Glossary

A Flink Application Cluster is a dedicated Flink Cluster that only executes a single Flink Job. The lifetime of the Flink Cluster is bound to the lifetime of the Flink Job. Formerly Flink Application Clusters were also known as Flink Clusters in job mode. Compare to Flink Session Cluster.

A distributed system consisting of (typically) one Flink Master and one or more Flink TaskManager processes.

Event

An event is a statement about a change of the state of the domain modelled by the application. Events can be input and/or output of a stream or batch processing application. Events are special types of records.

ExecutionGraph

see Physical Graph

Function

Functions are implemented by the user and encapsulate the application logic of a Flink program. Most Functions are wrapped by a corresponding Operator.

Instance

The term instance is used to describe a specific instance of a specific type (usually Operator or Function) during runtime. As Apache Flink is mostly written in Java, this corresponds to the definition of Instance or Object in Java. In the context of Apache Flink, the term parallel instance is also frequently used to emphasize that multiple instances of the same Operator or Function type are running in parallel.

A Flink Job is the runtime representation of a Flink program. A Flink Job can either be submitted to a long running Flink Session Cluster or it can be started as a self-contained Flink Application Cluster.

JobGraph

see Logical Graph

JobManagers are one of the components running in the Flink Master. A JobManager is responsible for supervising the execution of the Tasks of a single job. Historically, the whole Flink Master was called JobManager.

Logical Graph

A logical graph is a directed graph describing the high-level logic of a stream processing program. The nodes are Operators and the edges indicate input/output-relationships or data streams or data sets.

Managed State

Managed State describes application state which has been registered with the framework. For Managed State, Apache Flink will take care about persistence and rescaling among other things.

The Flink Master is the master of a Flink Cluster. It contains three distinct components: Flink Resource Manager, Flink Dispatcher and one Flink JobManager per running Flink Job.

Operator

Node of a Logical Graph. An Operator performs a certain operation, which is usually executed by a Function. Sources and Sinks are special Operators for data ingestion and data egress.

Operator Chain

An Operator Chain consists of two or more consecutive Operators without any repartitioning in between. Operators within the same Operator Chain forward records to each other directly without going through serialization or Flink’s network stack.

Partition

A partition is an independent subset of the overall data stream or data set. A data stream or data set is divided into partitions by assigning each record to one or more partitions. Partitions of data streams or data sets are consumed by Tasks during runtime. A transformation which changes the way a data stream or data set is partitioned is often called repartitioning.

Physical Graph

A physical graph is the result of translating a Logical Graph for execution in a distributed runtime. The nodes are Tasks and the edges indicate input/output-relationships or partitions of data streams or data sets.

Record

Records are the constituent elements of a data set or data stream. Operators and Functions receive records as input and emit records as output.

A long-running Flink Cluster which accepts multiple Flink Jobs for execution. The lifetime of this Flink Cluster is not bound to the lifetime of any Flink Job. Formerly, a Flink Session Cluster was also known as a Flink Cluster in session mode. Compare to Flink Application Cluster.

State Backend

For stream processing programs, the State Backend of a Flink Job determines how its state is stored on each TaskManager (Java Heap of TaskManager or (embedded) RocksDB) as well as where it is written upon a checkpoint (Java Heap of Flink Master or Filesystem).

Sub-Task

A Sub-Task is a Task responsible for processing a partition of the data stream. The term “Sub-Task” emphasizes that there are multiple parallel Tasks for the same Operator or Operator Chain.

Task

Node of a Physical Graph. A task is the basic unit of work, which is executed by Flink’s runtime. Tasks encapsulate exactly one parallel instance of an Operator or Operator Chain.

TaskManagers are the worker processes of a Flink Cluster. Tasks are scheduled to TaskManagers for execution. They communicate with each other to exchange data between subsequent Tasks.

Transformation

A Transformation is applied on one or more data streams or data sets and results in one or more output data streams or data sets. A transformation might change a data stream or data set on a per-record basis, but might also only change its partitioning or perform an aggregation. While Operators and Functions) are the “physical” parts of Flink’s API, Transformations are only an API concept. Specifically, most - but not all - transformations are implemented by certain Operators.