Migration Guide¶

Migration guide between Sparkling Water versions.

From 3.28 to 3.30¶

Removal of Deprecated Methods and Classes¶

On PySparkling, passing authentication on H2OContext via auth param is removed in favor of methods setUserName and setPassword ond the H2OConf or via the Spark options spark.ext.h2o.user.name and spark.ext.h2o.password directly.

From 3.26 To 3.28¶

Passing Authentication in Scala¶

The users of Scala who set up any form of authentication on the backend side are now required to specify credentials on the H2OConf object via setUserName and setPassword. It is also possible to specify these directly as Spark options spark.ext.h2o.user.name and spark.ext.h2o.password. Note: Actually only users of external backend need to specify these options at this moment as the external backend is using communication via REST api but all our documentation is using these options already as the internal backend will start using the REST api soon as well.

String instead of enums in Sparkling Water Algo API¶

In scala, setters of the pipeline wrappers for H2O algorithms now accepts strings in places where they accepted enum values before. Before, we called, for example:

import hex.genmodel.utils.DistributionFamily
val gbm = H2OGBM()
gbm.setDistribution(DistributionFamily.multinomial)

Now, the correct code is:

val gbm = H2OGBM()
gbm.setDistribution("multinomial")

which makes the Python and Scala APIs consistent. Both upper case and lower case values are valid and if a wrong input is entered, warning is printed out with correct possible values.

Switch to Java 1.8 on Spark 2.1¶

Sparkling Water for Spark 2.1 now requires Java 1.8 and higher.

DRF exposed into Sparkling Water Algorithm API¶

DRF is now exposed in the Sparkling Water. Please see our documentation to learn how to use it Train DRF Model in Sparkling Water.

Also we can run our Grid Search API on DRF.

Change Default Name of Prediction Column¶

The default name of the prediction column has been changed from prediction_output to prediction.

Single value in prediction column¶

The prediction column contains directly the predicted value. For example, before this change, the prediction column contained another struct field called value (in case of regression issue), which contained the value. From now on, the predicted value is always stored directly in the prediction column. In case of regression issue, the predicted numeric value and in case of classification, the predicted label. If you are interested in more details created during the prediction, please make sure to set withDetailedPredictionCol to true via the setters on both PySparkling and Sparkling Water. When enabled, additional column named detailed_prediction is created which contains additional prediction details, such as probabilities, contributions and so on.

In manual mode of external backend always require a specification of cluster location¶

In previous versions, H2O client was able to discover nodes using the multicast search. That is now removed and IP:Port of any node of external cluster to which we need to connect is required. This also means that in the users of multicast cloud up in case of external H2O backend in manual standalone (no Hadoop) mode now need to pass the flatfile argument external H2O. For more information, please see Manual Mode of External Backend without Hadoop (standalone).

Removal of Deprecated Methods and Classes¶

getColsampleBytree and setColsampleBytree methods are removed from the XGBoost API. Please use the new getColSampleByTree and setColSampleByTree.
Removal of deprecated option spark.ext.h2o.external.cluster.num.h2o.nodes and corresponding setters. Please use spark.ext.h2o.external.cluster.size or the corresponding setter setClusterSize.
Removal of deprecated algorithm classes in package org.apache.spark.h2o.ml.algos. Please use the classes from the package ai.h2o.sparkling.ml.algos. Their API remains the same as before. This is the beginning of moving Sparkling Water classes to our distinct package ai.h2o.sparkling
Removal of deprecated option spark.ext.h2o.external.read.confirmation.timeout and related setters. This option is removed without a replacement as it is no longer needed.
Removal of deprecated parameter SelectBestModelDecreasing on the Grid Search API. Related getters and setters have been also removed. This method is removed without replacement as we now internally sort the models with the ordering meaningful to the specified sort metric.
TargetEncoder transformer now accepts the outputCols parameter which can be used to override the default output column names.
On PySparkling H2OGLM API, we removed deprecated parameter alpha in favor of alphaValue and lambda_ in favor of lambdaValue. On Both PySparkling and Sparkling Water H2OGLM API, we removed methods getAlpha in favor of getAlphaValue, getLambda in favor of getLambdaValue, setAlpha in favor of setAlphaValue and setLambda in favor of setLambdaValue. These changes ensure the consistency across Python and Scala APIs.
In Sparkling Water H2OConf API, we removed method h2oDriverIf in favor of externalH2ODriverIf and setH2ODriverIf in favor of setExternalH2ODriverIf. In PySparkling H2OConf API, we removed method h2o_driver_if in favor of externalH2ODriverIf and set_h2o_driver_if in favor of setExternalH2ODriverIf.
On PySparkling H2OConf API, the method user_name has been removed in favor of the userName method and method set_user_name had been removed in favor of the setUserName method.
The configurations spark.ext.h2o.external.kill.on.unhealthy.interval, spark.ext.h2o.external.health.check.interval and spark.ext.h2o.ui.update.interval have been removed and were replaced by a single option spark.ext.h2o.backend.heartbeat.interval. On H2OConf Scala API, the methods backendHeartbeatInterval and setBackendHeartbeatInterval were added and the following methods were removed: uiUpdateInterval, setUiUpdateInterval, killOnUnhealthyClusterInterval, setKillOnUnhealthyClusterInterval, healthCheckInterval and setHealthCheckInterval. On H2OConf Python API, the methods backendHeartbeatInterval and setBackendHeartbeatInterval were added and the following methods were removed: ui_update_interval, set_ui_update_interval, kill_on_unhealthy_cluster_interval, set_kill_on_unhealthy_cluster_interval, get_health_check_interval and set_health_check_interval. The added methods are used to configure single interval which was previously specified by these 3 different methods.
The configuration spark.ext.h2o.cluster.client.connect.timeout is removed without replacement as it is no longer needed. on H2OConf Scala API, the methods clientConnectionTimeout and setClientConnectionTimeout were removed and on H2OConf Python API, the methods set_client_connection_timeout and set_client_connection_timeout were removed.

Change of Versioning Scheme¶

Version of Sparkling Water is changed to the following pattern: H2OVersion-SWPatchVersion-SparkVersion, where: H2OVersion is full H2O Version which is integrated to Sparkling Water. SWPatchVersion is used to specify a patch version and SparkVersion is a Spark version. This change of scheme allows us to do releases of Sparkling Water without the need of releasing H2O if there is only change on the Sparkling Water side. In that case, we just increment the SWPatchVersion. The new version therefore looks, for example, like 3.26.0.9-2-2.4. This version tells us this Sparkling Water is integrating H2O 3.26.0.9, it is the second release with 3.26.0.9 version and is for Spark 2.4.

Renamed Property for Passing Extra HTTP Headers for Flow UI¶

The configuration property spark.ext.h2o.client.flow.extra.http.headers was renamed to to spark.ext.h2o.flow.extra.http.headers since Flow UI can also run on H2O nodes and the value of the property is also propagated to H2O nodes since the major version 3.28.0.1-1.

External Backend now keeps H2O Flow accessible on worker nodes¶

The option spark.ext.h2o.node.enable.web does not have any effect anymore for automatic mode of external backend as we required H2O Flow to be accessible on the worker nodes. The associated getters and setters do also not have any effect in this case.

It is also required that the users of manual mode of external backend keep REST api available on all worker nodes. In particular, the H2O option -disable_web can’t be specified when starting H2O.

Default Values of Some AutoML Parameters Have Changed¶

The default values of the following AutoML parameters have changed across all APIs.

Parameter Name	Old Value	New Value
`maxRuntimeSecs`	`3600.0`	`0.0` (unlimited)
`keepCrossValidationPredictions`	`true`	`false`
`keepCrossValidationModels`	`true`	`false`

From any previous version to 3.26.11¶

Users of Sparkling Water external cluster in manual mode on Hadoop need to update the command the external cluster is launched with. A new parameter -sw_ext_backend needs to be added to the h2odriver invocation.