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StarRocks Overview

Last updated: 2022-05-24 09:31:14

    StarRocks Overview

    • StarRocks is a new-generation and high-speed MPP database for nearly all data analytics scenarios.
    • StarRocks takes advantage of the relational Online Analytical Processing (OLAP) database and distributed storage system. Through architectural upgrades and functional optimization, StarRocks has developed into an enterprise-level product.
    • StarRocks is committed to accommodating multiple data analysis scenarios for enterprise users. It supports multiple data warehouse schemas (flat tables, pre-aggregations, star or snowflake schema) and various data import methods (batch and streaming) for up to 10,000 columns of data. It allows direct access to data from Hive, MySQL and Elasticsearch without importing.
    • StarRocks is compatible with the MySQL protocol. You can use the MySQL client and common Business Intelligence (BI) tools to connect to StarRocks for data analysis.
    • StarRocks uses a distributed architecture to divide the table horizontally and store it in multiple replications. The clusters are highly scalable and therefore support 10PB-level data analysis, Massively Parallel Processing (MPP), and data replication and elastic fault tolerance.
    • Leveraging a relational model, strong data typing, and a columnar storage engine, StarRocks reduces read-write amplification through encoding and compression techniques. Using vectorized query execution, it fully unleashes the power of parallel computing on multicore CPUs, therefore significantly improves query performance.

    StarRocks Features

    StarRocks integrates the design ideas of MPP databases and distributed systems into its architecture and has the following features:

    Simplified architecture

    StarRocks performs the specific execution of SQL through the MPP computing framework. The framework itself can make full use of the computing power of multiple nodes and execute the entire query in parallel, so as to deliver an excellent interactive analysis experience. StarRocks does not rely on any external systems. Its clean architecture makes it easier to deploy, maintain, and scale out. Its minimalist architectural design reduces its complexity and maintenance cost and increases its reliability and scalability. Admins only need to focus on the StarRocks system itself, with no need to learn and manage other external systems.

    Native vectorized SQL engine

    The computing layer of StarRocks fully adopts the vectorization technology to systematically optimize all operators, functions, scanning, filtering, and import and export modules. Through the columnar memory layout and the SIMD instruction set adapted to CPU, it makes full use of the parallel computing power of CPU, achieving sub-second query returns in multidimensional analyses.

    Query optimization

    StarRocks can optimize complex queries through cost-based optimizer (CBO). The execution costs can be reasonably estimated based on the statistical information with no human intervention required. With a better execution plan, the data analysis efficiency in ad hoc and ETL scenarios can be greatly improved.

    Query federation

    StarRocks enables you to perform federated queries by using external tables. Currently, it supports tables from Hive, MySQL, and Elasticsearch. In this way, you can quickly query data without importing data.

    Efficient update

    StarRocks supports multiple data models. Among them, the update model can perform UPSERT/DELETE operations according to the primary key and achieve efficient query during concurrent updates through storage and indexing optimization. This better serves real-time data warehouses.

    Intelligent materialized view

    StarRocks supports intelligent materialized views. Users can create materialized views and generate pre-aggregated tables to speed up aggregate queries. StarRocks' materialized view automatically runs the aggregation when data is imported, keeping it consistent with the original table. When querying, users do not need to specify a materialized view, StarRocks can automatically select the best-materialized view to satisfy the query.

    Standard SQL

    StarRocks supports standard SQL syntax, including aggregation, join, sorting, window functions, and custom functions. It also fully supports 22 SQL queries from TPC-H and 99 SQL queries from TPC-DS. In addition, it is compatible with the MySQL protocol, so you can use various existing client tools and BI software programs to access StarRocks and perform data analysis with simple drag-and-drops in StarRocks.

    Unified batch processing and streaming

    StarRocks supports batch and streaming import of up to 10,000 columns of data in ORC, Parquet, and CSV formats from Kafka, HDFS, and local files. It can consume real-time Kafka data to import the data, which avoids data loss or duplication (i.e., exactly once). It can also import data in batches from local or remote (HDFS) data sources.

    High availability and scalability

    StarRocks metadata and data are stored in multiple replicas. A StarRocks cluster provides hot standby services and can be deployed as multiple instances, eliminating single points of failure. It has the ability of self-healing and elastic recovery. Therefore, the overall stability of the cluster service will not be affected by node failures, disconnections, or exceptions. StarRocks adopts a distributed architecture that makes possible to horizontally scale the storage capacity and computing power. Specifically, a cluster can be expanded to hundreds of nodes to support up to 10 PB data storage. It can normally provide the query service during scaling. In addition, the table schema of StarRocks supports hot changes, so you can use a simple SQL command to dynamically modify the table definition, for example, adding or deleting a column or creating a materialized view. You can also import data into or query data from tables during schema changes.

    Use Cases

    StarRocks can meet a variety of analysis needs, including OLAP analysis, custom reporting, real-time data analysis, and ad hoc data analysis. Specific business scenarios include:

    • Multidimensional OLAP. User behavior analysis.
      • User profiling, tag analysis, and user tagging.
      • High-dimensional business metric reporting.
      • Self-service reporting.
      • Business problem identification and analysis.
      • Cross-topic business analysis.
      • Financial reporting.
      • System monitoring analysis.
    • Real-time data analysis. Ecommerce promotion data analysis.
      • Education live streaming quality analysis.
      • Waybill analysis.
      • Finance performance analysis and metric calculation.
      • Ad placement analysis.
      • Management cockpit.
      • Application performance management (APM).
    • High-concurrency queries. Advertiser report analysis.
      • Retail channel personnel analysis.
      • User-oriented analysis reporting in the SaaS industry.
      • Multi-page dashboard analysis.
    • Unified analysis. The same system is used to address the needs in different scenarios, such as multidimensional analysis, high-concurrency queries, precomputing, real-time analysis, and ad hoc query, simplifying the system and reducing the cost of multi-technology stack development and maintenance.
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