Spark中文手册11：Spark 配置指南

Posted 2022-11-26 wanmeilingdu

问题导读

1.Spark可以在哪三个地方配置系统？
2.如何实现动态加载Spark属性？






Spark可以在三个地方配置系统:

• Spark属性控制大部分的应用参数。 这些属性可以通过SparkConf对象, 或者Java系统属性.
• 环境变量可以为每台机器配置，比如IP地址， 通过每个节点上的conf/spark-env.sh脚本.
• 可同通过log4j.properties配置日志.
  

Spark属性 Spark属性控制应用的大部分设置， 可以为不同的应用分别设置. 这些属性在SparkConf对象上设置， SparkConf被传给SparkContext. SparkConf允许你配置一些通用的属性(比如master URL 和应用名), 也可以通过set() 方法设置键值对. 例如，我们可以这样初始化一个应用:

1. val conf = new SparkConf()
   
2.              .setMaster("local")
   
3.              .setAppName("CountingSheep")
   
4.              .set("spark.executor.memory", "1g")
   
5. val sc = new SparkContext(conf)

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动态加载Spark属性 在一些情况下你可能想避免在SparkConf上硬编码. 举例来说， 如果你想在不同的master上或者不同的内存上运行同样的应用， Spark允许你简单创建一个空的conf:

1. val sc = new SparkContext(new SparkConf())
   

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你可以在运行时提供这些配置值:

1. ./bin/spark-submit --name "My app" --master local[4] --conf spark.shuffle.spill=false 
   
2.   --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar
   

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Spark shell 和 spark-submit脚本支持两个动态加载配置的方法. 第一种是命令行参数， 如上面用到的 —master. spark-submit通过—conf可以接收任意的spark属性, 但会使用一些其它参数来启动Spark应用. 运行./bin/spark-submit —hp 会显示完整的参数列表. bin/spark-submit会从conf/spark-defaults.conf读取缺省的配置参数, 每一行包括一个键和一个值， 由空格分隔. 比如下面的例子:

1. spark.master            spark://5.6.7.8:7077
   
2. spark.executor.memory   512m
   
3. spark.eventLog.enabled  true
   
4. spark.serializer        org.apache.spark.serializer.KryoSerializer
   

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命令行参数和文件中配置的属性都会传给应用，由SparkConf合并这些配置. SparkConf上设置的属性有最高优先级，然后是命令行传入给spark-submit或spark-shell的参数， 最后才是缺省文件中配置的属性. 查看Spark属性 应用的web UI （http://:4040）在”Environment”标签页列出了所有的Spark属性. 这是一个很有用的页面，可以帮助你检查你的属性是否设置正确。 注意只有显式在spark-defaults.conf 或 SparkConf 设置的属性才显示。 其它的配置属性将使用缺省值。 可用的属性 大部分控制内部设置的属性都有合适的缺省值。一些常用的属性分门别类的列在这里: 应用属性

属性名	缺省值	意义
spark.app.name	(none)	The name of your application. This will appear in the UI and in log data.
spark.master	(none)	The cluster manager to connect to. See the list ofallowed master URL’s.
spark.executor.memory	512m	Amount of memory to use per executor process, in the same format as JVM memory strings (e.g.512m,2g).
spark.serializer	org.apache.spark.serializer. JavaSerializer	Class to use for serializing objects that will be sent over the network or need to be cached in serialized form. The default of Java serialization works with any Serializable Java object but is quite slow, so we recommendusingorg.apache.spark.serializer.KryoSerializer and configuring Kryo serialization when speed is necessary. Can be any subclass oforg.apache.spark.Serializer.
spark.kryo.registrator	(none)	If you use Kryo serialization, set this class to register your custom classes with Kryo. It should be set to a class that extendsKryoRegistrator. See thetuning guide for more details.
spark.local.dir	/tmp	Directory to use for “scratch” space in Spark, including map output files and RDDs that get stored on disk. This should be on a fast, local disk in your system. It can also be a comma-separated list of multiple directories on different disks. NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or LOCAL_DIRS (YARN) environment variables set by the cluster manager.
spark.logConf	false	Logs the effective SparkConf as INFO when a SparkContext is started.

除了这些，下面的属性也可用，在某些情况下需要设置: 运行时环境Runtime Environment

属性名	缺省值	意义
spark.executor.extraJavaOptions	(none)	A string of extra JVM options to pass to executors. For instance, GC settings or other logging. Note that it is illegal to set Spark properties or heap size settings with this option. Spark properties should be set using a SparkConf object or the spark-defaults.conf file used with the spark-submit script. Heap size settings can be set with spark.executor.memory.
spark.executor.extraClassPath	(none)	Extra classpath entries to append to the classpath of executors. This exists primarily for backwards-compatibility with older versions of Spark. Users typically should not need to set this option.
spark.executor.extraLibraryPath	(none)	Set a special library path to use when launching executor JVM’s.
spark.files.userClassPathFirst	false	(Experimental) Whether to give user-added jars precedence over Spark’s own jars when loading classes in Executors. This feature can be used to mitigate conflicts between Spark’s dependencies and user dependencies. It is currently an experimental feature.
spark.python.worker.memory	512m	Amount of memory to use per python worker process during aggregation, in the same format as JVM memory strings (e.g.512m,2g). If the memory used during aggregation goes above this amount, it will spill the data into disks.
spark.executorEnv.[EnvironmentVariableName]	(none)	Add the environment variable specified byEnvironmentVariableName to the Executor process. The user can specify multiple of these and to set multiple environment variables.
spark.mesos.executor.home	driver sideSPARK_HOME	Set the directory in which Spark is installed on the executors in Mesos. By default, the executors will simply use the driver’s Spark home directory, which may not be visible to them. Note that this is only relevant if a Spark binary package is not specified throughspark.executor.uri.
spark.mesos.executor.memoryOverhead	executor memory * 0.07, with minimum of 384	This value is an additive forspark.executor.memory, specified in MiB, which is used to calculate the total Mesos task memory. A value of384 implies a 384MiB overhead. Additionally, there is a hard-coded 7% minimum overhead. The final overhead will be the larger of either spark.mesos.executor.memoryOverhead or 7% ofspark.executor.memory.

Shuffle Behavior

属性名	缺省值	意义
spark.shuffle.consolidateFiles	false	If set to “true”, consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to “true” when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations.
spark.shuffle.spill	true	If set to “true”, limits the amount of memory used during reduces by spilling data out to disk. This spilling threshold is specified byspark.shuffle.memoryFraction.
spark.shuffle.spill.compress	true	Whether to compress data spilled during shuffles. Compression will usespark.io.compression.codec.
spark.shuffle.memoryFraction	0.2	Fraction of Java heap to use for aggregation and cogroups during shuffles, ifspark.shuffle.spill is true. At any given time, the collective size of all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will begin to spill to disk. If spills are often, consider increasing this value at the expense ofspark.storage.memoryFraction.
spark.shuffle.compress	true	Whether to compress map output files. Generally a good idea. Compression will usespark.io.compression.codec.
spark.shuffle.file.buffer.kb	32	Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
spark.reducer.maxMbInFlight	48	Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since each output requires us to create a buffer to receive it, this represents a fixed memory overhead per reduce task, so keep it small unless you have a large amount of memory.
spark.shuffle.manager	HASH	Implementation to use for shuffling data. A hash-based shuffle manager is the default, but starting in Spark 1.1 there is an experimental sort-based shuffle manager that is more memory-efficient in environments with small executors, such as YARN. To use that, change this value toSORT.
spark.shuffle.sort.bypassMergeThreshold	200	(Advanced) In the sort-based shuffle manager, avoid merge-sorting data if there is no map-side aggregation and there are at most this many reduce partitions.

Spark UI

属性名	缺省值	意义
spark.ui.port	4040	Port for your application’s dashboard, which shows memory and workload data.
spark.ui.retainedStages	1000	How many stages the Spark UI remembers before garbage collecting.
spark.ui.killEnabled	true	Allows stages and corresponding jobs to be killed from the web ui.
spark.eventLog.enabled	false	Whether to log Spark events, useful for reconstructing the Web UI after the application has finished.
spark.eventLog.compress	false	Whether to compress logged events, ifspark.eventLog.enabled is true.
spark.eventLog.dir	file:///tmp/spark-events	Base directory in which Spark events are logged, ifspark.eventLog.enabled is true. Within this base directory, Spark creates a sub-directory for each application, and logs the events specific to the application in this directory. Users may want to set this to a unified location like an HDFS directory so history files can be read by the history server.

Compression and Serialization

属性名	缺省值	意义
spark.broadcast.compress	true	Whether to compress broadcast variables before sending them. Generally a good idea.
spark.rdd.compress	false	Whether to compress serialized RDD partitions (e.g. forStorageLevel. MEMORY_ONLY_SER). Can save substantial space at the cost of some extra CPU time.
spark.io.compression.codec	snappy	The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides three codecs:lz4,lzf, andsnappy. You can also use fully qualified class names to specify the codec, e.g.org.apache.spark.io.LZ4CompressionCodec, org.apache.spark.io.LZFCompressionCodec, andorg.apache.spark.io.SnappyCompressionCodec.
spark.io.compression.snappy.block.size	32768	Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used. Lowering this block size will also lower shuffle memory usage when Snappy is used.
spark.io.compression.lz4.block.size	32768	Block size (in bytes) used in LZ4 compression, in the case when LZ4 compression codec is used. Lowering this block size will also lower shuffle memory usage when LZ4 is used.
spark.closure.serializer	org.apache.spark.serializer. JavaSerializer	Serializer class to use for closures. Currently only the Java serializer is supported.
spark.serializer.objectStreamReset	100	When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches objects to prevent writing redundant data, however that stops garbage collection of those objects. By calling ‘reset’ you flush that info from the serializer, and allow old objects to be collected. To turn off this periodic reset set it to -1. By default it will reset the serializer every 100 objects.
spark.kryo.referenceTracking	true	Whether to track references to the same object when serializing data with Kryo, which is necessary if your object graphs have loops and useful for efficiency if they contain multiple copies of the same object. Can be disabled to improve performance if you know this is not the case.
spark.kryo.registrationRequired	false	Whether to require registration with Kryo. If set to ‘true’, Kryo will throw an exception if an unregistered class is serialized. If set to false (the default), Kryo will write unregistered class names along with each object. Writing class names can cause significant performance overhead, so enabling this option can enforce strictly that a user has not omitted classes from registration.
spark.kryoserializer.buffer.mb	0.064	Initial size of Kryo’s serialization buffer, in megabytes. Note that there will be one bufferper core on each worker. This buffer will grow up tospark.kryoserializer.buffer.max.mb if needed.
spark.kryoserializer.buffer.max.mb	64	Maximum allowable size of Kryo serialization buffer, in megabytes. This must be larger than any object you attempt to serialize. Increase this if you get a “buffer limit exceeded” exception inside Kryo.

Execution Behavior

属性名	缺省值	意义
spark.default.parallelism	Local mode: number of cores on the local machine Mesos fine grained mode: 8 Others: total number of cores on all executor nodes or 2, whichever is larger	Default number of tasks to use across the cluster for distributed shuffle operations (groupByKey,reduceByKey, etc) when not set by user.
spark.broadcast.factory	org.apache.spark.broadcast. TorrentBroadcastFactory	Which broadcast implementation to use.
spark.broadcast.blockSize	4096	Size of each piece of a block in kilobytes forTorrentBroadcastFactory. Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small,BlockManager might take a performance hit.
spark.files.overwrite	false	Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source.
spark.files.fetchTimeout	false	Communication timeout to use when fetching files added through SparkContext.addFile() from the driver.
spark.storage.memoryFraction	0.6	Fraction of Java heap to use for Spark’s memory cache. This should not be larger than the “old” generation of objects in the JVM, which by default is given 0.6 of the heap, but you can increase it if you configure your own old generation size.
spark.storage.unrollFraction	0.2	Fraction ofspark.storage.memoryFraction to use for unrolling blocks in memory. This is dynamically allocated by dropping existing blocks when there is not enough free storage space to unroll the new block in its entirety.
spark.tachyonStore.baseDir	System.getProperty(“java.io.tmpdir”)	Directories of the Tachyon File System that store RDDs. The Tachyon file system’s URL is set byspark.tachyonStore.url. It can also be a comma-separated list of multiple directories on Tachyon file system.
spark.storage.memoryMapThreshold	8192	Size of a block, in bytes, above which Spark memory maps when reading a block from disk. This prevents Spark from memory mapping very small blocks. In general, memory mapping has high overhead for blocks close to or below the page size of the operating system.
spark.tachyonStore.url	tachyon://localhost:19998	The URL of the underlying Tachyon file system in the TachyonStore.
spark.cleaner.ttl	(infinite)	Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.). Periodic cleanups will ensure that metadata older than this duration will be forgotten. This is useful for running Spark for many hours / days (for example, running 24/7 in case of Spark Streaming applications). Note that any RDD that persists in memory for more than this duration will be cleared as well.
spark.hadoop.validateOutputSpecs	true	If set to true, validates the output specification (e.g. checking if the output directory already exists) used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing output directories. We recommend that users do not disable this except if trying to achieve compatibility with previous versions of Spark. Simply use Hadoop’s FileSystem API to delete output directories by hand.
spark.hadoop.cloneConf	false	If set to true, clones a new HadoopConfiguration object for each task. This option should be enabled to work aroundConfigurationthread-safety issues (seeSPARK-2546 for more details). This is disabled by default in order to avoid unexpected performance regressions for jobs that are not affected by these issues.
spark.executor.heartbeatInterval	10000	Interval (milliseconds) between each executor’s heartbeats to the driver. Heartbeats let the driver know that the executor is still alive and update it with metrics for in-progress tasks.

Networking

属性名	缺省值	意义
spark.driver.host	(local hostname)	Hostname or IP address for the driver to listen on. This is used for communicating with the executors and the standalone Master.
spark.driver.port	(random)	Port for the driver to listen on. This is used for communicating with the executors and the standalone Master.
spark.fileserver.port	(random)	Port for the driver’s HTTP file server to listen on.
spark.broadcast.port	(random)	Port for the driver’s HTTP broadcast server to listen on. This is not relevant for torrent broadcast.
spark.replClassServer.port	(random)	Port for the driver’s HTTP class server to listen on. This is only relevant for the Spark shell.
spark.blockManager.port	(random)	Port for all block managers to listen on. These exist on both the driver and the executors.
spark.executor.port	(random)	Port for the executor to listen on. This is used for communicating with the driver.
spark.port.maxRetries	16	Default maximum number of retries when binding to a port before giving up.
spark.akka.frameSize	10	Maximum message size to allow in “control plane” communication (for serialized tasks and task results), in MB. Increase this if your tasks need to send back large results to the driver (e.g. usingcollect() on a large dataset).
spark.akka.threads	4	Number of actor threads to use for communication. Can be useful to increase on large clusters when the driver has a lot of CPU cores.
spark.akka.timeout	100	Communication timeout between Spark nodes, in seconds.
spark.akka.heartbeat.pauses	600	This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in combination of spark.akka.heartbeat.interval and spark.akka.failure-detector.threshold if you need to.
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