infoverload commented on a change in pull request #19107:
URL: https://github.com/apache/flink/pull/19107#discussion_r829105460
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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>
+
+This is a type of [keyed state](#keyed-state) that keeps a list of elements.
You can append elements
+and retrieve an Iterable over all currently stored elements. Elements are
added using add(T) or
+addAll(List<T>). The Iterable can be retrieved using Iterable<T> get().
#### 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).
+This is a directed graph where the nodes are [operators](#operator) and the
edges define input/output
+relationships of the operators and correspond to [DataStreams](#datastreams).
A logical graph is created
+by submitting jobs to a [Flink cluster](#(flink)-cluster) from a [Flink
application](#(flink)-application).
-Logical graphs are also often referred to as *dataflow graphs*.
+Logical graphs are also often referred to as [dataflow](#dataflow).
#### 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 is application state which has been registered with the stream
processing framework,
+which will take care of the persistence and rescaling of this state.
+
+This type of state is represented in data structures controlled by the Flink
runtime, such as internal
+hash tables, or RocksDB. Flink’s runtime encodes the states and writes them
into the checkpoints.
+
+[Keyed state](#keyed-state) and [operator state](#operator-state) exist in two
forms: managed and [raw](#raw-state).
+
+#### MapState<UK, UV>
+
+This is a type of [keyed state](#keyed-state) that keeps a list of mappings.
You can put key-value
+pairs into the state and retrieve an Iterable over all currently stored
mappings. Mappings are added
+using put(UK, UV) or putAll(Map<UK, UV>). The value associated with a key can
be retrieved using get(UK).
+
+#### Non-keyed State
+
+This type of state is bound to one parallel operator instance and is also
called [operator state](#operator-state).
+
+It is possible to work with [managed state](#managed-state) in non-keyed
contexts but it is unusual
+for user-defined functions to need non-keyed state and the interfaces involved
would be different.
+
+This feature is most often used in the implementation of [sources](#source)
and [sinks](#sink).
+
+#### Offset
+
+A number identifying how far you are from the beginning of a certain
[DataStream](#datastream).
#### 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
+An operator is a 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.
#### 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.
+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. This is
a useful optimization
+and increases overall throughput while decreasing latency. The chaining
behavior can be configured.
+
+#### Operator State
+
+See [non-keyed state](#non-keyed-state).
+
+#### Parallelism
+
+This is a technique for making programs run faster by performing several
computations simultaneously.
#### 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 [DataStream](#datastream).
A DataStream is divided
+into partitions by assigning each [record](#record) to one or more partitions
via keys. Partitions of
+DataStreams are consumed by [tasks](#task) during runtime. A transformation
that changes the way a
+DataStream is partitioned is often called repartitioning.
#### 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 [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 DataStreams.
+
+#### POJO
+
+This is a composite data type and can be serialized with Flink's serializer.
Flink recognizes a data
+type as a POJO type (and allows “by-name” field referencing) if the following
conditions are met:
+
+- the class is public and standalone (no non-static inner class)
+- the class has a public no-argument constructor
+- all non-static, non-transient fields in the class (and all superclasses) are
either public (and
+ non-final) or have public getter- and setter- methods that follow the Java
naming conventions for
+ getters and setters
+
+Flink analyzes the structure of POJO types and can process POJOs more
efficiently than general types.
+
+#### Process Functions
+
+This type of function combines event processing with timers and state and is
the basis for creating
+event-driven applications with Flink.
+
+#### Processing Time
+
+The time when a specific operator in your pipeline is processing the event.
Computing analytics based
+on processing time can cause inconsistencies and make it difficult to
re-analyze historic data or test
+new implementations.
+
+#### Queryable State
+
+This is managed keyed (partitioned) state that can be accessed from outside of
Flink during runtime.
+
+#### Raw State
+
+This is state that operators keep in their own data structures. When
checkpointed, only a sequence of
+bytes is written into the checkpoint and Flink knows nothing about the state’s
data structures and will
+see only the raw bytes.
+
+[Keyed state](#keyed-state) and [operator state](#operator-state) exist in two
forms: [managed](#managed-state) and raw.
#### 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.
+Records are the elements that make up a [DataStream](#datastream).
[Operators](#operator) and [functions](#function)
+receive records as input and emit records as output.
+
+#### ResourceManager
+
+This is part of the [JobManager](#JobManager) and is responsible for resource
de-/allocation and
+provisioning in a Flink cluster.
+
+#### Rich Functions
+
+A RichFunction is a "rich" variant of Flink's function interfaces for data
transformation. These functions
+have some additional methods needed for working with managed keyed state such
as `open(Configuration c)`,
+`close()`, `getRuntimeContext()`.
+
+#### Rolling Total
+
+The sum of a sequence of numbers which is updated each time a new number is
added to the sequence,
+by adding the value of the new number to the previous rolling total.
#### (Runtime) Execution Mode
-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.
+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.
+
+#### Savepoint
+
+A [snapshot](#snapshot) triggered manually by a user (or an API call) for some
operational purpose,
+such as a stateful redeploy/upgrade/rescaling. Savepoints are always complete
and aligned and are
+optimized for operational flexibility.
+
+#### Scalar
+
+A scalar refers to a single value. This is in contrast to a set of values.
+
+#### Schema
+
+This refers to the organization or structure of data as a blueprint.
+
+#### Serialization
+
+This is the process of turning a data element in memory into a stream of bytes
so that you can more
+efficiently store it on disk or send it over the network.
+
+Flink handles data types and serialization in a unique way, containing its own
type descriptors,
+generic type extraction, and type serialization framework.
+
+#### Session Cluster
+
+A long-running [Flink cluster](#(flink)-cluster) which accepts multiple [Flink
jobs](#(flink)-job) for
+execution. The lifetime of this cluster is not bound to the lifetime of any
Flink job. Formerly, a
+Session Cluster was also known as a Flink Cluster in *session mode*.
+
+#### Session Windows
+
+This is a [window](#window) that groups elements by sessions of activity.
Session windows do not overlap
+and do not have a fixed start and end time, in contrast to [tumbling
windows](#tumbling-window) and
+[sliding windows](#sliding-window). A session window closes when it does not
receive elements for a
+certain period of time (i.e. when a gap of inactivity occurred).
+
+#### Shuffling
+
+This is a process of redistributing data across [partitions](#partition) (aka
repartitioning) that
+may or may not cause moving data across JVM processes or over the network.
+
+#### Side Outputs
+
+This is an extra output stream from a Flink operator. Beyond error reporting,
side outputs are also
+a good way to implement an n-way split of a stream.
+
+#### Sink
+
+A sink is a component that consumes incoming processed
[DataStreams](#datastream) from Flink and
+forwards them to files, sockets, external systems, or print them.
+
+A few predefined data sinks are built into Flink, such as support for writing
to files, to stdout/stderr,
+and to sockets.
+
+#### Sliding Window
+
+This is a [window](#window) that groups elements to windows of fixed length.
Similar to [tumbling windows](#tumbling-window),
+the size of sliding windows are configured by the window size parameter. An
additional window slide
+parameter controls how frequently a sliding window is started. Hence, sliding
windows can be overlapping
+if the slide is smaller than the window size. In this case, elements are
assigned to multiple windows.
+
+#### Snapshot
+
+A generic term referring to a global, consistent image of the state of a
[Flink job](#(flink)-job).
+A snapshot can be full or incremental and includes a pointer into each of the
data sources as well as
+a copy of the state from each of the job’s stateful operators that resulted
from having processed all
+the events up to those positions in the sources.
+
+Flink periodically takes persistent snapshots of all the state in every
operator and copies these
+snapshots somewhere more durable, such as a distributed file system.
+
+Flink uses a variant of the Chandy-Lamport algorithm known as asynchronous
barrier snapshotting.
+
+#### Source
+
+This is the source of the data that gets piped into a [Flink
application](#(flink)-application) to be
+processed. As long as data keeps flowing in, Flink can keep performing
calculations.
+
+A few basic data sources are built into Flink and are always available, such
as reading from files,
+directories, sockets, and ingesting data from collections and iterators.
-#### Flink Session Cluster
+#### Spilling
-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).
+This is a technique where state data is spilled to disk before JVM heap memory
is exhausted.
#### 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) determines how its
+[state](#managed-state) is stored on each [TaskManager](#taskmanager).
-#### Sub-Task
+Two implementations of state backends are available. One is based on RocksDB,
an embedded key/value
+store that keeps its working state on disk, and the other is heap-based that
keeps its working state
+in memory, on the Java heap.
-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).
+#### Stream Barriers
+
+A core element of Flink's distributed snapshotting. Stream barriers are
injected into the [DataStream](#datastream)
+and flow with the [records](#record) as part of the DataStream. Barriers never
overtake records and
+flow strictly in line. A barrier separates the records in the DataStream into
the set of records that
+goes into the current snapshot, and the records that go into the next snapshot.
+
+#### Subtask
+
+A subtask is a [task](#task) responsible for processing a
[partition](#partition) of the [DataStream](#datastream).
+The term "subtask" emphasizes that there are multiple parallel tasks for the
same [operator](#operator)
+or [operator chain](#operator-chain).
#### 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).
+This is a node in 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).
+
+#### Task Chaining
+
+This is an optimization where Flink puts two subsequent
[transformations](#transformation) in the same thread, if possible.
+
+#### Task Parallelism
+
+This is the number of parallel instances of a task. A [Flink
application](#(flink)-application) consists
+of multiple [tasks](#task) ([transformations](#transformation),
[operators](#operator), [sources](#source),
+[sinks](#sink)). A task is split into several parallel instances for execution
and each parallel instance
+processes a subset of the task's input data.
+
+#### Task Slot
+
+This is one unit of resource scheduling in a [Flink
cluster](#(flink)-cluster). Each task slot
+represents a fixed subset of resources of the [TaskManager](#taskmanager). The
number of task slots
+in a TaskManager indicates the number of concurrent processing tasks.
+
+#### TaskManager
-#### Flink TaskManager
+TaskManagers are the worker processes of a [Flink cluster](#flink-cluster),
execute the tasks of a
+dataflow, and buffer and exchange the [DataStreams](#datastreams). They
connect to [JobManagers](#jobmanagers),
+announce themselves as available, and are assigned work. [Tasks](#task) are
scheduled to TaskManagers
+for execution. They communicate with each other to exchange data between
subsequent tasks. Each TaskManager
+is a JVM process and may execute one or more subtasks in separate threads.
-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.
+There must always be at least one TaskManager. The smallest unit of resource
scheduling in a TaskManager
+is a [task slot](#task-slot).
+
+#### Timer
+
+Timers allow applications to react to changes in [processing
time](#processing-time) and in [event time](#event-time).
+There are at most one timer per key and per second.
+
+Timers are fault-tolerant and checkpointed along with the state of the
application. In case of a failure
+recovery or when starting an application from a [savepoint](#savepoint),
timers are restored.
#### 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](#operator) and [Functions](#function) are the "physical" parts of
Flink's API,
-Transformations are only an API concept. Specifically, most transformations are
-implemented by certain [Operators](#operator).
+A transformation is applied to one or more [DataStreams](#datastreams) and
results in one or more
+output DataStreams. A transformation might change a DataStream on a
[per-record](#record) basis, but
+might also only change its [partitioning](#partition) or perform an
[aggregation](#aggregation). While
+[operators](#operator) and [functions](#function) are the "physical" parts of
Flink's API, transformations
+are an API concept. Specifically, most transformations are implemented by
certain [operators](#operator).
+
+#### Tumbling Window
+
+This is a [window](#window) that groups elements by a specified window size.
Tumbling windows have a
+fixed size and do not overlap. For example, if you specify a tumbling window
with a size of 5 minutes,
+the current window will be evaluated and a new window will be started every
five minutes.
+
+#### Tuple
+
+A composite data type that has a finite ordered list of immutable elements.
+
+#### Unbounded streams
+
+Unbounded [DataStreams](#datastream) have a start but no defined end. They do
not terminate, provide
+data as it is generated, and must be continuously processed.
+
+#### (User-Defined) Functions
+
+Functions are implemented by the user and encapsulate the application logic of
a [Flink application](#(flink)-application).
+Most functions are wrapped by a corresponding [operator](#operator).
+
+#### User-Defined Aggregate Function (UDAF)
+
+This type of user-defined function aggregates multiple values into a single
value.
+
+#### User-Defined Scalar Function (UDSF)
+
+This type of user-defined function maps zero, one, or more [scalar](#scalar)
values to a new scalar value.
+
+#### User-Defined Table-valued Function (UDTF)
+
+This type of user-defined function uses zero, one, or multiple
[scalar](#scalar) values as input parameters
+(including variable-length parameters). A UDTF returns any number of rows,
rather than a single value.
+The returned rows can consist of one or more columns.
+
+#### ValueState<T>
+
+This is a type of [keyed state](#keyed-state) where Flink will store a single
object for each key.
+ValueState keeps a value that can be updated and retrieved (scoped to key of
the input element so there
+will possibly be one value for each key that the operation sees). The value
can be set using update(T)
+and retrieved using T value().
+
+#### Watermark
+
+This is the mechanism in Flink to measure progress in event time. Watermarks
are special timestamped
+elements that get inserted by watermark generators into a
[DataStream](#datastream). They flow as part
Review comment:
What is the difference between a stream and a DataStream?
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