Compression[04]

Pinot leverages dictionary encoding and bit packing for columns in segments to reduce storage overhead. The typical space a segment consumes varies from hundreds of megabytes to several gigabytes.

Data Model[04]

Pinot uses relational data model. In terms of data types, attributes in a relation can be integers with various length, floating-point numbers, strings, booleans, arrays, and timestamps. In terms of analyst, attributes can be dimensions and metrics.

Indexes[04][03]

Joins[04]

Query Execution[04]

Query Interface[04]

Pinot uses PQL query interface, which is a subset of SQL. PQL supports selection, projection, aggregations, and top-n. But it does not support joins, nested queries, record-level creation, updates, deletion or any data definition language (DDL).

Storage Model[04]

Pinot uses a hybrid data model, which divides rows into segments and stores data inside each segment in Columnar manner. A segment is a basic unit of replication. It's immutable and typically contains tens of millions of rows.

Storage Organization[04]

Pinot stores segments in directories of UNIX filesystem. Each such directory contains a metadata file and an index file. The metadata file stores information about record columns in the segment. The index file stores indexes for all the columns. The global metadata about segments, including the mapping of a segment to its position, is maintained in controller clusters.

System Architecture[04]

Pinot consists of four parts: servers, controllers, brokers, and minions. They together support the functionality of data storage, data management, and query processing.

Servers

Servers are responsible for data storage. Pinot stores segments in each server node in a distributed manner. Each segment has multiple replicas and transactions are executed in active-active manner.

Controllers

Controllers are responsible for maintaining global metadata. They are implemented with Apache Helix and Zookeeper.

Brokers

Brokers are responsible for query routing. They control the flow of query such as where each query should go to and how to generate the final result with intermediate results from different nodes.

Minions

Minions are responsible for running maintenance tasks, which are usually time consuming and should not influence the running queries.