Elasticsearch vs Rockset
A detailed comparison
Compare Elasticsearch and Rockset for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 Elasticsearch and Rockset so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Elasticsearch 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.
Elasticsearch vs Rockset Breakdown
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| Database Model | Distributed search and analytics engine, document-oriented |
Real time database |
| Architecture | Elasticsearch is built on top of Apache Lucene and uses a RESTful API for communication. It stores data in a flexible JSON document format, and the data is automatically indexed for fast search and retrieval. Elasticsearch can be deployed as a single node, in a cluster configuration, or as a managed cloud service (Elastic Cloud) |
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. |
| License | Elastic License |
Closed source |
| Use Cases | Full-text search, log and event data analysis, real-time application monitoring, analytics |
Real-time analytics, event-driven applications, search and aggregations, personalized user experiences, IoT data analysis |
| Scalability | Horizontally scalable with support for data sharding, replication, and distributed querying |
Horizontally scalable with distributed storage and compute |
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Elasticsearch Overview
Elasticsearch is an open-source distributed search and analytics engine built on top of Apache Lucene. It was first released in 2010 and has since become popular for its scalability, near real-time search capabilities, and ease of use. Elasticsearch is designed to handle a wide variety of data types, including structured, unstructured, and time-based data. It is often used in conjunction with other tools from the Elastic Stack, such as Logstash for data ingestion and Kibana for data visualization.
Rockset Overview
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.
Elasticsearch for Time Series Data
Elasticsearch can be used for time series data storage and analysis, thanks to its distributed architecture, near real-time search capabilities, and support for aggregations. However, it might not be as optimized for time series data as dedicated time series databases. Despite this, Elasticsearch is widely used for log and event data storage and analysis which can be considered time series data.
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.
Elasticsearch Key Concepts
- Inverted Index: A data structure used by Elasticsearch to enable fast and efficient full-text searches.
- Cluster: A group of Elasticsearch nodes that work together to distribute data and processing tasks.
- Shard: A partition of an Elasticsearch index that allows data to be distributed across multiple nodes for improved performance and fault tolerance.
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.
Elasticsearch Architecture
Elasticsearch is a distributed, RESTful search and analytics engine that uses a schema-free JSON document data model. It is built on top of Apache Lucene and provides a high-level API for indexing, searching, and analyzing data. Elasticsearch’s architecture is designed to be horizontally scalable, with data distributed across multiple nodes in a cluster. Data is indexed using inverted indices, which enable fast and efficient full-text searches.
Rockset Architecture
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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Elasticsearch Features
Full-Text Search
Elasticsearch provides powerful full-text search capabilities with support for complex queries, scoring, and relevance ranking.
Scalability
Elasticsearch’s distributed architecture enables horizontal scalability, allowing it to handle large volumes of data and high query loads.
Aggregations
Elasticsearch supports various aggregation operations, such as sum, average, and percentiles, which are useful for analyzing and summarizing data.
Rockset Features
Serverless Scaling
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.
Elasticsearch Use Cases
Log and Event Data Analysis
Elasticsearch is widely used for storing and analyzing log and event data, such as web server logs, application logs, and network events, to help identify patterns, troubleshoot issues, and monitor system performance.
Full-Text Search
Elasticsearch is a popular choice for implementing full-text search functionality in applications, websites, and content management systems due to its powerful search capabilities and flexible data model.
Security Analytics
Elasticsearch, in combination with other Elastic Stack components, can be used for security analytics, such as monitoring network traffic, detecting anomalies, and identifying potential threats.
Rockset Use Cases
Real-Time Analytics
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.
Full-Text Search
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.
Machine Learning
Rockset’s ability to ingest and query large-scale, semi-structured data in real-time makes it a suitable choice for machine learning applications.
Elasticsearch Pricing Model
Elasticsearch is open-source software and can be self-hosted without any licensing fees. However, operational costs, such as hardware, hosting, and maintenance, should be considered. Elasticsearch also offers a managed cloud service called Elastic Cloud, which provides various pricing tiers based on factors like storage, computing resources, and support. Elastic Cloud includes additional features and tools, such as Kibana, machine learning, and security features.
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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