California-based Star Rock has announced a cloud-based version of its SQL engine for enterprises to provide them with faster and more affordable analytical capabilities.
As data continues to explode, companies are increasingly looking for ways to extract value from the growing pool within their own backyards. Real-time data has been leveraged to create an application that allows people to see the status of their orders as they happen. It is the new Holy Grail, with a number of companies processing and reacting to event stream data for use cases such as outage detection.
However, when it comes to analytics, which involves finding patterns in data, working in real time can be a major challenge for some companies. Analytical queries become difficult to perform when information is constantly being added, updated, and even deleted from databases. When dozens of people try to query data at once, the problem becomes even worse.
Universal Engine Of Star Rocks
Star Rocks solves the problem of having too many different tools for analyzing data by combining them into a single tool called Star Rocks.
“This is a new kind of analytic database that addresses the critical technical challenges in big data analytics, such as the ability to handle massive amounts of data, the need to normalize data, and the challenge of scaling up for thousands of concurrent users.” We created a brand new query engine using many breakthrough technologies.
The engine was purposely designed to support real-time data and a large number of concurrent users, with multi-table joins. According to the company’s claims, it can ingest data at 100 MB/s per node and perform more than 10,000 searches per second. This eventually allows enterprises to combine their latest streaming transaction records with historical records for effective recommendation and decision-making.
More than 500 companies have already adopted the solution, including Airbnb, TripAdvisor, and Lenovo.
New Cloud Native Version
With the new cloud-native version, available as a fully managed SaaS platform called Star Rocks, Star Rocks is increasing its value for enterprises.
Basically, StarRockets Cloud allows organizations to integrate their existing data infrastructures in the cloud and do them without regular engineering and administrative tasks needed for real-time analytics. From setting up the servers/VMS to deploying the software.
Besides this, cloud computing also brings in various cloud-specific features such as separation of computation and storage, automatic resource management, etc, that not only reduce costs but also give data teams extra time to focus on query performance and improve the time to insights for end-users.
Competition Level
There are currently several organizations looking into the real-time analytics market, including ClickHouse, Imply (Apache Druid), Starmtree (Apache Pinot), and Rocket (ROCKSdb). However, StarRockets claims to offer a much lower price-performance ratio than its competitors.
“StarRockets has better performance than its competitors – we have published benchmark tests showing we have three to five times the performance of our competitors.
In addition, StarRockets is less expensive than its competitors – with our industry-leading query engine, we are capable of achieving superior query performance without complex data processing and heavy indexing processes.
As a result, we’re able to achieve a much better price-performance ratio.” Finally, Star Rocks is the most flexible. Our unique design can easily handle frequent updates to past transaction records while still maintaining high query speed in real time.
This allows us to provide real-time analytics for use cases that were previously considered unsuitable for real-time analytics.
Star Rocks Cloud is expected to become generally available in Q3 2022 on Amazon Web Services, with support for the Google cloud platform coming in later.
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Wide-table Testing Using SSB
Star Schema Benchmark (SSB) is a tool used to measure the basic performance metrics of various online analytical processing (OLAP) databases. SSB uses a Star Schema Test Set [1] that is commonly used in academia and industry.
ClickHouse flattens out the star schema into a wider flat table and then rewrites the
SSBs into a flat table benchmark. Therefore, in flat table scenarios, we use the CREATE TABLE statement in ClickHouse to create a PK test on Star Rock, ClickHouse, and Apache Druid.
We perform additional performance testing on the aggregation for low cardinality fields.
Multi-table Testing Using TPC-H
TPC-H benchmarks are used by companies to determine whether their applications run efficiently enough for them to be able to handle large amounts of transactions. TPC-H (Tiny Parquet Compressed Hierarchical) can be used to build data warehouses for simulating the data warehouse of a business system.
The main performance metrics include the response time for each query. The TPC-C benchmark evaluates a database system by testing its ability to handle queries with various types of joins.
- Analyze large amounts of data.
- Complex queries require complex processing.
- Answer critical business questions.
ClickHouse and Apache Drill cannot complete the TPC-H benchmark. Since there is no primary key between StarRocks and Trio, we perform a non-unique constraint test between them.