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Target customers: Enterprise users who have requirements in data analytics scenarios, including multi-dimensional online analytical processing (OLAP) analysis, real-time data analysis, high-concurrency queries, and unified data analysis. StarRocks is widely used in industries such as new retail, Internet finance, and Internet entertainment. Features released: Comprehensive vectorized engine: StarRocks adopts vectorization technologies at the computing layer to optimize all operators, functions, scanning and filtering modules, and import and export modules in a systematic manner. StarRocks makes full use of the parallel computing power of CPUs by using methods such as columnar memory layout and single instruction, multiple data (SIMD) in CPUs. This way, you can perform multi-dimensional analysis on data at sub-second speeds. Optimized intelligent queries: StarRocks uses a cost-based optimizer (CBO) to implement automatic optimization for complex queries. The CBO uses statistical information to reasonably estimate execution costs and generate a better execution plan, without manual intervention. This greatly improves efficiency of data analysis in ad hoc queries and extract, transform, and load (ETL) scenarios. Federated queries: StarRocks supports federated queries by using external tables. Hive, MySQL, and Elasticsearch external tables are supported. You can directly query data without the need to import data first. This accelerates data queries. Intelligent materialized views: StarRocks supports intelligent materialized views. You can create a materialized view and perform precalculation to generate a pre-aggregate table to accelerate aggregation query requests. Data can be automatically aggregated into the desired materialized view when data is imported. Therefore, the materialized view contains the same data as the source table. When you query data, you do not need to specify a materialized view. StarRocks selects the optimal materialized view. This accelerates queries.