NEWS.md

Sparklyr 0.6.0 (UNRELEASED)

Distributed R

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

External Data

• 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

• 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

• 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 csrf_header to livy_config() to enable connections to Livy servers with CSRF protection enabled.

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

• 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.