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.
see Physical Graph
Functions are implemented by the user and encapsulate the application logic of a Flink program. Most Functions are wrapped by a corresponding Operator.
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 application is a Java Application that submits one or multiple Flink
Jobs from the
main() method (or by some other means). Submitting
jobs is usually done by calling
execute() on an execution environment.
see Logical Graph
A logical graph is a directed graph where the nodes are Operators and the edges define input/output-relationships of the operators and correspond to data streams or data sets. A logical graph is created by submitting jobs from a Flink Application.
Logical graphs are also often referred to as dataflow graphs.
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.
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.
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.
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.
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.
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 JobManager or Filesystem).
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 transformations are implemented by certain Operators.