sparklyr v0.9.3

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R Interface to Apache Spark

R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.

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sparklyr: R interface for Apache Spark

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  • Connect to Spark from R. The sparklyr package provides a complete dplyr backend.
  • Filter and aggregate Spark datasets then bring them into R for analysis and visualization.
  • Use Spark’s distributed machine learning library from R.
  • Create extensions that call the full Spark API and provide interfaces to Spark packages.

Installation

You can install the sparklyr package from CRAN as follows:

install.packages("sparklyr")

You should also install a local version of Spark for development purposes:

library(sparklyr)
spark_install(version = "2.1.0")

To upgrade to the latest version of sparklyr, run the following command and restart your r session:

devtools::install_github("rstudio/sparklyr")

If you use the RStudio IDE, you should also download the latest preview release of the IDE which includes several enhancements for interacting with Spark (see the RStudio IDE section below for more details).

Connecting to Spark

You can connect to both local instances of Spark as well as remote Spark clusters. Here we’ll connect to a local instance of Spark via the spark_connect function:

library(sparklyr)
sc <- spark_connect(master = "local")

The returned Spark connection (sc) provides a remote dplyr data source to the Spark cluster.

For more information on connecting to remote Spark clusters see the Deployment section of the sparklyr website.

Using dplyr

We can now use all of the available dplyr verbs against the tables within the cluster.

We’ll start by copying some datasets from R into the Spark cluster (note that you may need to install the nycflights13 and Lahman packages in order to execute this code):

install.packages(c("nycflights13", "Lahman"))
library(dplyr)
iris_tbl <- copy_to(sc, iris)
flights_tbl <- copy_to(sc, nycflights13::flights, "flights")
batting_tbl <- copy_to(sc, Lahman::Batting, "batting")
src_tbls(sc)
## [1] "batting" "flights" "iris"

To start with here’s a simple filtering example:

# filter by departure delay and print the first few records
flights_tbl %>% filter(dep_delay == 2)
## # Source:   lazy query [?? x 19]
## # Database: spark_connection
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  1  2013     1     1      517            515         2      830
##  2  2013     1     1      542            540         2      923
##  3  2013     1     1      702            700         2     1058
##  4  2013     1     1      715            713         2      911
##  5  2013     1     1      752            750         2     1025
##  6  2013     1     1      917            915         2     1206
##  7  2013     1     1      932            930         2     1219
##  8  2013     1     1     1028           1026         2     1350
##  9  2013     1     1     1042           1040         2     1325
## 10  2013     1     1     1231           1229         2     1523
## # ... with more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

Introduction to dplyr provides additional dplyr examples you can try. For example, consider the last example from the tutorial which plots data on flight delays:

delay <- flights_tbl %>% 
  group_by(tailnum) %>%
  summarise(count = n(), dist = mean(distance), delay = mean(arr_delay)) %>%
  filter(count > 20, dist < 2000, !is.na(delay)) %>%
  collect

# plot delays
library(ggplot2)
ggplot(delay, aes(dist, delay)) +
  geom_point(aes(size = count), alpha = 1/2) +
  geom_smooth() +
  scale_size_area(max_size = 2)
## `geom_smooth()` using method = 'gam'

Window Functions

dplyr window functions are also supported, for example:

batting_tbl %>%
  select(playerID, yearID, teamID, G, AB:H) %>%
  arrange(playerID, yearID, teamID) %>%
  group_by(playerID) %>%
  filter(min_rank(desc(H)) <= 2 & H > 0)
## # Source:     lazy query [?? x 7]
## # Database:   spark_connection
## # Groups:     playerID
## # Ordered by: playerID, yearID, teamID
##    playerID  yearID teamID     G    AB     R     H
##    <chr>      <int> <chr>  <int> <int> <int> <int>
##  1 aaronha01   1959 ML1      154   629   116   223
##  2 aaronha01   1963 ML1      161   631   121   201
##  3 abbotji01   1999 MIL       20    21     0     2
##  4 abnersh01   1992 CHA       97   208    21    58
##  5 abnersh01   1990 SDN       91   184    17    45
##  6 acklefr01   1963 CHA        2     5     0     1
##  7 acklefr01   1964 CHA        3     1     0     1
##  8 adamecr01   2016 COL      121   225    25    49
##  9 adamecr01   2015 COL       26    53     4    13
## 10 adamsac01   1943 NY1       70    32     3     4
## # ... with more rows

For additional documentation on using dplyr with Spark see the dplyr section of the sparklyr website.

Using SQL

It’s also possible to execute SQL queries directly against tables within a Spark cluster. The spark_connection object implements a DBI interface for Spark, so you can use dbGetQuery to execute SQL and return the result as an R data frame:

library(DBI)
iris_preview <- dbGetQuery(sc, "SELECT * FROM iris LIMIT 10")
iris_preview
##    Sepal_Length Sepal_Width Petal_Length Petal_Width Species
## 1           5.1         3.5          1.4         0.2  setosa
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 5           5.0         3.6          1.4         0.2  setosa
## 6           5.4         3.9          1.7         0.4  setosa
## 7           4.6         3.4          1.4         0.3  setosa
## 8           5.0         3.4          1.5         0.2  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 10          4.9         3.1          1.5         0.1  setosa

Machine Learning

You can orchestrate machine learning algorithms in a Spark cluster via the machine learning functions within sparklyr. These functions connect to a set of high-level APIs built on top of DataFrames that help you create and tune machine learning workflows.

Here’s an example where we use ml_linear_regression to fit a linear regression model. We’ll use the built-in mtcars dataset, and see if we can predict a car’s fuel consumption (mpg) based on its weight (wt), and the number of cylinders the engine contains (cyl). We’ll assume in each case that the relationship between mpg and each of our features is linear.

# copy mtcars into spark
mtcars_tbl <- copy_to(sc, mtcars)

# transform our data set, and then partition into 'training', 'test'
partitions <- mtcars_tbl %>%
  filter(hp >= 100) %>%
  mutate(cyl8 = cyl == 8) %>%
  sdf_partition(training = 0.5, test = 0.5, seed = 1099)

# fit a linear model to the training dataset
fit <- partitions$training %>%
  ml_linear_regression(response = "mpg", features = c("wt", "cyl"))
fit
## Formula: mpg ~ wt + cyl
## 
## Coefficients:
## (Intercept)          wt         cyl 
##   33.499452   -2.818463   -0.923187

For linear regression models produced by Spark, we can use summary() to learn a bit more about the quality of our fit, and the statistical significance of each of our predictors.

summary(fit)
## Deviance Residuals:
##    Min     1Q Median     3Q    Max 
## -1.752 -1.134 -0.499  1.296  2.282 
## 
## Coefficients:
## (Intercept)          wt         cyl 
##   33.499452   -2.818463   -0.923187 
## 
## R-Squared: 0.8274
## Root Mean Squared Error: 1.422

Spark machine learning supports a wide array of algorithms and feature transformations and as illustrated above it’s easy to chain these functions together with dplyr pipelines. To learn more see the machine learning section.

Reading and Writing Data

You can read and write data in CSV, JSON, and Parquet formats. Data can be stored in HDFS, S3, or on the local filesystem of cluster nodes.

temp_csv <- tempfile(fileext = ".csv")
temp_parquet <- tempfile(fileext = ".parquet")
temp_json <- tempfile(fileext = ".json")

spark_write_csv(iris_tbl, temp_csv)
iris_csv_tbl <- spark_read_csv(sc, "iris_csv", temp_csv)

spark_write_parquet(iris_tbl, temp_parquet)
iris_parquet_tbl <- spark_read_parquet(sc, "iris_parquet", temp_parquet)

spark_write_json(iris_tbl, temp_json)
iris_json_tbl <- spark_read_json(sc, "iris_json", temp_json)

src_tbls(sc)
## [1] "batting"      "flights"      "iris"         "iris_csv"    
## [5] "iris_json"    "iris_parquet" "mtcars"

Distributed R

You can execute arbitrary r code across your cluster using spark_apply. For example, we can apply rgamma over iris as follows:

spark_apply(iris_tbl, function(data) {
  data[1:4] + rgamma(1,2)
})
## # Source:   table<sparklyr_tmp_d1564a7b34a4> [?? x 4]
## # Database: spark_connection
##    Sepal_Length Sepal_Width Petal_Length Petal_Width
##           <dbl>       <dbl>        <dbl>       <dbl>
##  1         8.47        6.87         4.77        3.57
##  2         8.27        6.37         4.77        3.57
##  3         8.07        6.57         4.67        3.57
##  4         7.97        6.47         4.87        3.57
##  5         8.37        6.97         4.77        3.57
##  6         8.77        7.27         5.07        3.77
##  7         7.97        6.77         4.77        3.67
##  8         8.37        6.77         4.87        3.57
##  9         7.77        6.27         4.77        3.57
## 10         8.27        6.47         4.87        3.47
## # ... with more rows

You can also group by columns to perform an operation over each group of rows and make use of any package within the closure:

spark_apply(
  iris_tbl,
  function(e) broom::tidy(lm(Petal_Width ~ Petal_Length, e)),
  names = c("term", "estimate", "std.error", "statistic", "p.value"),
  group_by = "Species"
)
## # Source:   table<sparklyr_tmp_d15652cdc540> [?? x 6]
## # Database: spark_connection
##   Species    term         estimate std.error statistic  p.value
##   <chr>      <chr>           <dbl>     <dbl>     <dbl>    <dbl>
## 1 versicolor (Intercept)   -0.0843    0.161     -0.525 6.02e- 1
## 2 versicolor Petal_Length   0.331     0.0375     8.83  1.27e-11
## 3 virginica  (Intercept)    1.14      0.379      2.99  4.34e- 3
## 4 virginica  Petal_Length   0.160     0.0680     2.36  2.25e- 2
## 5 setosa     (Intercept)   -0.0482    0.122     -0.396 6.94e- 1
## 6 setosa     Petal_Length   0.201     0.0826     2.44  1.86e- 2

Extensions

The facilities used internally by sparklyr for its dplyr and machine learning interfaces are available to extension packages. Since Spark is a general purpose cluster computing system there are many potential applications for extensions (e.g. interfaces to custom machine learning pipelines, interfaces to 3rd party Spark packages, etc.).

Here’s a simple example that wraps a Spark text file line counting function with an R function:

# write a CSV 
tempfile <- tempfile(fileext = ".csv")
write.csv(nycflights13::flights, tempfile, row.names = FALSE, na = "")

# define an R interface to Spark line counting
count_lines <- function(sc, path) {
  spark_context(sc) %>% 
    invoke("textFile", path, 1L) %>% 
      invoke("count")
}

# call spark to count the lines of the CSV
count_lines(sc, tempfile)
## [1] 336777

To learn more about creating extensions see the Extensions section of the sparklyr website.

Table Utilities

You can cache a table into memory with:

tbl_cache(sc, "batting")

and unload from memory using:

tbl_uncache(sc, "batting")

Connection Utilities

You can view the Spark web console using the spark_web function:

spark_web(sc)

You can show the log using the spark_log function:

spark_log(sc, n = 10)
## 18/05/25 09:19:44 INFO ContextCleaner: Cleaned accumulator 2135
## 18/05/25 09:19:44 INFO ContextCleaner: Cleaned accumulator 2136
## 18/05/25 09:19:44 INFO ContextCleaner: Cleaned accumulator 2146
## 18/05/25 09:19:44 INFO ContextCleaner: Cleaned accumulator 2138
## 18/05/25 09:19:44 INFO ContextCleaner: Cleaned accumulator 2126
## 18/05/25 09:19:44 INFO Executor: Finished task 0.0 in stage 69.0 (TID 115). 918 bytes result sent to driver
## 18/05/25 09:19:44 INFO TaskSetManager: Finished task 0.0 in stage 69.0 (TID 115) in 177 ms on localhost (executor driver) (1/1)
## 18/05/25 09:19:44 INFO TaskSchedulerImpl: Removed TaskSet 69.0, whose tasks have all completed, from pool 
## 18/05/25 09:19:44 INFO DAGScheduler: ResultStage 69 (count at NativeMethodAccessorImpl.java:0) finished in 0.180 s
## 18/05/25 09:19:44 INFO DAGScheduler: Job 47 finished: count at NativeMethodAccessorImpl.java:0, took 0.183200 s

Finally, we disconnect from Spark:

spark_disconnect(sc)

RStudio IDE

The latest RStudio Preview Release of the RStudio IDE includes integrated support for Spark and the sparklyr package, including tools for:

  • Creating and managing Spark connections
  • Browsing the tables and columns of Spark DataFrames
  • Previewing the first 1,000 rows of Spark DataFrames

Once you’ve installed the sparklyr package, you should find a new Spark pane within the IDE. This pane includes a New Connection dialog which can be used to make connections to local or remote Spark instances:

Once you’ve connected to Spark you’ll be able to browse the tables contained within the Spark cluster and preview Spark DataFrames using the standard RStudio data viewer:

You can also connect to Spark through Livy through a new connection dialog:

The RStudio IDE features for sparklyr are available now as part of the RStudio Preview Release.

Using H2O

rsparkling is a CRAN package from H2O that extends sparklyr to provide an interface into Sparkling Water. For instance, the following example installs, configures and runs h2o.glm:

library(rsparkling)
library(sparklyr)
library(dplyr)
library(h2o)

sc <- spark_connect(master = "local", version = "2.1.0")
mtcars_tbl <- copy_to(sc, mtcars, "mtcars")

mtcars_h2o <- as_h2o_frame(sc, mtcars_tbl, strict_version_check = FALSE)

mtcars_glm <- h2o.glm(x = c("wt", "cyl"), 
                      y = "mpg",
                      training_frame = mtcars_h2o,
                      lambda_search = TRUE)
mtcars_glm
## Model Details:
## ==============
## 
## H2ORegressionModel: glm
## Model ID:  GLM_model_R_1527265202599_1 
## GLM Model: summary
##     family     link                              regularization
## 1 gaussian identity Elastic Net (alpha = 0.5, lambda = 0.1013 )
##                                                                lambda_search
## 1 nlambda = 100, lambda.max = 10.132, lambda.min = 0.1013, lambda.1se = -1.0
##   number_of_predictors_total number_of_active_predictors
## 1                          2                           2
##   number_of_iterations                                training_frame
## 1                  100 frame_rdd_31_ad5c4e88ec97eb8ccedae9475ad34e02
## 
## Coefficients: glm coefficients
##       names coefficients standardized_coefficients
## 1 Intercept    38.941654                 20.090625
## 2       cyl    -1.468783                 -2.623132
## 3        wt    -3.034558                 -2.969186
## 
## H2ORegressionMetrics: glm
## ** Reported on training data. **
## 
## MSE:  6.017684
## RMSE:  2.453097
## MAE:  1.940985
## RMSLE:  0.1114801
## Mean Residual Deviance :  6.017684
## R^2 :  0.8289895
## Null Deviance :1126.047
## Null D.o.F. :31
## Residual Deviance :192.5659
## Residual D.o.F. :29
## AIC :156.2425
spark_disconnect(sc)

Connecting through Livy

Livy enables remote connections to Apache Spark clusters. Connecting to Spark clusters through Livy is under experimental development in sparklyr. Please post any feedback or questions as a GitHub issue as needed.

Before connecting to Livy, you will need the connection information to an existing service running Livy. Otherwise, to test livy in your local environment, you can install it and run it locally as follows:

livy_install()
livy_service_start()

To connect, use the Livy service address as master and method = "livy" in spark_connect. Once connection completes, use sparklyr as usual, for instance:

sc <- spark_connect(master = "http://localhost:8998", method = "livy")
copy_to(sc, iris)
## # Source:   table<iris> [?? x 5]
## # Database: spark_connection
##    Sepal_Length Sepal_Width Petal_Length Petal_Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # ... with more rows
spark_disconnect(sc)

Once you are done using livy locally, you should stop this service with:

livy_service_stop()

To connect to remote livy clusters that support basic authentication connect as:

config <- livy_config(username="<username>", password="<password">)
sc <- spark_connect(master = "<address>", method = "livy", config = config)
spark_disconnect(sc)

Functions in sparklyr

Name Description
compile_package_jars Compile Scala sources into a Java Archive (jar)
DBISparkResult-class DBI Spark Result.
checkpoint_directory Set/Get Spark checkpoint directory
ft_ngram Feature Transformation -- NGram (Transformer)
ft_feature_hasher Feature Transformation -- FeatureHasher (Transformer)
ft_hashing_tf Feature Transformation -- HashingTF (Transformer)
copy_to.spark_connection Copy an R Data Frame to Spark
download_scalac Downloads default Scala Compilers
ft_count_vectorizer Feature Transformation -- CountVectorizer (Estimator)
ft_chisq_selector Feature Transformation -- ChiSqSelector (Estimator)
ft_index_to_string Feature Transformation -- IndexToString (Transformer)
ft_normalizer Feature Transformation -- Normalizer (Transformer)
ft_interaction Feature Transformation -- Interaction (Transformer)
ft_idf Feature Transformation -- IDF (Estimator)
ft_standard_scaler Feature Transformation -- StandardScaler (Estimator)
ft_dct Feature Transformation -- Discrete Cosine Transform (DCT) (Transformer)
ft_stop_words_remover Feature Transformation -- StopWordsRemover (Transformer)
ft_elementwise_product Feature Transformation -- ElementwiseProduct (Transformer)
ft_string_indexer Feature Transformation -- StringIndexer (Estimator)
hive_context_config Runtime configuration interface for Hive
ft_imputer Feature Transformation -- Imputer (Estimator)
invoke Invoke a Method on a JVM Object
ml_als Spark ML -- ALS
ft_tokenizer Feature Transformation -- Tokenizer (Transformer)
ft_pca Feature Transformation -- PCA (Estimator)
ft_one_hot_encoder Feature Transformation -- OneHotEncoder (Transformer)
livy_service_start Start Livy
ft_max_abs_scaler Feature Transformation -- MaxAbsScaler (Estimator)
ft_r_formula Feature Transformation -- RFormula (Estimator)
ft_regex_tokenizer Feature Transformation -- RegexTokenizer (Transformer)
ml-params Spark ML -- ML Params
ml_corr Compute correlation matrix
jobj_class Superclasses of object
invoke_method Generic Call Interface
ft_min_max_scaler Feature Transformation -- MinMaxScaler (Estimator)
ml_chisquare_test Chi-square hypothesis testing for categorical data.
ml_clustering_evaluator Spark ML - Clustering Evaluator
ft_vector_assembler Feature Transformation -- VectorAssembler (Transformer)
ft_vector_indexer Feature Transformation -- VectorIndexer (Estimator)
ml_bisecting_kmeans Spark ML -- Bisecting K-Means Clustering
livy_config Create a Spark Configuration for Livy
ml_glm_tidiers Tidying methods for Spark ML linear models
ml_generalized_linear_regression Spark ML -- Generalized Linear Regression
ml-tuning Spark ML -- Tuning
ml_linear_svc Spark ML -- LinearSVC
livy_install Install Livy
ml_logistic_regression Spark ML -- Logistic Regression
ml_aft_survival_regression Spark ML -- Survival Regression
ml_fpgrowth Frequent Pattern Mining -- FPGrowth
ml_evaluator Spark ML - Evaluators
ml_gaussian_mixture Spark ML -- Gaussian Mixture clustering.
ml_stage Spark ML -- Pipeline stage extraction
ml_summary Spark ML -- Extraction of summary metrics
ml_logistic_regression_tidiers Tidying methods for Spark ML Logistic Regression
ml_gbt_classifier Spark ML -- Gradient Boosted Trees
ml_decision_tree_classifier Spark ML -- Decision Trees
register_extension Register a Package that Implements a Spark Extension
ml_isotonic_regression Spark ML -- Isotonic Regression
ml_multilayer_perceptron_classifier Spark ML -- Multilayer Perceptron
ml_feature_importances Spark ML - Feature Importance for Tree Models
sdf-saveload Save / Load a Spark DataFrame
ml_naive_bayes Spark ML -- Naive-Bayes
ml_uid Spark ML -- UID
ml_lda Spark ML -- Latent Dirichlet Allocation
ml_unsupervised_tidiers Tidying methods for Spark ML unsupervised models
ml_linear_regression Spark ML -- Linear Regression
sdf-transform-methods Spark ML -- Transform, fit, and predict methods (sdf_ interface)
ml_pipeline Spark ML -- Pipelines
ml_random_forest_classifier Spark ML -- Random Forest
reactiveSpark Reactive spark reader
sdf_along Create DataFrame for along Object
sdf_checkpoint Checkpoint a Spark DataFrame
ml_model_data Extracts data associated with a Spark ML model
reexports Objects exported from other packages
sdf_coalesce Coalesces a Spark DataFrame
sdf_dim Support for Dimension Operations
sdf_num_partitions Gets number of partitions of a Spark DataFrame
ml_survival_regression_tidiers Tidying methods for Spark ML Survival Regression
sdf_fast_bind_cols Fast cbind for Spark DataFrames
ml_tree_tidiers Tidying methods for Spark ML tree models
sdf_partition Partition a Spark Dataframe
sdf_repartition Repartition a Spark DataFrame
sdf_collect Collect a Spark DataFrame into R.
na.replace Replace Missing Values in Objects
sdf_copy_to Copy an Object into Spark
sdf_project Project features onto principal components
sdf_bind Bind multiple Spark DataFrames by row and column
sdf_broadcast Broadcast hint
%>% Pipe operator
sdf_sort Sort a Spark DataFrame
sdf_quantile Compute (Approximate) Quantiles with a Spark DataFrame
sdf_is_streaming Spark DataFrame is Streaming
sdf_last_index Returns the last index of a Spark DataFrame
sdf_persist Persist a Spark DataFrame
sdf_read_column Read a Column from a Spark DataFrame
sdf_sql Spark DataFrame from SQL
ensure Enforce Specific Structure for R Objects
sdf_pivot Pivot a Spark DataFrame
find_scalac Discover the Scala Compiler
sdf_register Register a Spark DataFrame
ft_binarizer Feature Transformation -- Binarizer (Transformer)
spark_apply_log Log Writer for Spark Apply
sdf_residuals.ml_model_generalized_linear_regression Model Residuals
ft_bucketizer Feature Transformation -- Bucketizer (Transformer)
spark-api Access the Spark API
ft_lsh Feature Transformation -- LSH (Estimator)
spark_compilation_spec Define a Spark Compilation Specification
sdf_with_sequential_id Add a Sequential ID Column to a Spark DataFrame
spark-connections Manage Spark Connections
sdf_with_unique_id Add a Unique ID Column to a Spark DataFrame
spark_context_config Runtime configuration interface for the Spark Context.
sdf_separate_column Separate a Vector Column into Scalar Columns
ft_lsh_utils Utility functions for LSH models
spark_config_exists A helper function to check value exist under spark_config()
sdf_seq Create DataFrame for Range
ft_polynomial_expansion Feature Transformation -- PolynomialExpansion (Transformer)
spark_config_kubernetes Kubernetes Configuration
spark_install_sync helper function to sync sparkinstall project to sparklyr
spark_connection_find Find Spark Connection
spark_connection Retrieve the Spark Connection Associated with an R Object
spark_jobj-class spark_jobj class
spark_dataframe Retrieve a Spark DataFrame
spark_home_set Set the SPARK_HOME environment variable
ft_quantile_discretizer Feature Transformation -- QuantileDiscretizer (Estimator)
spark_default_compilation_spec Default Compilation Specification for Spark Extensions
spark_install_find Find a given Spark installation by version.
ft_vector_slicer Feature Transformation -- VectorSlicer (Transformer)
spark_read_libsvm Read libsvm file into a Spark DataFrame.
spark_apply Apply an R Function in Spark
spark_read_parquet Read a Parquet file into a Spark DataFrame
spark_read_source Read from a generic source into a Spark DataFrame.
spark_apply_bundle Create Bundle for Spark Apply
spark_session_config Runtime configuration interface for the Spark Session
spark_write_csv Write a Spark DataFrame to a CSV
spark_write_jdbc Writes a Spark DataFrame into a JDBC table
spark_read_orc Read a ORC file into a Spark DataFrame
ft_word2vec Feature Transformation -- Word2Vec (Estimator)
spark_connection-class spark_connection class
ml-persistence Spark ML -- Model Persistence
stream_id Spark Stream's Identifier
ml_default_stop_words Default stop words
ml-transform-methods Spark ML -- Transform, fit, and predict methods (ml_ interface)
stream_name Spark Stream's Name
spark_dependency Define a Spark dependency
spark_read_table Reads from a Spark Table into a Spark DataFrame.
spark_default_version determine the version that will be used by default if version is NULL
ml_evaluate Spark ML -- Evaluate prediction frames with evaluators
spark_read_text Read a Text file into a Spark DataFrame
spark_versions Retrieves a dataframe available Spark versions that van be installed.
spark_read_jdbc Read from JDBC connection into a Spark DataFrame.
spark_version Get the Spark Version Associated with a Spark Connection
spark_home_dir Find the SPARK_HOME directory for a version of Spark
spark_read_json Read a JSON file into a Spark DataFrame
stream_read_csv Read CSV Stream
spark_web Open the Spark web interface
stream_read_parquet Read Parquet Stream
spark_write_table Writes a Spark DataFrame into a Spark table
spark_write_text Write a Spark DataFrame to a Text file
stream_read_text Read Text Stream
spark_version_from_home Get the Spark Version Associated with a Spark Installation
ml_isotonic_regression_tidiers Tidying methods for Spark ML Isotonic Regression
stream_find Find Stream
stream_read_json Read JSON Stream
stream_write_kafka Write Kafka Stream
stream_write_json Write JSON Stream
stream_write_parquet Write Parquet Stream
spark_save_table Saves a Spark DataFrame as a Spark table
spark_table_name Generate a Table Name from Expression
ml_kmeans Spark ML -- K-Means Clustering
stream_write_text Write Text Stream
ft_sql_transformer Feature Transformation -- SQLTransformer
stream_generate_test Generate Test Stream
src_databases Show database list
stream_stop Stops a Spark Stream
ml_naive_bayes_tidiers Tidying methods for Spark ML Naive Bayes
stream_render Render Stream
ml_one_vs_rest Spark ML -- OneVsRest
tbl_cache Cache a Spark Table
stream_trigger_continuous Spark Stream Continuous Trigger
stream_stats Stream Statistics
tbl_change_db Use specific database
stream_trigger_interval Spark Stream Interval Trigger
stream_view View Stream
tbl_uncache Uncache a Spark Table
stream_write_orc Write a ORC Stream
stream_write_memory Write Memory Stream
worker_spark_apply_unbundle Extracts a bundle of dependencies required by spark_apply()
print_jobj Generic method for print jobj for a connection type
random_string Random string generation
sdf_debug_string Debug Info for Spark DataFrame
sdf_describe Compute summary statistics for columns of a data frame
sdf_len Create DataFrame for Length
sdf_mutate Mutate a Spark DataFrame
sdf_sample Randomly Sample Rows from a Spark DataFrame
sdf_schema Read the Schema of a Spark DataFrame
spark_compile Compile Scala sources into a Java Archive
spark_config_value A helper function to retrieve values from spark_config()
spark_config Read Spark Configuration
spark_config_settings Retrieve Available Settings
spark_jobj Retrieve a Spark JVM Object Reference
spark_log View Entries in the Spark Log
spark_read_csv Read a CSV file into a Spark DataFrame
spark_load_table Reads from a Spark Table into a Spark DataFrame.
spark_write_json Write a Spark DataFrame to a JSON file
spark_write_parquet Write a Spark DataFrame to a Parquet file
spark_write_orc Write a Spark DataFrame to a ORC file
stream_read_kafka Read Kafka Stream
spark_write_source Writes a Spark DataFrame into a generic source
stream_read_orc Read ORC Stream
stream_watermark Watermark Stream
stream_write_csv Write CSV Stream
connection_config Read configuration values for a connection
connection_is_open Check whether the connection is open
connection_spark_shinyapp A Shiny app that can be used to construct a spark_connect statement
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