diff options
20 files changed, 762 insertions, 42 deletions
diff --git a/conf/metrics.properties.template b/conf/metrics.properties.template index 6c36f3cca4..ae10f615d1 100644 --- a/conf/metrics.properties.template +++ b/conf/metrics.properties.template @@ -31,7 +31,7 @@ # 1. To add a new sink, set the "class" option to a fully qualified class # name (see examples below). # 2. Some sinks involve a polling period. The minimum allowed polling period -# is 1 second. +# is 1 second. # 3. Wild card properties can be overridden by more specific properties. # For example, master.sink.console.period takes precedence over # *.sink.console.period. @@ -47,11 +47,45 @@ # instance master and applications. MetricsServlet may not be configured by self. # +## List of available sinks and their properties. + +# org.apache.spark.metrics.sink.ConsoleSink +# Name: Default: Description: +# period 10 Poll period +# unit seconds Units of poll period + +# org.apache.spark.metrics.sink.CSVSink +# Name: Default: Description: +# period 10 Poll period +# unit seconds Units of poll period +# directory /tmp Where to store CSV files + +# org.apache.spark.metrics.sink.GangliaSink +# Name: Default: Description: +# host NONE Hostname or multicast group of Ganglia server +# port NONE Port of Ganglia server(s) +# period 10 Poll period +# unit seconds Units of poll period +# ttl 1 TTL of messages sent by Ganglia +# mode multicast Ganglia network mode ('unicast' or 'mulitcast') + +# org.apache.spark.metrics.sink.JmxSink + +# org.apache.spark.metrics.sink.MetricsServlet +# Name: Default: Description: +# path VARIES* Path prefix from the web server root +# sample false Whether to show entire set of samples for histograms ('false' or 'true') +# +# * Default path is /metrics/json for all instances except the master. The master has two paths: +# /metrics/aplications/json # App information +# /metrics/master/json # Master information + +## Examples # Enable JmxSink for all instances by class name -#*.sink.jmx.class=spark.metrics.sink.JmxSink +#*.sink.jmx.class=org.apache.spark.metrics.sink.JmxSink # Enable ConsoleSink for all instances by class name -#*.sink.console.class=spark.metrics.sink.ConsoleSink +#*.sink.console.class=org.apache.spark.metrics.sink.ConsoleSink # Polling period for ConsoleSink #*.sink.console.period=10 @@ -64,7 +98,7 @@ #master.sink.console.unit=seconds # Enable CsvSink for all instances -#*.sink.csv.class=spark.metrics.sink.CsvSink +#*.sink.csv.class=org.apache.spark.metrics.sink.CsvSink # Polling period for CsvSink #*.sink.csv.period=1 @@ -80,11 +114,11 @@ #worker.sink.csv.unit=minutes # Enable jvm source for instance master, worker, driver and executor -#master.source.jvm.class=spark.metrics.source.JvmSource +#master.source.jvm.class=org.apache.spark.metrics.source.JvmSource -#worker.source.jvm.class=spark.metrics.source.JvmSource +#worker.source.jvm.class=org.apache.spark.metrics.source.JvmSource -#driver.source.jvm.class=spark.metrics.source.JvmSource +#driver.source.jvm.class=org.apache.spark.metrics.source.JvmSource -#executor.source.jvm.class=spark.metrics.source.JvmSource +#executor.source.jvm.class=org.apache.spark.metrics.source.JvmSource diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index edf71c9db6..29407bcd30 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -282,8 +282,8 @@ class SparkContext( // Post init taskScheduler.postStartHook() - val dagSchedulerSource = new DAGSchedulerSource(this.dagScheduler) - val blockManagerSource = new BlockManagerSource(SparkEnv.get.blockManager) + val dagSchedulerSource = new DAGSchedulerSource(this.dagScheduler, this) + val blockManagerSource = new BlockManagerSource(SparkEnv.get.blockManager, this) def initDriverMetrics() { SparkEnv.get.metricsSystem.registerSource(dagSchedulerSource) diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index d365804994..ceae3b8289 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -98,7 +98,7 @@ private[spark] class Executor( } ) - val executorSource = new ExecutorSource(this) + val executorSource = new ExecutorSource(this, executorId) // Initialize Spark environment (using system properties read above) val env = SparkEnv.createFromSystemProperties(executorId, slaveHostname, 0, false, false) diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala index bf8fb4fd21..18c9dc1c0a 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala @@ -27,7 +27,7 @@ import scala.collection.JavaConversions._ import org.apache.spark.metrics.source.Source -class ExecutorSource(val executor: Executor) extends Source { +class ExecutorSource(val executor: Executor, executorId: String) extends Source { private def fileStats(scheme: String) : Option[FileSystem.Statistics] = FileSystem.getAllStatistics().filter(s => s.getScheme.equals(scheme)).headOption @@ -39,7 +39,8 @@ class ExecutorSource(val executor: Executor) extends Source { } val metricRegistry = new MetricRegistry() - val sourceName = "executor" + // TODO: It would be nice to pass the application name here + val sourceName = "executor.%s".format(executorId) // Gauge for executor thread pool's actively executing task counts metricRegistry.register(MetricRegistry.name("threadpool", "activeTask", "count"), new Gauge[Int] { diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala index 0f9c4e00b1..caab748d60 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala @@ -37,10 +37,9 @@ private[spark] class MetricsConfig(val configFile: Option[String]) extends Loggi private def setDefaultProperties(prop: Properties) { prop.setProperty("*.sink.servlet.class", "org.apache.spark.metrics.sink.MetricsServlet") - prop.setProperty("*.sink.servlet.uri", "/metrics/json") - prop.setProperty("*.sink.servlet.sample", "false") - prop.setProperty("master.sink.servlet.uri", "/metrics/master/json") - prop.setProperty("applications.sink.servlet.uri", "/metrics/applications/json") + prop.setProperty("*.sink.servlet.path", "/metrics/json") + prop.setProperty("master.sink.servlet.path", "/metrics/master/json") + prop.setProperty("applications.sink.servlet.path", "/metrics/applications/json") } def initialize() { diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/GangliaSink.scala b/core/src/main/scala/org/apache/spark/metrics/sink/GangliaSink.scala new file mode 100644 index 0000000000..b924907070 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/metrics/sink/GangliaSink.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.metrics.sink + +import java.util.Properties +import java.util.concurrent.TimeUnit + +import com.codahale.metrics.ganglia.GangliaReporter +import com.codahale.metrics.MetricRegistry +import info.ganglia.gmetric4j.gmetric.GMetric + +import org.apache.spark.metrics.MetricsSystem + +class GangliaSink(val property: Properties, val registry: MetricRegistry) extends Sink { + val GANGLIA_KEY_PERIOD = "period" + val GANGLIA_DEFAULT_PERIOD = 10 + + val GANGLIA_KEY_UNIT = "unit" + val GANGLIA_DEFAULT_UNIT = TimeUnit.SECONDS + + val GANGLIA_KEY_MODE = "mode" + val GANGLIA_DEFAULT_MODE = GMetric.UDPAddressingMode.MULTICAST + + // TTL for multicast messages. If listeners are X hops away in network, must be at least X. + val GANGLIA_KEY_TTL = "ttl" + val GANGLIA_DEFAULT_TTL = 1 + + val GANGLIA_KEY_HOST = "host" + val GANGLIA_KEY_PORT = "port" + + def propertyToOption(prop: String) = Option(property.getProperty(prop)) + + if (!propertyToOption(GANGLIA_KEY_HOST).isDefined) { + throw new Exception("Ganglia sink requires 'host' property.") + } + + if (!propertyToOption(GANGLIA_KEY_PORT).isDefined) { + throw new Exception("Ganglia sink requires 'port' property.") + } + + val host = propertyToOption(GANGLIA_KEY_HOST).get + val port = propertyToOption(GANGLIA_KEY_PORT).get.toInt + val ttl = propertyToOption(GANGLIA_KEY_TTL).map(_.toInt).getOrElse(GANGLIA_DEFAULT_TTL) + val mode = propertyToOption(GANGLIA_KEY_MODE) + .map(u => GMetric.UDPAddressingMode.valueOf(u.toUpperCase)).getOrElse(GANGLIA_DEFAULT_MODE) + val pollPeriod = propertyToOption(GANGLIA_KEY_PERIOD).map(_.toInt) + .getOrElse(GANGLIA_DEFAULT_PERIOD) + val pollUnit = propertyToOption(GANGLIA_KEY_UNIT).map(u => TimeUnit.valueOf(u.toUpperCase)) + .getOrElse(GANGLIA_DEFAULT_UNIT) + + MetricsSystem.checkMinimalPollingPeriod(pollUnit, pollPeriod) + + val ganglia = new GMetric(host, port, mode, ttl) + val reporter: GangliaReporter = GangliaReporter.forRegistry(registry) + .convertDurationsTo(TimeUnit.MILLISECONDS) + .convertRatesTo(TimeUnit.SECONDS) + .build(ganglia) + + override def start() { + reporter.start(pollPeriod, pollUnit) + } + + override def stop() { + reporter.stop() + } +} + diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala index 4e90dd4323..99357fede6 100644 --- a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala +++ b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala @@ -31,18 +31,21 @@ import org.eclipse.jetty.server.Handler import org.apache.spark.ui.JettyUtils class MetricsServlet(val property: Properties, val registry: MetricRegistry) extends Sink { - val SERVLET_KEY_URI = "uri" + val SERVLET_KEY_PATH = "path" val SERVLET_KEY_SAMPLE = "sample" - val servletURI = property.getProperty(SERVLET_KEY_URI) + val SERVLET_DEFAULT_SAMPLE = false - val servletShowSample = property.getProperty(SERVLET_KEY_SAMPLE).toBoolean + val servletPath = property.getProperty(SERVLET_KEY_PATH) + + val servletShowSample = Option(property.getProperty(SERVLET_KEY_SAMPLE)).map(_.toBoolean) + .getOrElse(SERVLET_DEFAULT_SAMPLE) val mapper = new ObjectMapper().registerModule( new MetricsModule(TimeUnit.SECONDS, TimeUnit.MILLISECONDS, servletShowSample)) def getHandlers = Array[(String, Handler)]( - (servletURI, JettyUtils.createHandler(request => getMetricsSnapshot(request), "text/json")) + (servletPath, JettyUtils.createHandler(request => getMetricsSnapshot(request), "text/json")) ) def getMetricsSnapshot(request: HttpServletRequest): String = { diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala index 22e3723ac8..446d490cc9 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala @@ -20,10 +20,12 @@ package org.apache.spark.scheduler import com.codahale.metrics.{Gauge,MetricRegistry} import org.apache.spark.metrics.source.Source +import org.apache.spark.SparkContext -private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler) extends Source { +private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler, sc: SparkContext) + extends Source { val metricRegistry = new MetricRegistry() - val sourceName = "DAGScheduler" + val sourceName = "%s.DAGScheduler".format(sc.appName) metricRegistry.register(MetricRegistry.name("stage", "failedStages", "number"), new Gauge[Int] { override def getValue: Int = dagScheduler.failed.size diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala index 3d709cfde4..acc3951088 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala @@ -20,11 +20,13 @@ package org.apache.spark.storage import com.codahale.metrics.{Gauge,MetricRegistry} import org.apache.spark.metrics.source.Source +import org.apache.spark.SparkContext -private[spark] class BlockManagerSource(val blockManager: BlockManager) extends Source { +private[spark] class BlockManagerSource(val blockManager: BlockManager, sc: SparkContext) + extends Source { val metricRegistry = new MetricRegistry() - val sourceName = "BlockManager" + val sourceName = "%s.BlockManager".format(sc.appName) metricRegistry.register(MetricRegistry.name("memory", "maxMem", "MBytes"), new Gauge[Long] { override def getValue: Long = { diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala index 755f1a760e..632ff047d1 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala @@ -23,9 +23,9 @@ import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput} * Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory, * whether to drop the RDD to disk if it falls out of memory, whether to keep the data in memory * in a serialized format, and whether to replicate the RDD partitions on multiple nodes. - * The [[org.apache.spark.storage.StorageLevel$]] singleton object contains some static constants for - * commonly useful storage levels. To create your own storage level object, use the factor method - * of the singleton object (`StorageLevel(...)`). + * The [[org.apache.spark.storage.StorageLevel$]] singleton object contains some static constants + * for commonly useful storage levels. To create your own storage level object, use the + * factory method of the singleton object (`StorageLevel(...)`). */ class StorageLevel private( private var useDisk_ : Boolean, diff --git a/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala index 58c94a162d..1a9ce8c607 100644 --- a/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala @@ -30,14 +30,13 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { val conf = new MetricsConfig(Option("dummy-file")) conf.initialize() - assert(conf.properties.size() === 5) + assert(conf.properties.size() === 4) assert(conf.properties.getProperty("test-for-dummy") === null) val property = conf.getInstance("random") - assert(property.size() === 3) + assert(property.size() === 2) assert(property.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") - assert(property.getProperty("sink.servlet.uri") === "/metrics/json") - assert(property.getProperty("sink.servlet.sample") === "false") + assert(property.getProperty("sink.servlet.path") === "/metrics/json") } test("MetricsConfig with properties set") { @@ -45,22 +44,20 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { conf.initialize() val masterProp = conf.getInstance("master") - assert(masterProp.size() === 6) + assert(masterProp.size() === 5) assert(masterProp.getProperty("sink.console.period") === "20") assert(masterProp.getProperty("sink.console.unit") === "minutes") assert(masterProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") assert(masterProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") - assert(masterProp.getProperty("sink.servlet.uri") === "/metrics/master/json") - assert(masterProp.getProperty("sink.servlet.sample") === "false") + assert(masterProp.getProperty("sink.servlet.path") === "/metrics/master/json") val workerProp = conf.getInstance("worker") - assert(workerProp.size() === 6) + assert(workerProp.size() === 5) assert(workerProp.getProperty("sink.console.period") === "10") assert(workerProp.getProperty("sink.console.unit") === "seconds") assert(workerProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") assert(workerProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") - assert(workerProp.getProperty("sink.servlet.uri") === "/metrics/json") - assert(workerProp.getProperty("sink.servlet.sample") === "false") + assert(workerProp.getProperty("sink.servlet.path") === "/metrics/json") } test("MetricsConfig with subProperties") { @@ -84,6 +81,6 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { assert(consoleProps.size() === 2) val servletProps = sinkProps("servlet") - assert(servletProps.size() === 3) + assert(servletProps.size() === 2) } } diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index c897f8b36c..1a629994cc 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -3,4 +3,189 @@ layout: global title: Machine Learning Library (MLlib) --- -Coming soon. +MLlib is a Spark implementation of some common machine learning (ML) +functionality, as well associated unit tests and data generators. MLlib +currently supports four common types of machine learning problem settings, +namely, binary classification, regression, clustering and collaborative +filtering, as well as an underlying gradient descent optimization primitive. +This guide will outline the functionality supported in MLlib and also provides +an example of invoking MLlib. + +# Binary Classification + +Binary classification is a supervised learning problem in which we want to +classify entities into one of two distinct categories or labels, e.g., +predicting whether or not emails are spam. This problem involves executing a +learning *Algorithm* on a set of *labeled* examples, i.e., a set of entities +represented via (numerical) features along with underlying category labels. +The algorithm returns a trained *Model* that can predict the label for new +entities for which the underlying label is unknown. + +MLlib currently supports two standard model families for binary classification, +namely [Linear Support Vector Machines +(SVMs)](http://en.wikipedia.org/wiki/Support_vector_machine) and [Logistic +Regression](http://en.wikipedia.org/wiki/Logistic_regression), along with [L1 +and L2 regularized](http://en.wikipedia.org/wiki/Regularization_(mathematics)) +variants of each model family. The training algorithms all leverage an +underlying gradient descent primitive (described +[below](#gradient-descent-primitive)), and take as input a regularization +parameter (*regParam*) along with various parameters associated with gradient +descent (*stepSize*, *numIterations*, *miniBatchFraction*). + +The following code snippet illustrates how to load a sample dataset, execute a +training algorithm on this training data using a static method in the algorithm +object, and make predictions with the resulting model to compute the training +error. + +{% highlight scala %} +import org.apache.spark.SparkContext +import org.apache.spark.mllib.classification.SVMWithSGD +import org.apache.spark.mllib.regression.LabeledPoint + +// Load and parse the data file +val data = sc.textFile("mllib/data/sample_svm_data.txt") +val parsedData = data.map(line => { + val parts = line.split(' ') + LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray) +}) + +// Run training algorithm +val numIterations = 20 +val model = SVMWithSGD.train( + parsedData, + numIterations) + +// Evaluate model on training examples and compute training error +val labelAndPreds = parsedData.map(r => { + val prediction = model.predict(r.features) + (r.label, prediction) +}) +val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count +println("trainError = " + trainErr) +{% endhighlight %} + +The `SVMWithSGD.train()` method by default performs L2 regularization with the +regularization parameter set to 1.0. If we want to configure this algorithm, we +can customize `SVMWithSGD` further by creating a new object directly and +calling setter methods. All other MLlib algorithms support customization in +this way as well. For example, the following code produces an L1 regularized +variant of SVMs with regularization parameter set to 0.1, and runs the training +algorithm for 200 iterations. + +{% highlight scala %} +import org.apache.spark.mllib.optimization.L1Updater + +val svmAlg = new SVMWithSGD() +svmAlg.optimizer.setNumIterations(200) + .setRegParam(0.1) + .setUpdater(new L1Updater) +val modelL1 = svmAlg.run(parsedData) +{% endhighlight %} + +Both of the code snippets above can be executed in `spark-shell` to generate a +classifier for the provided dataset. + +Available algorithms for binary classification: + +* [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD) +* [LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD) + +# Linear Regression + +Linear regression is another classical supervised learning setting. In this +problem, each entity is associated with a real-valued label (as opposed to a +binary label as in binary classification), and we want to predict labels as +closely as possible given numerical features representing entities. MLlib +supports linear regression as well as L1 +([lasso](http://en.wikipedia.org/wiki/Lasso_(statistics)#Lasso_method)) and L2 +([ridge](http://en.wikipedia.org/wiki/Ridge_regression)) regularized variants. +The regression algorithms in MLlib also leverage the underlying gradient +descent primitive (described [below](#gradient-descent-primitive)), and have +the same parameters as the binary classification algorithms described above. + +Available algorithms for linear regression: + +* [LinearRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD) +* [RidgeRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) +* [LassoWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD) + +# Clustering + +Clustering is an unsupervised learning problem whereby we aim to group subsets +of entities with one another based on some notion of similarity. Clustering is +often used for exploratory analysis and/or as a component of a hierarchical +supervised learning pipeline (in which distinct classifiers or regression +models are trained for each cluster). MLlib supports +[k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, arguably +the most commonly used clustering approach that clusters the data points into +*k* clusters. The MLlib implementation includes a parallelized +variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method +called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf). +The implementation in MLlib has the following parameters: + +* *k* is the number of clusters. +* *maxIterations* is the maximum number of iterations to run. +* *initializationMode* specifies either random initialization or +initialization via k-means\|\|. +* *runs* is the number of times to run the k-means algorithm (k-means is not +guaranteed to find a globally optimal solution, and when run multiple times on +a given dataset, the algorithm returns the best clustering result). +* *initializiationSteps* determines the number of steps in the k-means\|\| algorithm. +* *epsilon* determines the distance threshold within which we consider k-means to have converged. + +Available algorithms for clustering: + +* [KMeans](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) + +# Collaborative Filtering + +[Collaborative +filtering](http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering) +is commonly used for recommender systems. These techniques aim to fill in the +missing entries of a user-product association matrix. MLlib currently supports +model-based collaborative filtering, in which users and products are described +by a small set of latent factors that can be used to predict missing entries. +In particular, we implement the [alternating least squares +(ALS)](http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf) +algorithm to learn these latent factors. The implementation in MLlib has the +following parameters: + +* *numBlocks* is the number of blacks used to parallelize computation (set to -1 to auto-configure). +* *rank* is the number of latent factors in our model. +* *iterations* is the number of iterations to run. +* *lambda* specifies the regularization parameter in ALS. + +Available algorithms for collaborative filtering: + +* [ALS](api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS) + +# Gradient Descent Primitive + +[Gradient descent](http://en.wikipedia.org/wiki/Gradient_descent) (along with +stochastic variants thereof) are first-order optimization methods that are +well-suited for large-scale and distributed computation. Gradient descent +methods aim to find a local minimum of a function by iteratively taking steps +in the direction of the negative gradient of the function at the current point, +i.e., the current parameter value. Gradient descent is included as a low-level +primitive in MLlib, upon which various ML algorithms are developed, and has the +following parameters: + +* *gradient* is a class that computes the stochastic gradient of the function +being optimized, i.e., with respect to a single training example, at the +current parameter value. MLlib includes gradient classes for common loss +functions, e.g., hinge, logistic, least-squares. The gradient class takes as +input a training example, its label, and the current parameter value. +* *updater* is a class that updates weights in each iteration of gradient +descent. MLlib includes updaters for cases without regularization, as well as +L1 and L2 regularizers. +* *stepSize* is a scalar value denoting the initial step size for gradient +descent. All updaters in MLlib use a step size at the t-th step equal to +stepSize / sqrt(t). +* *numIterations* is the number of iterations to run. +* *regParam* is the regularization parameter when using L1 or L2 regularization. +* *miniBatchFraction* is the fraction of the data used to compute the gradient +at each iteration. + +Available algorithms for gradient descent: + +* [GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) diff --git a/docs/monitoring.md b/docs/monitoring.md index e9832e0466..0e3606f71a 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -42,6 +42,15 @@ set of sinks to which metrics are reported. The following instances are currentl * `executor`: A Spark executor. * `driver`: The Spark driver process (the process in which your SparkContext is created). +Each instance can report to zero or more _sinks_. Sinks are contained in the +`org.apache.spark.metrics.sink` package: + +* `ConsoleSink`: Logs metrics information to the console. +* `CSVSink`: Exports metrics data to CSV files at regular intervals. +* `GangliaSink`: Sends metrics to a Ganglia node or multicast group. +* `JmxSink`: Registers metrics for viewing in a JXM console. +* `MetricsServlet`: Adds a servlet within the existing Spark UI to serve metrics data as JSON data. + The syntax of the metrics configuration file is defined in an example configuration file, `$SPARK_HOME/conf/metrics.conf.template`. diff --git a/mllib/data/sample_svm_data.txt b/mllib/data/sample_svm_data.txt new file mode 100644 index 0000000000..7ab30bd93c --- /dev/null +++ b/mllib/data/sample_svm_data.txt @@ -0,0 +1,322 @@ +1 0 2.52078447201548 0 0 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0 +0 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 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2.061393766919624 2.619965104088255 0 2.004684436494304 0 2.122974378789621 0 0 0 0 0 0 0 0 +1 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0 +0 2.857738033247042 0 0 2.619965104088255 0 2.004684436494304 2.000347299268466 2.122974378789621 0 0 0 0 0 0 0 0 +1 2.857738033247042 0 0 2.619965104088255 0 0 2.000347299268466 0 2.228387042742021 2.228387042742023 0 0 0 0 0 0 +1 0 2.52078447201548 0 2.619965104088255 0 0 0 0 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0 +1 2.857738033247042 0 0 2.619965104088255 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0 diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 5177325887..a60b553b5a 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -33,6 +33,10 @@ object SparkBuild extends Build { // HBase version; set as appropriate. val HBASE_VERSION = "0.94.6" + // Target JVM version + val SCALAC_JVM_VERSION = "jvm-1.5" + val JAVAC_JVM_VERSION = "1.5" + lazy val root = Project("root", file("."), settings = rootSettings) aggregate(allProjects: _*) lazy val core = Project("core", file("core"), settings = coreSettings) @@ -77,7 +81,9 @@ object SparkBuild extends Build { organization := "org.apache.spark", version := "0.8.0-SNAPSHOT", scalaVersion := "2.9.3", - scalacOptions := Seq("-unchecked", "-optimize", "-deprecation"), + scalacOptions := Seq("-unchecked", "-optimize", "-deprecation", + "-target:" + SCALAC_JVM_VERSION), + javacOptions := Seq("-target", JAVAC_JVM_VERSION, "-source", JAVAC_JVM_VERSION), unmanagedJars in Compile <<= baseDirectory map { base => (base / "lib" ** "*.jar").classpath }, retrieveManaged := true, retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]", @@ -207,6 +213,7 @@ object SparkBuild extends Build { "com.codahale.metrics" % "metrics-core" % "3.0.0", "com.codahale.metrics" % "metrics-jvm" % "3.0.0", "com.codahale.metrics" % "metrics-json" % "3.0.0", + "com.codahale.metrics" % "metrics-ganglia" % "3.0.0", "com.twitter" % "chill_2.9.3" % "0.3.1", "com.twitter" % "chill-java" % "0.3.1" ) diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py index fd5972d381..1f35f6f939 100644 --- a/python/pyspark/__init__.py +++ b/python/pyspark/__init__.py @@ -30,6 +30,8 @@ Public classes: An "add-only" shared variable that tasks can only add values to. - L{SparkFiles<pyspark.files.SparkFiles>} Access files shipped with jobs. + - L{StorageLevel<pyspark.storagelevel.StorageLevel>} + Finer-grained cache persistence levels. """ import sys import os @@ -39,6 +41,7 @@ sys.path.insert(0, os.path.join(os.environ["SPARK_HOME"], "python/lib/py4j0.7.eg from pyspark.context import SparkContext from pyspark.rdd import RDD from pyspark.files import SparkFiles +from pyspark.storagelevel import StorageLevel -__all__ = ["SparkContext", "RDD", "SparkFiles"] +__all__ = ["SparkContext", "RDD", "SparkFiles", "StorageLevel"] diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 8fbf296509..597110321a 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -27,6 +27,7 @@ from pyspark.broadcast import Broadcast from pyspark.files import SparkFiles from pyspark.java_gateway import launch_gateway from pyspark.serializers import dump_pickle, write_with_length, batched +from pyspark.storagelevel import StorageLevel from pyspark.rdd import RDD from py4j.java_collections import ListConverter @@ -279,6 +280,16 @@ class SparkContext(object): """ self._jsc.sc().setCheckpointDir(dirName, useExisting) + def _getJavaStorageLevel(self, storageLevel): + """ + Returns a Java StorageLevel based on a pyspark.StorageLevel. + """ + if not isinstance(storageLevel, StorageLevel): + raise Exception("storageLevel must be of type pyspark.StorageLevel") + + newStorageLevel = self._jvm.org.apache.spark.storage.StorageLevel + return newStorageLevel(storageLevel.useDisk, storageLevel.useMemory, + storageLevel.deserialized, storageLevel.replication) def _test(): import atexit diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 914118ccdd..58e1849cad 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -70,6 +70,25 @@ class RDD(object): self._jrdd.cache() return self + def persist(self, storageLevel): + """ + Set this RDD's storage level to persist its values across operations after the first time + it is computed. This can only be used to assign a new storage level if the RDD does not + have a storage level set yet. + """ + self.is_cached = True + javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel) + self._jrdd.persist(javaStorageLevel) + return self + + def unpersist(self): + """ + Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + """ + self.is_cached = False + self._jrdd.unpersist() + return self + def checkpoint(self): """ Mark this RDD for checkpointing. It will be saved to a file inside the diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index 54823f8037..dc205b306f 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -24,6 +24,7 @@ import os import platform import pyspark from pyspark.context import SparkContext +from pyspark.storagelevel import StorageLevel # this is the equivalent of ADD_JARS add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") != None else None diff --git a/python/pyspark/storagelevel.py b/python/pyspark/storagelevel.py new file mode 100644 index 0000000000..b31f4762e6 --- /dev/null +++ b/python/pyspark/storagelevel.py @@ -0,0 +1,43 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +__all__ = ["StorageLevel"] + +class StorageLevel: + """ + Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory, + whether to drop the RDD to disk if it falls out of memory, whether to keep the data in memory + in a serialized format, and whether to replicate the RDD partitions on multiple nodes. + Also contains static constants for some commonly used storage levels, such as MEMORY_ONLY. + """ + + def __init__(self, useDisk, useMemory, deserialized, replication = 1): + self.useDisk = useDisk + self.useMemory = useMemory + self.deserialized = deserialized + self.replication = replication + +StorageLevel.DISK_ONLY = StorageLevel(True, False, False) +StorageLevel.DISK_ONLY_2 = StorageLevel(True, False, False, 2) +StorageLevel.MEMORY_ONLY = StorageLevel(False, True, True) +StorageLevel.MEMORY_ONLY_2 = StorageLevel(False, True, True, 2) +StorageLevel.MEMORY_ONLY_SER = StorageLevel(False, True, False) +StorageLevel.MEMORY_ONLY_SER_2 = StorageLevel(False, True, False, 2) +StorageLevel.MEMORY_AND_DISK = StorageLevel(True, True, True) +StorageLevel.MEMORY_AND_DISK_2 = StorageLevel(True, True, True, 2) +StorageLevel.MEMORY_AND_DISK_SER = StorageLevel(True, True, False) +StorageLevel.MEMORY_AND_DISK_SER_2 = StorageLevel(True, True, False, 2) |