10 配置的详细说明

Configuration

Overview

The default configuration parameters allow Flink to run out-of-the-box in single node setups.
This page lists the most common options that are typically needed to set up a well performing (distributed) installation. In addition a full list of all available configuration parameters is listed here.
All configuration is done in

conf/flink-conf.yaml

, which is expected to be
a flat collection of YAML key value pairs with format

key:
value

.
The system and run scripts parse the config at startup time. Changes to the configuration file require restarting the Flink JobManager and TaskManagers.
The configuration files for the TaskManagers can be different, Flink does not assume uniform machines in the cluster.

Overview
Common
Options
Advanced
Options

Managed
Memory
Kerberos
Other

Full
Reference

HDFS
JobManager
& TaskManager
Distributed
Coordination (via Akka)
JobManager
Web Frontend
Webclient
File
Systems
Compiler/Optimizer
Runtime
Algorithms

YARN
High
Availability Mode
Background

Configuring
the Network Buffers
Configuring
Temporary I/O Directories
Configuring
TaskManager processing slots

Common Options

env.java.home

: The path to the Java installation to use (DEFAULT: system’s
default Java installation, if found). Needs to be specified if the startup scipts fail to automatically resolve the java home directory. Can be specified to point to a specific java installation or version. If this option is not specified, the startup scripts
also evaluate the

$JAVA_HOME

environment variable.

jobmanager.rpc.address

: The IP address of the JobManager, which is the master/coordinator
of the distributed system (DEFAULT: localhost).

jobmanager.rpc.port

: The port number of the JobManager (DEFAULT: 6123).

jobmanager.heap.mb

: JVM heap size (in megabytes) for the JobManager. You
may have to increase the heap size for the JobManager if you are running very large applications (with many operators), or if you are keeping a long history of them.

taskmanager.heap.mb

: JVM heap size (in megabytes) for the TaskManagers, which
are the parallel workers of the system. In contrast to Hadoop, Flink runs operators (e.g., join, aggregate) and user-defined functions (e.g., Map, Reduce, CoGroup) inside the TaskManager (including sorting/hashing/caching), so this value should be as large
as possible. If the cluster is exclusively running Flink, the total amount of available memory per machine minus some memory for the operating system (maybe 1-2 GB) is a good value. On YARN setups, this value is automatically configured to the size of the
TaskManager’s YARN container, minus a certain tolerance value.

taskmanager.numberOfTaskSlots

: The number of parallel operator or user function
instances that a single TaskManager can run (DEFAULT: 1). If this value is larger than 1, a single TaskManager takes multiple instances of a function or operator. That way, the TaskManager can utilize multiple CPU cores, but at the same time, the available
memory is divided between the different operator or function instances. This value is typically proportional to the number of physical CPU cores that the TaskManager’s machine has (e.g., equal to the number of cores, or half the number of cores). More
about task slots.

parallelism.default

: The default parallelism to use for programs that have
no parallelism specified. (DEFAULT: 1). For setups that have no concurrent jobs running, setting this value to NumTaskManagers * NumSlotsPerTaskManager will cause the system to use all available execution resources for the program’s execution. Note:
The default parallelism can be overwriten for an entire job by calling

setParallelism(int parallelism)

on the

ExecutionEnvironment

or
by passing

-p <parallelism>

to the Flink Command-line frontend. It can be overwritten for single transformations by calling

setParallelism(int
parallelism)

on an operator. See theprogramming
guide for more information about the parallelism.

fs.hdfs.hadoopconf

: The absolute path to the Hadoop File System’s (HDFS)
configuration directory (OPTIONAL VALUE). Specifying this value allows programs to reference HDFS files using short URIs (

hdfs:///path/to/files

,
without including the address and port of the NameNode in the file URI). Without this option, HDFS files can be accessed, but require fully qualified URIs like

hdfs://address:port/path/to/files

.
This option also causes file writers to pick up the HDFS’s default values for block sizes and replication factors. Flink will look for the “core-site.xml” and “hdfs-site.xml” files in teh specified directory.

Advanced Options

Managed Memory

By default, Flink allocates a fraction of 0.7 of the total memory configured via

taskmanager.heap.mb

for
its managed memory. Managed memory helps Flink to run the operators efficiently. It prevents OutOfMemoryExceptions because Flink knows how much memory it can use to execute operations. If Flink runs out of managed memory, it utilizes disk space. Using managed
memory, some operations can be performed directly on the raw data without having to deserialize the data to convert it into Java objects. All in all, managed memory improves the robustness and speed of the system.
The default fraction for managed memory can be adjusted using the

taskmanager.memory.fraction

parameter.
An absolute value may be set using

taskmanager.memory.size

(overrides the fraction parameter). If desired, the managed
memory may be allocated outside the JVM heap. This may improve performance in setups with large memory sizes.

taskmanager.memory.size

: The amount of memory (in megabytes) that the task
manager reserves on the JVM’s heap space for sorting, hash tables, and caching of intermediate results. If unspecified (-1), the memory manager will take a fixed ratio of the heap memory available to the JVM, as specified by

taskmanager.memory.fraction

.
(DEFAULT: -1)

taskmanager.memory.fraction

: The relative amount of memory that the task
manager reserves for sorting, hash tables, and caching of intermediate results. For example, a value of 0.8 means that TaskManagers reserve 80% of the JVM’s heap space for internal data buffers, leaving 20% of the JVM’s heap space free for objects created
by user-defined functions. (DEFAULT: 0.7) This parameter is only evaluated, if

taskmanager.memory.size

is not set.

taskmanager.memory.off-heap

: If set to

true

,
the task manager allocates memory which is used for sorting, hash tables, and caching of intermediate results outside of the JVM heap. For setups with larger quantities of memory, this can improve the efficiency of the operations performed on the memory (DEFAULT:
false).

taskmanager.memory.segment-size

: The size of memory buffers used by the memory
manager and the network stack in bytes (DEFAULT: 32768 (= 32 KiBytes)).

Kerberos

Flink supports Kerberos authentication of Hadoop services such as HDFS, YARN, or HBase.
While Hadoop uses Kerberos tickets to authenticate users with services initially, the authentication process continues differently afterwards. Instead of saving the ticket to authenticate on a later access, Hadoop
creates its own security tockens (DelegationToken) that it passes around. These are authenticated to Kerberos periodically but are independent of the token renewal time. The tokens have a maximum life span identical to the Kerberos ticket maximum life span.
Please make sure to set the maximum ticket life span high long running jobs. The renewal time of the ticket, on the other hand, is not important because Hadoop abstracts this away using its own security tocken renewal
system. Hadoop makes sure that tickets are renewed in time and you can be sure to be authenticated until the end of the ticket life time.
If you are on YARN, then it is sufficient to authenticate the client with Kerberos. On a Flink standalone cluster you need to ensure that, initially, all nodes are authenticated with Kerberos using the

kinit

tool.

Other

taskmanager.tmp.dirs

: The directory for temporary files, or a list of directories
separated by the systems directory delimiter (for example ‘:’ (colon) on Linux/Unix). If multiple directories are specified, then the temporary files will be distributed across the directories in a round-robin fashion. The I/O manager component will spawn
one reading and one writing thread per directory. A directory may be listed multiple times to have the I/O manager use multiple threads for it (for example if it is physically stored on a very fast disc or RAID) (DEFAULT: The system’s tmp dir).

jobmanager.web.port

: Port of the JobManager’s web interface (DEFAULT: 8081).

fs.overwrite-files

: Specifies whether file output writers should overwrite
existing files by default. Set to true to overwrite by default, falseotherwise. (DEFAULT: false)

fs.output.always-create-directory

: File writers running with a parallelism
larger than one create a directory for the output file path and put the different result files (one per parallel writer task) into that directory. If this option is set to true, writers with a parallelism of 1 will also create a directory
and place a single result file into it. If the option is set to false, the writer will directly create the file directly at the output path, without creating a containing directory. (DEFAULT: false)

taskmanager.network.numberOfBuffers

: The number of buffers available to the
network stack. This number determines how many streaming data exchange channels a TaskManager can have at the same time and how well buffered the channels are. If a job is rejected or you get a warning that the system has not enough buffers available, increase
this value (DEFAULT: 2048).

env.java.opts

: Set custom JVM options. This value is respected by Flink’s
start scripts and Flink’s YARN client. This can be used to set different garbage collectors or to include remote debuggers into the JVMs running Flink’s services.

state.backend

: The backend that will be used to store operator state checkpoints
if checkpointing is enabled.
Supported backends:

jobmanager

(in-memory)

filesystem

(all filesystems supported by Flink, for example HDFS)

state.backend.fs.checkpointdir

: Directory for storing checkpoints in a flink
supported filesystem Note: State backend must be accessible from the JobManager, use file:// only for local setups.

blob.storage.directory

: Directory for storing blobs (such as user jar’s)
on the TaskManagers.

execution-retries.delay

: Delay between execution retries. Default value “100
s”. Note that values have to be specified as strings with a unit.

execution-retries.default

: Default number of execution retries (Can also
be set on a per-job basis).

Full Reference

HDFS

These parameters configure the default HDFS used by Flink. Setups that do not specify a HDFS configuration have to specify the full path to HDFS files (

hdfs://address:port/path/to/files

)
Files will also be written with default HDFS parameters (block size, replication factor).

fs.hdfs.hadoopconf

: The absolute path to the Hadoop configuration directory. The system will look for the
“core-site.xml” and “hdfs-site.xml” files in that directory (DEFAULT: null).

fs.hdfs.hdfsdefault

: The absolute path of Hadoop’s own configuration file “hdfs-default.xml” (DEFAULT: null).

fs.hdfs.hdfssite

: The absolute path of Hadoop’s own configuration file “hdfs-site.xml” (DEFAULT: null).

JobManager & TaskManager

The following parameters configure Flink’s JobManager and TaskManagers.

jobmanager.rpc.address

: The IP address of the JobManager, which is the master/coordinator of the distributed
system (DEFAULT: localhost).

jobmanager.rpc.port

: The port number of the JobManager (DEFAULT: 6123).

taskmanager.rpc.port

: The task manager’s IPC port (DEFAULT: 6122).

taskmanager.data.port

: The task manager’s port used for data exchange operations (DEFAULT: 6121).

jobmanager.heap.mb

: JVM heap size (in megabytes) for the JobManager (DEFAULT: 256).

taskmanager.heap.mb

: JVM heap size (in megabytes) for the TaskManagers, which are the parallel workers of
the system. In contrast to Hadoop, Flink runs operators (e.g., join, aggregate) and user-defined functions (e.g., Map, Reduce, CoGroup) inside the TaskManager (including sorting/hashing/caching), so this value should be as large as possible (DEFAULT: 512).
On YARN setups, this value is automatically configured to the size of the TaskManager’s YARN container, minus a certain tolerance value.

taskmanager.numberOfTaskSlots

: The number of parallel operator or user function instances that a single TaskManager
can run (DEFAULT: 1). If this value is larger than 1, a single TaskManager takes multiple instances of a function or operator. That way, the TaskManager can utilize multiple CPU cores, but at the same time, the available memory is divided between the different
operator or function instances. This value is typically proportional to the number of physical CPU cores that the TaskManager’s machine has (e.g., equal to the number of cores, or half the number of cores).

taskmanager.tmp.dirs

: The directory for temporary files, or a list of directories separated by the systems
directory delimiter (for example ‘:’ (colon) on Linux/Unix). If multiple directories are specified, then the temporary files will be distributed across the directories in a round robin fashion. The I/O manager component will spawn one reading and one writing
thread per directory. A directory may be listed multiple times to have the I/O manager use multiple threads for it (for example if it is physically stored on a very fast disc or RAID) (DEFAULT: The system’s tmp dir).

taskmanager.network.numberOfBuffers

: The number of buffers available to the network stack. This number determines
how many streaming data exchange channels a TaskManager can have at the same time and how well buffered the channels are. If a job is rejected or you get a warning that the system has not enough buffers available, increase this value (DEFAULT: 2048).

taskmanager.memory.size

: The amount of memory (in megabytes) that the task manager reserves on the JVM’s heap
space for sorting, hash tables, and caching of intermediate results. If unspecified (-1), the memory manager will take a fixed ratio of the heap memory available to the JVM, as specified by

taskmanager.memory.fraction

.
(DEFAULT: -1)

taskmanager.memory.fraction

: The relative amount of memory that the task manager reserves for sorting, hash
tables, and caching of intermediate results. For example, a value of 0.8 means that TaskManagers reserve 80% of the JVM’s heap space for internal data buffers, leaving 20% of the JVM’s heap space free for objects created by user-defined functions. (DEFAULT:
0.7) This parameter is only evaluated, if

taskmanager.memory.size

is not set.

jobclient.polling.interval

: The interval (in seconds) in which the client polls the JobManager for the status
of its job (DEFAULT: 2).

taskmanager.heartbeat-interval

: The interval in which the TaskManager sends heartbeats to the JobManager.

jobmanager.max-heartbeat-delay-before-failure.msecs

: The maximum time that a TaskManager hearbeat may be missing
before the TaskManager is considered failed.

Distributed Coordination (via Akka)

akka.ask.timeout

: Timeout used for all futures and blocking Akka calls. If Flink fails due to timeouts then
you should try to increase this value. Timeouts can be caused by slow machines or a congested network. The timeout value requires a time-unit specifier (ms/s/min/h/d) (DEFAULT: 100 s).

akka.lookup.timeout

: Timeout used for the lookup of the JobManager. The timeout value has to contain a time-unit
specifier (ms/s/min/h/d) (DEFAULT: 10 s).

akka.framesize

: Maximum size of messages which are sent between the JobManager and the TaskManagers. If Flink
fails because messages exceed this limit, then you should increase it. The message size requires a size-unit specifier (DEFAULT: 10485760b).

akka.watch.heartbeat.interval

: Heartbeat interval for Akka’s DeathWatch mechanism to detect dead TaskManagers.
If TaskManagers are wrongly marked dead because of lost or delayed heartbeat messages, then you should increase this value. A thorough description of Akka’s DeathWatch can be found here (DEFAULT: akka.ask.timeout/10).

akka.watch.heartbeat.pause

: Acceptable heartbeat pause for Akka’s DeathWatch mechanism. A low value does not
allow a irregular heartbeat. A thorough description of Akka’s DeathWatch can be found here (DEFAULT: akka.ask.timeout).

akka.watch.threshold

: Threshold for the DeathWatch failure detector. A low value is prone to false positives
whereas a high value increases the time to detect a dead TaskManager. A thorough description of Akka’s DeathWatch can be found here (DEFAULT: 12).

akka.transport.heartbeat.interval

: Heartbeat interval for Akka’s transport failure detector. Since Flink uses
TCP, the detector is not necessary. Therefore, the detector is disabled by setting the interval to a very high value. In case you should need the transport failure detector, set the interval to some reasonable value. The interval value requires a time-unit
specifier (ms/s/min/h/d) (DEFAULT: 1000 s).

akka.transport.heartbeat.pause

: Acceptable heartbeat pause for Akka’s transport failure detector. Since Flink
uses TCP, the detector is not necessary. Therefore, the detector is disabled by setting the pause to a very high value. In case you should need the transport failure detector, set the pause to some reasonable value. The pause value requires a time-unit specifier
(ms/s/min/h/d) (DEFAULT: 6000 s).

akka.transport.threshold

: Threshold for the transport failure detector. Since Flink uses TCP, the detector
is not necessary and, thus, the threshold is set to a high value (DEFAULT: 300).

akka.tcp.timeout

: Timeout for all outbound connections. If you should experience problems with connecting
to a TaskManager due to a slow network, you should increase this value (DEFAULT: akka.ask.timeout).

akka.throughput

: Number of messages that are processed in a batch before returning the thread to the pool.
Low values denote a fair scheduling whereas high values can increase the performance at the cost of unfairness (DEFAULT: 15).

akka.log.lifecycle.events

: Turns on the Akka’s remote logging of events. Set this value to ‘on’ in case of
debugging (DEFAULT: off).

akka.startup-timeout

: Timeout after which the startup of a remote component is considered being failed (DEFAULT: akka.ask.timeout).

JobManager Web Frontend

jobmanager.web.port

: Port of the JobManager’s web interface that displays status of running jobs and execution
time breakdowns of finished jobs (DEFAULT: 8081). Setting this value to

-1

disables the web frontend.

jobmanager.web.history

: The number of latest jobs that the JobManager’s web front-end in its history (DEFAULT:
5).

Webclient

These parameters configure the web interface that can be used to submit jobs and review the compiler’s execution plans.

webclient.port

: The port of the webclient server (DEFAULT: 8080).

webclient.tempdir

: The temp directory for the web server. Used for example for caching file fragments during
file-uploads (DEFAULT: The system’s temp directory).

webclient.uploaddir

: The directory into which the web server will store uploaded programs (DEFAULT: ${webclient.tempdir}/webclient-jobs/).

webclient.plandump

: The directory into which the web server will dump temporary JSON files describing the
execution plans (DEFAULT: ${webclient.tempdir}/webclient-plans/).

File Systems

The parameters define the behavior of tasks that create result files.

fs.overwrite-files

: Specifies whether file output writers should overwrite existing files by default. Set
to true to overwrite by default, falseotherwise. (DEFAULT: false)

fs.output.always-create-directory

: File writers running with a parallelism larger than one create a directory
for the output file path and put the different result files (one per parallel writer task) into that directory. If this option is set to true, writers with a parallelism of 1 will also create a directory and place a single result file
into it. If the option is set to false, the writer will directly create the file directly at the output path, without creating a containing directory. (DEFAULT: false)

Compiler/Optimizer

compiler.delimited-informat.max-line-samples

: The maximum number of line samples taken by the compiler for
delimited inputs. The samples are used to estimate the number of records. This value can be overridden for a specific input with the input format’s parameters (DEFAULT: 10).

compiler.delimited-informat.min-line-samples

: The minimum number of line samples taken by the compiler for
delimited inputs. The samples are used to estimate the number of records. This value can be overridden for a specific input with the input format’s parameters (DEFAULT: 2).

compiler.delimited-informat.max-sample-len

: The maximal length of a line sample that the compiler takes for
delimited inputs. If the length of a single sample exceeds this value (possible because of misconfiguration of the parser), the sampling aborts. This value can be overridden for a specific input with the input format’s parameters (DEFAULT: 2097152 (= 2 MiBytes)).

Runtime Algorithms

taskmanager.runtime.max-fan

: The maximal fan-in for external merge joins and fan-out for spilling hash tables.
Limits the number of file handles per operator, but may cause intermediate merging/partitioning, if set too small (DEFAULT: 128).

taskmanager.runtime.sort-spilling-threshold

: A sort operation starts spilling when this fraction of its memory
budget is full (DEFAULT: 0.8).

taskmanager.runtime.hashjoin-bloom-filters

: If true, the hash join uses bloom filters to pre-filter records
against spilled partitions. (DEFAULT: true)

YARN

yarn.heap-cutoff-ratio

: (Default 0.15) Percentage of heap space to remove from containers started by YARN.
When a user requests a certain amount of memory for each TaskManager container (for example 4 GB), we can not pass this amount as the maximum heap space for the JVM (

-Xmx

argument)
because the JVM is also allocating memory outside the heap. YARN is very strict with killing containers which are using more memory than requested. Therefore, we remove a 15% of the memory from the requested heap as a safety margin.

yarn.heap-cutoff-min

: (Default 384 MB) Minimum amount of memory to cut off
the requested heap size.

yarn.reallocate-failed

(Default ‘true’) Controls whether YARN should reallocate
failed containers

yarn.maximum-failed-containers

(Default: number of requested containers).
Maximum number of containers the system is going to reallocate in case of a failure.

yarn.application-attempts

(Default: 1). Number of ApplicationMaster restarts.
Note that that the entire Flink cluster will restart and the YARN Client will loose the connection. Also, the JobManager address will change and you’ll need to set the JM host:port manually. It is recommended to leave this option at 1.

yarn.heartbeat-delay

(Default: 5 seconds). Time between heartbeats with the
ResourceManager.

yarn.properties-file.location

(Default: temp directory). When a Flink job is submitted to YARN, the JobManager’s
host and the number of available processing slots is written into a properties file, so that the Flink client is able to pick those details up. This configuration parameter allows changing the default location of that file (for example for environments sharing
a Flink installation between users)

High Availability Mode

recovery.mode

: (Default ‘standalone’) Defines the recovery mode used for
the cluster execution. Currently, Flink supports the ‘standalone’ mode where only a single JobManager runs and no JobManager state is checkpointed. The high availability mode ‘zookeeper’ supports the execution of multiple JobManagers and JobManager state checkpointing.
Among the group of JobManagers, ZooKeeper elects one of them as the leader which is responsible for the cluster execution. In case of a JobManager failure, a standby JobManager will be elected as the new leader and is given the last checkpointed JobManager
state. In order to use the ‘zookeeper’ mode, it is mandatory to also define the

recovery.zookeeper.quorum

configuration
value.

recovery.zookeeper.quorum

: Defines the ZooKeeper quorum URL which is used
to connet to the ZooKeeper cluster when the ‘zookeeper’ recovery mode is selected

recovery.zookeeper.path.root

: (Default ‘/flink’) Defines the root dir under
which the ZooKeeper recovery mode will create znodes.

recovery.zookeeper.path.latch

: (Default ‘/leaderlatch’) Defines the znode
of the leader latch which is used to elect the leader.

recovery.zookeeper.path.leader

: (Default ‘/leader’) Defines the znode of
the leader which contains the URL to the leader and the current leader session ID

recovery.zookeeper.client.session-timeout

: (Default ‘60000’) Defines the
session timeout for the ZooKeeper session in ms.

recovery.zookeeper.client.connection-timeout

: (Default ‘15000’) Defines the
connection timeout for ZooKeeper in ms.

recovery.zookeeper.client.retry-wait

: (Default ‘5000’) Defines the pause
between consecutive retries in ms.

recovery.zookeeper.client.max-retry-attempts

: (Default ‘3’) Defines the number
of connection retries before the client gives up.

Background

Configuring the Network Buffers

Network buffers are a critical resource for the communication layers. They are used to buffer records before transmission over a network, and to buffer incoming data before dissecting it into records and handing
them to the application. A sufficient number of network buffers is critical to achieve a good throughput.
In general, configure the task manager to have enough buffers that each logical network connection on you expect to be open at the same time has a dedicated buffer. A logical network connection exists for each point-to-point
exchange of data over the network, which typically happens at repartitioning- or broadcasting steps. In those, each parallel task inside the TaskManager has to be able to talk to all other parallel tasks. Hence, the required number of buffers on a task manager
is total-degree-of-parallelism (number of targets) * intra-node-parallelism (number of sources in one task manager) * n. Here, n is a constant that defines how many repartitioning-/broadcasting
steps you expect to be active at the same time.
Since the intra-node-parallelism is typically the number of cores, and more than 4 repartitioning or broadcasting channels are rarely active in parallel, it frequently boils down to #cores\^2\^ * #machines *
4. To support for example a cluster of 20 8-core machines, you should use roughly 5000 network buffers for optimal throughput.
Each network buffer has by default a size of 32 KiBytes. In the above example, the system would allocate roughly 300 MiBytes for network buffers.
The number and size of network buffers can be configured with the following parameters:

taskmanager.network.numberOfBuffers

, and

taskmanager.memory.segment-size

.

Configuring Temporary I/O Directories

Although Flink aims to process as much data in main memory as possible, it is not uncommon that more data needs to be processed than memory is available. Flink’s runtime is designed to write temporary data to disk
to handle these situations.
The

taskmanager.tmp.dirs

parameter specifies a list of directories into which
Flink writes temporary files. The paths of the directories need to be separated by ‘:’ (colon character). Flink will concurrently write (or read) one temporary file to (from) each configured directory. This way, temporary I/O can be evenly distributed over
multiple independent I/O devices such as hard disks to improve performance. To leverage fast I/O devices (e.g., SSD, RAID, NAS), it is possible to specify a directory multiple times.
If the

taskmanager.tmp.dirs

parameter is not explicitly specified, Flink
writes temporary data to the temporary directory of the operating system, such as /tmp in Linux systems.

Configuring TaskManager processing slots

Flink executes a program in parallel by splitting it into subtasks and scheduling these subtasks to processing slots.
Each Flink TaskManager provides processing slots in the cluster. The number of slots is typically proportional to the number of available CPU coresof each TaskManager. As a general
recommendation, the number of available CPU cores is a good default for

taskmanager.numberOfTaskSlots

.
When starting a Flink application, users can supply the default number of slots to use for that job. The command line value therefore is called

-p

(for
parallelism). In addition, it is possible to set
the number of slots in the programming APIs for the whole application and individual operators.