nictownsend commented on code in PR #28863:
URL: https://github.com/apache/flink/pull/28863#discussion_r3913199023
##########
docs/content/docs/concepts/glossary.md:
##########
@@ -46,137 +48,331 @@ Cluster](#flink-cluster) is bound to the lifetime of the
Flink Application.
#### 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.
+(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.
+
+#### Channel
+
+Also called *Stream Partitions*.
+
+A Channel is the physical link between a [Sub-Task](#sub-task) and a
downstream Sub-Task, and the
+edge of a [Physical Graph](#physical-graph). Parts of the documentation refer
to Channels as *Stream
+Partitions*, in the sense of internal, physical Partitions.
+
+Channels carry data records as well as signals such as
[Watermarks](#watermark), Watermark Status
+updates and Checkpoint barriers. Transmission over a Channel is always
unidirectional (upstream to
+downstream) and asynchronous.
+
+A Sub-Task may have one or more input Channels and one or more output
Channels. Source Sub-Tasks have
+no input Channels, since they begin the graph, and Sink Sub-Tasks have no
output Channels, since they
+end it.
+
+A Sub-Task routes each record to one of its output Channels according to the
[Physical
+Partitioning](#partition) of the stream. Hash partitioning (`keyBy()` in the
DataStream API, `GROUP
+BY` in SQL) routes a record to the Channel connected to the downstream
Sub-Task that handles the
+record's key, whereas `rebalance()` or `rescale()` may round-robin records
across output Channels.
+
+A Channel is *local* when both Sub-Tasks run in the same [Flink
+TaskManager](#flink-taskmanager), in which case records are handed over
through an in-memory buffer,
+or *remote* when the Sub-Tasks run in different TaskManagers, in which case
the data crosses the
+network.
+
+#### Checkpoint
+
+A consistent snapshot of the State of a [Flink Job](#flink-job) at a logical
point in time, taken
+with a variant of the Chandy-Lamport algorithm and written to [Checkpoint
+Storage](#checkpoint-storage).
+
+A Checkpoint contains the [State](#managed-state) of all stateful
[Operators](#operator). This also
+includes source positions (for example Kafka partition offsets), assignment of
[Source Splits](#source-split)
+to [Sub-Tasks](#sub-task), and Sink transaction metadata. Async I/O in-flight
data and buffered data
+of some asynchronous Sink connectors are also part of the
[Operator](#operator) [State](#managed-state)
+and are saved in the Checkpoint.
+When [Unaligned Checkpoints]({{< ref
"docs/concepts/stateful-stream-processing" >}}#unaligned-checkpointing)
+are enabled, it may also contain data in flight between Sub-Tasks.
+
+Checkpoints are triggered automatically and periodically while the Job is
running, and are used to
+recover from failures such as a TaskManager crash or a network problem: the
Job restarts from the
+latest completed Checkpoint. They are designed for low overhead and run mostly
asynchronously,
+without blocking record processing, apart from a synchronous phase in each
Sub-Task.
+Transactional [Sources and Sinks](#operator) tie their transactions to the
Checkpoint; the
+Kafka Sink, for instance, commits its Kafka transactions when a Checkpoint
completes.
+
+Checkpoints are only used in the `STREAMING` [Execution
Mode](#runtime-execution-mode). In `BATCH`
+mode, Flink recovers instead by backtracking to previous processing stages
whose intermediate results
+are still available, so that potentially only the failed [Tasks](#task) and
their predecessors are
+restarted. As a consequence, Sinks that rely on Checkpoints to commit their
transactions do not work
+in `BATCH` mode unless they are implemented with the Unified Sink API, which
commits once the whole
+input has been processed.
+
+Compare to [Savepoint](#savepoint).
+
+Checkpoints and [Savepoints](#savepoint) are also referred to, collectively,
as *State Snapshots* or
+*Snapshots*.
#### Checkpoint Storage
-The location where the [State Backend](#state-backend) will store its snapshot
during a checkpoint (Java Heap of [JobManager](#flink-jobmanager) or
Filesystem).
+The durable location where [Checkpoints](#checkpoint) and
[Savepoints](#savepoint) are saved. It can
+be either the Java Heap of the [Flink JobManager](#flink-jobmanager) or a
filesystem. Production
+deployments use a filesystem, typically remote object storage, since
Checkpoint Storage is what makes
+State survive the loss of a [TaskManager](#flink-taskmanager) or of the whole
[Flink Cluster](#flink-cluster).
+
+The relationship between the State Backend and Checkpoint Storage changes with
[Disaggregated
+State]({{< ref "docs/ops/state/disaggregated_state" >}}), where remote storage
becomes the primary
+location of the State and the local State Backend acts as a cache, the two
being synchronized
+asynchronously.
#### Flink Cluster
A distributed system consisting of (typically) one
[JobManager](#flink-jobmanager) and one or more
-[Flink TaskManager](#flink-taskmanager) processes.
+[Flink TaskManager](#flink-taskmanager) processes. Each of these processes
runs in a separate JVM,
+usually on a separate container or machine, although this is not a requirement.
+
+See also [Flink Architecture: Anatomy of a Flink Cluster]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster).
#### 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](#Record).
+An Event is a statement about a change of the state of the domain modeled by
the
+Application. Events can be input and/or output of a stream or batch processing
Application.
+Events are special types of records.
+
+#### Execution Graph
-#### ExecutionGraph
+Also called *ExecutionGraph*.
-see [Physical Graph](#physical-graph)
+See [Physical Graph](#physical-graph)
#### Function
-Functions are implemented by the user and encapsulate the
+Functions are implemented by the user, in Java or Python, and encapsulate the
application logic of a Flink program. Most Functions are wrapped by a
corresponding
-[Operator](#operator).
+[Operator](#operator). In the DataStream API, Functions are passed to the
+[Transformations](#transformation) they implement. In the Table API and SQL,
they are declared
+separately as [User-Defined Functions]({{< ref "docs/dev/table/functions/udfs"
>}}) (UDF) or
+[Process Table Functions]({{< ref "docs/dev/table/functions/ptfs" >}}) (PTF).
#### 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.
+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.
#### Instance
The term *instance* is used to describe a specific instance of a specific type
(usually
-[Operator](#operator) or [Function](#function)) during runtime. As Apache
Flink is mostly written in
+[Operator](#operator) or [Function](#function)) at 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](#operator) or [Function](#function) type are running in
parallel.
#### Flink Job
-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).
+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
+(See [Restart Pipelined Region Failover Strategy]({{< ref
"docs/ops/state/task_failure_recovery"
>}}#restart-pipelined-region-failover-strategy)).
+
+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).
+
+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.
+[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
-#### JobGraph
+Also called *JobGraph* or *Optimized Dataflow*.
-see [Logical Graph](#logical-graph)
+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).
+
+Producing the Job Graph is mainly a matter of chaining [Operators](#operator):
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.
#### 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 called *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).
+
+See also [Flink Architecture: JobManager]({{< ref
"docs/concepts/flink-architecture" >}}#jobmanager).
#### 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.
+(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.
+
+#### Key Group
+
+A Key Group is the atomic unit of key distribution and state assignment across
parallel
+[Sub-Tasks](#sub-task). Every key is mapped deterministically to a Key Group
based on
+`keyGroupIndex = MathUtils.murmurHash(key.hashCode()) % maxParallelism`.
+This allows stateful [Operators](#operator) to rescale without rehashing
individual keys.
+
+The total number of Key Groups is equal to the `maxParallelism` configuration,
set at [Job](#flink-job)
+level or overridden at [Operator](#operator) level.
+A contiguous range of Key Groups is assigned to each [Sub-Task](#sub-task),
and Key Groups are evenly
+distributed across all [Sub-Tasks](#sub-task).
#### 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.
+A 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.
+
+See also [Flink Architecture: Tasks and Operator Chains]({{< ref
"docs/concepts/flink-architecture" >}}#tasks-and-operator-chains).
+
+#### 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
Review Comment:
Re-read, that's fair
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