Process and System Components Glossary for StarRocks

[alberttwong]

Vectorized Query Engine Process

The core components of a database query engine work together to interpret and execute user queries, enabling efficient data retrieval and manipulation. These components include:

• Lexer: The lexer, also known as a tokenizer, breaks down the user-entered query into individual tokens, such as keywords, operators, and identifiers. It separates the query into meaningful units for further processing.
• Parser: The parser analyzes the tokens identified by the lexer and constructs a parse tree, which represents the syntactic structure of the query. It checks the query for grammar and syntax errors to ensure it adheres to the database's query language rules.
• Binder: The binder binds identifiers, such as table names and column names, to their corresponding entries in the database schema. It resolves aliases and ensures that the query references valid database objects.
• Optimizer: The optimizer determines the most efficient way to execute the query. It considers various factors, such as table sizes, indexes, and query execution plans, to minimize the time required to retrieve the desired data.
• Executor: The executor translates the optimized query plan into machine-executable code. It interacts with the database's storage engine to fetch the relevant data from the database tables.
• Storage Engine: The storage engine manages the physical storage of data on disk or other storage devices. It provides efficient access to data blocks and handles data retrieval, insertion, and modification operations.
• Query Evaluator: The query evaluator processes the data retrieved from the storage engine and applies any necessary operations, such as filtering, sorting, or aggregation, as specified in the query. It transforms the raw data into the desired output format.
• Result Compiler: The result compiler formats the query results into a displayable format, such as a table or a chart. It presents the processed data in a user-friendly manner.
   

Materialized View Process

Building a data materialized view involves several key components that work together to create and maintain a persistent representation of data for efficient querying and analysis. These components include:

• Data Source: The data source provides the raw data that will be materialized into the view. This can be a relational database, a data warehouse, or any other data storage system.
• Materialized View Definition (MVD): The MVD defines the view's structure and content, specifying the source tables, columns, and any transformations or aggregations to be applied. It outlines the data elements and operations involved in creating the view.
• Materialization Engine: The materialization engine is responsible for generating and maintaining the materialized view. It extracts data from the source, applies the MVD's transformations, and stores the resulting data in a physical format.
  Refresh Mechanism: The refresh mechanism ensures that the materialized view remains up-to-date with the source data. It detects changes in the source data and triggers a refresh process to update the view accordingly.
• View Metadata: View metadata stores information about the view's structure, dependencies, and refresh history. It provides context and insights into the view's creation and maintenance.
• Access Control: Access control mechanisms regulate user access to the materialized view, ensuring that only authorized users can query and modify the view data.
• Performance Optimization: Performance optimization techniques are employed to improve the efficiency of materialized view creation, querying, and maintenance. This includes indexing, data partitioning, and query caching.
  Monitoring and Alerting: Monitoring and alerting mechanisms track the health and performance of the materialized view system, identifying potential issues and triggering notifications for corrective actions.
• Change Data Capture (CDC): Change Data Capture (CDC) technology enables efficient incremental updates to materialized views when the source data changes. It captures the specific changes made to the source data, rather than rematerializing the entire view.
• Distributed Materialized Views: Distributed materialized views span multiple nodes or servers, allowing for large-scale data materialization and querying across distributed data sources. It enables efficient processing and analysis of data stored in multiple locations.
   

Caching System

A database cache system is a software layer that sits between a database and its users and applications. It stores a subset of the database's data in memory, making it faster to access frequently used data. Here are the major components of a database cache system:

• Cache Manager: The cache manager is the central component of the cache system. It is responsible for deciding which data to cache, how to store it in memory, and when to refresh it. It manages the cache's contents and ensures efficient data retrieval.
  Caching Algorithm: The caching algorithm determines which data to cache and how to replace cached data when the cache is full. It employs techniques such as LRU (Least Recently Used) and LFU (Least Frequently Used) to prioritize caching of frequently accessed data.
• Cache Storage: The cache storage provides a place to store the cached data in memory. It utilizes efficient data structures, such as hash tables or B-trees, to enable quick access and retrieval of cached entries.
• Cache Consistency: Cache consistency mechanisms ensure that the cached data remains synchronized with the database. It employs techniques like cache invalidation and write-through caching to maintain data integrity.
• Cache Monitoring: Cache monitoring tools track the performance and health of the cache system. They provide insights into cache hit rates, eviction rates, and memory usage.
• Cache Replacement Policy: The cache replacement policy dictates when to evict cached data from the cache when it reaches its capacity. It considers factors like data access patterns, recency of access, and data size to determine which cached entries to remove.
• Data Prefetching: Data prefetching anticipates future data access patterns and proactively loads data into the cache before it is actually requested. It can improve cache hit rates and reduce query latency.
• Cache Locking: Cache locking mechanisms ensure data consistency when multiple clients access the cache concurrently. It prevents concurrent updates and maintains data integrity in a multi-user environment.
• Cache Security: Cache security measures protect cached data from unauthorized access and modification. It employs access control mechanisms, encryption, and audit logging to safeguard sensitive data.
• Cache Scalability: Cache scalability techniques allow the cache system to handle increasing data volumes and workloads. It involves partitioning the cache across multiple servers or using distributed caching algorithms.
   

Data Storage

Columnar storage is a data storage format that organizes data in columns, rather than rows, which can improve performance for analytical workloads. Here are the major components of a columnar storage system:

• Column Store: The column store is the physical storage structure that holds the data in columns. It organizes data by individual attributes or fields, allowing for efficient retrieval and processing of specific columns.
• Metadata Manager: The metadata manager maintains information about the column store's structure, including table definitions, column data types, and file locations. It provides context and facilitates data access and manipulation.
• Data Compression: Data compression techniques are applied to reduce the size of the stored data, saving disk space and improving I/O performance. Algorithms like run-length encoding, dictionary encoding, and delta encoding are commonly used.
  Index Management: Columnar storage often employs indexes to speed up data retrieval. Indexes are data structures that point to specific data locations, enabling efficient filtering, sorting, and aggregation operations.
• Query Optimizer: The query optimizer analyzes user queries and determines the most efficient way to execute them using the columnar storage format. It considers factors like column size, compression, and indexes to optimize query performance.
• Data Encoding: Data encoding techniques transform raw data into a format suitable for storage and processing. Encodings like fixed-width, variable-width, and self-describing encoding are commonly used.
• Data Partitioning: Data partitioning divides large tables into smaller, more manageable partitions. It allows for parallel processing and reduces I/O overhead, particularly for large-scale analytical workloads.
• Columnar File Format: The columnar file format defines how data is stored in physical files. It specifies the layout of columns, indexes, and metadata, ensuring consistent data representation.
• Query Execution Engine: The query execution engine processes user queries and retrieves data from the column store. It utilizes optimized algorithms and data structures to efficiently execute queries and deliver results.
• Data Loading and Updates: Data loading mechanisms are responsible for inserting and updating data into the column store. They handle data ingestion, format conversion, and integration with the existing data.
   

JOIN Operations Process

Executing a JOIN operation within a database involves several steps, including:

• Query Parsing and Analysis: The database engine parses the SQL query and analyzes the JOIN conditions and table relationships. It identifies the tables to be joined and the join predicates that specify the matching criteria.
• Join Algorithm Selection: The database engine selects an appropriate join algorithm based on the query structure, table sizes, and available join indexes. Common join algorithms include Nested Loops Join, Hash Join, and Merge Join.
• Join Order Determination: The database engine determines the optimal join order using join reordering techniques. It evaluates different join orders based on estimated costs and considers factors like table sizes, join selectivity, and available indexes.
• Table Access and Data Retrieval: The database engine accesses the relevant tables and retrieves the required data. It may utilize indexes to efficiently locate matching rows and minimize data access overhead.
• Join Predicate Evaluation: The database engine evaluates the join predicates for each pair of rows from the joined tables. It compares the specified join columns and determines whether the rows satisfy the join conditions.
• Join Result Construction: The database engine constructs the join result by combining the matching rows from the joined tables. It forms the output table based on the JOIN clause's specified columns and join type.
• Join Result Sorting and Aggregation: If necessary, the database engine sorts the join result based on specified ORDER BY clauses or performs aggregations using GROUP BY and HAVING clauses.
• Join Result Materialization: The database engine materializes the join result, either by storing it temporarily in memory or writing it to an intermediate file.
• Join Result Processing: The database engine processes the materialized join result, applying any additional filtering, sorting, or aggregation operations specified in the query.
• Join Result Presentation: The database engine presents the final join result to the user or application, typically as a table or an iterator over the rows.