- StarRocks
- Introduction to StarRocks
- Quick Start
- Table Design
- Data Loading
- Concepts
- Overview of data loading
- Load data from a local file system or a streaming data source using HTTP PUT
- Load data from HDFS or cloud storage
- Continuously load data from Apache Kafka®
- Bulk load using Apache Spark™
- Load data using INSERT
- Synchronize data from MySQL in real time
- Continuously load data from Apache Flink®
- Change data through loading
- Transform data at loading
- Data Unloading
- Query Data Sources
- Query Acceleration
- Administration
- Deployment
- Management
- Data Recovery
- User Privilege and Authentication
- Performance Tuning
- Reference
- SQL Reference
- User Account Management
- Cluster Management
- ADD SQLBLACKLIST
- ADMIN CANCEL REPAIR TABLE
- ADMIN CHECK TABLET
- ADMIN REPAIR TABLE
- ADMIN SET CONFIG
- ADMIN SET REPLICA STATUS
- ADMIN SHOW CONFIG
- ADMIN SHOW REPLICA DISTRIBUTION
- ADMIN SHOW REPLICA STATUS
- ALTER RESOURCE GROUP
- ALTER SYSTEM
- CANCEL DECOMMISSION
- CREATE FILE
- CREATE RESOURCE GROUP
- DELETE SQLBLACKLIST
- DROP FILE
- DROP RESOURCE GROUP
- EXPLAIN
- INSTALL PLUGIN
- KILL
- SET
- SHOW BACKENDS
- SHOW BROKER
- SHOW FILE
- SHOW FRONTENDS
- SHOW FULL COLUMNS
- SHOW INDEX
- SHOW PLUGINS
- SHOW PROC
- SHOW PROCESSLIST
- SHOW RESOURCE GROUP
- SHOW SQLBLACKLIST
- SHOW TABLE STATUS
- SHOW VARIABLES
- UNINSTALL PLUGIN
- DDL
- ALTER DATABASE
- ALTER TABLE
- ALTER VIEW
- ALTER RESOURCE
- BACKUP
- CANCEL BACKUP
- CANCEL RESTORE
- CREATE DATABASE
- CREATE INDEX
- CREATE MATERIALIZED VIEW
- CREATE REPOSITORY
- CREATE RESOURCE
- CREATE TABLE AS SELECT
- CREATE TABLE LIKE
- CREATE TABLE
- CREATE VIEW
- CREATE FUNCTION
- DROP DATABASE
- DROP INDEX
- DROP MATERIALIZED VIEW
- DROP REPOSITORY
- DROP RESOURCE
- DROP TABLE
- DROP VIEW
- DROP FUNCTION
- HLL
- RECOVER
- RESTORE
- SHOW RESOURCES
- SHOW FUNCTION
- TRUNCATE TABLE
- USE
- DML
- ALTER ROUTINE LOAD
- BROKER LOAD
- CANCEL LOAD
- CANCEL EXPORT
- CANCEL REFRESH MATERIALIZED VIEW
- DELETE
- EXPORT
- GROUP BY
- INSERT
- PAUSE ROUTINE LOAD
- RESUME ROUTINE LOAD
- ROUTINE LOAD
- SELECT
- SHOW ALTER TABLE
- SHOW BACKUP
- SHOW CREATE TABLE
- SHOW CREATE VIEW
- SHOW DATA
- SHOW DATABASES
- SHOW DELETE
- SHOW DYNAMIC PARTITION TABLES
- SHOW EXPORT
- SHOW LOAD
- SHOW PARTITIONS
- SHOW PROPERTY
- SHOW REPOSITORIES
- SHOW RESTORE
- SHOW ROUTINE LOAD
- SHOW ROUTINE LOAD TASK
- SHOW SNAPSHOT
- SHOW TABLES
- SHOW TABLET
- SHOW TRANSACTION
- SPARK LOAD
- STOP ROUTINE LOAD
- STREAM LOAD
- Auxiliary Commands
- Data Types
- Function Reference
- Java UDFs
- Window functions
- Aggregate Functions
- Array Functions
- Bit Functions
- Bitmap Functions
- base64_to_bitmap
- bitmap_agg
- bitmap_and
- bitmap_andnot
- bitmap_contains
- bitmap_count
- bitmap_from_string
- bitmap_empty
- bitmap_has_any
- bitmap_hash
- bitmap_intersect
- bitmap_max
- bitmap_min
- bitmap_or
- bitmap_remove
- bitmap_to_array
- bitmap_to_string
- bitmap_union
- bitmap_union_count
- bitmap_union_int
- bitmap_xor
- intersect_count
- to_bitmap
- Conditional Functions
- Cryptographic Functions
- Date Functions
- add_months
- adddate
- convert_tz
- current_date
- current_time
- current_timestamp
- date
- date_add
- date_format
- date_sub, subdate
- date_trunc
- datediff
- day
- dayname
- dayofmonth
- dayofweek
- dayofyear
- days_add
- days_diff
- days_sub
- from_days
- from_unixtime
- hour
- hours_add
- hours_diff
- hours_sub
- microseconds_add
- microseconds_sub
- minute
- minutes_add
- minutes_diff
- minutes_sub
- month
- monthname
- months_add
- months_diff
- months_sub
- now
- quarter
- second
- seconds_add
- seconds_diff
- seconds_sub
- str_to_date
- str2date
- time_slice
- time_to_sec
- timediff
- timestamp
- timestampadd
- timestampdiff
- to_date
- to_days
- unix_timestamp
- utc_timestamp
- week
- weekofyear
- weeks_add
- weeks_diff
- weeks_sub
- year
- years_add
- years_diff
- years_sub
- Geographic Functions
- JSON Functions
- Overview of JSON functions and operators
- JSON operators
- JSON constructor functions
- JSON query and processing functions
- Math Functions
- String Functions
- Pattern Matching Functions
- Percentile Functions
- Scalar Functions
- Utility Functions
- cast function
- hash function
- System variables
- Error code
- System limits
- SQL Reference
- FAQ
- Benchmark
- Developers
- Contribute to StarRocks
- Code Style Guides
- Use the debuginfo file for debugging
- Development Environment
- Trace Tools
- Integration
What is StarRocks?
StarRocks is a next-generation, blazing-fast massively parallel processing (MPP) database designed to make real-time analytics easy for enterprises. It is built to power sub-second queries at scale.
StarRocks has an elegant design. It encompasses a rich set of features including fully vectorized engine, newly designed cost-based optimizer (CBO), and intelligent materialized view. As such, StarRocks can deliver a query speed far exceeding database products of its kind, especially for multi-table joins.
StarRocks is ideal for real-time analytics on fresh data. Data can be ingested at a high speed and updated and deleted in real time. StarRocks empowers users to create tables that use various schemas, such as flat, star, and snowflake schemas.
Compatible with MySQL protocols and standard SQL, StarRocks has out-of-the-box support for all major BI tools, such as Tableau and Power BI. StarRocks does not rely on any external components. It is an integrated data analytics platform that allows for high scalability, high availability, and simplified management and maintenance.
Scenarios
StarRocks meets varied enterprise analytics requirements, including OLAP multi-dimensional analytics, real-time analytics, high-concurrency analytics, customized reporting, ad-hoc queries, and unified analytics.
OLAP multi-dimensional analytics
The MPP framework and vectorized execution engine enable users to choose between various schemas to develop multi-dimensional analytical reports. Scenarios:
User behavior analysis
User profiling, label analysis, user tagging
High-dimensional metrics report
Self-service dashboard
Service anomaly probing and analysis
Cross-theme analysis
Financial data analysis
System monitoring analysis
Real-time analytics
StarRocks uses the Primary Key model to implement real-time updates. Data changes in a TP database can be synchronized to StarRocks in a matter of seconds to build a real-time warehouse.
Scenarios:
Online promotion analysis
Logistics tracking and analysis
Performance analysis and metrics computation for the financial industry
Quality analysis for livestreaming
Ad placement analysis
Cockpit management
Application Performance Management (APM)
High-concurrency analytics
StarRocks leverages performant data distribution, flexible indexing, and intelligent materialized views to facilitate user-facing analytics at high concurrency:
Advertiser report analysis
Channel analysis for the retail industry
User-facing analysis for SaaS
Multi-tabbed dashboard analysis
Unified analytics
StarRocks provides a unified data analytics experience.
One system can power various analytical scenarios, reducing system complexity and lowering TCO.
StarRocks unifies data lakes and data warehouses. Data in a lakehouse can be managed all in StarRocks. Latency-sensitive queries that require high concurrency can run on StarRocks. Data in data lakes can be accessed by using external catalogs or external tables provided by StarRocks.