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)
ml_cross_validator() for
regression models. (#3273)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.
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.
dplyr actions before
sampling (#3276)dbplyrEnsures 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)
Addresses deprecation of rlang::is_env() function.
(@lionel-
#3217)
Updates pivot_wider() to support new version of
tidyr (@DavisVaughan #3215)
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.
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.
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.
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).
ml_prefixspan().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().
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.
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.
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.
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.
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
dplyr::compute() caching
a Spark view needed to be further revised to take effect with dbplyr
backend API edition 2sdf_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
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.
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
Created convenience functions for working with Spark runtime configurations
Fixed buggy exit code from the spark-submit process
launched by sparklyr
Implemented R interface for Power Iteration Clustering
The handle_invalid option is added to
ft_vector_indexer() (supported by Spark 2.3 or
above)
~ within some path components not
being normalized in sparklyr::livy_install()Fixed op_vars() specification in
dplyr::distinct() verb for Spark dataframes
spark_disconnect() now closes the Spark monitoring
connection correctly
Implement support for stratified sampling in
ft_dplyr_transformer()
Added support for na.rm in dplyr
rowSums() function for Spark dataframes
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)
tbl() methods were revised to be compatible with
dbplyr 2.0 when handling inputs of the form
"<schema name>.<table name>"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.
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.
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().
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)
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)
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
Fixed a rendering issue with HTML reference pages
Made test reporting in Github CI workflows more informative (https://github.com/sparklyr/sparklyr/pull/2672)
ft_robust_scaler was created as the R interface for the
RobustScaler functionality in Spark 3 or abovehof_* method is specified
with a R formula and the lambda takes 2 parametersml_evaluate() methods are implemented for ML clustering
and classification modelsCreated 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)
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)
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)
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")
spark_dependency(). The
repositories parameter of spark_dependency()
now works as expected.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
Add support for using Spark as a foreach parallel backend
Fixed a bug with how columns parameter was
interpreted in spark_apply
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).
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.
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
barrier = TRUE in spark_apply() (@samuelmacedo83,
#2216).Add support for stream_read_delta() and
stream_write_delta().
Fixed typo in stream_read_socket().
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).
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.
partition_by when using
spark_write_delta() (#2228).java.util.Map[Object, Object] (#1058).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).
spark_connect() supports new packages
parameter to easily enable kafka and delta
(#2148).
spark_disconnect() returns invisibly
(#2028).
SPARKLYR_CONFIG_FILE environment variable (@AgrawalAmey,
#2153).curl_fetch_memory error when using YARN Cluster
mode (#2157).compute() in Spark 1.6 (#2099)spark_read_() functions now support multiple
parameters (@jozefhajnala, #2118).mode = "quobole"
(@vipul1409,
#2039).invoke() fails due to mismatched parameters,
warning with info is logged.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_corr() retrieve a tibble for better
formatting.The infer_schema parameter now defaults to
is.null(column).
The spark_read_() functions support loading data
with named path but no explicit name.
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).
Added sdf_crosstab() to create contingency
tables.
Fix tibble::as.tibble() deprecation
warning.
spark-submit with R file to pass
additional arguments to R file (#1942).spark.r.libpaths (@mattpollock, #1956).Support for creating an Spark extension package using
spark_extension().
Add support for repositories in
spark_dependency().
sdf_bind_cols() when using dbplyr
1.4.0.spark_config_kubernetes()
configuration helper.arrow package.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.
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.
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.
tidy(), augment(), and
glance() for ml_lda()and ml_als()
models (@samuelmacedo83)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.
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).
memory parameter in spark_apply() now
defaults to FALSE when the name parameter is
not specified.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.
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.
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.
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.
tidy(), augment(), and
glance() for ml_linear_svc()and
ml_pca() models (@samuelmacedo83)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.
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)
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().
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.
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().
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.
spark_submit() to assist submitting
non-interactive Spark jobs.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).ml_logistic_regression() (@shabbybanks, #1596).lazy val and def attributes have been
converted to closures, so they are not evaluated at object instantiation
(#1453).ml_binary_classification_eval()ml_classification_eval()ml_multilayer_perceptron()ml_survival_regression()ml_als_factorization()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.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).
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().
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).
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.
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).
Date types to support correct local
JVM timezone to UTC ().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).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)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.
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).
Support for resource managers using https in
yarn-cluster mode (#1459).
Fixed regression for connections using Livy and Spark 1.6.X.
mode with
databricks.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.
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).
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).
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).
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.
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.
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.
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.
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.
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.
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.
Added src_databases(). This function list all the
available databases.
Added tbl_change_db(). This function changes current
database.
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().
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.
tidy(), augment(), and
glance() from tidyverse/broom for
ml_model_generalized_linear_regression and
ml_model_linear_regression models.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.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
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.
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.
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.
connectionViewer interface
required in RStudio 1.1 and spark_connect with
mode="databricks".dplyr 0.6 and Spark 2.1.x.DBI 0.6.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.
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)