MartijnVisser commented on a change in pull request #19107:
URL: https://github.com/apache/flink/pull/19107#discussion_r829945284
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File path: docs/content/docs/concepts/glossary.md
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@@ -25,182 +25,605 @@ under the License.
# Glossary
+#### Aggregation
+
+Aggregation is an operation that takes multiple values and returns a single
value. When working with
+streams, it generally makes more sense to think in terms of aggregations over
finite windows, rather
+than over the entire stream.
+
+#### (Flink) Application
+
+A Flink application is any user program that submits one or multiple [Flink
Jobs](#flink-job) from its
+`main()` method. The execution of these jobs can happen in a local JVM or on a
remote setup of clusters
+with multiple machines.
+
+The jobs of an application can either be submitted to a long-running [Session
Cluster](#session-cluster),
+to a dedicated [Application Cluster](#application-cluster), or to a [Job
Cluster](#job-cluster).
+
+#### Application Cluster
+
+A Flink application cluster is a dedicated [Flink cluster](#(flink)-cluster)
that only executes
+[Flink jobs](#flink-job) from one [Flink application](#(flink)-application).
The lifetime of the Flink
+cluster is bound to the lifetime of the Flink application.
+
+#### Asynchronous Snapshotting
+
+A form of [snapshotting](#snapshot) that doesn't impede the ongoing stream
processing by allowing an
+operator to continue processing while it stores its state snapshot,
effectively letting the state
+snapshots happen asynchronously in the background.
+
+#### At-least-once
+
+A fault-tolerance guarantee and data delivery approach where multiple attempts
are made at delivering
+an event such that at least one succeeds. This guarantees that nothing is
lost, but you may experience
+duplicated results.
+
+#### At-most-once
+
+A data delivery approach where each event is delivered zero or one times.
There is lower latency but
+events may be lost.
+
+#### Backpressure
+
+A situation where a system is receiving data at a higher rate than it can
process during a temporary
+load spike.
+
+#### Barrier Alignment
+
+For providing exactly-once guarantees, Flink aligns the streams at operators
that receive multiple
+input streams, so that the snapshot will reflect the state resulting from
consuming events from both
+input streams up to (but not past) both barriers.
+
+#### Batch Processing
+
+This is the processing and analysis on a set of data that have already been
stored over a period
+of time (i.e. in groups or batches). The results are usually not available in
real-time. Flink
+executes batch programs as a special case of streaming programs.
+
+#### Bounded Streams
+
+Bounded [DataStreams](#datastream) have a defined start and end. They can be
processed by ingesting
+all data before performing any computations. Ordered ingestion is not required
to process bounded streams
+because a bounded data set can always be sorted. Processing of bounded streams
is also known as
+[batch processing](#batch-processing).
+
+#### Checkpoint
+
+A [snapshot](#snapshot) taken automatically by Flink for the purpose of being
able to recover from
+faults. A checkpoint marks a specific point in each of the input streams along
with the corresponding
+state for each of the operators. Checkpoints can be incremental and unaligned,
and are optimized for
+being restored quickly.
+
+#### Checkpoint Barrier
+
+A special marker that flows along the graph and triggers the checkpointing
process on each of the
+parallel instances of the operators. Checkpoint barriers are injected into the
source operators and
+flow together with the data. If an operator has multiple outputs, it gets
"split" into both of them.
+
+#### Checkpoint Coordinator
+
+This coordinates the distributed snapshots of operators and state. It is part
of the JobManager and
+instructs the TaskManager when to begin a checkpoint by sending the messages
to the relevant tasks
+and collecting the checkpoint acknowledgements.
+
#### 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 location where the [state backend](#state-backend) will store its snapshot
during a checkpoint.
+This could be on the Java heap of the [JobManager](#flink-jobmanager) or on a
file system.
+
+#### (Flink) Client
+
+This is not part of the runtime and program execution but is used to prepare
and send a dataflow graph
+to the JobManager. The Flink client runs either as part of the program that
triggers the execution or
+in the command line process via `./bin/flink run`.
+
+#### (Flink) Cluster
-#### Flink Application Cluster
+A distributed system consisting of (typically) one [JobManager](#jobmanager)
and one or more
+[TaskManager](#taskmanager) processes.
-A Flink Application Cluster is a dedicated [Flink Cluster](#flink-cluster) that
-only executes [Flink Jobs](#flink-job) from one [Flink
-Application](#flink-application). The lifetime of the [Flink
-Cluster](#flink-cluster) is bound to the lifetime of the Flink Application.
+#### Connected Streams
-#### Flink Job Cluster
+A pattern in Flink where a single operator has two input streams. Connected
streams can also be used
+to implement streaming joins.
-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.
+#### Connectors
-#### Flink Cluster
+Connectors allow [Flink applications](#(flink)-applications) to read from and
write to various external
+systems. They support multiple formats in order to encode and decode data to
match Flink’s data structures.
-A distributed system consisting of (typically) one
[JobManager](#flink-jobmanager) and one or more
-[Flink TaskManager](#flink-taskmanager) processes.
+#### Dataflow
+
+See [logical graph](#logical-graph).
+
+#### DataStream
+
+This is a collection of data in a Flink application. You can think of them as
immutable collections
+of data that can contain duplicates. This data can either be finite or
unbounded.
+
+#### Directed Acyclic Graph (DAG)
+
+This is a graph that is directed and without cycles connecting the other
edges. It can be used to
+conceptually represent a [dataflow](#dataflow) where you never look back to
previous events.
+
+#### Dispatcher
+
+This is a component of the [JobManager](#jobmanager) and provides a REST
interface to submit Flink
+applications for execution and starts a new [JobMaster](#jobmaster) for each
submitted job. It also
+runs the Flink web UI to provide information about job executions.
#### 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 modelled by
the application. Events
+can be input and/or output of a stream processing application. Events are
special types of
+[records](#Record).
+
+#### Event Time
+
+The time when an [event](#event) occurred, as recorded by the device producing
(or storing) the event.
+For reproducible results, you should use event time because the result does
not depend on when the
+calculation is performed.
+
+If you want to use event time, you will also need to supply a Timestamp
Extractor and Watermark Generator
+that Flink will use to track the progress of event time.
+
+#### Exactly-once
+
+A fault-tolerance guarantee and data delivery approach where nothing is lost
or duplicated. This does
+not mean that every event will be processed exactly once. Instead, it means
that every event will affect
+the state being managed by Flink exactly once.
#### ExecutionGraph
-see [Physical Graph](#physical-graph)
+See [Physical Graph](#physical-graph).
+
+#### Externalized Checkpoint
-#### Function
+A checkpoint that is configured to be retained instead of being deleted when a
job is cancelled.
+Flink normally retains only the n-most-recent checkpoints (n being
configurable) while a job is running
+and deletes them when a job is cancelled.
-Functions are implemented by the user and encapsulate the
-application logic of a Flink program. Most Functions are wrapped by a
corresponding
-[Operator](#operator).
+You can manually resume from an externalized checkpoint.
-#### Instance
+#### Format
-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
-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.
+A table format is a storage format that defines how to map binary data onto
table columns.
+Flink comes with a variety of built-in output formats that can be used with
table [connectors](#connector).
-#### Flink Application
+#### Ingestion Time
-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 timestamp recorded by Flink at the moment it ingests the event.
-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).
+#### (Flink) Job
-#### Flink Job
+This 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 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).
+#### Job Cluster
+
+This is a dedicated [Flink cluster](#(flink)-cluster) that only executes a
single [Flink job](#(flink)-job).
+The lifetime of the Flink cluster is bound to the lifetime of the Flink job.
This deployment mode has
+been deprecated since Flink 1.15.
#### JobGraph
-see [Logical Graph](#logical-graph)
+See [Logical Graph](#logical-graph).
+
+#### JobManager
-#### Flink JobManager
+The JobManager is the orchestrator of a [Flink cluster](#(flink)-cluster). It
contains three distinct
+components: ResourceManager, Dispatcher, and a [JobMaster](#jobmaster) per
running [Flink job](#(flink)-job).
-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).
+There is always at least one JobManager. A high-availability setup might have
multiple JobManagers,
+one of which is always the leader.
-#### Flink JobMaster
+#### 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.
+This is one of the components that run in the [JobManager](#jobmanager). It is
responsible for supervising
+the execution of the [tasks](#task) of a single [job](#(flink)-job). Multiple
jobs can run simultaneously
+in a [Flink cluster](#(flink)-cluster), each having its own JobMaster.
#### 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. These results are then used by Flink to determine whether jobs
should
-be subject to recovery in highly-available clusters.
+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. These results
+are then used by Flink to determine whether jobs should be subject to recovery
in highly-available clusters.
+
+#### Key Group
+
+These are the atomic unit by which Flink can redistribute [keyed
state](#keyed-state). There are
+exactly as many key groups as the defined maximum parallelism. During
execution, each parallel instance
+of a keyed operator works with the keys for one or more key groups.
+
+#### Keyed State
+
+Keyed state is one of the two basic types of state in Apache Flink (the other
being operator state).
+In order to have all events with the same value of an attribute grouped
together, you can partition
+a stream around that attribute, and maintain it as an embedded key/value
store. This results in a keyed
+state.
+
+A keyed state is always bound to keys and is only available to functions and
operators that process
+data from a keyed stream.
+
+Flink supports several different types of keyed state, with the simplest one
being [ValueState](#valuestate).
+
+#### Keyed Stream
+
+A keyed stream is a [DataStream](#DataStream) on which [operator
state](#operator-state) is partitioned
+by a key. Typical operations supported by a DataStream are also possible on a
keyed stream, except for
+partitioning methods such as shuffle, forward, and keyBy.
+
+#### Lateness
+
+Lateness is defined relative to the [watermarks](#watermark). A watermark(t)
asserts that the stream
+is complete up through to time t. Any event is considered late if it comes
after the watermark whose
+timestamp is ≤ t.
+
+#### ListState<T>
Review comment:
I would rather have a generic term List State that explains what this
is, without mentioning the classname.
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