Sparklyr 1.9.2

  • Avoids the cross-wire when pulling an object from a lazy table instead of pulling a field (#3494)

  • Converts spark_write_delta() to method

  • In simulate_vars_spark(), it avoids calling a function named ‘unknown’ in case a package has added such a function name in its environment (#3497)

Sparklyr 1.9.1

  • Removes use of %||% in worker’s R scripts to avoid reference error (#3487)

  • Restores support for Spark 2.4 with Scala 2.11 (#3485)

  • Addresses changes in Spark 4.0 from when it was in preview to now.

  • ml_load() now uses Spark’s file read to obtain the metadata for the models instead of R’s file read. This approach accounts for when the Spark Context is reading different mounted file protocols and mounted paths (#3478).

Sparklyr 1.9.0

Improvements

  • Adds support for Spark 4 and Scala 2.13 (#3479):

    • Adds new Java folder for Spark 4.0.0 with updated code

    • Adds new JAR file to handle Spark 4+

    • Updates to different spots in the R code to start handling version 4, as well as releases marked as “preview” by the Spark project

    • Removes JARs using Scala 2.11

  • Updates the Spark versions to use for CI

Fixes

  • sdf_sql() now returns nothing, including an error, when the query outputs an empty dataset (#3439)

Sparklyr 1.8.6

  • Addresses issues with R 4.4.0. The root cause was that version checking functions changed how the work.
    • package_version() no longer accepts numeric_version() output. Wrapped the package_version() function to coerce the argument if it’s a numeric_version class
    • Comparison operators (<, >=, etc.) for packageVersion() do no longer accept numeric values. The changes were to pass the version as a character
  • Adding support for Databricks “autoloader” (format: cloudFiles) for streaming ingestion of files(stream_read_cloudfiles)(@zacdav-db #3432):
    • stream_write_table()
    • stream_read_table()
  • Made changes to stream_write_generic (@zacdav-db #3432):
    • toTable method doesn’t allow calling start, added to_table param that adjusts logic
    • path option not propagated when to_table is TRUE
  • Upgrades to Roxygen version 7.3.1

Sparklyr 1.8.5

Fixes

  • Fixes quoting issue with dbplyr 2.5.0 (#3429)

  • Fixes Windows OS identification (#3426)

Package improvements

  • Removes dependency on tibble, all calls are now redirected to dplyr (#3399)

  • Removes dependency on rapddirs (#3401):

    • Backwards compatibility with sparklyr 0.5 is no longer needed
    • Replicates selection of cache directory

  • Converts spark_apply() to a method (#3418)

Spark improvements

  • Spark 2.3 is no longer considered maintained as of September 2019
    • Removes Java folder for versions 2.3 and below
    • Merges Scala file sets into Spark version 2.4
    • Re-compiles JARs for version 2.4 and above
  • Updates Delta-to-Spark version matching when using delta as one of the packages when connecting (#3414)

Sparklyr 1.8.4

Compatability with new dbplyr version

  • Fixes db_connection_describe() S3 consistency error (@t-kalinowski)

  • Addresses new error from dbplyr that fails when you try to access components from a remote tbl using $

  • Bumps the version of dbplyr to switch between the two methods to create temporary tables

  • Addresses new translate_sql() hard requirement to pass a con object. Done by passing the current connection or simulate_hive()

Fixes

  • Small fix to spark_connect_method() arguments. Removes ‘hadoop_version’

  • Improvements to handling pysparklyr load (@t-kalinowski)

  • Fixes ‘subscript out of bounds’ issue found by pysparklyr (@t-kalinowski)

  • Updates available Spark download links

Improvements

  • Removes dependency on the following packages:
    • digest
    • base64enc
    • ellipsis
  • Converts ml_fit() into a S3 method for pysparklyr compatibility

Test improvements

  • Improvements and fixes to tests (@t-kalinowski)

  • Fixes test jobs that include should have included Arrow but did not

  • Updates to the Spark versions to be tested

  • Re-adds tests for development dbplyr

Sparklyr 1.8.3

Improvements

  • Spark error message relays are now cached instead of the entire content displayed as an R error. This used to overwhelm the interactive session’s console or Notebook, because of the amount of lines returned by the Spark message. Now, by default, it will return the top of the Spark error message, which is typically the most relevant part. The full error can still be accessed using a new function called spark_last_error()

  • Reduces redundancy on several tests

  • Handles SQL quoting when the table reference contains multiple levels. The common time someone would encounter an issue is when a table name is passed using in_catalog(), or in_schema().

Java

  • Adds Scala scripts to handle changes in the upcoming version of Spark (3.5)
  • Adds new JAR file to handle Spark 3.0 to 3.4
  • Adds new JAR file to handle Spark 3.5 and above

Fixes

  • It prevents an error when na.rm = TRUE is explicitly set within pmax() and pmin(). It will now also purposely fail if na.rm is set to FALSE. The default of these functions in base R is for na.rm to be FALSE, but ever since these functions were released, there has been no warning or error. For now, we will keep that behavior until a better approach can be figured out. (#3353)

  • spark_install() will now properly match when a partial version is passed to the function. The issue was that passing ‘2.3’ would match to ‘3.2.3’, instead of ‘2.3.x’ (#3370)

Package integration

  • Adds functionality to allow other packages to provide sparklyr additional back-ends. This effort is mainly focused on adding the ability to integrate with Spark Connect and Databricks Connect through a new package.

  • New exported functions to integrate with the RStudio IDE. They all have the same spark_ide_ prefix

  • Modifies several read functions to become exported methods, such as sdf_read_column().

  • Adds spark_integ_test_skip() function. This is to allow other packages to use sparklyr’s test suite. It enables a way to the external package to indicate if a given test should run or be skipped.

  • If installed, sparklyr will load the pysparklyr package

Sparklyr 1.8.2

New Features

  • Adds Azure Synapse Analytics connectivity (@Bob-Chou , #3336)

  • Adds support for “parameterized” queries now available in Spark 3.4 (@gregleleu #3335)

  • Adds new DBI methods: dbValid and dbDisconnect (@alibell, #3296)

  • Adds overwrite parameter to dbWriteTable() (@alibell, #3296)

  • Adds database parameter to dbListTables() (@alibell, #3296)

  • Adds ability to turn off predicate support (where(), across()) using options(“sparklyr.support.predicates” = FALSE). Defaults to TRUE. This should accelerate dplyr commands because it won’t need to process column types for every single piped command

Fixes

  • Fixes Spark download locations (#3331)

  • Fix various rlang deprecation warnings (@mgirlich, #3333).

Misc

  • Switches upper version of Spark to 3.4, and updates JARS (#3334)

Sparklyr 1.8.1

Bug Fixes

  • Fixes consistency issues with dplyr’s sample_n(), slice(), op_vars(), and sample_frac()

Internal functionality

  • Adds R-devel to GHA testing

Sparklyr 1.8.0

Bug Fixes

  • Addresses Warning from CRAN checks

  • Addresses option(stringsAsFactors) usage

  • Fixes root cause of issue processing pivot wider and distinct (#3317 & #3320)

  • Updates local Spark download sources

Sparklyr 1.7.9

Bug Fixes

  • Better resolves intermediate column names when using dplyr verbs for data transformation (#3286)

  • Fixes pivot_wider() issues with simpler cases (#3289)

  • Updates Spark download locations (#3298)

  • Better resolution of intermediate column names (#3286)

Sparklyr 1.7.8

New features

  • Adds new metric extraction functions: ml_metrics_binary(), ml_metrics_regression() and ml_metrics_multiclass(). They work closer to how yardstick metric extraction functions work. They expect a table with the predictions and actual values, and returns a concise tibble with the metrics. (#3281)

  • Adds new spark_insert_table() function. This allows one to insert data into an existing table definition without redefining the table, even when overwriting the existing data. (#3272 @jimhester)

Bug Fixes

  • Restores “validator” functions to regression models. Removing them in a previous version broke ml_cross_validator() for regression models. (#3273)

Spark

  • Adds support to Spark 3.3 local installation. This includes the ability to enable and setup log4j version 2. (#3269)

  • Updates the JSON file that sparklyr uses to find and download Spark for local use. It is worth mentioning that starting with Spark 3.3, the Hadoop version number is no longer using a minor version for its download link. So, instead of requesting 3.2, the version to request is 3.

Internal functionality

  • Removes workaround for older versions of arrow. Bumps arrow version dependency, from 0.14.0 to 0.17.0 (#3283 @nealrichardson)

  • Removes code related to backwards compatibility with dbplyr. sparklyr requires dbplyr version 2.2.1 or above, so the code is no longer needed. (#3277)

  • Begins centralizing ML parameter validation into a single function that will run the proper cast function for each Spark parameter. It also starts using S3 methods, instead of searching for a concatenated function name, to find the proper parameter validator. Regression models are the first ones to use this new method. (#3279)

  • sparklyr compilation routines have been improved and simplified.
    spark_compile() now provides more informative output when used. It also adds tests to compilation to make sure. It also adds a step to install Scala in the corresponding GHAs. This is so that the new JAR build tests are able to run. (#3275)

  • Stops using package environment variables directly. Any package level variable will be handled by a genv prefixed function to set and retrieve values. This avoids the risk of having the exact same variable initialized on more than on R script. (#3274)

  • Adds more tests to improve coverage.

Misc

  • Addresses new CRAN HTML check NOTEs. It also adds a new GHA action to run the same checks to make sure we avoid new issues with this in the future.

Sparklyr 1.7.7

dplyr

  • Makes sure to run previous dplyr actions before sampling (#3276)

Misc

  • Ensures compatibility with the upcoming, and current, versions of dbplyr

Sparklyr 1.7.6

Misc

  • Ensures compatibility with Spark version 3.2 (#3261)

  • Compatibility with new dbplyr version (@mgirlich)

  • Removes stringr dependency

  • Fixes augment() when the model was fitted via parsnip (#3233)

Sparklyr 1.7.5

Misc

  • Addresses deprecation of rlang::is_env() function. (@lionel- #3217)

  • Updates pivot_wider() to support new version of tidyr (@DavisVaughan #3215)

Sparklyr 1.7.4

Misc

  • Edgar Ruiz (https://github.com/edgararuiz) will be the new maintainer of {sparklyr} moving forward.

Sparklyr 1.7.3

Data

  • Implemented support for the .groups parameter for dplyr::summarize() operations on Spark dataframes

  • Fixed the incorrect handling of the remove = TRUE option for separate.tbl_spark()

  • Optimized away an extra count query when collecting Spark dataframes from Spark to R.

Misc

  • By default, use links from the https://dlcdn.apache.org site for downloading Apache Spark when possible.

  • Attempt to continue spark_install() process even if the Spark version specified is not present in inst/extdata/versions*.json files (in which case sparklyr will guess the URL of the tar ball based on the existing and well-known naming convention used by https://archive.apache.org, i.e., https://archive.apache.org/dist/spark/spark-\({spark version}/spark-\){spark version}-bin-hadoop${hadoop version}.tgz)

  • Revised inst/extdata/versions*.json files to reflect recent releases of Apache Spark.

  • Implemented sparklyr_get_backend_port() for querying the port number used by the sparklyr backend.

Sparklyr 1.7.2

Connections

  • Added support for notebook-scoped libraries on Databricks connections. R library tree paths (i.e., those returned from .libPaths()) are now shared between driver and worker in sparklyr for Databricks connection use cases.

  • Java version validation function of sparklyr was revised to be able to parse java -version outputs containing only major version or outputs containing data values.

  • Spark configuration logic was revised to ensure “sparklyr.cores.local” takes precedence over “sparklyr.connect.cores.local”, as the latter is deprecated.

  • Renamed “sparklyr.backend.threads” (an undocumented, non-user-facing, sparklyr internal-only configuration) to “spark.sparklyr-backend.threads” so that it has the required “spark.” prefix and is configurable through sparklyr::spark_config().

  • For Spark 2.0 or above, if org.apache.spark.SparkEnv.get() returns a non- null env object, then sparklyr will use that env object to configure “spark.sparklyr-backend.threads”.

  • Support for running custom callbacks before the sparklyr backend starts processing JVM method calls was added for Databricks-related use cases, which will be useful for implementing ADL credential pass-through.

Data

  • Revised spark_write_delta() to use delta.io library version 1.0 when working with Apache Spark 3.1 or above.

  • Fixed a problem with dbplyr::remote_name() returning NULL on Spark dataframes returned from a dplyr::arrange() operation followed by dplyr::compute() (e.g., <a spark_dataframe> %>% arrange(<some column>) %>% compute()).

  • Implemented tidyr::replace_na() interface for Spark dataframes.

  • The n_distinct() summarizer for Spark dataframes was revised substantially to properly support na.rm = TRUE or na.rm = FALSE use cases when performing dplyr::summarize(<colname> = n_distinct(...)) types of operations on Spark dataframes.

  • Spark data interface functions that create Spark dataframes will no longer check whether any Spark dataframe with identical name exists when the dataframe being created has a randomly generated name (as randomly generated table name will contain a UUID and any chance of name collision is vanishingly small).

Documentation

  • Create usage example for ml_prefixspan().

Sparklyr 1.7.1

Connections

  • Fixed an issue with connecting to Apache Spark 3.1 or above.

Sparklyr 1.7.0

Data

  • Revised tidyr::fill() implementation to respect any ‘ORDER BY’ clause from the input while ensuring the same ‘ORDER BY’ operation is never duplicated twice in the generated Spark SQL query

  • Helper functions such as sdf_rbeta(), sdf_rbinom(), etc were implemented for generating Spark dataframes containing i.i.d. samples from commonly used probability distributions.

  • Fixed a bug with compute.tbl_spark()’s handling of positional args.

  • Fixed a bug that previously affected dplyr::tbl() when the source table is specified using dbplyr::in_schema().

  • Internal calls to sdf_schema.tbl_spark() and spark_dataframe.tbl_spark() are memoized to reduce performance overhead from repeated spark_invoke()s.

  • spark_read_image() was implemented to support image files as data sources.

  • spark_read_binary() was implemented to support binary data sources.

  • A specialized version of tbl_ptype() was implemented so that no data will be collected from Spark to R when dplyr calls tbl_ptype() on a Spark dataframe.

  • Added support for database parameter to src_tbls.spark_connection() (e.g., src_tbls(sc, database = "default") where sc is a Spark connection).

  • Fixed a null pointer issue with spark_read_jdbc() and spark_write_jdbc().

Distributed R

  • spark_apply() was improved to support tibble inputs containing list columns.

  • Spark dataframes created by spark_apply() will be cached by default to avoid re-computations.

  • spark_apply() and do_spark() now support qs and custom serializations.

  • The experimental auto_deps = TRUE mode was implemented for spark_apply() to infer required R packages for the closure, and to only copy required R packages to Spark worker nodes when executing the closure.

Extensions

  • Sparklyr extensions can now customize dbplyr SQL translator env used by sparklyr by supplying their own dbplyr SQL variant when calling spark_dependency() (see https://github.com/r-spark/sparklyr.sedona/blob/1455d3dea51ad16114a8112f2990ec542458aee2/R/dependencies.R#L38 for an example).

  • jarray() was implemented to convert a R vector into an Array[T] reference. A reference returned by jarray() can be passed to invoke* family of functions requiring an Array[T] as a parameter where T is some type that is more specific than java.lang.Object.

  • jfloat() function was implemented to cast any numeric type in R to java.lang.Float.

  • jfloat_array() was implemented to instantiate Array[java.lang.Float] from numeric values in R.

Serialization

  • Added null checks that were previously missing when collecting array columns from Spark dataframe to R.

  • array<byte> and array<boolean> columns in a Spark dataframe will be collected as raw() and logical() vectors, respectively, in R rather than integer arrays.

  • Fixed a bug that previously caused invoke params containing NaNs to be serialized incorrectly.

Spark ML

  • ml_compute_silhouette_measure() was implemented to evaluate the Silhouette measure of k-mean clustering results.

  • spark_read_libsvm() now supports specifications of additional options via the options parameter. Additional libsvm data source options currently supported by Spark include numFeatures and vectorType (see https://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/source/libsvm/LibSVMDataSource.html).

  • ml_linear_svc() will emit a warning if weight_col is specified while working with Spark 3.0 or above, as it is no longer supported in recent versions of Spark.

  • Fixed an issue with ft_one_hot_encoder.ml_pipeline() not working as expected.

Sparklyr 1.6.3

Data

  • Reduced the number of invoke() calls needed for sdf_schema() to avoid performance issues when processing Spark dataframes with non-trivial number of columns

  • Implement memoization for spark_dataframe.tbl_spark() and sdf_schema.tbl_spark() to reduce performance overhead for some dplyr use cases involving Spark dataframes with non-trivial number of columns

Sparklyr 1.6.2

Data

  • A previous bug fix related to dplyr::compute() caching a Spark view needed to be further revised to take effect with dbplyr backend API edition 2

Sparklyr 1.6.1

Data

  • sdf_distinct() is implemented to be an R interface for distinct() operation on Spark dataframes (NOTE: this is different from the dplyr::distinct() operation, as dplyr::distinct() operation on a Spark dataframe now supports .keep_all = TRUE and has more complex ordering requirements)

  • Fixed a problem of some expressions being evaluated twice in transmute.tbl_spark() (see tidyverse/dbplyr#605)

  • dbExistsTable() now performs case insensitive comparison with table names to be consistent with how table names are handled by Spark catalog API

  • Fixed a bug with sql_query_save() not overwriting a temp table with identical name

  • Revised sparklyr:::process_tbl_name() to correctly handle inputs that are not table names

  • Bug fix: db_save_query.spark_connection() should also cache the view it created in Spark

Sparklyr 1.6.0

Data

  • Made sparklyr compatible with both dbplyr edition 1 and edition 2 APIs

  • Revised sparklyr’s integration with dbplyr API so that dplyr::select(), dplyr::mutate(), and dplyr::summarize() verbs on Spark dataframes involving where() predicates can be correctly translated to Spark SQL (e.g., one can have sdf %>% select(where(is.numeric)) and sdf %>% summarize(across(starts_with("Petal"), mean)), etc)

  • Implemented dplyr::if_all() and dplyr::if_any() support for Spark dataframes

  • Added support for partition_by option in stream_write_* methods

  • Fixed a bug with URI handling affecting all spark_read_* methods

  • Avoided repeated creations of SimpleDataFormat objects and setTimeZone calls while collecting Data columns from a Spark dataframe

  • Schema specification for struct columns in spark_read_*() methods are now supported (e.g., spark_read_json(sc, path, columns = list(s = list(a = "integer, b = "double"))) says expect a struct column named s with each element containing a field named a and a field named b)

  • sdf_quantile() and ft_quantile_discretizer() now support approximation of weighted quantiles using a modified version of the Greenwald-Khanna algorithm that takes relative weight of each data point into consideration.

  • Fixed a problem of some expressions being evaluated twice in transmute.tbl_spark() (see tidyverse/dbplyr#605)

  • Made dplyr::distinct() behavior for Spark dataframes configurable: setting options(sparklyr.dplyr_distinct.impl = "tbl_lazy) will switch dplyr::distinct() implementation to a basic one that only adds ‘DISTINCT’ clause to the current Spark SQL query, does not support the .keep_all = TRUE option, and (3) does not have any ordering guarantee for the output.

Serialization

  • spark_write_rds() was implemented to support exporting all partitions of a Spark dataframe in parallel into RDS (version 2) files. Such RDS files will be written to the default file system of the Spark instance (i.e., local file if the Spark instance is running locally, or a distributed file system such as HDFS if the Spark instance is deployed over a cluster). The resulting RDS files, once downloaded onto the local file system, should be deserialized into R dataframes using collect_from_rds() (which calls readRDS() internally and also performs some important post-processing steps to support timestamp columns, date columns, and struct columns properly in R).

  • copy_to() can now import list columns of temporal values within a R dataframe as arrays of Spark SQL date/timestamp types when working with Spark 3.0 or above

  • Fixed a bug with copy_to()’s handling of NA values in list columns of a R dataframe

  • Spark map type will be collected as list instead of environment in R in order to support empty string as key

  • Fixed a configuration-related bug in sparklyr:::arrow_enabled()

  • Implemented spark-apply-specific configuration option for Arrow max records per batch, which can be different from the spark.sql.execution.arrow.maxRecordsPerBatch value from Spark session config

Connections

  • Created convenience functions for working with Spark runtime configurations

  • Fixed buggy exit code from the spark-submit process launched by sparklyr

Spark ML

  • Implemented R interface for Power Iteration Clustering

  • The handle_invalid option is added to ft_vector_indexer() (supported by Spark 2.3 or above)

Misc

  • Fixed a bug with ~ within some path components not being normalized in sparklyr::livy_install()

Sparklyr 1.5.2

Connections

  • Fixed op_vars() specification in dplyr::distinct() verb for Spark dataframes

  • spark_disconnect() now closes the Spark monitoring connection correctly

Data

  • Implement support for stratified sampling in ft_dplyr_transformer()

  • Added support for na.rm in dplyr rowSums() function for Spark dataframes

Sparklyr 1.5.1

Connections

  • A bug in how multiple --conf values were handled in some scenarios within the spark-submit shell args which was introduced in sparklyr 1.4 has been fixed now.

  • A bug with livy.jars configuration was fixed (#2843)

Data

  • tbl() methods were revised to be compatible with dbplyr 2.0 when handling inputs of the form "<schema name>.<table name>"

Sparklyr 1.5.0

Connections

  • spark_web() has been revised to work correctly in environments such as RStudio Server or RStudio Cloud where the Spark web UI URLs such as “http://localhost:4040/jobs/” needs to be translated with rstudioapi::translateLocalUrl() to be accessible.

  • The problem with bundle file name collisions when session_id is not provided has been fixed in spark_apply_bundle().

  • Support for sparklyr.livy.sources is removed completely as it is no longer needed as a workaround when Spark version is specified.

Data

  • stream_lag() is implemented to provide the equivalent functionality of dplyr::lag() for streaming Spark dataframes while also supporting additional filtering of “outdated” records based on timestamp threshold.

  • A specialized version of dplyr::distinct() is implemented for Spark dataframes that supports .keep_all = TRUE and correctly satisfies the “rows are a subset of the input but appear in the same order” requirement stated in the dplyr documentation.

  • The default value for the repartition parameter of sdf_seq() has been corrected.

  • Some implementation detail was revised to make sparklyr 1.5 fully compatible with dbplyr 2.0.

  • sdf_expand_grid() was implemented to support roughly the equivalent of expand.grid() for Spark dataframes while also offering additional Spark- specific options such as broadcast hash joins, repartitioning, and caching of the resulting Spark dataframe in memory.

  • sdf_quantile() now supports calculation for multiple columns.

  • Both lead() and lag() methods for dplyr interface of sparklyr are fixed to correctly accept the order_by parameter.

  • The cumprod() window aggregation function for dplyr was reimplemented to correctly handle null values in Spark dataframes.

  • Support for missing parameter is implemented for the ifelse()/if_else() function for dplyr.

  • A weighted.mean() summarizer was implemented for dplyr interface of sparklyr.

  • A workaround was created to ensure NA_real_ is handled correctly within the contexts of dplyr::mutate() and dplyr::transmute() methods (e.g., sdf %>% dplyr::mutate(z = NA_real_) should result in a column named “z” with double-precision SQL type)

  • Support for R-like subsetting operator ([) was implemented for selecting a subset of columns from a Spark dataframe.

  • The rowSums() function was implemented for dplyr interface of sparklyr.

  • The sdf_partition_sizes() function was created to enable efficient query of partition sizes within a Spark dataframe.

  • Stratified sampling for Spark dataframes has been implemented and can be expressed using dplyr grammar as <spark dataframe> %>% dplyr::group_by(<columns>) %>% dplyr::sample_n(...) or <spark dataframe> %>% dplyr::group_by(<columns>) %>% dplyr::sample_frac(...) where <columns> is a list of grouping column(s) defining the strata (i.e., the sampling specified by dplyr::sample_n() or dplyr::sample_frac() will be applied to each group defined by dplyr::group_by(<columns>))

  • The implementations of dplyr::sample_n() and dplyr::sample_frac() have been revised to first perform aggregations on individual partitions before merging aggregated results from all partitions, which is more efficient than mapPartitions() followed by reduce().

  • sdf_unnest_longer() and sdf_unnest_wider() were implemented and offer the equivalents of tidyr::unnest_longer() and tidyr::unnest_wider() for for Spark dataframes.

Serialization

  • copy_to() now serializes R dataframes into RDS format instead of CSV format if arrow is unavailable. RDS serialization is approximately 48% faster than CSV and allows multiple correctness issues related to CSV serialization to be fixed easily in sparklyr.

  • copy_to() and collect() now correctly preserve NA_real_ (NA_real_ from a R dataframe, once translated as null in a Spark dataframe, used to be incorrectly collected as NaN in previous versions of sparklyr).

  • copy_to() can now distinguish "NA" from NA as expected.

  • copy_to() now supports importing binary columns from R dataframes to Spark.

  • Reduced serialization overhead in Spark-based foreach parallel backend created with registerDoSpark().

Sparklyr 1.4.0

Connections

  • RAPIDS GPU acceleration plugin can now be enabled with spark_connect(..., package = "rapids") and configured with spark_config options prefixed with “spark.rapids.”

  • Enabled support for http{,s} proxy plus additional CURL options for Livy connections

  • In sparklyr error message, suggest options(sparklyr.log.console = TRUE) as a trouble-shooting step whenever the “sparklyr gateway not responding” error occurs

  • Addressed an inter-op issue with Livy + Spark 2.4 (https://github.com/sparklyr/sparklyr/issues/2641)

  • Added configurable retries for Gateway ports query (https://github.com/sparklyr/sparklyr/pull/2654)

  • App name setting now takes effect as expected in YARN cluster mode (https://github.com/sparklyr/sparklyr/pull/2675)

Data

  • Support for newly introduced higher-order functions in Spark 3.0 (e.g., array_sort, map_filter, map_zip_with, and many others)

  • Implemented parallelizable weighted sampling methods for sampling from a Spark data frames with and without replacement using exponential variates

  • Replaced dplyr::sample_* implementations based on TABLESAMPLE with alternative implementation that can return exactly the number of rows or fraction specified and also properly support sampling with-replacement, without-replacement, and repeatable sampling use cases

  • All higher-order functions and sampling methods are made directly accessible through dplyr verbs

  • Made grepl part of the dplyr interface for Spark data frames

  • Tidyr verbs such as pivot_wider, pivot_longer, nest, unnest, separate, unite, and fill now have specialized implementations in sparklyr for working with Spark data frames

  • Made dplyr::inner_join, dplyr::left_join, dplyr::right_join, and dplyr::full_join replace '.' with '_' in suffix parameter when working with Spark data frames (https://github.com/sparklyr/sparklyr/issues/2648)

Distributed R

  • Fixed an issue with global variables in registerDoSpark (https://github.com/sparklyr/sparklyr/pull/2608)

  • Revised spark_read_compat_param to avoid collision on names assigned to different Spark data frames

Misc

  • Fixed a rendering issue with HTML reference pages

  • Made test reporting in Github CI workflows more informative (https://github.com/sparklyr/sparklyr/pull/2672)

Spark ML

  • ft_robust_scaler was created as the R interface for the RobustScaler functionality in Spark 3 or above

Sparklyr 1.3.1

Distributed R

  • Fixed a bug in ordering of parameters for a lamba expression when the lambda expression passed to a hof_* method is specified with a R formula and the lambda takes 2 parameters

Sparklyr 1.3.0

Spark ML

  • ml_evaluate() methods are implemented for ML clustering and classification models

Distributed R

  • Created helper methods to integrate Spark SQL higher-order functions with dplyr::mutate

  • Implemented option to pass partition index as a named parameter to spark_apply() transform function

  • Enabled transform function of spark_apply() to return nested lists

  • Added option to return R objects instead of Spark data frame rows from transform function of spark_apply

  • sdf_collect() now supports fetching Spark data frame row-by-row rather than column-by-column, and fetching rows using iterator instead of collecting all rows into memory

  • Support for partition when using barrier execution in spark_apply (#2454)

Connections

  • Sparklyr can now connect with Spark 2.4 built with Scala 2.12 using spark_connect(..., scala_version = "2.12")

  • Hive integration can now be disabled by configuration in spark_connect() (#2465)

  • A JVM object reference counting bug affecting secondary Spark connections was fixed (#2515)

  • Revised JObj envs initialization for Databricks connections (#2533)

Serialization

  • Timezones, if present in data, are correctly represented now in Arrow serialization

  • Embedded nul bytes are removed from strings when reading strings from Spark to R (#2250)

  • Support to collect objectts of type SeqWrapper (#2441)

Data

  • Created helper methods to integrate Spark SQL higher-order functions with dplyr::mutate

  • New spark_read() method to allow user-defined R functions to be run on Spark workers to import data into a Spark data frame

  • spark_write() method is implemented allow user-defined functions to be run on Spark workers to export data from a Spark data frame

  • Avro functionalities such as spark_read_avro(), spark_write_avro(), sdf_from_avro(), and sdf_to_avro() are implemented and can be optionally enabled with spark_connect(..., package = "avro")

Extensions

  • Fixed a bug where Spark package repositories specification was not honored by spark_dependency(). The repositories parameter of spark_dependency() now works as expected.

Misc

  • Fixed warnings for deprecated functions (#2431)

  • More test coverage for Databricks Connect and Databricks Notebook modes

  • Embedded R sources are now included as resources rather than as a Scala string literal in sparklyr-*.jar files, so that they can be updated without re-compilation of Scala source files

  • A mechanism is created to verify embedded sources in sparklyr-*.jar files are in-sync with current R source files and this verification is now part of the Github CI workflow for sparklyr

Sparklyr 1.2.0

Distributed R

  • Add support for using Spark as a foreach parallel backend

  • Fixed a bug with how columns parameter was interpreted in spark_apply

Data

  • Allow sdf_query_plan to also get analyzed plan

  • Add support for serialization of R date values into corresponding Hive date values

  • Fixed the issue of date or timestamp values representing the UNIX epoch (1970-01-01) being deserialized incorrectly into NAs

  • Better support for querying and deserializing Spark SQL struct columns when working with Spark 2.4 or above

  • Add support in copy_to() for columns with nested lists (#2247).

  • Significantly improve collect() performance for columns with nested lists (#2252).

Connection

  • Add support for Databricks Connect

  • Add support for copy_to in Databricks connection

  • Ensure spark apply bundle files created by multiple Spark sessions don’t overwrite each other

  • Fixed an interop issue with spark-submit when running with Spark 3 preview

  • Fixed an interop issue with Sparklyr gateway connection when running with Spark 3 preview

  • Fixed a race condition of JVM object with refcount 1 being removed from JVM object tracker before pending method invocation(s) on them could be initiated (NOTE: previously this would only happen when the R process was running under high memory pressure)

  • Allow a chain of JVM method invocations to be batched into 1 invoke call

  • Removal of unneeded objects from JVM object tracker no longer blocks subsequent JVM method invocations

  • Add support for JDK11 for Spark 3 preview.

Misc

  • Support for installing Spark 3.0 Preview 2.

  • Emit more informative error message if network interface required for spark_connect is not up

  • Fixed a bug preventing more than 10 rows of a Spark table to be printed from R

  • Fixed a spelling error in print method for ml_model_naive_bayes objects

  • Made sdf_drop_duplicates an exported function (previously it was not exported by mistake)

  • Fixed a bug in summary() of ml_linear_regression

Sparklyr 1.1.0

Distributed R

  • Add support for barrier execution mode with barrier = TRUE in spark_apply() (@samuelmacedo83, #2216).

Streaming

  • Add support for stream_read_delta() and stream_write_delta().

  • Fixed typo in stream_read_socket().

Data

  • Allow using Scala types in schema specifications. For example, StringType in the columns parameter for spark_read_csv() (@jozefhajnala, #2226)

  • Add support for DBI 1.1 to implement missing dbQuoteLiteral signature (#2227).

Livy

  • Add support for Livy 0.6.0.

  • Deprecate uploading sources to Livy, a jar is now always used and the version parameter in spark_connect() is always required.

  • Add config sparklyr.livy.branch to specify the branch used for the sparklyr JAR.

  • Add config sparklyr.livy.jar to configure path or URL to sparklyr JAR.

Data

  • Add support for partition_by when using spark_write_delta() (#2228).

Sparklyr 1.0.5

Serialization

  • R environments are now sent to Scala Maps rather than java.util.Map[Object, Object] (#1058).

Data

  • Allow sdf_sql() to accept glue strings (@yutannihilation, #2171).

  • Support to read and write from Delta Lake using spark_read_delta() and spark_write_delta() (#2148).

Connections

  • spark_connect() supports new packages parameter to easily enable kafka and delta (#2148).

  • spark_disconnect() returns invisibly (#2028).

Configuration

  • Support to specify config file location using the SPARKLYR_CONFIG_FILE environment variable (@AgrawalAmey, #2153).

Compilation

  • Support for Scala 12 (@lu-wang-dl, #2154).

YARN

  • Fix curl_fetch_memory error when using YARN Cluster mode (#2157).

Sparklyr 1.0.4

Arrow

  • Support for Apache Arrow 0.15 (@nealrichardson, #2132).

Sparklyr 1.0.3

Kuberenetes

  • Support for port forwarding in Windows using RStudio terminal.

dplyr

  • Fix support for compute() in Spark 1.6 (#2099)

Data

  • The spark_read_() functions now support multiple parameters (@jozefhajnala, #2118).

Connections

  • Fix for Qubole connections for single user and multiple sessions (@vipul1409, #2128).

Sparklyr 1.0.2

Connections

  • Support for Qubole connections using mode = "quobole" (@vipul1409, #2039).

Extensions

  • When invoke() fails due to mismatched parameters, warning with info is logged.

RStudio

  • Spark UI path can now be accessed even when the R session and Spark are bussy.

Distributed

  • Configuration setting sparklyr.apply.serializer can be used to select serializer version in spark_apply().

  • Fix for spark_apply_log() and use RClosure as logging component.

ML

  • ml_corr() retrieve a tibble for better formatting.

Misc

  • Support for Spark 2.3.3 and 2.4.3.

Data

  • The infer_schema parameter now defaults to is.null(column).

  • The spark_read_() functions support loading data with named path but no explicit name.

Sparklyr 1.0.1

ML

  • ml_lda(): Allow passing of optional arguments via ... to regex tokenizer, stop words remover, and count vectorizer components in the formula API.

  • Implemented ml_evaluate() for logistic regression, linear regression, and GLM models.

  • Implemented print() method for ml_summary objects.

  • Deprecated compute_cost() for KMeans in Spark 2.4 (#1772).

  • Added missing internal constructor for clustering evaluator (#1936).

  • sdf_partition() has been renamed to sdf_random_split().

  • Added ft_one_hot_encoder_estimator() (#1337).

Misc

  • Added sdf_crosstab() to create contingency tables.

  • Fix tibble::as.tibble() deprecation warning.

Connections

  • Reduced default memory for local connections when Java x64 is not installed (#1931).

Batches

  • Add support in spark-submit with R file to pass additional arguments to R file (#1942).

Distributed R

  • Fix support for multiple library paths when using spark.r.libpaths (@mattpollock, #1956).

Extensions

  • Support for creating an Spark extension package using spark_extension().

  • Add support for repositories in spark_dependency().

DataFrames

  • Fix sdf_bind_cols() when using dbplyr 1.4.0.

Kubernetes

  • Fix regression in spark_config_kubernetes() configuration helper.

Sparklyr 1.0.0

Arrow

  • Support for Apache Arrow using the arrow package.

ML

  • The dataset parameter for estimator feature transformers has been deprecated (#1891).

  • ml_multilayer_perceptron_classifier() gains probabilistic classifier parameters (#1798).

  • Removed support for all undocumented/deprecated parameters. These are mostly dot case parameters from pre-0.7.

  • Remove support for deprecated function(pipeline_stage, data) signature in sdf_predict/transform/fit functions.

  • Soft deprecate sdf_predict/transform/fit functions. Users are advised to use ml_predict/transform/fit functions instead.

  • Utilize the ellipsis package to provide warnings when unsupported arguments are specified in ML functions.

Livy

  • Support for sparklyr extensions when using Livy.

  • Significant performance improvements by using version in spark_connect() which enables using the sparklyr JAR rather than sources.

  • Improved memory use in Livy by using string builders and avoid print backs.

Data

  • Fix for DBI::sqlInterpolate() and related methods to properly quote parameterized queries.

  • copy_to() names tables sparklyr_tmp_ instead of sparklyr_ for consistency with other temp tables and to avoid rendering them under the connections pane.

  • copy_to() and collect() are not re-exported since they are commonly used even when using DBI or outside data analysis use cases.

  • Support for reading path as the second parameter in spark_read_*() when no name is specified (e.g. spark_read_csv(sc, "data.csv")).

  • Support for batches in sdf_collect() and dplyr::collect() to retrieve data incrementally using a callback function provided through a callback parameter. Useful when retrieving larger datasets.

  • Support for batches in sdf_copy_to() and dplyr::copy_to() by passing a list of callbacks that retrieve data frames. Useful when uploading larger datasets.

  • spark_read_source() now has a path parameter for specifying file path.

  • Support for whole parameter for spark_read_text() to read an entire text file without splitting contents by line.

Broom

  • Implemented tidy(), augment(), and glance() for ml_lda()and ml_als() models (@samuelmacedo83)

Connections

  • Local connection defaults now to 2GB.

  • Support to install and connect based on major Spark versions, for instance: spark_connect(master = "local", version = "2.4").

  • Support for installing and connecting to Spark 2.4.

Serialization

  • Faster retrieval of string arrays.

YARN

  • New YARN action under RStudio connection pane extension to launch YARN UI. Configurable through the sparklyr.web.yarn configuration setting.

  • Support for property expansion in yarn-site.xml (@lgongmsft, #1876).

Distributed R

  • The memory parameter in spark_apply() now defaults to FALSE when the name parameter is not specified.

Other

  • Removed dreprecated sdf_mutate().

  • Remove exported ensure_ functions which were deprecated.

  • Fixed missing Hive tables not rendering under some Spark distributions (#1823).

  • Remove dependency on broom.

  • Fixed re-entrancy job progress issues when running RStudio 1.2.

  • Tables with periods supported by setting sparklyr.dplyr.period.splits to FALSE.

  • sdf_len(), sdf_along() and sdf_seq() default to 32 bit integers but allow support for 64 bits through bits parameter.

  • Support for detecting Spark version using spark-submit.

Sparklyr 0.9.4

  • Improved multiple streaming documentation examples (#1801, #1805, #1806).

  • Fix issue while printing Spark data frames under tibble 2.0.0 (#1829).

  • Support for stream_write_console() to write to console log.

  • Support for stream_read_scoket() to read socket streams.

  • Fix to spark_read_kafka() to remove unused path.

Sparklyr 0.9.3

  • Fix to make spark_config_kubernetes() work with variable jar parameters.

  • Support to install and use Spark 2.4.0.

  • Improvements and fixes to spark_config_kubernetes() parameters.

  • Support for sparklyr.connect.ondisconnect config setting to allow cleanup of resources when using kubernetes.

  • spark_apply() and spark_apply_bundle() properly dereference symlinks when creating package bundle (@awblocker, #1785)

  • Fix tableName warning triggered while connecting.

  • Deprecate sdf_mutate() (#1754).

  • Fix requirement to specify SPARK_HOME_VERSION when version parameter is set in spark_connect().

  • Cloudera autodetect Spark version improvements.

  • Fixed default for session in reactiveSpark().

  • Removed stream_read_jdbc() and stream_write_jdbc() since they are not yet implemented in Spark.

  • Support for collecting NA values from logical columns (#1729).

  • Proactevely clean JVM objects when R object is deallocated.

Sparklyr 0.9.2

  • Support for Spark 2.3.2.

  • Fix installation error with older versions of rstudioapi (#1716).

  • Fix missing callstack and error case while logging in spark_apply().

  • Proactevely clean JVM objects when R object is deallocated.

Broom

  • Implemented tidy(), augment(), and glance() for ml_linear_svc()and ml_pca() models (@samuelmacedo83)

Sparklyr 0.9.2

  • Support for Spark 2.3.2.

  • Fix installation error with older versions of rstudioapi (#1716).

  • Fix missing callstack and error case while logging in spark_apply().

  • Fix regression in sdf_collect() failing to collect tables.

  • Fix new connection RStudio selectors colors when running under OS X Mojave.

  • Support for launching Livy logs from connection pane.

Sparklyr 0.9.2

  • Removed overwrite parameter in spark_read_table() (#1698).

  • Fix regression preventing using R 3.2 (#1695).

  • Additional jar search paths under Spark 2.3.1 (#1694)

Sparklyr 0.9.1

  • Terminate streams when Shiny app terminates.

  • Fix dplyr::collect() with Spark streams and improve printing.

  • Fix regression in sparklyr.sanitize.column.names.verbose setting which would cause verbose column renames.

  • Fix to stream_write_kafka() and stream_write_jdbc().

Sparklyr 0.9.0

Streaming

  • Support for stream_read_*() and stream_write_*() to read from and to Spark structured streams.

  • Support for dplyr, sdf_sql(), spark_apply() and scoring pipeline in Spark streams.

  • Support for reactiveSpark() to create a shiny reactive over a Spark stream.

  • Support for convenience functions stream_*() to stop, change triggers, print, generate test streams, etc.

Monitoring

  • Support for interrupting long running operations and recover gracefully using the same connection.

  • Support cancelling Spark jobs by interrupting R session.

  • Support for monitoring job progress within RStudio, required RStudio 1.2.

  • Progress reports can be turned off by setting sparklyr.progress to FALSE in spark_config().

Kubernetes

  • Added config sparklyr.gateway.routing to avoid routing to ports since Kubernetes clusters have unique spark masters.

  • Change backend ports to be choosen deterministically by searching for free ports starting on sparklyr.gateway.port which default to 8880. This allows users to enable port forwarding with kubectl port-forward.

  • Added support to set config sparklyr.events.aftersubmit to a function that is called after spark-submit which can be used to automatically configure port forwarding.

Batches

  • Added support for spark_submit() to assist submitting non-interactive Spark jobs.

Spark ML

  • (Breaking change) The formula API for ML classification algorithms no longer indexes numeric labels, to avoid the confusion of 0 being mapped to "1" and vice versa. This means that if the largest numeric label is N, Spark will fit a N+1-class classification model, regardless of how many distinct labels there are in the provided training set (#1591).
  • Fix retrieval of coefficients in ml_logistic_regression() (@shabbybanks, #1596).
  • (Breaking change) For model objects, lazy val and def attributes have been converted to closures, so they are not evaluated at object instantiation (#1453).
  • Input and output column names are no longer required to construct pipeline objects to be consistent with Spark (#1513).
  • Vector attributes of pipeline stages are now printed correctly (#1618).
  • Deprecate various aliases favoring method names in Spark.
    • ml_binary_classification_eval()
    • ml_classification_eval()
    • ml_multilayer_perceptron()
    • ml_survival_regression()
    • ml_als_factorization()
  • Deprecate incompatible signatures for sdf_transform() and ml_transform() families of methods; the former should take a tbl_spark as the first argument while the latter should take a model object as the first argument.
  • Input and output column names are no longer required to construct pipeline objects to be consistent with Spark (#1513).

Data

  • Implemented support for DBI::db_explain() (#1623).

  • Fixed for timestamp fields when using copy_to() (#1312, @yutannihilation).

  • Added support to read and write ORC files using spark_read_orc() and spark_write_orc() (#1548).

Livy

  • Fixed must share the same src error for sdf_broadcast() and other functions when using Livy connections.

  • Added support for logging sparklyr server events and logging sparklyr invokes as comments in the Livy UI.

  • Added support to open the Livy UI from the connections viewer while using RStudio.

  • Improve performance in Livy for long execution queries, fixed livy.session.command.timeout and support for livy.session.command.interval to control max polling while waiting for command response (#1538).

  • Fixed Livy version with MapR distributions.

  • Removed install column from livy_available_versions().

Distributed R

  • Added name parameter to spark_apply() to optionally name resulting table.

  • Fix to spark_apply() to retain column types when NAs are present (#1665).

  • spark_apply() now supports rlang anonymous functions. For example, sdf_len(sc, 3) %>% spark_apply(~.x+1).

  • Breaking Change: spark_apply() no longer defaults to the input column names when the columns parameter is nos specified.

  • Support for reading column names from the R data frame returned by spark_apply().

  • Fix to support retrieving empty data frames in grouped spark_apply() operations (#1505).

  • Added support for sparklyr.apply.packages to configure default behavior for spark_apply() parameters (#1530).

  • Added support for spark.r.libpaths to configure package library in spark_apply() (#1530).

Connections

  • Default to Spark 2.3.1 for installation and local connections (#1680).

  • ml_load() no longer keeps extraneous table views which was cluttering up the RStudio Connections pane (@randomgambit, #1549).

  • Avoid preparing windows environment in non-local connections.

Extensions

  • The ensure_* family of functions is deprecated in favor of forge which doesn’t use NSE and provides more informative errors messages for debugging (#1514).

  • Support for sparklyr.invoke.trace and sparklyr.invoke.trace.callstack configuration options to trace all invoke() calls.

  • Support to invoke methods with char types using single character strings (@lawremi, #1395).

Serialization

  • Fixed collection of Date types to support correct local JVM timezone to UTC ().

Documentation

  • Many new examples for ft_binarizer(), ft_bucketizer(), ft_min_max_scaler, ft_max_abs_scaler(), ft_standard_scaler(), ml_kmeans(), ml_pca(), ml_bisecting_kmeans(), ml_gaussian_mixture(), ml_naive_bayes(), ml_decision_tree(), ml_random_forest(), ml_multilayer_perceptron_classifier(), ml_linear_regression(), ml_logistic_regression(), ml_gradient_boosted_trees(), ml_generalized_linear_regression(), ml_cross_validator(), ml_evaluator(), ml_clustering_evaluator(), ml_corr(), ml_chisquare_test() and sdf_pivot() (@samuelmacedo83).

Broom

  • Implemented tidy(), augment(), and glance() for ml_aft_survival_regression(), ml_isotonic_regression(), ml_naive_bayes(), ml_logistic_regression(), ml_decision_tree(), ml_random_forest(), ml_gradient_boosted_trees(), ml_bisecting_kmeans(), ml_kmeans()and ml_gaussian_mixture() models (@samuelmacedo83)

Configuration

  • Deprecated configuration option sparklyr.dplyr.compute.nocache.

  • Added spark_config_settings() to list all sparklyr configuration settings and describe them, cleaned all settings and grouped by area while maintaining support for previous settings.

  • Static SQL configuration properties are now respected for Spark 2.3, and spark.sql.catalogImplementation defaults to hive to maintain Hive support (#1496, #415).

  • spark_config() values can now also be specified as options().

  • Support for functions as values in entries to spark_config() to enable advanced configuration workflows.

Sparklyr 0.8.4

  • Added support for spark_session_config() to modify spark session settings.

  • Added support for sdf_debug_string() to print execution plan for a Spark DataFrame.

  • Fixed DESCRIPTION file to include test packages as requested by CRAN.

  • Support for sparklyr.spark-submit as config entry to allow customizing the spark-submit command.

  • Changed spark_connect() to give precedence to the version parameter over SPARK_HOME_VERSION and other automatic version detection mechanisms, improved automatic version detection in Spark 2.X.

  • Fixed sdf_bind_rows() with dplyr 0.7.5 and prepend id column instead of appending it to match behavior.

  • broom::tidy() for linear regression and generalized linear regression models now give correct results (#1501).

Sparklyr 0.8.3

  • Support for Spark 2.3 in local windows clusters (#1473).

Sparklyr 0.8.2

  • Support for resource managers using https in yarn-cluster mode (#1459).

  • Fixed regression for connections using Livy and Spark 1.6.X.

Sparklyr 0.8.1

  • Fixed regression for connections using mode with databricks.

Sparklyr 0.8.0

Spark ML

  • Added ml_validation_metrics() to extract validation metrics from cross validator and train split validator models.

  • ml_transform() now also takes a list of transformers, e.g. the result of ml_stages() on a PipelineModel (#1444).

  • Added collect_sub_models parameter to ml_cross_validator() and ml_train_validation_split() and helper function ml_sub_models() to allow inspecting models trained for each fold/parameter set (#1362).

  • Added parallelism parameter to ml_cross_validator() and ml_train_validation_split() to allow tuning in parallel (#1446).

  • Added support for feature_subset_strategy parameter in GBT algorithms (#1445).

  • Added string_order_type to ft_string_indexer() to allow control over how strings are indexed (#1443).

  • Added ft_string_indexer_model() constructor for the string indexer transformer (#1442).

  • Added ml_feature_importances() for extracing feature importances from tree-based models (#1436). ml_tree_feature_importance() is maintained as an alias.

  • Added ml_vocabulary() to extract vocabulary from count vectorizer model and ml_topics_matrix() to extract matrix from LDA model.

  • ml_tree_feature_importance() now works properly with decision tree classification models (#1401).

  • Added ml_corr() for calculating correlation matrices and ml_chisquare_test() for performing chi-square hypothesis testing (#1247).

  • ml_save() outputs message when model is successfully saved (#1348).

  • ml_ routines no longer capture the calling expression (#1393).

  • Added support for offset argument in ml_generalized_linear_regression() (#1396).

  • Fixed regression blocking use of response-features syntax in some ml_functions (#1302).

  • Added support for Huber loss for linear regression (#1335).

  • ft_bucketizer() and ft_quantile_discretizer() now support multiple input columns (#1338, #1339).

  • Added ft_feature_hasher() (#1336).

  • Added ml_clustering_evaluator() (#1333).

  • ml_default_stop_words() now returns English stop words by default (#1280).

  • Support the sdf_predict(ml_transformer, dataset) signature with a deprecation warning. Also added a deprecation warning to the usage of sdf_predict(ml_model, dataset). (#1287)

  • Fixed regression blocking use of ml_kmeans() in Spark 1.6.x.

Extensions

  • invoke*() method dispatch now supports Char and Short parameters. Also, Long parameters now allow numeric arguments, but integers are supported for backwards compatibility (#1395).

  • invoke_static() now supports calling Scala’s package objects (#1384).

  • spark_connection and spark_jobj classes are now exported (#1374).

Distributed R

  • Added support for profile parameter in spark_apply() that collects a profile to measure perpformance that can be rendered using the profvis package.

  • Added support for spark_apply() under Livy connections.

  • Fixed file not found error in spark_apply() while working under low disk space.

  • Added support for sparklyr.apply.options.rscript.before to run a custom command before launching the R worker role.

  • Added support for sparklyr.apply.options.vanilla to be set to FALSE to avoid using --vanilla while launching R worker role.

  • Fixed serialization issues most commonly hit while using spark_apply() with NAs (#1365, #1366).

  • Fixed issue with dates or date-times not roundtripping with `spark_apply() (#1376).

  • Fixed data frame provided by spark_apply() to not provide characters not factors (#1313).

Miscellaneous

  • Fixed typo in sparklyr.yarn.cluster.hostaddress.timeot (#1318).

  • Fixed regression blocking use of livy.session.start.timeout parameter in Livy connections.

  • Added support for Livy 0.4 and Livy 0.5.

  • Livy now supports Kerberos authentication.

  • Default to Spark 2.3.0 for installation and local connections (#1449).

  • yarn-cluster now supported by connecting with master="yarn" and config entry sparklyr.shell.deploy-mode set to cluster (#1404).

  • sample_frac() and sample_n() now work properly in nontrivial queries (#1299)

  • sdf_copy_to() no longer gives a spurious warning when user enters a multiline expression for x (#1386).

  • spark_available_versions() was changed to only return available Spark versions, Hadoop versions can be still retrieved using hadoop = TRUE.

  • spark_installed_versions() was changed to retrieve the full path to the installation folder.

  • cbind() and sdf_bind_cols() don’t use NSE internally anymore and no longer output names of mismatched data frames on error (#1363).

Sparklyr 0.7.0

  • Added support for Spark 2.2.1.

  • Switched copy_to serializer to use Scala implementation, this change can be reverted by setting the sparklyr.copy.serializer option to csv_file.

  • Added support for spark_web() for Livy and Databricks connections when using Spark 2.X.

  • Fixed SIGPIPE error under spark_connect() immediately after a spark_disconnect() operation.

  • spark_web() is is more reliable under Spark 2.X by making use of a new API to programmatically find the right address.

  • Added support in dbWriteTable() for temporary = FALSE to allow persisting table across connections. Changed default value for temporary to TRUE to match DBI specification, for compatibility, default value can be reverted back to FALSE using the sparklyr.dbwritetable.temp option.

  • ncol() now returns the number of columns instead of NA, and nrow() now returns NA_real_.

  • Added support to collect VectorUDT column types with nested arrays.

  • Fixed issue in which connecting to Livy would fail due to long user names or long passwords.

  • Fixed error in the Spark connection dialog for clusters using a proxy.

  • Improved support for Spark 2.X under Cloudera clusters by prioritizing use of spark2-submit over spark-submit.

  • Livy new connection dialog now prompts for password using rstudioapi::askForPassword().

  • Added schema parameter to spark_read_parquet() that enables reading a subset of the schema to increase performance.

  • Implemented sdf_describe() to easily compute summary statistics for data frames.

  • Fixed data frames with dates in spark_apply() retrieved as Date instead of doubles.

  • Added support to use invoke() with arrays of POSIXlt and POSIXct.

  • Added support for context parameter in spark_apply() to allow callers to pass additional contextual information to the f() closure.

  • Implemented workaround to support in spark_write_table() for mode = 'append'.

  • Various ML improvements, including support for pipelines, additional algorithms, hyper-parameter tuning, and better model persistence.

  • Added spark_read_libsvm() for reading libsvm files.

  • Added support for separating struct columns in sdf_separate_column().

  • Fixed collection of short, float and byte to properly return NAs.

  • Added sparklyr.collect.datechars option to enable collecting DateType and TimestampTime as characters to support compatibility with previos versions.

  • Fixed collection of DateType and TimestampTime from character to proper Date and POSIXct types.

Sparklyr 0.6.4

  • Added support for HTTPS for yarn-cluster which is activated by setting yarn.http.policy to HTTPS_ONLY in yarn-site.xml.

  • Added support for sparklyr.yarn.cluster.accepted.timeout under yarn-cluster to allow users to wait for resources under cluster with high waiting times.

  • Fix to spark_apply() when package distribution deadlock triggers in environments where multiple executors run under the same node.

  • Added support in spark_apply() for specifying a list of packages to distribute to each worker node.

  • Added support inyarn-cluster for sparklyr.yarn.cluster.lookup.prefix, sparklyr.yarn.cluster.lookup.username and sparklyr.yarn.cluster.lookup.byname to control the new application lookup behavior.

Sparklyr 0.6.3

  • Enabled support for Java 9 for clusters configured with Hadoop 2.8. Java 9 blocked on ‘master=local’ unless ‘options(sparklyr.java9 = TRUE)’ is set.

  • Fixed issue in spark_connect() where using set.seed() before connection would cause session ids to be duplicates and connections to be reused.

  • Fixed issue in spark_connect() blocking gateway port when connection was never started to the backend, for isntasnce, while interrupting the r session while connecting.

  • Performance improvement for quering field names from tables impacting tables and dplyr queries, most noticeable in na.omit with several columns.

  • Fix to spark_apply() when closure returns a data.frame that contains no rows and has one or more columns.

  • Fix to spark_apply() while using tryCatch() within closure and increased callstack printed to logs when error triggers within closure.

  • Added support for the SPARKLYR_LOG_FILE environment variable to specify the file used for log output.

  • Fixed regression for union_all() affecting Spark 1.6.X.

  • Added support for na.omit.cache option that when set to FALSE will prevent na.omit from caching results when rows are dropped.

  • Added support in spark_connect() for yarn-cluster with hight-availability enabled.

  • Added support for spark_connect() with master="yarn-cluster" to query YARN resource manager API and retrieve the correct container host name.

  • Fixed issue in invoke() calls while using integer arrays that contain NA which can be commonly experienced while using spark_apply().

  • Added topics.description under ml_lda() result.

  • Added support for ft_stop_words_remover() to strip out stop words from tokens.

  • Feature transformers (ft_* functions) now explicitly require input.col and output.col to be specified.

  • Added support for spark_apply_log() to enable logging in worker nodes while using spark_apply().

  • Fix to spark_apply() for SparkUncaughtExceptionHandler exception while running over large jobs that may overlap during an, now unnecesary, unregister operation.

  • Fix race-condition first time spark_apply() is run when more than one partition runs in a worker and both processes try to unpack the packages bundle at the same time.

  • spark_apply() now adds generic column names when needed and validates f is a function.

  • Improved documentation and error cases for metric argument in ml_classification_eval() and ml_binary_classification_eval().

  • Fix to spark_install() to use the /logs subfolder to store local log4j logs.

  • Fix to spark_apply() when R is used from a worker node since worker node already contains packages but still might be triggering different R session.

  • Fix connection from closing when invoke() attempts to use a class with a method that contains a reference to an undefined class.

  • Implemented all tuning options from Spark ML for ml_random_forest(), ml_gradient_boosted_trees(), and ml_decision_tree().

  • Avoid tasks failing under spark_apply() and multiple concurrent partitions running while selecting backend port.

  • Added support for numeric arguments for n in lead() for dplyr.

  • Added unsupported error message to sample_n() and sample_frac() when Spark is not 2.0 or higher.

  • Fixed SIGPIPE error under spark_connect() immediately after a spark_disconnect() operation.

  • Added support for sparklyr.apply.env. under spark_config() to allow spark_apply() to initializae environment varaibles.

  • Added support for spark_read_text() and spark_write_text() to read from and to plain text files.

  • Addesd support for RStudio project templates to create an “R Package using sparklyr”.

  • Fix compute() to trigger refresh of the connections view.

  • Added a k argument to ml_pca() to enable specification of number of principal components to extract. Also implemented sdf_project() to project datasets using the results of ml_pca() models.

  • Added support for additional livy session creation parameters using the livy_config() function.

Sparklyr 0.6.2

  • Fix connection_spark_shinyapp() under RStudio 1.1 to avoid error while listing Spark installation options for the first time.

Sparklyr 0.6.1

  • Fixed error in spark_apply() that may triggered when multiple CPUs are used in a single node due to race conditions while accesing the gateway service and another in the JVMObjectTracker.

  • spark_apply() now supports explicit column types using the columns argument to avoid sampling types.

  • spark_apply() with group_by no longer requires persisting to disk nor memory.

  • Added support for Spark 1.6.3 under spark_install().

  • Added support for Spark 1.6.3 under spark_install()

  • spark_apply() now logs the current callstack when it fails.

  • Fixed error triggered while processing empty partitions in spark_apply().

  • Fixed slow printing issue caused by print calculating the total row count, which is expensive for some tables.

  • Fixed sparklyr 0.6 issue blocking concurrent sparklyr connections, which required to set config$sparklyr.gateway.remote = FALSE as workaround.

Sparklyr 0.6.0

Distributed R

  • Added packages parameter to spark_apply() to distribute packages across worker nodes automatically.

  • Added sparklyr.closures.rlang as a spark_config() value to support generic closures provided by the rlang package.

  • Added config options sparklyr.worker.gateway.address and sparklyr.worker.gateway.port to configure gateway used under worker nodes.

  • Added group_by parameter to spark_apply(), to support operations over groups of dataframes.

  • Added spark_apply(), allowing users to use R code to directly manipulate and transform Spark DataFrames.

External Data

  • Added spark_write_source(). This function writes data into a Spark data source which can be loaded through an Spark package.

  • Added spark_write_jdbc(). This function writes from a Spark DataFrame into a JDBC connection.

  • Added columns parameter to spark_read_*() functions to load data with named columns or explicit column types.

  • Added partition_by parameter to spark_write_csv(), spark_write_json(), spark_write_table() and spark_write_parquet().

  • Added spark_read_source(). This function reads data from a Spark data source which can be loaded through an Spark package.

  • Added support for mode = "overwrite" and mode = "append" to spark_write_csv().

  • spark_write_table() now supports saving to default Hive path.

  • Improved performance of spark_read_csv() reading remote data when infer_schema = FALSE.

  • Added spark_read_jdbc(). This function reads from a JDBC connection into a Spark DataFrame.

  • Renamed spark_load_table() and spark_save_table() into spark_read_table() and spark_write_table() for consistency with existing spark_read_*() and spark_write_*() functions.

  • Added support to specify a vector of column names in spark_read_csv() to specify column names without having to set the type of each column.

  • Improved copy_to(), sdf_copy_to() and dbWriteTable() performance under yarn-client mode.

dplyr

  • Support for cumprod() to calculate cumulative products.

  • Support for cor(), cov(), sd() and var() as window functions.

  • Support for Hive built-in operators %like%, %rlike%, and %regexp% for matching regular expressions in filter() and mutate().

  • Support for dplyr (>= 0.6) which among many improvements, increases performance in some queries by making use of a new query optimizer.

  • sample_frac() takes a fraction instead of a percent to match dplyr.

  • Improved performance of sample_n() and sample_frac() through the use of TABLESAMPLE in the generated query.

Databases

  • Added src_databases(). This function list all the available databases.

  • Added tbl_change_db(). This function changes current database.

DataFrames

  • Added sdf_len(), sdf_seq() and sdf_along() to help generate numeric sequences as Spark DataFrames.

  • Added spark_set_checkpoint_dir(), spark_get_checkpoint_dir(), and sdf_checkpoint() to enable checkpointing.

  • Added sdf_broadcast() which can be used to hint the query optimizer to perform a broadcast join in cases where a shuffle hash join is planned but not optimal.

  • Added sdf_repartition(), sdf_coalesce(), and sdf_num_partitions() to support repartitioning and getting the number of partitions of Spark DataFrames.

  • Added sdf_bind_rows() and sdf_bind_cols() – these functions are the sparklyr equivalent of dplyr::bind_rows() and dplyr::bind_cols().

  • Added sdf_separate_column() – this function allows one to separate components of an array / vector column into separate scalar-valued columns.

  • sdf_with_sequential_id() now supports from parameter to choose the starting value of the id column.

  • Added sdf_pivot(). This function provides a mechanism for constructing pivot tables, using Spark’s ‘groupBy’ + ‘pivot’ functionality, with a formula interface similar to that of reshape2::dcast().

MLlib

  • Added vocabulary.only to ft_count_vectorizer() to retrieve the vocabulary with ease.

  • GLM type models now support weights.column to specify weights in model fitting. (#217)

  • ml_logistic_regression() now supports multinomial regression, in addition to binomial regression [requires Spark 2.1.0 or greater]. (#748)

  • Implemented residuals() and sdf_residuals() for Spark linear regression and GLM models. The former returns a R vector while the latter returns a tbl_spark of training data with a residuals column added.

  • Added ml_model_data(), used for extracting data associated with Spark ML models.

  • The ml_save() and ml_load() functions gain a meta argument, allowing users to specify where R-level model metadata should be saved independently of the Spark model itself. This should help facilitate the saving and loading of Spark models used in non-local connection scenarios.

  • ml_als_factorization() now supports the implicit matrix factorization and nonnegative least square options.

  • Added ft_count_vectorizer(). This function can be used to transform columns of a Spark DataFrame so that they might be used as input to ml_lda(). This should make it easier to invoke ml_lda() on Spark data sets.

Broom

  • Implemented tidy(), augment(), and glance() from tidyverse/broom for ml_model_generalized_linear_regression and ml_model_linear_regression models.

R Compatibility

  • Implemented cbind.tbl_spark(). This method works by first generating index columns using sdf_with_sequential_id() then performing inner_join(). Note that dplyr _join() functions should still be used for DataFrames with common keys since they are less expensive.

Connections

  • Increased default number of concurrent connections by setting default for spark.port.maxRetries from 16 to 128.

  • Support for gateway connections sparklyr://hostname:port/session and using spark-submit --class sparklyr.Shell sparklyr-2.1-2.11.jar <port> <id> --remote.

  • Added support for sparklyr.gateway.service and sparklyr.gateway.remote to enable/disable the gateway in service and to accept remote connections required for Yarn Cluster mode.

  • Added support for Yarn Cluster mode using master = "yarn-cluster". Either, explicitly set config = list(sparklyr.gateway.address = "<driver-name>") or implicitly sparklyr will read the site-config.xml for the YARN_CONF_DIR environment variable.

  • Added spark_context_config() and hive_context_config() to retrieve runtime configurations for the Spark and Hive contexts.

  • Added sparklyr.log.console to redirect logs to console, useful to troubleshooting spark_connect.

  • Added sparklyr.backend.args as config option to enable passing parameters to the sparklyr backend.

  • Improved logging while establishing connections to sparklyr.

  • Improved spark_connect() performance.

  • Implemented new configuration checks to proactively report connection errors in Windows.

  • While connecting to spark from Windows, setting the sparklyr.verbose option to TRUE prints detailed configuration steps.

  • Added custom_headers to livy_config() to add custom headers to the REST call to the Livy server

Compilation

  • Added support for jar_dep in the compilation specification to support additional jars through spark_compile().

  • spark_compile() now prints deprecation warnings.

  • Added download_scalac() to assist downloading all the Scala compilers required to build using compile_package_jars and provided support for using any scalac minor versions while looking for the right compiler.

Backend

  • Improved backend logging by adding type and session id prefix.

Miscellaneous

  • copy_to() and sdf_copy_to() auto generate a name when an expression can’t be transformed into a table name.

  • Implemented type_sum.jobj() (from tibble) to enable better printing of jobj objects embedded in data frames.

  • Added the spark_home_set() function, to help facilitate the setting of the SPARK_HOME environment variable. This should prove useful in teaching environments, when teaching the basics of Spark and sparklyr.

  • Added support for the sparklyr.ui.connections option, which adds additional connection options into the new connections dialog. The rstudio.spark.connections option is now deprecated.

  • Implemented the “New Connection Dialog” as a Shiny application to be able to support newer versions of RStudio that deprecate current connections UI.

Bug Fixes

  • When using spark_connect() in local clusters, it validates that java exists under JAVA_HOME to help troubleshoot systems that have an incorrect JAVA_HOME.

  • Improved argument is of length zero error triggered while retrieving data with no columns to display.

  • Fixed Path does not exist referencing hdfs exception during copy_to under systems configured with HADOOP_HOME.

  • Fixed session crash after “No status is returned” error by terminating invalid connection and added support to print log trace during this error.

  • compute() now caches data in memory by default. To revert this beavior use sparklyr.dplyr.compute.nocache set to TRUE.

  • spark_connect() with master = "local" and a given version overrides SPARK_HOME to avoid existing installation mismatches.

  • Fixed spark_connect() under Windows issue when newInstance0 is present in the logs.

  • Fixed collecting long type columns when NAs are present (#463).

  • Fixed backend issue that affects systems where localhost does not resolve properly to the loopback address.

  • Fixed issue collecting data frames containing newlines \n.

  • Spark Null objects (objects of class NullType) discovered within numeric vectors are now collected as NAs, rather than lists of NAs.

  • Fixed warning while connecting with livy and improved 401 message.

  • Fixed issue in spark_read_parquet() and other read methods in which spark_normalize_path() would not work in some platforms while loading data using custom protocols like s3n:// for Amazon S3.

  • Resolved issue in spark_save() / load_table() to support saving / loading data and added path parameter in spark_load_table() for consistency with other functions.

Sparklyr 0.5.5

  • Implemented support for connectionViewer interface required in RStudio 1.1 and spark_connect with mode="databricks".

Sparklyr 0.5.4

  • Implemented support for dplyr 0.6 and Spark 2.1.x.

Sparklyr 0.5.3

  • Implemented support for DBI 0.6.

Sparklyr 0.5.2

  • Fix to spark_connect affecting Windows users and Spark 1.6.x.

  • Fix to Livy connections which would cause connections to fail while connection is on ‘waiting’ state.

Sparklyr 0.5.0

  • Implemented basic authorization for Livy connections using livy_config_auth().

  • Added support to specify additional spark-submit parameters using the sparklyr.shell.args environment variable.

  • Renamed sdf_load() and sdf_save() to spark_read() and spark_write() for consistency.

  • The functions tbl_cache() and tbl_uncache() can now be using without requiring the dplyr namespace to be loaded.

  • spark_read_csv(..., columns = <...>, header = FALSE) should now work as expected – previously, sparklyr would still attempt to normalize the column names provided.

  • Support to configure Livy using the livy. prefix in the config.yml file.

  • Implemented experimental support for Livy through: livy_install(), livy_service_start(), livy_service_stop() and spark_connect(method = "livy").

  • The ml routines now accept data as an optional argument, to support calls of the form e.g. ml_linear_regression(y ~ x, data = data). This should be especially helpful in conjunction with dplyr::do().

  • Spark DenseVector and SparseVector objects are now deserialized as R numeric vectors, rather than Spark objects. This should make it easier to work with the output produced by sdf_predict() with Random Forest models, for example.

  • Implemented dim.tbl_spark(). This should ensure that dim(), nrow() and ncol() all produce the expected result with tbl_sparks.

  • Improved Spark 2.0 installation in Windows by creating spark-defaults.conf and configuring spark.sql.warehouse.dir.

  • Embedded Apache Spark package dependencies to avoid requiring internet connectivity while connecting for the first through spark_connect. The sparklyr.csv.embedded config setting was added to configure a regular expression to match Spark versions where the embedded package is deployed.

  • Increased exception callstack and message length to include full error details when an exception is thrown in Spark.

  • Improved validation of supported Java versions.

  • The spark_read_csv() function now accepts the infer_schema parameter, controlling whether the columns schema should be inferred from the underlying file itself. Disabling this should improve performance when the schema is known beforehand.

  • Added a do_.tbl_spark implementation, allowing for the execution of dplyr::do statements on Spark DataFrames. Currently, the computation is performed in serial across the different groups specified on the Spark DataFrame; in the future we hope to explore a parallel implementation. Note that do_ always returns a tbl_df rather than a tbl_spark, as the objects produced within a do_ query may not necessarily be Spark objects.

  • Improved errors, warnings and fallbacks for unsupported Spark versions.

  • sparklyr now defaults to tar = "internal" in its calls to untar(). This should help resolve issues some Windows users have seen related to an inability to connect to Spark, which ultimately were caused by a lack of permissions on the Spark installation.

  • Resolved an issue where copy_to() and other R => Spark data transfer functions could fail when the last column contained missing / empty values. (#265)

  • Added sdf_persist() as a wrapper to the Spark DataFrame persist() API.

  • Resolved an issue where predict() could produce results in the wrong order for large Spark DataFrames.

  • Implemented support for na.action with the various Spark ML routines. The value of getOption("na.action") is used by default. Users can customize the na.action argument through the ml.options object accepted by all ML routines.

  • On Windows, long paths, and paths containing spaces, are now supported within calls to spark_connect().

  • The lag() window function now accepts numeric values for n. Previously, only integer values were accepted. (#249)

  • Added support to configure Ppark environment variables using spark.env.* config.

  • Added support for the Tokenizer and RegexTokenizer feature transformers. These are exported as the ft_tokenizer() and ft_regex_tokenizer() functions.

  • Resolved an issue where attempting to call copy_to() with an R data.frame containing many columns could fail with a Java StackOverflow. (#244)

  • Resolved an issue where attempting to call collect() on a Spark DataFrame containing many columns could produce the wrong result. (#242)

  • Added support to parameterize network timeouts using the sparklyr.backend.timeout, sparklyr.gateway.start.timeout and sparklyr.gateway.connect.timeout config settings.

  • Improved logging while establishing connections to sparklyr.

  • Added sparklyr.gateway.port and sparklyr.gateway.address as config settings.

  • The spark_log() function now accepts the filter parameter. This can be used to filter entries within the Spark log.

  • Increased network timeout for sparklyr.backend.timeout.

  • Moved spark.jars.default setting from options to Spark config.

  • sparklyr now properly respects the Hive metastore directory with the sdf_save_table() and sdf_load_table() APIs for Spark < 2.0.0.

  • Added sdf_quantile() as a means of computing (approximate) quantiles for a column of a Spark DataFrame.

  • Added support for n_distinct(...) within the dplyr interface, based on call to Hive function count(DISTINCT ...). (#220)

Sparklyr 0.4.0

  • First release to CRAN.