Up to date February 2023
We designed our platform with a mission to simplify real-time analytics and make them accessible at an affordable price point within the cloud. We prioritize our customers’ needs, relentlessly focusing on delivering pace, scale, and ease for their rapidly evolving real-time data stacks. While we prioritize efficiency, we treat our performance metrics with utmost gravity.
Benchmarking Responsibly
We’re in complete agreement with Databricks on one key point: that any individual publishing benchmarks should conduct them in a transparent, straightforward, and reproducible manner. The manner in which distributors navigate benchmarking exercises provides a telling indicator of their operational ethos and underlying values. Here is the rewritten text:
Recently, a prominent firm affiliated with Apache Druid published a tongue-in-cheek blog post that appeared to boast about being more environmentally friendly than Rockset. As a discerning buyer, consider these potential drawbacks to Indicate’s benchmark:
- The company’s developers have leveraged a {hardware} setup boasting a 20% boost in processing power relative to the Rockset architecture. What are good benchmark goals for achieving hardware parity and ensuring an apples-to-apples comparability in testing?
- Rockset’s cloud consumption mannequin permits independently scaling compute & storage. The company’s marketing materials have inaccurately stated that their product offers superior value compared to industry peers, despite being priced similarly or even higher than competitive offerings?
SSB Benchmark Outcomes
The system assesses the efficacy of 13 standardised search requests commonly used for knowledge-seeking purposes. This benchmark is primarily focused on and tailored for knowledge warehouse workloads. Recently, it has gained traction for assessing query performance, particularly those incorporating aggregations and metrics, in column-store databases such as ClickHouse and Druid.
To achieve meaningful resource parity, we employed a consistent hardware setup identical to that used by Altinity in its published SSB performance benchmark. The instance was a single m5.8xlarge Amazon EC2 occurrence. Indicated to have further expanded its offerings by introducing revised SSB numbers for Druid, compatible with hardware configurations featuring increased vCPU resources. Despite this, Rockset still managed to outperform Druid’s figures for absolute phrases.
To further amplify the dataset’s dimensions, we expanded it to 100 gigabytes in size and augmented it with 600 million rows of knowledge, achieving a similar scaling factor of 100 as that employed by Altinity and InData. As Altinity and Indicata released comprehensive SSB efficiency findings on normalized data, we synchronized our approach accordingly. The introduction of Rockset eliminated the need for manual question joins, freeing users from a previously necessary step.
All queries executed under 88 milliseconds on Rockset, with a combined runtime of just 664 milliseconds across the entire suite of SSB queries. The ClickHouse’s combination runtime was a relatively slow 1,112 milliseconds. The Druid’s combination runtime was a relatively sluggish 747 milliseconds. With these outcomes, Rockset demonstrates a general speedup of 1.67 compared to ClickHouse and 1.12 relative to Druid.
You may delve deeper into the configuration and efficiency enhancements outlined in the whitepaper. The paper provides a comprehensive overview of the benchmark’s fundamental concepts and query sets, outlines the necessary configurations to execute the benchmark, and presents the findings resulting from the investigation.
Knowledge in Real-Time within the World
Automotive manufacturers tout their vehicles’ acceleration rates from 0 to 60 miles per hour as key selling points, but as a discerning buyer, you evaluate a car’s performance based on a multitude of factors beyond just speed. Similarly, as you choose your real-time response, consider the technical issues and diverse aspects aligning with Rockset’s capabilities.
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The following five traits are fundamental challenges that most analytical methods face when grappling with real-time knowledge:
- . With the advent of clickstream and sensor technologies, the volume of data generated can reach astonishing levels – potentially exceeding many terabytes daily – while also exhibiting extreme unpredictability, necessitating scalable solutions that can rapidly adapt to fluctuations.
- . With modern databases such as MongoDB and Amazon DynamoDB, you’re now empowered to make constant, on-the-fly changes to your operational data. The issue? Most analytics databases, including Apache Druid and ClickHouse, adhere to an immutable design paradigm, rendering knowledge fixed and resistant to updates or rewriting. That makes it extremely challenging for the system to stay synchronized in real-time with the OLTP database.
- . As real-time streams process data, inconsistencies arise from delayed or redundant information being transmitted, resulting in duplicate entries.
- . Actual-time knowledge streams occasionally emerge unprocessed and semi-structured, manifesting as JSON documents with various levels of nesting. Continuously emerging are fresh fields and columns of knowledge, constantly expanding our understanding.
- . Actual-time knowledge streams often energize analytical inquiry. This pivotal transformation arises from the fact that builders now act as end-users, encouraging them to rapidly prototype and test novel approaches, thereby necessitating more flexibility than initially envisioned for early analytical databases like Apache Druid.
What’s driving your interest in evaluating Rockset, Apache Druid, and ClickHouse?
To effectively integrate with real-time data in today’s fast-paced world, it’s essential to understand the key features that align well with Rockset, Apache Druid, and ClickHouse: While Apache Pinot isn’t a typical candidate for this comparison table, its inclusion could still provide valuable insights into similar databases featuring horizontal scaling – an open-source platform developed during the on-premise era. As of our most recent data updates, all competitor comparisons are based on publicly available information from their official sources.
| Rockset | Apache Druid | ClickHouse | |
|---|---|---|---|
| Cloud-based learning platforms offer a multitude of benefits for individuals seeking to expand their knowledge. By creating an account and beginning the process of ingesting knowledge, one can access a vast array of educational resources from across the globe.
What are you waiting for? |
Deploy and orchestrate nodes seamlessly across both on-premise and cloud environments. | Enable seamless node deployment by crafting a comprehensive plan that integrates capabilities for on-premise and cloud-based infrastructure. | |
| You can easily ingest a nested JSON object without flattening it in Python using libraries such as `json` and `pandas`. Here’s how you can do this:
“` # Load the JSON file into a string # Convert the JSON object to a pandas DataFrame print(df) |
Flatten nested JSON | JSON data structures are typically flat, comprising of key-value pairs, rather than being deeply nested. | |
| Mutable databases efficiently manage data modifications by performing updates, inserts, and deletes directly on the underlying storage. | Insert solely | Largely inserting data solely, with asynchronous updates executed through efficient ALTER TABLE UPDATE statements. | |
| The ability to ingest knowledge in its raw form without adhering to a predetermined schema is a hallmark of intelligent systems. By embracing the complexity and nuances of real-world data, these systems can uncover patterns, relationships, and insights that might otherwise remain hidden. This capacity for schema-less ingestion enables the development of more comprehensive models, better equipped to tackle the intricacies of human experience. | Schema specified for optimal ingestion, partitioning, and sorting of data is crucial to enhance efficiency. | Schema specified on desk creation | |
| Data pipelines are optimized when SQL-based ingest transformations and DBT converge. | The following specifications shall govern the ingestion of data into the system:
The ingest system shall filter out any data that contains restricted words or phrases as specified in the attached list. The filter shall be case-insensitive and apply to all forms of data, including but not limited to text files, CSV files, JSON files, and database exports. Additionally, the following conditions shall also trigger filtering: The ingest system shall log all attempts to bypass these filters and notify the administrator of any suspicious activity. |
Materializing views enables efficient data summarization across multiple tables, streamlining insights and decision-making processes. By leveraging this feature, organizations can optimize the management of complex relationships between disparate datasets, fostering a more comprehensive understanding of their operations. | |
| SQL-based rollups with aggregations on various subjects? | Ingestion specifications for precise time-based rollups are paramount to ensure seamless data processing and accurate insights. | Materialized views provide a flexible mechanism for reworking knowledge between tables by precomputing complex join operations and storing the results in a physical table. This approach enables you to query the precomputed results directly, which can significantly improve query performance and reduce the overhead of complex joins on large datasets. By leveraging materialized views, you can efficiently rework knowledge between tables and simplify complex queries. | |
| SQL | Druids utilize a proprietary language to specify complex operations on their data structures. This language is then translated into a SQL-like query that can be executed against the Druid data source.
The proprietary language allows users to define filters, aggregations, and joins in a concise and expressive manner. It includes support for various data types, such as integers, strings, and timestamps, which are used to specify the structure of the data being queried. To facilitate querying of large-scale data sets, Druid provides a parser that can handle complex SQL-like queries. The parser is designed to parse the proprietary language and generate an efficient query plan that can be executed against the Druid data source. The parser supports various features, including: * Filter clauses: Users can specify filter conditions using the `WHERE` clause. For example, `SELECT * FROM table WHERE timestamp > ‘2022-01-01’`. Overall, the proprietary language and parser enable efficient querying of large-scale data sets in Druid, while providing a flexible and expressive way to specify complex operations. SKIP |
SQL | |
| Helps JOINs | Broadcasting joins with inefficient overhead, denormalizing data to circumvent complex joins. | Helps JOINs | |
| Effortlessly manage computational resources across the cloud. | Effortlessly scaling multi-node clusters to accommodate increased computational demands; seamlessly integrating new nodes to optimize resource utilization. | ?Tighten cluster configuration and scalability by seamlessly integrating additional computing resources. | |
| Elevate storage capacity in the cloud through flexible scaling. | Nodes configured; additional storage appended. | Configuring and tuning multi-node clusters requires careful planning and execution to ensure seamless data replication, high availability, and optimal performance. To augment storage capacity, adding new nodes is a straightforward approach that involves updating the cluster configuration, rebalancing data, and verifying node status.
Tuning the cluster for optimal performance necessitates monitoring system metrics, adjusting parameters such as buffer sizes, compression ratios, and query optimization techniques to minimize latency and maximize throughput. |
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| Expertly designed managed services for seamless cloud integration and enhanced developer productivity. | What’s the most cost-effective way to optimize our data ingestion process using Apache Druid? | To streamline operations and optimize pricing, organizations demand a robust analytics platform that integrates seamlessly with their existing systems. That’s where ClickHouse comes in – a powerful tool for data professionals seeking to unlock the secrets of their business. By leveraging its exceptional performance and scalability, companies can gain real-time insights into market trends, customer behavior, and product performance, enabling data-driven decision making. | |
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While uncooked price-performance remains a crucial consideration, it’s equally important to highlight our advancements in cloud efficiency and developer productivity today. Cloud effectiveness enables organizations to avoid overprovisioning compute or storage resources, allowing for flexible and efficient scaling in response to changing demands. In the complex real world, knowledge is often multifaceted and nuanced, making it challenging to efficiently consume and process. By streamlining this complexity, Rockset empowers customers with significant time and effort savings, no longer requiring them to simplify their data before utilization. We ensure that customers don’t need to sacrifice performance or compromise their understanding of data normalisation with unnecessarily complex JOIN operations, even if they’re initially introduced in the early stages; instead, we guarantee a seamless experience by minimising the overhead of denormalization on customer effort and iteration speed. By leveraging subject indexing, we eliminate the need for sophisticated knowledge modeling. With commonplace SQL, our aim is to truly democratize access to real-time insights. The space where Rockset truly excels is its ability to handle both time-series data streams and change data capture (CDC) streams with updates, inserts, and deletes, thereby enabling real-time synchronization with databases such as DynamoDB, MongoDB, PostgreSQL, and MySQL without incurring reindexing overhead.
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We focus specifically on a single buyer. As Marvel Industries embarks on the ambitious project of Shapeshift, aiming to bridge the gap with Rockset’s cloud efficiency, the challenge lies in seamlessly integrating datacenter-era technology into the cloud, a feat that requires a significant amount of luck and perseverance. While proponents of Apache Druid claim a focus on real-world usability, the technology surprisingly falls short in delivering practical benefits, such as seamless deployment, intuitive operation, adaptability to changing requirements, and effortless scalability – all critical concerns in high-stakes, real-time analytics environments that demand prompt insights. Rockset is committed to innovating and enhancing its real-time analytics capabilities within the cloud, specifically focusing on developing tailored solutions that cater to customers’ unique needs and use cases. Worth-performance does matter. To maintain transparency, Rockset will publish consistent benchmarking results, ensuring that we neither misrepresent ourselves nor our competitors throughout the process, and more importantly, upholding our commitment to honest communication with our clients. While leveraging the power of real-time analytics at cloud scale for yourself and your expertise?
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