Spark Operations
| Function(s) | Description |
|---|---|
| get_spark_sql_catalog_implementation() | Retrieve the Spark connection’s SQL catalog implementation property |
| spark_config() | Read Spark Configuration |
| spark_connect() spark_connection_is_open() spark_disconnect() spark_disconnect_all() spark_submit() | Manage Spark Connections |
| spark_install() spark_uninstall() spark_install_dir() spark_install_tar() spark_installed_versions() spark_available_versions() | Download and install various versions of Spark |
| spark_log() | View Entries in the Spark Log |
| spark_web() | Open the Spark web interface |
| 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 |
| spark_session_config() | Runtime configuration interface for the Spark Session |
| spark_set_checkpoint_dir() spark_get_checkpoint_dir() | Set/Get Spark checkpoint directory |
| spark_table_name() | Generate a Table Name from Expression |
| spark_version_from_home() | Get the Spark Version Associated with a Spark Installation |
| spark_versions() | Retrieves a dataframe available Spark versions that van be installed. |
| spark_config_kubernetes() | Kubernetes Configuration |
| spark_config_settings() | Retrieve Available Settings |
| spark_connection_find() | Find Spark Connection |
| spark_dependency_fallback() | Fallback to Spark Dependency |
| spark_extension() | Create Spark Extension |
| spark_load_table() | Reads from a Spark Table into a Spark DataFrame. |
| list_sparklyr_jars() | list all sparklyr-*.jar files that have been built |
| spark_config_packages() | Creates Spark Configuration |
| spark_connection() | Retrieve the Spark Connection Associated with an R Object |
| spark_adaptive_query_execution() | Retrieves or sets status of Spark AQE |
| spark_advisory_shuffle_partition_size() | Retrieves or sets advisory size of the shuffle partition |
| spark_auto_broadcast_join_threshold() | Retrieves or sets the auto broadcast join threshold |
| spark_coalesce_initial_num_partitions() | Retrieves or sets initial number of shuffle partitions before coalescing |
| spark_coalesce_min_num_partitions() | Retrieves or sets the minimum number of shuffle partitions after coalescing |
| spark_coalesce_shuffle_partitions() | Retrieves or sets whether coalescing contiguous shuffle partitions is enabled |
Spark Data
| Function(s) | Description |
|---|---|
| spark_read() | Read file(s) into a Spark DataFrame using a custom reader |
| spark_read_avro() | Read Apache Avro data into a Spark DataFrame. |
| spark_read_binary() | Read binary data into a Spark DataFrame. |
| spark_read_csv() | Read a CSV file into a Spark DataFrame |
| spark_read_delta() | Read from Delta Lake into a Spark DataFrame. |
| spark_read_image() | Read image data into a Spark DataFrame. |
| spark_read_jdbc() | Read from JDBC connection into a Spark DataFrame. |
| spark_read_json() | Read a JSON file into a Spark DataFrame |
| spark_read_libsvm() | Read libsvm file into a Spark DataFrame. |
| spark_read_parquet() | Read a Parquet file into a Spark DataFrame |
| spark_read_source() | Read from a generic source into a Spark DataFrame. |
| spark_read_table() | Reads from a Spark Table into a Spark DataFrame. |
| spark_read_orc() | Read a ORC file into a Spark DataFrame |
| spark_read_text() | Read a Text file into a Spark DataFrame |
| spark_save_table() | Saves a Spark DataFrame as a Spark table |
| spark_write() | Write Spark DataFrame to file using a custom writer |
| spark_write_avro() | Serialize a Spark DataFrame into Apache Avro format |
| spark_write_orc() | Write a Spark DataFrame to a ORC file |
| spark_write_text() | Write a Spark DataFrame to a Text file |
| spark_write_csv() | Write a Spark DataFrame to a CSV |
| spark_write_delta() | Writes a Spark DataFrame into Delta Lake |
| spark_write_jdbc() | Writes a Spark DataFrame into a JDBC table |
| spark_write_json() | Write a Spark DataFrame to a JSON file |
| spark_write_parquet() | Write a Spark DataFrame to a Parquet file |
| spark_write_source() | Writes a Spark DataFrame into a generic source |
| spark_write_table() | Writes a Spark DataFrame into a Spark table |
| spark_write_rds() | Write Spark DataFrame to RDS files |
| collect_from_rds() | Collect Spark data serialized in RDS format into R |
Spark Tables
| Function(s) | Description |
|---|---|
| src_databases() | Show database list |
| tbl_cache() | Cache a Spark Table |
| tbl_change_db() | Use specific database |
| tbl_uncache() | Uncache a Spark Table |
Spark DataFrames
| Function(s) | Description |
|---|---|
[(<tbl_spark>) |
Subsetting operator for Spark dataframe |
| copy_to(<spark_connection>) | Copy an R Data Frame to Spark |
| sdf_along() | Create DataFrame for along Object |
| sdf_bind_rows() sdf_bind_cols() | Bind multiple Spark DataFrames by row and column |
| sdf_broadcast() | Broadcast hint |
| sdf_checkpoint() | Checkpoint a Spark DataFrame |
| sdf_coalesce() | Coalesces a Spark DataFrame |
| sdf_copy_to() sdf_import() | Copy an Object into Spark |
| sdf_distinct() | Invoke distinct on a Spark DataFrame |
| sdf_drop_duplicates() | Remove duplicates from a Spark DataFrame |
| sdf_expand_grid() | Create a Spark dataframe containing all combinations of inputs |
| sdf_from_avro() | Convert column(s) from avro format |
| sdf_len() | Create DataFrame for Length |
| sdf_num_partitions() | Gets number of partitions of a Spark DataFrame |
| sdf_random_split() sdf_partition() | Partition a Spark Dataframe |
| sdf_partition_sizes() | Compute the number of records within each partition of a Spark DataFrame |
| sdf_pivot() | Pivot a Spark DataFrame |
| sdf_predict() sdf_transform() sdf_fit() sdf_fit_and_transform() | Spark ML – Transform, fit, and predict methods (sdf_ interface) |
| sdf_rbeta() | Generate random samples from a Beta distribution |
| sdf_rbinom() | Generate random samples from a binomial distribution |
| sdf_rcauchy() | Generate random samples from a Cauchy distribution |
| sdf_rchisq() | Generate random samples from a chi-squared distribution |
| sdf_rexp() | Generate random samples from an exponential distribution |
| sdf_rgamma() | Generate random samples from a Gamma distribution |
| sdf_rgeom() | Generate random samples from a geometric distribution |
| sdf_rhyper() | Generate random samples from a hypergeometric distribution |
| sdf_rlnorm() | Generate random samples from a log normal distribution |
| sdf_rnorm() | Generate random samples from the standard normal distribution |
| sdf_rpois() | Generate random samples from a Poisson distribution |
| sdf_rt() | Generate random samples from a t-distribution |
| sdf_runif() | Generate random samples from the uniform distribution U(0, 1). |
| sdf_rweibull() | Generate random samples from a Weibull distribution. |
| sdf_read_column() | Read a Column from a Spark DataFrame |
| sdf_register() | Register a Spark DataFrame |
| sdf_repartition() | Repartition a Spark DataFrame |
| sdf_residuals() | Model Residuals |
| sdf_sample() | Randomly Sample Rows from a Spark DataFrame |
| sdf_separate_column() | Separate a Vector Column into Scalar Columns |
| sdf_seq() | Create DataFrame for Range |
| sdf_sort() | Sort a Spark DataFrame |
| sdf_to_avro() | Convert column(s) to avro format |
| sdf_with_unique_id() | Add a Unique ID Column to a Spark DataFrame |
| sdf_collect() | Collect a Spark DataFrame into R. |
| sdf_crosstab() | Cross Tabulation |
| sdf_debug_string() | Debug Info for Spark DataFrame |
| sdf_describe() | Compute summary statistics for columns of a data frame |
| sdf_dim() sdf_nrow() sdf_ncol() | Support for Dimension Operations |
| sdf_is_streaming() | Spark DataFrame is Streaming |
| sdf_last_index() | Returns the last index of a Spark DataFrame |
| sdf_save_table() sdf_load_table() sdf_save_parquet() sdf_load_parquet() | Save / Load a Spark DataFrame |
| sdf_persist() | Persist a Spark DataFrame |
| sdf_project() | Project features onto principal components |
| sdf_quantile() | Compute (Approximate) Quantiles with a Spark DataFrame |
| sdf_schema() | Read the Schema of a Spark DataFrame |
| sdf_sql() | Spark DataFrame from SQL |
| sdf_unnest_longer() | Unnest longer |
| sdf_unnest_wider() | Unnest wider |
| sdf_with_sequential_id() | Add a Sequential ID Column to a Spark DataFrame |
| inner_join(<tbl_spark>) left_join(<tbl_spark>) right_join(<tbl_spark>) full_join(<tbl_spark>) | Join Spark tbls. |
| hof_aggregate() | Apply Aggregate Function to Array Column |
| hof_array_sort() | Sorts array using a custom comparator |
| hof_exists() | Determine Whether Some Element Exists in an Array Column |
| hof_filter() | Filter Array Column |
| hof_forall() | Checks whether all elements in an array satisfy a predicate |
| hof_map_filter() | Filters a map |
| hof_map_zip_with() | Merges two maps into one |
| hof_transform() | Transform Array Column |
| hof_transform_keys() | Transforms keys of a map |
| hof_transform_values() | Transforms values of a map |
| hof_zip_with() | Combines 2 Array Columns |
| sdf_weighted_sample() | Perform Weighted Random Sampling on a Spark DataFrame |
| transform_sdf() | transform a subset of column(s) in a Spark Dataframe |
Spark Machine Learning
Spark Feature Transformers
| Function(s) | Description |
|---|---|
| ft_binarizer() | Feature Transformation – Binarizer (Transformer) |
| ft_bucketizer() | Feature Transformation – Bucketizer (Transformer) |
| ft_count_vectorizer() ml_vocabulary() | Feature Transformation – CountVectorizer (Estimator) |
| ft_dct() ft_discrete_cosine_transform() | Feature Transformation – Discrete Cosine Transform (DCT) (Transformer) |
| ft_elementwise_product() | Feature Transformation – ElementwiseProduct (Transformer) |
| ft_index_to_string() | Feature Transformation – IndexToString (Transformer) |
| ft_one_hot_encoder() | Feature Transformation – OneHotEncoder (Transformer) |
| ft_quantile_discretizer() | Feature Transformation – QuantileDiscretizer (Estimator) |
| ft_sql_transformer() ft_dplyr_transformer() | Feature Transformation – SQLTransformer |
| ft_string_indexer() ml_labels() ft_string_indexer_model() | Feature Transformation – StringIndexer (Estimator) |
| ft_vector_assembler() | Feature Transformation – VectorAssembler (Transformer) |
| ft_tokenizer() | Feature Transformation – Tokenizer (Transformer) |
| ft_regex_tokenizer() | Feature Transformation – RegexTokenizer (Transformer) |
| ft_bucketed_random_projection_lsh() ft_minhash_lsh() | Feature Transformation – LSH (Estimator) |
| ft_chisq_selector() | Feature Transformation – ChiSqSelector (Estimator) |
| ft_feature_hasher() | Feature Transformation – FeatureHasher (Transformer) |
| ft_hashing_tf() | Feature Transformation – HashingTF (Transformer) |
| ft_idf() | Feature Transformation – IDF (Estimator) |
| ft_imputer() | Feature Transformation – Imputer (Estimator) |
| ft_interaction() | Feature Transformation – Interaction (Transformer) |
| ft_max_abs_scaler() | Feature Transformation – MaxAbsScaler (Estimator) |
| ft_min_max_scaler() | Feature Transformation – MinMaxScaler (Estimator) |
| ft_ngram() | Feature Transformation – NGram (Transformer) |
| ft_normalizer() | Feature Transformation – Normalizer (Transformer) |
| ft_one_hot_encoder_estimator() | Feature Transformation – OneHotEncoderEstimator (Estimator) |
| ft_pca() ml_pca() | Feature Transformation – PCA (Estimator) |
| ft_polynomial_expansion() | Feature Transformation – PolynomialExpansion (Transformer) |
| ft_r_formula() | Feature Transformation – RFormula (Estimator) |
| ft_standard_scaler() | Feature Transformation – StandardScaler (Estimator) |
| ft_stop_words_remover() | Feature Transformation – StopWordsRemover (Transformer) |
| ft_vector_indexer() | Feature Transformation – VectorIndexer (Estimator) |
| ft_vector_slicer() | Feature Transformation – VectorSlicer (Transformer) |
| ft_word2vec() ml_find_synonyms() | Feature Transformation – Word2Vec (Estimator) |
| ft_robust_scaler() | Feature Transformation – RobustScaler (Estimator) |
Spark Machine Learning Utilities
Extensions
| Function(s) | Description |
|---|---|
| compile_package_jars() | Compile Scala sources into a Java Archive (jar) |
| connection_config() | Read configuration values for a connection |
| download_scalac() | Downloads default Scala Compilers |
| find_scalac() | Discover the Scala Compiler |
| spark_context() java_context() hive_context() spark_session() | Access the Spark API |
| hive_context_config() | Runtime configuration interface for Hive |
| invoke() invoke_static() invoke_new() | Invoke a Method on a JVM Object |
| j_invoke() j_invoke_static() j_invoke_new() | Invoke a Java function. |
| jarray() | Instantiate a Java array with a specific element type. |
| jfloat() | Instantiate a Java float type. |
| jfloat_array() | Instantiate an Array[Float]. |
| register_extension() registered_extensions() | Register a Package that Implements a Spark Extension |
| spark_compilation_spec() | Define a Spark Compilation Specification |
| spark_default_compilation_spec() | Default Compilation Specification for Spark Extensions |
| spark_context_config() | Runtime configuration interface for the Spark Context. |
| spark_dataframe() | Retrieve a Spark DataFrame |
| spark_dependency() | Define a Spark dependency |
| spark_home_set() | Set the SPARK_HOME environment variable |
| spark_jobj() | Retrieve a Spark JVM Object Reference |
| spark_version() | Get the Spark Version Associated with a Spark Connection |
Distributed Computing
| Function(s) | Description |
|---|---|
| spark_apply() | Apply an R Function in Spark |
| spark_apply_bundle() | Create Bundle for Spark Apply |
| spark_apply_log() | Log Writer for Spark Apply |
| registerDoSpark() | Register a Parallel Backend |
Livy
| Function(s) | Description |
|---|---|
| livy_install() livy_available_versions() livy_install_dir() livy_installed_versions() livy_home_dir() | Install Livy |
| livy_config() | Create a Spark Configuration for Livy |
| livy_service_start() livy_service_stop() | Start Livy |
Streaming
| Function(s) | Description |
|---|---|
| stream_find() | Find Stream |
| stream_generate_test() | Generate Test Stream |
| stream_id() | Spark Stream’s Identifier |
| stream_lag() | Apply lag function to columns of a Spark Streaming DataFrame |
| stream_name() | Spark Stream’s Name |
| stream_read_csv() | Read CSV Stream |
| stream_read_json() | Read JSON Stream |
| stream_read_delta() | Read Delta Stream |
| stream_read_kafka() | Read Kafka Stream |
| stream_read_orc() | Read ORC Stream |
| stream_read_parquet() | Read Parquet Stream |
| stream_read_socket() | Read Socket Stream |
| stream_read_text() | Read Text Stream |
| stream_render() | Render Stream |
| stream_stats() | Stream Statistics |
| stream_stop() | Stops a Spark Stream |
| stream_trigger_continuous() | Spark Stream Continuous Trigger |
| stream_trigger_interval() | Spark Stream Interval Trigger |
| stream_view() | View Stream |
| stream_watermark() | Watermark Stream |
| stream_write_console() | Write Console Stream |
| stream_write_csv() | Write CSV Stream |
| stream_write_delta() | Write Delta Stream |
| stream_write_json() | Write JSON Stream |
| stream_write_kafka() | Write Kafka Stream |
| stream_write_memory() | Write Memory Stream |
| stream_write_orc() | Write a ORC Stream |
| stream_write_parquet() | Write Parquet Stream |
| stream_write_text() | Write Text Stream |
| reactiveSpark() | Reactive spark reader |