As mentioned earlier, TimescaleDB extends PostgreSQL’s query planner for writing and querying time series data, including optimizations like time-based indexing and chunk pruning.
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.
TimescaleDB Use Cases
Monitoring and metrics
TimescaleDB is well-suited for storing and analyzing monitoring and metrics data, such as server performance metrics, application logs, and sensor data. Its hypertable structure and query optimizations make it easy to store, query, and visualize large volumes of time series data.
IoT data storage
TimescaleDB can be used to store and analyze IoT data, such as sensor readings and device status information. Its support for automatic partitioning and specialized SQL interfaces simplifies the management and querying of large-scale IoT datasets.
TimescaleDB is suitable for storing and analyzing financial data, such as stock prices, exchange rates, and trading volumes. Its query optimizations and specialized SQL functions make it easy to perform time-based aggregations and analyze trends in financial data.
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.
TimescaleDB Pricing Model
TimescaleDB is available in two editions: TimescaleDB Open Source and TimescaleDB Cloud. The open-source edition is free to use and can be self-hosted, while the cloud edition is a managed service with a pay-as-you-go pricing model based on storage, compute, and data transfer usage. TimescaleDB Cloud offers various pricing tiers with different levels of resources and features, such as continuous backups and high availability.
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