davidradl commented on code in PR #28863:
URL: https://github.com/apache/flink/pull/28863#discussion_r3820177801
##########
docs/content/docs/concepts/glossary.md:
##########
@@ -72,165 +167,347 @@ Java, this corresponds to the definition of *Instance*
or *Object* in Java. In t
Flink, the term *parallel instance* is also frequently used to emphasize that
multiple instances of
the same [Operator](#operator) or [Function](#function) type are running in
parallel.
-#### Flink Application
+#### Flink Job
-A Flink application is a Java Application that submits one or multiple [Flink
-Jobs](#flink-job) from the `main()` method (or by some other means). Submitting
-jobs is usually done by calling `execute()` on an execution environment.
+A Flink Job is the unit of data processing execution in Flink: a Job as a
whole is submitted,
+started, stopped and resumed, although under some conditions Flink may restart
a Job only partially.
-The jobs of an application can either be submitted to a long running [Flink
-Session Cluster](#flink-session-cluster), to a dedicated [Flink Application
-Cluster](#flink-application-cluster), or to a [Flink Job
-Cluster](#flink-job-cluster).
+A Job is submitted either by a [Flink Application](#flink-application), by
calling `execute()` on an
+execution environment, or as a single [Flink SQL
Statement](#flink-sql-statement) or [Statement
+Set](#statement-set).
-#### Flink Job
+A Flink Job is the runtime representation of a [Logical Graph](#logical-graph)
(also often called
+*Dataflow Graph*). The Logical Graph is optimized into a [Job
Graph](#job-graph), from which the
+[Physical Graph](#physical-graph) that actually runs in a [Flink
Cluster](#flink-cluster) is derived.
+
+#### Flink Job Cluster
+
+A Flink Job Cluster is a dedicated [Flink Cluster](#flink-cluster) that only
+executes a single [Flink Job](#flink-job). The lifetime of the
+[Flink Cluster](#flink-cluster) is bound to the lifetime of the Flink Job.
+This deployment mode has been deprecated since Flink 1.15.
+
+#### Job Graph
-A Flink Job is the runtime representation of a [logical graph](#logical-graph)
-(also often called dataflow graph) that is created and submitted by calling
-`execute()` in a [Flink Application](#flink-application).
+Also *JobGraph*.
-#### JobGraph
+A Job Graph is the optimized representation of a [Logical
Graph](#logical-graph), and the
+representation that a [Flink Application](#flink-application) submits to the
[Flink
+Cluster](#flink-cluster).
-see [Logical Graph](#logical-graph)
+Producing the Job Graph is mainly a matter of chaining: consecutive
[Operators](#operator) that are
+not separated by a repartitioning are merged into a single [Task](#task). The
nodes of a Job Graph
+are therefore [Tasks](#task), each implementing one Operator or one [Operator
+Chain](#operator-chain).
+
+The Job Graph is translated into a [Physical Graph](#physical-graph) for
execution.
+
+Job Graph is sometimes referred to as *Optimized Dataflow*.
#### Flink JobManager
-The JobManager is the orchestrator of a [Flink Cluster](#flink-cluster). It
contains three distinct
-components: Flink Resource Manager, Flink Dispatcher and one [Flink
JobMaster](#flink-jobmaster)
-per running [Flink Job](#flink-job).
+Also *Job Manager*.
+
+The JobManager is the orchestrator of a [Flink Cluster](#flink-cluster). It
does not process any
+data itself: it translates the submitted [Job Graph](#job-graph) into a
[Physical
+Graph](#physical-graph), schedules the resulting [Sub-Tasks](#sub-task) on the
+[TaskManagers](#flink-taskmanager), and coordinates [Checkpoints](#checkpoint)
and
+[Savepoints](#savepoint). It contains three distinct components: Flink
Resource Manager, Flink
+Dispatcher and one [Flink JobMaster](#flink-jobmaster) per running [Flink
Job](#flink-job).
#### Flink JobMaster
JobMasters are one of the components running in the
[JobManager](#flink-jobmanager). A JobMaster is
-responsible for supervising the execution of the [Tasks](#task) of a single
job.
+responsible for supervising the execution of the [Sub-Tasks](#sub-task) of a
single Job. It derives
+the [Physical Graph](#physical-graph) from the Job's [Job Graph](#job-graph),
requests the slots
+needed to run it, deploys the Sub-Tasks to the
[TaskManagers](#flink-taskmanager), and triggers the
+Job's [Checkpoints](#checkpoint).
#### JobResultStore
The JobResultStore is a Flink component that persists the results of globally
terminated
-(i.e. finished, cancelled or failed) jobs to a filesystem, allowing the
results to outlive
-a finished job. Each result contains the job's identifier, final state, name,
the application it
-belongs to, etc. These results are then used by Flink to determine whether
jobs should
-be subject to recovery in highly-available clusters.
-
-#### ApplicationResultStore
-
-The ApplicationResultStore is a Flink component that persists the results of
terminated
-(i.e. finished, cancelled or failed) applications to a filesystem, allowing
the results to outlive
-a terminated application. Each result contains the application's identifier,
final state, name,
-etc. These results are then used by Flink to determine whether applications
should
-be subject to recovery in highly-available clusters.
-
-#### History Server
-
-The History Server is a standalone service that serves the detailed history of
completed Flink
-applications and jobs, using archives generated by the JobManager. Unlike the
-[ApplicationResultStore](#applicationresultstore) and
[JobResultStore](#jobresultstore), which store
-minimal metadata for internal recovery decisions in highly-available clusters,
the History Server
-provides detailed archives for analysis via Web UI or REST API after the
cluster has been shut down.
+(i.e. finished, cancelled or failed) Jobs to a filesystem, allowing the
results to outlive
+a finished Job. Each result contains the Job's identifier, final state, name,
the Application it
+belongs to, etc. These results are then used by Flink to determine whether
Jobs should
+be subject to recovery in highly-available Clusters.
#### Logical Graph
-A logical graph is a directed graph where the nodes are [Operators](#operator)
-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](#flink-application).
+A Logical Graph is a Directed Acyclic Graph (DAG) where the nodes are
[Operators](#operator)
+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](#flink-application). For the Table API and SQL, the
Logical Graph is the
+result of parsing and optimizing the [Table Program](#table-program) in the
table planner.
-Logical graphs are also often referred to as *dataflow graphs*.
+Logical Graphs are also often referred to as *Dataflow Graphs* or, for the
DataStream API, as
+*StreamGraphs*. A Logical Graph is optimized into a [Job Graph](#job-graph)
before execution.
#### 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.
+Managed State describes Application State which has been registered with the
framework. This includes
+both [keyed state]({{< ref "docs/dev/datastream/fault-tolerance/state"
>}}#using-keyed-state) and
+non-keyed state (also known as [Operator State]({{< ref
"docs/dev/datastream/fault-tolerance/state" >}}#operator-state)).
+For Managed State, Apache Flink takes care of persistence and rescaling, among
other things.
#### Operator
-Node of a [Logical Graph](#logical-graph). An Operator performs a certain
operation, which is
-usually executed by a [Function](#function). Sources and Sinks are special
Operators for data
-ingestion and data egress.
+Node of a [Logical Graph](#logical-graph). An Operator performs a certain
operation, such as a join,
+an aggregation or a stateless transformation, which is usually executed by a
[Function](#function).
+
+Sources and Sinks are special Operators for data ingestion and data egress: a
Logical Graph always
+begins with one or more Source Operators and ends with one or more Sink
Operators.
+
+Note that parts of the Flink documentation and of the Web UI use the term
*Operator* loosely, also
+referring to a [Task](#task) or a [Sub-Task](#sub-task), leaving the precise
meaning to be inferred
+from the context.
#### Operator Chain
An Operator Chain consists of two or more consecutive [Operators](#operator)
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.
+directly without going through serialization or Flink's network stack, which
removes the overhead of
+the handover between them.
+
+An Operator Chain becomes a single [Task](#task) in the [Job
Graph](#job-graph). Chains are
+recognizable in graphical representations of the Job Graph, such as the Flink
Web UI, because the
+name of the Task is the composition of the names of the chained Operators.
+
+#### Parallelism
+
+The number of parallel flows Flink uses to process the data, and therefore the
way a [Flink
+Job](#flink-job) scales horizontally. The Parallelism of an
[Operator](#operator) determines the
+number of [Sub-Tasks](#sub-task) and of [Physical Partitions](#partition) it
is executed with.
+
+The *Job Parallelism* is the default Parallelism of all Operators of a Job.
The *Operator
+Parallelism* may override it for an individual Operator.
+
+Parallelism is a property of the Job, independent of the number of [Flink
+TaskManagers](#flink-taskmanager) in the [Flink Cluster](#flink-cluster).
#### 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](#Record) to one
or more partitions.
-Partitions of data streams or data sets are consumed by [Tasks](#task) during
runtime. A
-transformation which changes the way a data stream or data set is partitioned
is often called
-repartitioning.
+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.
+A [Transformation](#transformation) which changes the way a data stream or
data set is partitioned is
+often called repartitioning.
+
+*Logical Partitioning* is how records and State are divided in the [Logical
+Graph](#logical-graph) and the [Job Graph](#job-graph), in order to implement
the semantics of an
+operation. A `JOIN` or `GROUP BY` in SQL, or a `keyBy()` in DataStream API,
for example, requires the
+data to be logically partitioned by a key, and the number of Logical
Partitions is then the number of
+distinct keys. Keyed State is isolated per Logical Partition: a
[Function](#function) can only access
+the State of the key of the record or timer it is currently processing.
Operator State and Broadcast
+State, in contrast, are not keyed.
+
+*Physical Partitioning* is how records and State are divided in the [Physical
+Graph](#physical-graph), across the [Sub-Tasks](#sub-task) that Flink executes
in parallel. Each
+Sub-Task handles exactly one Physical Partition and holds only the State
belonging to it, so the
+number of Physical Partitions equals the [Parallelism](#parallelism) of the
[Operators](#operator)
+the Sub-Task implements. Physical Partitioning follows from Logical
Partitioning: in a stream
+partitioned by key, each Physical Partition holds a fixed subset of the keys.
+
+Note that the Flink documentation uses the word *partition* both for these
internal Partitions and
+for the partitions of an external system, such as the Kafka partitions of a
source topic. The
+intended meaning has to be inferred from the context.
#### Physical Graph
-A physical graph is the result of translating a [Logical
Graph](#logical-graph) for execution in a
-distributed runtime. The nodes are [Tasks](#task) and the edges indicate
input/output-relationships
-or [partitions](#partition) of data streams or data sets.
+A Physical Graph is the result of translating a [Job Graph](#job-graph) for
execution in a
+distributed runtime, taking [Parallelism](#parallelism) into account. The
nodes are
+[Sub-Tasks](#sub-task) and the edges are the [Channels](#channel) connecting
them.
+
+Physical Graphs are also referred to as *Parallel Dataflows* or as
*ExecutionGraphs*.
#### Record
Records are the constituent elements of a data set or data stream.
[Operators](#operator) and
-[Functions](#Function) receive records as input and emit records as output.
+[Functions](#function) receive records as input and emit records as output.
#### (Runtime) Execution Mode
-DataStream API programs can be executed in one of two execution modes: `BATCH`
+DataStream API programs can be executed in one of two Execution Modes: `BATCH`
or `STREAMING`. See [Execution Mode]({{< ref
"/docs/dev/datastream/execution_mode" >}}) for more details.
+In `STREAMING` mode, Flink processes unbounded data as it arrives, uses
[Watermarks](#watermark) to implement
+event-time semantics, keeps State in the [State Backend](#state-backend) and
relies on
+[Checkpoints](#checkpoint) for fault tolerance.
+
+In `BATCH` mode, Flink processes a bounded data set with a known beginning and
end. Operators may
+consume their entire input before emitting any output, and Watermarks are not
used for event-time
+semantics. The configured State Backend is ignored: the input of a keyed
operation is instead grouped
+by key through sorting, so that Flink only has to hold the State of one key at
a time, spilling to
+local disk when memory is insufficient.
+
+Note that a bounded data set can also be processed in `STREAMING` mode, for
example by setting
+`scan.bounded.mode` on the Kafka Source (SQL and Table API) or `.setBounded()`
in DataStream API.
+
+#### Savepoint
+
+A consistent snapshot of the [State](#managed-state) of a [Flink
Job](#flink-job), triggered on
+demand. A Job can be resumed from a Savepoint later, for instance across an
Application upgrade or a
+Flink version upgrade.
+
+When a Job is *stopped with a Savepoint*, every [Sub-Task](#sub-task) stops
right after its State has
+been snapshotted, which minimizes the chances of records being reprocessed
when the Job is resumed.
+
+Savepoints are similar to [Checkpoints](#checkpoint). See
+[Checkpoints vs. Savepoints]({{< ref
"docs/ops/state/checkpoints_vs_savepoints" >}}) for a detailed
+comparison.
+
+[Checkpoints](#checkpoint) and Savepoints are also referred to, collectively,
as *State Snapshots* or
+*Snapshots*.
+
#### Flink Session Cluster
A long-running [Flink Cluster](#flink-cluster) which accepts multiple [Flink
Jobs](#flink-job) 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](#flink-application-cluster).
+#### Source Split
+
+Also *Split*.
+
+A Source Split is the unit of work a [Source Operator](#operator) distributes
across its parallel
+[Sub-Tasks](#sub-task): the smallest portion of the input that one Source
Sub-Task reads
+independently. Splits are what make reading from an external system
parallelizable. For example, in
+the Kafka Source, a Source Split is one topic partition.
+
+#### Flink SQL Statement
+
+The unit of execution submitted to Flink when using SQL. A single
data-processing Statement, such as
+an `INSERT INTO ... SELECT`, is executed as one [Flink Job](#flink-job).
Several Statements can be
+submitted as a single Job by grouping them into a [Statement
Set](#statement-set).
+
#### State Backend
-For stream processing programs, the State Backend of a [Flink Job](#flink-job)
determines how its
-[state](#managed-state) is stored on each TaskManager (Java Heap of
TaskManager or (embedded)
-RocksDB).
+For stream processing programs, the State Backend of a [Flink Job](#flink-job)
holds the
+[keyed state]({{< ref "docs/dev/datastream/fault-tolerance/state"
>}}#using-keyed-state) of the
+[Job](#flink-job)'s [Operators](#operator). This is local to each
[TaskManager](#flink-taskmanager):
+either on the Java Heap of the TaskManager (`HashMapStateBackend`) or in
off-heap memory and on
+local disk (`EmbeddedRocksDBStateBackend`).
+
+The State Backend is working storage, not long-term storage: it is [Checkpoint
+Storage](#checkpoint-storage) that makes the State durable and recoverable.
+
+Note that non-keyed state (also known as
+[Operator State]({{< ref "docs/dev/datastream/fault-tolerance/state"
>}}#operator-state)) is always
+maintained in memory, in the JVM heap of the
[TaskManager](#flink-taskmanager), regardless of the
+configured State Backend.
+
+#### Statement Set
+
+A group of SQL DML Statements wrapped in `EXECUTE STATEMENT SET BEGIN ...
END`, which Flink submits
+and optimizes as a single Statement and executes as a single [Flink
Job](#flink-job).
+
+Because the Statements are optimized together, they may share some Source and
Sink
+[Operators](#operator), avoiding reading the same data more than once. See
[INSERT
+Statement]({{< ref "docs/sql/reference/dml/insert"
>}}#insert-into-multiple-tables) for the syntax.
+
+#### StreamExchange Operator
+
+An [Operator](#operator) that only appears in [Logical Graphs](#logical-graph)
generated from
+[Table Programs](#table-program). It repartitions a stream, and is the
equivalent of a hash (key-by)
+connection between two Operators in the DataStream API.
+
+#### StreamGraphs
+
+See [Logical Graph](#logical-graph)
#### Sub-Task
-A Sub-Task is a [Task](#task) responsible for processing a
[partition](#partition) of
-the data stream. The term "Sub-Task" emphasizes that there are multiple
parallel Tasks for the same
-[Operator](#operator) or [Operator Chain](#operator-chain).
+Also *Subtask*.
+
+A Sub-Task is a node of the [Physical Graph](#physical-graph) and the smallest
unit of execution in
+the Flink runtime, distributed across the [Flink Cluster](#flink-cluster) to
process data. Each
+[Task](#task) results in as many Sub-Tasks as the [Parallelism](#parallelism)
of the
+[Operators](#operator) it implements, which is why the term emphasizes that
there are multiple
+parallel Sub-Tasks for the same Task.
+
+Because a Task may implement a single Operator or a whole [Operator
Chain](#operator-chain), a
+Sub-Task may execute one or more Operators. All Operators in a Chain
necessarily share the same
+Parallelism, otherwise they would not have been chained.
+
+Each Sub-Task processes one [Physical Partition](#partition) of the data and
holds only the State
+belonging to that Partition. Within a Sub-Task, a single thread generally
carries a record through
+all the Operators the Sub-Task implements, although some internal buffering
happens and some
+Operators are partly asynchronous.
+
+Operators that call a user-defined [Function](#function) create a separate
instance of that Function
+per Sub-Task, and processing within one instance always runs on a single
thread, so instance fields
+of a Function implementation are not subject to concurrent access.
#### Table Program
A generic term for pipelines declared with Flink's relational APIs (Table API
or SQL).
#### Task
-Node of a [Physical Graph](#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](#operator) or [Operator Chain](#operator-chain).
+A Task is a node of the [Job Graph](#job-graph), implementing either a single
[Operator](#operator)
+or several Operators [chained](#operator-chain) together. Tasks are the blocks
shown in the graphical
+representation of a Job in the Flink Web UI.
+
+At runtime, each Task is executed as one [Sub-Task](#sub-task) per [Physical
+Partition](#partition) of the data.
#### Flink TaskManager
-TaskManagers are the worker processes of a [Flink Cluster](#flink-cluster).
[Tasks](#task) are
-scheduled to TaskManagers for execution. They communicate with each other to
exchange data between
-subsequent Tasks.
+Also *Task Manager*.
+
+TaskManagers are the worker processes of a [Flink Cluster](#flink-cluster),
and the processes that do
+the actual data processing. [Sub-Tasks](#sub-task) are scheduled to
TaskManagers for execution, and
+TaskManagers communicate with each other over [Channels](#channel) to exchange
data between
+subsequent Sub-Tasks.
+
+Each TaskManager manages its own local [State Backend](#state-backend), and
reads from and writes to
+[Checkpoint Storage](#checkpoint-storage) independently during
[Checkpoints](#checkpoint) and
+[Savepoints](#savepoint).
#### 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
+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](#operator) and [Functions](#function) are the "physical" parts of
Flink's API,
-Transformations are only an API concept. Specifically, most transformations are
+Transformations are only an API concept. Specifically, most Transformations are
Review Comment:
I am not sure what " most Transformations are implemented by certain
[Operators](#operator)" is telling me. I would either remove this sentence or
be explicit about the certain operators you are referring to or point to how to
find the details.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
