BEIJING, Jan. 28, 2022 /PRNewswire/ -- StarRocks, a
new-generation massively parallel processing (MPP) database service
designed for all analytical scenarios, launched the 2.0 version.
This new version delivers a myriad of performance improvements in
both single-table and multi-table query scenarios. The single-table
query performance is twice that of its competitors. The multi-table
query performance is five to ten times that of other database
systems. StarRocks 2.0 introduces a new model, the primary key
model, which enhances real-time update performance by three to ten
times. In addition, the memory management scheme is redesigned in
2.0 to accommodate customers' requirements for higher availability
and stability.
Last September, StarRocks opened its source code to global
communities and communities have become a key driving force behind
the improvement of StarRocks. StarRocks has received more than
2,000 GitHub stars within the first 135 days after the code is
open. Hundreds of large and medium-sized enterprises are attracted
to use StarRocks.
2X Single-Table Query Performance Compared to
Competitors
StarRocks 2.0 is ideal for single-table and multi-table queries.
For single-table queries, StarRocks 2.0 innovatively uses global
dictionaries to optimize queries on low-cardinality fields,
delivering a single-table query performance twice that of its
earlier versions and also other leading database service providers.
For multi-table queries, StarRocks 2.0 has resigned the cost-based
optimizer (CBO) to handle complex multi-table queries, improving
multi-table query performance by two times and making StarRocks 2.0
five to ten times faster than other database systems.
In terms of data updates, traditional OLAP systems use the
merge-on-read mode to update data, which is not the best solution
because it pursues data loading efficiency at the cost of query
performance. As real-time data update requirements keep rising in
the finance and logistics sectors, this model no longer lives up to
expectations. StarRocks 2.0 introduces a novel data model, the
primary key model, to update data in delete-and-insert mode. This
innovation enhances query performance by three to ten times in
real-time update scenarios.
In addition, the memory management scheme is redesigned in
StarRocks 2.0 to improve system stability. A pipeline execution
engine built for higher concurrency and faster complex queries on
multi-core machines has been released for trial use. This engine
will be officially released in StarRocks 2.1.
Five Technical Highlights and R&D Directions in
2022
StarRocks announced its five major R&D directions in 2022 to
the community.
Resource Management
StarRocks will introduce a new resource management mechanism to
provide separate resource groups for different businesses. This
mechanism guarantees sufficient resource quotas and isolated
resources for businesses. This way, different services can run on
the same cluster, which simplifies O&M and improves cluster
resource utilization.
Materialized Views with JOINs
Data modeling in a majority of companies requires complex data
development from data engineers. Materialized views with JOINs
enable data engineers to create various types of materialized views
to construct data models. This significantly reduces the workload
of data engineers and simplifies data modeling.
StarRocks also introduces intelligent materialized views. This
feature intelligently recommends materialized views to users based
on query behavior to accelerate queries.
Separation of Storage and Compute
In the earlier versions of StarRocks, compute and storage are
tightly coupled for excellent query performance. However, this
architecture cannot achieve on-demand resource allocation and may
result in unnecessary costs. In 2022, StarRocks will implement a
new architecture where storage and compute are decoupled. This new
architecture supports offline analytics in parallel with real-time
analytics and can be deployed on public, private, and multiple
clouds.
Lightning Fast Data Lake Analytics
Currently, StarRocks serves more like a data warehouse.
Customers import high-value data from data lakes to StarRocks for
ultra-fast data analytics. In 2022, StarRocks will press ahead with
its endeavors to enhance data lake analytics capabilities and
provide unified and blazing fast analytics experience for
customers.
The StarRocks community has completed the first-phase
development of data queries on Iceberg, with the collaboration from
renowned communities and developers in world's leading cloud
computing companies. Test results show that StarRocks offers a 5X
performance improvement compared to Trino. In the future, the
StarRocks community will extend its support for Hudi and offer more
feature enhancements.
Unified Batch and Stream Processing
StarRocks plans to implement unified stream and batch processing
across hundreds of nodes. This way, customers' raw data can be
processed and then analyzed all in StarRocks. This guarantees a
one-stop, unified, and blazing fast data processing and analytics
experience, bringing the vision of unification to a new level.
About StarRocks
StarRocks is a new-generation MPP database designed for all
analytical scenarios. It features a simple architecture, vectorized
engine, redesigned CBO, and a query speed (especially for
multi-table join queries) beyond the reach of other database
products. StarRocks supports real-time data analytics and achieves
efficient queries on data that is updated in real time. StarRocks
provides materialized views to further accelerate queries.
Customers can use StarRocks to flexibly build various schemas such
as flat tables and the star and snowflake schemas. StarRocks is
compatible with the MySQL protocol and can interconnect with
various MySQL clients and tools. StarRocks does not rely on any
external systems. The simple architecture makes it highly
available, scalable, and easy for O&M.
StarRocks meets requirements in various data analytics
scenarios, such as multi-dimensional filtering and analytics,
real-time data analytics, and ad hoc queries. It allows access from
thousands of users at the same time. Typical use scenarios include
business intelligence, real-time data warehousing, user profiling,
reports and dashboards, order analysis, O&M and monitoring,
anti-fraud analysis, and risk management. Hundreds of large and
medium-sized enterprises from various sectors have deployed
StarRocks to their production environments and have seen thousands
of StarRocks servers run stably and steadily on their
platforms.
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