Flink programs are regular programs that implement transformations on distributed collections (e.g., filtering, mapping, updating state, joining, grouping, defining windows, aggregating). Collections are initially created from sources (e.g., by reading from files, kafka topics, or from local, in-memory collections). Results are returned via sinks, which may for example write the data to (distributed) files, or to standard output (for example, the command line terminal). Flink programs run in a variety of contexts, standalone, or embedded in other programs. The execution can happen in a local JVM, or on clusters of many machines.
Depending on the type of data sources, i.e. bounded or unbounded sources, you would either write a batch program or a streaming program where the DataSet API is used for batch and the DataStream API is used for streaming. This guide will introduce the basic concepts that are common to both APIs but please see our Streaming Guide and Batch Guide for concrete information about writing programs with each API.
NOTE: When showing actual examples of how the APIs can be used we will use
StreamingExecutionEnvironment
and the DataStream
API. The concepts are exactly the same
in the DataSet
API, just replace by ExecutionEnvironment
and DataSet
.
Flink has the special classes DataSet
and DataStream
to represent data in a program. You
can think of them as immutable collections of data that can contain duplicates. In the case
of DataSet
the data is finite while for a DataStream
the number of elements can be unbounded.
These collections differ from regular Java collections in some key ways. First, they are immutable, meaning that once they are created you cannot add or remove elements. You can also not simply inspect the elements inside.
A collection is initially created by adding a source in a Flink program and new collections are
derived from these by transforming them using API methods such as map
, filter
and so on.
Flink programs look like regular programs that transform collections of data. Each program consists of the same basic parts:
execution environment
,We will now give an overview of each of those steps, please refer to the respective sections for more details. Note that all core classes of the Java DataSet API are found in the package org.apache.flink.api.java while the classes of the Java DataStream API can be found in org.apache.flink.streaming.api.
The StreamExecutionEnvironment
is the basis for all Flink programs. You can
obtain one using these static methods on StreamExecutionEnvironment
:
Typically, you only need to use getExecutionEnvironment()
, since this
will do the right thing depending on the context: if you are executing
your program inside an IDE or as a regular Java program it will create
a local environment that will execute your program on your local machine. If
you created a JAR file from your program, and invoke it through the
command line, the Flink cluster manager
will execute your main method and getExecutionEnvironment()
will return
an execution environment for executing your program on a cluster.
For specifying data sources the execution environment has several methods to read from files using various methods: you can just read them line by line, as CSV files, or using completely custom data input formats. To just read a text file as a sequence of lines, you can use:
This will give you a DataStream on which you can then apply transformations to create new derived DataStreams.
You apply transformations by calling methods on DataStream with a transformation functions. For example, a map transformation looks like this:
This will create a new DataStream by converting every String in the original collection to an Integer.
Once you have a DataStream containing your final results, you can write it to an outside system by creating a sink. These are just some example methods for creating a sink:
We will now give an overview of each of those steps, please refer to the respective sections for more details. Note that all core classes of the Scala DataSet API are found in the package org.apache.flink.api.scala while the classes of the Scala DataStream API can be found in org.apache.flink.streaming.api.scala.
The StreamExecutionEnvironment
is the basis for all Flink programs. You can
obtain one using these static methods on StreamExecutionEnvironment
:
Typically, you only need to use getExecutionEnvironment()
, since this
will do the right thing depending on the context: if you are executing
your program inside an IDE or as a regular Java program it will create
a local environment that will execute your program on your local machine. If
you created a JAR file from your program, and invoke it through the
command line, the Flink cluster manager
will execute your main method and getExecutionEnvironment()
will return
an execution environment for executing your program on a cluster.
For specifying data sources the execution environment has several methods to read from files using various methods: you can just read them line by line, as CSV files, or using completely custom data input formats. To just read a text file as a sequence of lines, you can use:
This will give you a DataStream on which you can then apply transformations to create new derived DataStreams.
You apply transformations by calling methods on DataSet with a transformation functions. For example, a map transformation looks like this:
This will create a new DataStream by converting every String in the original collection to an Integer.
Once you have a DataStream containing your final results, you can write it to an outside system by creating a sink. These are just some example methods for creating a sink:
Once you specified the complete program you need to trigger the program execution by calling
execute()
on the StreamExecutionEnvironment
.
Depending on the type of the ExecutionEnvironment
the execution will be triggered on your local
machine or submit your program for execution on a cluster.
The execute()
method is returning a JobExecutionResult
, this contains execution
times and accumulator results.
Please see the Streaming Guide for information about streaming data sources and sink and for more in-depths information about the supported transformations on DataStream.
Check out the Batch Guide for information about batch data sources and sink and for more in-depths information about the supported transformations on DataSet.
All Flink programs are executed lazily: When the program’s main method is executed, the data loading
and transformations do not happen directly. Rather, each operation is created and added to the
program’s plan. The operations are actually executed when the execution is explicitly triggered by
an execute()
call on the execution environment. Whether the program is executed locally
or on a cluster depends on the type of execution environment
The lazy evaluation lets you construct sophisticated programs that Flink executes as one holistically planned unit.
Some transformations (join, coGroup, keyBy, groupBy) require that a key be defined on a collection of elements. Other transformations (Reduce, GroupReduce, Aggregate, Windows) allow data being grouped on a key before they are applied.
A DataSet is grouped as
while a key can be specified on a DataStream using
The data model of Flink is not based on key-value pairs. Therefore, you do not need to physically pack the data set types into keys and values. Keys are “virtual”: they are defined as functions over the actual data to guide the grouping operator.
NOTE: In the following discussion we will use the DataStream
API and keyBy
.
For the DataSet API you just have to replace by DataSet
and groupBy
.
The simplest case is grouping Tuples on one or more fields of the Tuple:
The tuples are grouped on the first field (the one of Integer type).
Here, we group the tuples on a composite key consisting of the first and the second field.
A note on nested Tuples: If you have a DataStream with a nested tuple, such as:
Specifying keyBy(0)
will cause the system to use the full Tuple2
as a key (with the Integer and Float being the key). If you want to “navigate” into the nested Tuple2
, you have to use field expression keys which are explained below.
You can use String-based field expressions to reference nested fields and define keys for grouping, sorting, joining, or coGrouping.
Field expressions make it very easy to select fields in (nested) composite types such as Tuple and POJO types.
In the example below, we have a WC
POJO with two fields “word” and “count”. To group by the field word
, we just pass its name to the keyBy()
function.
Field Expression Syntax:
Select POJO fields by their field name. For example "user"
refers to the “user” field of a POJO type.
Select Tuple fields by their field name or 0-offset field index. For example "f0"
and "5"
refer to the first and sixth field of a Java Tuple type, respectively.
You can select nested fields in POJOs and Tuples. For example "user.zip"
refers to the “zip” field of a POJO which is stored in the “user” field of a POJO type. Arbitrary nesting and mixing of POJOs and Tuples is supported such as "f1.user.zip"
or "user.f3.1.zip"
.
You can select the full type using the "*"
wildcard expressions. This does also work for types which are not Tuple or POJO types.
Field Expression Example:
These are valid field expressions for the example code above:
"count"
: The count field in the WC
class.
"complex"
: Recursively selects all fields of the field complex of POJO type ComplexNestedClass
.
"complex.word.f2"
: Selects the last field of the nested Tuple3
.
"complex.hadoopCitizen"
: Selects the Hadoop IntWritable
type.
In the example below, we have a WC
POJO with two fields “word” and “count”. To group by the field word
, we just pass its name to the keyBy()
function.
Field Expression Syntax:
Select POJO fields by their field name. For example "user"
refers to the “user” field of a POJO type.
Select Tuple fields by their 1-offset field name or 0-offset field index. For example "_1"
and "5"
refer to the first and sixth field of a Scala Tuple type, respectively.
You can select nested fields in POJOs and Tuples. For example "user.zip"
refers to the “zip” field of a POJO which is stored in the “user” field of a POJO type. Arbitrary nesting and mixing of POJOs and Tuples is supported such as "_2.user.zip"
or "user._4.1.zip"
.
You can select the full type using the "_"
wildcard expressions. This does also work for types which are not Tuple or POJO types.
Field Expression Example:
These are valid field expressions for the example code above:
"count"
: The count field in the WC
class.
"complex"
: Recursively selects all fields of the field complex of POJO type ComplexNestedClass
.
"complex.word._3"
: Selects the last field of the nested Tuple3
.
"complex.hadoopCitizen"
: Selects the Hadoop IntWritable
type.
An additional way to define keys are “key selector” functions. A key selector function takes a single element as input and returns the key for the element. The key can be of any type and be derived from deterministic computations.
The following example shows a key selector function that simply returns the field of an object:
Most transformations require user-defined functions. This section lists different ways of how they can be specified
The most basic way is to implement one of the provided interfaces:
You can pass a function as an anonymous class:
Flink also supports Java 8 Lambdas in the Java API.
All transformations that require a user-defined function can instead take as argument a rich function. For example, instead of
you can write
and pass the function as usual to a map
transformation:
Rich functions can also be defined as an anonymous class:
As already seen in previous examples all operations accept lambda functions for describing the operation:
All transformations that take as argument a lambda function can instead take as argument a rich function. For example, instead of
you can write
and pass the function to a map
transformation:
Rich functions can also be defined as an anonymous class:
Rich functions provide, in addition to the user-defined function (map,
reduce, etc), four methods: open
, close
, getRuntimeContext
, and
setRuntimeContext
. These are useful for parameterizing the function
(see Passing Parameters to Functions),
creating and finalizing local state, accessing broadcast variables (see
Broadcast Variables), and for accessing runtime
information such as accumulators and counters (see
Accumulators and Counters), and information
on iterations (see Iterations).
Flink places some restrictions on the type of elements that can be in a DataSet or DataStream. The reason for this is that the system analyzes the types to determine efficient execution strategies.
There are six different categories of data types:
Tuples are composite types that contain a fixed number of fields with various types.
The Java API provides classes from Tuple1
up to Tuple25
. Every field of a tuple
can be an arbitrary Flink type including further tuples, resulting in nested tuples. Fields of a
tuple can be accessed directly using the field’s name as tuple.f4
, or using the generic getter method
tuple.getField(int position)
. The field indices start at 0. Note that this stands in contrast
to the Scala tuples, but it is more consistent with Java’s general indexing.
Scala case classes (and Scala tuples which are a special case of case classes), are composite types that contain a fixed number of fields with various types. Tuple fields are addressed by their 1-offset names such as _1
for the first field. Case class fields are accessed by their name.
Java and Scala classes are treated by Flink as a special POJO data type if they fulfill the following requirements:
The class must be public.
It must have a public constructor without arguments (default constructor).
All fields are either public or must be accessible through getter and setter functions. For a field called foo
the getter and setter methods must be named getFoo()
and setFoo()
.
The type of a field must be supported by Flink. At the moment, Flink uses Avro to serialize arbitrary objects (such as Date
).
Flink analyzes the structure of POJO types, i.e., it learns about the fields of a POJO. As a result POJO types are easier to use than general types. Moreover, Flink can process POJOs more efficiently than general types.
The following example shows a simple POJO with two public fields.
Flink supports all Java and Scala primitive types such as Integer
, String
, and Double
.
Flink supports most Java and Scala classes (API and custom). Restrictions apply to classes containing fields that cannot be serialized, like file pointers, I/O streams, or other native resources. Classes that follow the Java Beans conventions work well in general.
All classes that are not identified as POJO types (see POJO requirements above) are handled by Flink as general class types. Flink treats these data types as black boxes and is not able to access their content (i.e., for efficient sorting). General types are de/serialized using the serialization framework Kryo.
Value types describe their serialization and deserialization manually. Instead of going through a
general purpose serialization framework, they provide custom code for those operations by means of
implementing the org.apache.flinktypes.Value
interface with the methods read
and write
. Using
a Value type is reasonable when general purpose serialization would be highly inefficient. An
example would be a data type that implements a sparse vector of elements as an array. Knowing that
the array is mostly zero, one can use a special encoding for the non-zero elements, while the
general purpose serialization would simply write all array elements.
The org.apache.flinktypes.CopyableValue
interface supports manual internal cloning logic in a
similar way.
Flink comes with pre-defined Value types that correspond to basic data types. (ByteValue
,
ShortValue
, IntValue
, LongValue
, FloatValue
, DoubleValue
, StringValue
, CharValue
,
BooleanValue
). These Value types act as mutable variants of the basic data types: Their value can
be altered, allowing programmers to reuse objects and take pressure off the garbage collector.
You can use types that implement the org.apache.hadoop.Writable
interface. The serialization logic
defined in the write()
and readFields()
methods will be used for serialization.
You can use special types, including Scala’s Either
, Option
, and Try
.
The Java API has its own custom implementation of Either
.
Similarly to Scala’s Either
, it represents a value of one two possible types, Left or Right.
Either
can be useful for error handling or operators that need to output two different types of records.
Note: This Section is only relevant for Java.
The Java compiler throws away much of the generic type information after compilation. This is
known as type erasure in Java. It means that at runtime, an instance of an object does not know
its generic type any more. For example, instances of DataStream<String>
and DataStream<Long>
look the
same to the JVM.
Flink requires type information at the time when it prepares the program for execution (when the
main method of the program is called). The Flink Java API tries to reconstruct the type information
that was thrown away in various ways and store it explicitly in the data sets and operators. You can
retrieve the type via DataStream.getType()
. The method returns an instance of TypeInformation
,
which is Flink’s internal way of representing types.
The type inference has its limits and needs the “cooperation” of the programmer in some cases.
Examples for that are methods that create data sets from collections, such as
ExecutionEnvironment.fromCollection(),
where you can pass an argument that describes the type. But
also generic functions like MapFunction<I, O>
may need extra type information.
The ResultTypeQueryable interface can be implemented by input formats and functions to tell the API explicitly about their return type. The input types that the functions are invoked with can usually be inferred by the result types of the previous operations.
Accumulators are simple constructs with an add operation and a final accumulated result, which is available after the job ended.
The most straightforward accumulator is a counter: You can increment it using the
Accumulator.add(V value)
method. At the end of the job Flink will sum up (merge) all partial
results and send the result to the client. Accumulators are useful during debugging or if you
quickly want to find out more about your data.
Flink currently has the following built-in accumulators. Each of them implements the Accumulator interface.
How to use accumulators:
First you have to create an accumulator object (here a counter) in the user-defined transformation function where you want to use it.
Second you have to register the accumulator object, typically in the open()
method of the
rich function. Here you also define the name.
You can now use the accumulator anywhere in the operator function, including in the open()
and
close()
methods.
The overall result will be stored in the JobExecutionResult
object which is
returned from the execute()
method of the execution environment
(currently this only works if the execution waits for the
completion of the job).
All accumulators share a single namespace per job. Thus you can use the same accumulator in different operator functions of your job. Flink will internally merge all accumulators with the same name.
A note on accumulators and iterations: Currently the result of accumulators is only available after the overall job has ended. We plan to also make the result of the previous iteration available in the next iteration. You can use Aggregators to compute per-iteration statistics and base the termination of iterations on such statistics.
Custom accumulators:
To implement your own accumulator you simply have to write your implementation of the Accumulator interface. Feel free to create a pull request if you think your custom accumulator should be shipped with Flink.
You have the choice to implement either Accumulator or SimpleAccumulator.
Accumulator<V,R>
is most flexible: It defines a type V
for the value to add, and a
result type R
for the final result. E.g. for a histogram, V
is a number and R
is
a histogram. SimpleAccumulator
is for the cases where both types are the same, e.g. for counters.