BDV concepts

Overview

To understand BDV, you must first understand the terms and concepts used throughout the BDV documentation.

While it is easy to understand statements and queries, as an end-user you should have familiarity with concepts such as stages and splits to take full advantage of BDV to execute efficient queries. As a BDV administrator you should understand how BDV’s concepts of stages map to tasks and how tasks contain a set of drivers which process data.

This section provides a solid definition for the core concepts referenced throughout BDV, and these sections are sorted from most general to most specific.

Note

The book Trino: The Definitive Guide and the research paper Presto: SQL on Everything can provide further information about the concepts in use by BDV.

Server types

There are two types of BDV servers: coordinators and workers. The following section explains the difference between the two.

Coordinator

The BDV coordinator is the server that is responsible for parsing statements, planning queries, and managing BDV worker nodes. It is the “brain” of a BDV installation and is also the node to which a client connects to submit statements for execution. Every BDV installation must have a BDV coordinator alongside one or more BDV workers. For development or testing purposes, a single instance of BDV can be configured to perform both roles.

The coordinator keeps track of the activity on each worker and coordinates the execution of a query. The coordinator creates a logical model of a query involving a series of stages, which is then translated into a series of connected tasks running on a cluster of BDV workers.

Coordinators communicate with workers and clients using a REST API.

Worker

A BDV worker is a server in a BDV installation, which is responsible for executing tasks and processing data. Worker nodes fetch data from connectors and exchange intermediate data with each other. The coordinator is responsible for fetching results from the workers and returning the final results to the client.

When a BDV worker process starts up, it advertises itself to the discovery server in the coordinator, which makes it available to the BDV coordinator for task execution.

Workers communicate with other workers and BDV coordinators using a REST API.

Data sources

Throughout this documentation, you’ll read terms such as connector, catalog, schema, and table. These fundamental concepts cover BDV’s model of a particular data source and are described in the following section.

Connector

A connector adapts BDV to a data source such as Hive or a relational database. You can think of a connector the same way you think of a driver for a database. It is an implementation of BDV’s SPI, which allows BDV to interact with a resource using a standard API.

BDV contains several built-in connectors: a connector for JMX, a System connector which provides access to built-in system tables, a Hive connector, and a TPCH connector designed to serve TPC-H benchmark data. Many connectors are included so that BDV can access data in a variety of data sources.

Every catalog is associated with a specific connector. If you examine a catalog configuration file, you see that each contains a mandatory property connector.name, which is used by the catalog manager to create a connector for a given catalog. It is possible to have more than one catalog use the same connector to access two different instances of a similar database. For example, if you have two Hive clusters, you can configure two catalogs in a single BDV cluster that both use the Hive connector, allowing you to query data from both Hive clusters, even within the same SQL query.

Catalog

A BDV catalog contains schemas and references a data source via a connector. For example, you can configure a JMX catalog to provide access to JMX information via the JMX connector. When you run a SQL statement in BDV, you are running it against one or more catalogs. Other examples of catalogs include the Hive catalog to connect to a Hive data source.

When addressing a table in BDV, the fully-qualified table name is always rooted in a catalog. For example, a fully-qualified table name of hive.test_data.test refers to the test table in the test_data schema in the hive catalog.

Catalogs are defined in properties files stored in the BDV configuration directory.

Schema

Schemas are a way to organize tables. Together, a catalog and schema define a set of tables that can be queried. When accessing Hive or a relational database such as MySQL with BDV, a schema translates to the same concept in the target database. Other types of connectors may choose to organize tables into schemas in a way that makes sense for the underlying data source.

Table

A table is a set of unordered rows, which are organized into named columns with types. This is the same as in any relational database. The mapping from source data to tables is defined by the connector.

Query execution model

BDV executes SQL statements and turns these statements into queries, that are executed across a distributed cluster of coordinator and workers.

Statement

BDV executes ANSI-compatible SQL statements. When the BDV documentation refers to a statement, it is referring to statements as defined in the ANSI SQL standard, which consists of clauses, expressions, and predicates.

Some readers might be curious why this section lists separate concepts for statements and queries. This is necessary because, in BDV, statements simply refer to the textual representation of a SQL statement. When a statement is executed, BDV creates a query along with a query plan that is then distributed across a series of BDV workers.

Query

When BDV parses a statement, it converts it into a query and creates a distributed query plan, which is then realized as a series of interconnected stages running on BDV workers. When you retrieve information about a query in BDV, you receive a snapshot of every component that is involved in producing a result set in response to a statement.

The difference between a statement and a query is simple. A statement can be thought of as the SQL text that is passed to BDV, while a query refers to the configuration and components instantiated to execute that statement. A query encompasses stages, tasks, splits, connectors, and other components and data sources working in concert to produce a result.

Stage

When BDV executes a query, it does so by breaking up the execution into a hierarchy of stages. For example, if BDV needs to aggregate data from one billion rows stored in Hive, it does so by creating a root stage to aggregate the output of several other stages, all of which are designed to implement different sections of a distributed query plan.

The hierarchy of stages that comprises a query resembles a tree. Every query has a root stage, which is responsible for aggregating the output from other stages. Stages are what the coordinator uses to model a distributed query plan, but stages themselves don’t run on BDV workers.

Task

As mentioned in the previous section, stages model a particular section of a distributed query plan, but stages themselves don’t execute on BDV workers. To understand how a stage is executed, you need to understand that a stage is implemented as a series of tasks distributed over a network of BDV workers.

Tasks are the “work horse” in the BDV architecture as a distributed query plan is deconstructed into a series of stages, which are then translated to tasks, which then act upon or process splits. A BDV task has inputs and outputs, and just as a stage can be executed in parallel by a series of tasks, a task is executing in parallel with a series of drivers.

Split

Tasks operate on splits, which are sections of a larger data set. Stages at the lowest level of a distributed query plan retrieve data via splits from connectors, and intermediate stages at a higher level of a distributed query plan retrieve data from other stages.

When BDV is scheduling a query, the coordinator queries a connector for a list of all splits that are available for a table. The coordinator keeps track of which machines are running which tasks, and what splits are being processed by which tasks.

Driver

Tasks contain one or more parallel drivers. Drivers act upon data and combine operators to produce output that is then aggregated by a task and then delivered to another task in another stage. A driver is a sequence of operator instances, or you can think of a driver as a physical set of operators in memory. It is the lowest level of parallelism in the BDV architecture. A driver has one input and one output.

Operator

An operator consumes, transforms and produces data. For example, a table scan fetches data from a connector and produces data that can be consumed by other operators, and a filter operator consumes data and produces a subset by applying a predicate over the input data.

Exchange

Exchanges transfer data between BDV nodes for different stages of a query. Tasks produce data into an output buffer and consume data from other tasks using an exchange client.