Spark 1 64 F1

[Erminia Scharnberg]

SparkRis an R package that provides a light-weight frontend to use Apache Spark from R.In Spark 3.5.1, SparkR provides a distributed data frame implementation thatsupports operations like selection, filtering, aggregation etc. (similar to R data frames,dplyr) but on large datasets. SparkR also supports distributedmachine learning using MLlib.

A SparkDataFrame is a distributed collection of data organized into named columns. It is conceptuallyequivalent to a table in a relational database or a data frame in R, but with richeroptimizations under the hood. SparkDataFrames can be constructed from a wide array of sources such as:structured data files, tables in Hive, external databases, or existing local R data frames.

The entry point into SparkR is the SparkSession which connects your R program to a Spark cluster.You can create a SparkSession using sparkR.session and pass in options such as the application name, any spark packages depended on, etc. Further, you can also work with SparkDataFrames via SparkSession. If you are working from the sparkR shell, the SparkSession should already be created for you, and you would not need to call sparkR.session.

You can also start SparkR from RStudio. You can connect your R program to a Spark cluster fromRStudio, R shell, Rscript or other R IDEs. To start, make sure SPARK_HOME is set in environment(you can check Sys.getenv),load the SparkR package, and call sparkR.session as below. It will check for the Spark installation, and, if not found, it will be downloaded and cached automatically. Alternatively, you can also run install.spark manually.

In addition to calling sparkR.session, you could also specify certain Spark driver properties. Normally theseApplication properties andRuntime Environment cannot be set programmatically, as thedriver JVM process would have been started, in this case SparkR takes care of this for you. To setthem, pass them as you would other configuration properties in the sparkConfig argument tosparkR.session().

The simplest way to create a data frame is to convert a local R data frame into a SparkDataFrame. Specifically, we can use as.DataFrame or createDataFrame and pass in the local R data frame to create a SparkDataFrame. As an example, the following creates a SparkDataFrame based using the faithful dataset from R.

SparkR supports operating on a variety of data sources through the SparkDataFrame interface. This section describes the general methods for loading and saving data using Data Sources. You can check the Spark SQL programming guide for more specific options that are available for the built-in data sources.

The general method for creating SparkDataFrames from data sources is read.df. This method takes in the path for the file to load and the type of data source, and the currently active SparkSession will be used automatically.SparkR supports reading JSON, CSV and Parquet files natively, and through packages available from sources like Third Party Projects, you can find data source connectors for popular file formats like Avro. These packages can either be added byspecifying --packages with spark-submit or sparkR commands, or if initializing SparkSession with sparkPackages parameter when in an interactive R shell or from RStudio.

We can see how to use data sources using an example JSON input file. Note that the file that is used here is not a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON. As a consequence, a regular multi-line JSON file will most often fail.

You can also create SparkDataFrames from Hive tables. To do this we will need to create a SparkSession with Hive support which can access tables in the Hive MetaStore. Note that Spark should have been built with Hive support and more details can be found in the SQL programming guide. In SparkR, by default it will attempt to create a SparkSession with Hive support enabled (enableHiveSupport = TRUE).

Apply a function to each partition of a SparkDataFrame. The function to be applied to each partition of the SparkDataFrameand should have only one parameter, to which a data.frame corresponds to each partition will be passed. The output of function should be a data.frame. Schema specifies the row format of the resulting a SparkDataFrame. It must match to data types of returned value.

Like dapply, apply a function to each partition of a SparkDataFrame and collect the result back. The output of functionshould be a data.frame. But, Schema is not required to be passed. Note that dapplyCollect can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.

Like gapply, applies a function to each partition of a SparkDataFrame and collect the result back to R data.frame. The output of the function should be a data.frame. But, the schema is not required to be passed. Note that gapplyCollect can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.

Similar to lapply in native R, spark.lapply runs a function over a list of elements and distributes the computations with Spark.Applies a function in a manner that is similar to doParallel or lapply to elements of a list. The results of all the computationsshould fit in a single machine. If that is not the case they can do something like df

If eager execution is enabled, the data will be returned to R client immediately when the SparkDataFrame is created. By default, eager execution is not enabled and can be enabled by setting the configuration property spark.sql.repl.eagerEval.enabled to true when the SparkSession is started up.

Maximum number of rows and maximum number of characters per column of data to display can be controlled by spark.sql.repl.eagerEval.maxNumRows and spark.sql.repl.eagerEval.truncate configuration properties, respectively. These properties are only effective when eager execution is enabled. If these properties are not set explicitly, by default, data up to 20 rows and up to 20 characters per column will be showed.

A SparkDataFrame can also be registered as a temporary view in Spark SQL and that allows you to run SQL queries over its data.The sql function enables applications to run SQL queries programmatically and returns the result as a SparkDataFrame.

SparkR supports the Structured Streaming API. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. For more information see the R API on the Structured Streaming Programming Guide

Apache Arrow is an in-memory columnar data format that is used in Spark to efficiently transfer data between JVM and R processes. See also PySpark optimization done, PySpark Usage Guide for Pandas with Apache Arrow. This guide targets to explain how to use Arrow optimization in SparkR with some key points.

Note that you must ensure that Arrow R package is installed and available on all cluster nodes.The current supported minimum version is 1.0.0; however, this might change between the minor releases since Arrow optimization in SparkR is experimental.

Whether the optimization is enabled or not, SparkR produces the same results. In addition, the conversionbetween Spark DataFrame and R DataFrame falls back automatically to non-Arrow optimization implementationwhen the optimization fails for any reasons before the actual computation.

Since part of SparkR is modeled on the dplyr package, certain functions in SparkR share the same names with those in dplyr. Depending on the load order of the two packages, some functions from the package loaded first are masked by those in the package loaded after. In such case, prefix such calls with the package name, for instance, SparkR::cume_dist(x) or dplyr::cume_dist(x).

This is an appeal from a decree of the District Court for Massachusetts granting the petition of the owners of the schooner Commonwealth, seeking to have their liability limited under sections 4283-4289 of the Revised Statutes (46 USCA 175, 183-188), and alleging that the schooner was seaworthy, that the petitioners were without privity or knowledge of any fault, denying liability, and praying to be exonerated from all liability. While off the coast of Nova Scotia on a fishing trip, the schooner took fire on Friday, April 8, 1927, sank, and was a total loss. Twelve of the crew were drowned, and some of the others were injured.

The Commonwealth was a fishing schooner with auxiliary power. She was built in 1913 and was 103 feet long and 24 feet wide. Originally she was equipped with two 50 horse power gasoline twin-screw engines. In 1919 the two gasoline engines were removed and a crude oil full Diesel engine of 180 horse power was installed. A part of the equipment of this engine included a small one-cylinder gasoline engine, known as the Palmer engine, which was used to pump air into the bottles that supplied the air pressure to vaporize the crude oil and ignite the same in the chamber of the Diesel engine. Ordinarily it was used only in starting up the Diesel engine after it had been shut down for a time.

In 1924 an electric lighting plant was installed on the schooner, which was known as a two-kilowatt unimote generator. This lighting unit consisted of a reservoir holding about 2 gallons of gasoline, which formed the base; above that was the gasoline engine, and above that the generator; and connected with it were four storage batteries. The gasoline was pumped from the reservoir into the carburetor of the engine. The exhaust from this engine was carried through the side of the vessel. The schooner also was equipped with an electric bilge pump, which was operated through a rheostat from these batteries.

The Diesel engine, the Palmer engine, the electric lighting plant with its gasoline engine, generator, and batteries, and the electric bilge pump, were at the time of the fire installed in the engine room.

The engine room was about 18 feet long, 14 feet wide, and 7 or 8 feet high. It was located immediately in front of the cabin and at the rear of the hold where the fish were kept. The Diesel engine was installed lengthwise of the ship in the center of the engine room, and the bilge pump was on the port side. On the starboard side, and, about 2 feet distant from the Diesel engine, was a recess opening into the engine room. The dimensions of this recess were about 4 by 5 feet, and it was high enough for a man to stand in. At the right side of this recess, as one faced it, was located the Palmer engine. On the further side and about 2 feet distant from the Palmer engine and on a bracket 2 feet above the floor was located the unit of the electric lighting system consisting of the reservoir, the gasoline engine, and the generator. And on the left-hand side of the recess were located the four storage batteries. On the deck and over the front portion of the engine room was what is described as the gurry kit. In this, among other things, was located a drum holding 50 gallons of gasoline. A pipe three-eighths of an inch in diameter led from this drum down through the deck to the Palmer engine, at a point about 2 feet distant from the electric lighting plant's main unit. This pipe connected directly with the carburetor of the Palmer engine and supplied it with gasoline. In this pipe near where it entered the Palmer engine was a shut-off valve. In the pipe and above the shut-off valve was a cock, from which gasoline was drawn. And on deck, the petitioners' evidence tended to show there was located in this feed pipe, at a point near the drum, another shut-off valve. There was no *10 main pipe or branch pipe that supplied gasoline from the drum to the reservoir of the engine of the electric lighting plant. This reservoir had to be supplied with gasoline by pouring it from a heavy can holding 5 gallons (termed a safety can, the orifice of which was 2 inches in diameter) into an orifice in the reservoir which was an inch in diameter. This orifice was covered by a screw cap, which had to be removed when gasoline was supplied to the reservoir. Whether at the time in question a tunnel was furnished for use in pouring gasoline from the can into the reservoir, the evidence does not disclose. A 5-gallon can of gasoline was kept in the engine room. The top to this can, when in place, was held tight under pressure, and it was stated that it probably took 10 pounds of pressure to remove the top.

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