Package com.logicalclocks.hsfs

Class FeatureViewBase<T extends FeatureViewBase,T3 extends FeatureStoreBase<T4>,T4 extends QueryBase,T5>

java.lang.Object
com.logicalclocks.hsfs.FeatureViewBase<T,T3,T4,T5>
Direct Known Subclasses:
FeatureView, FeatureView, FeatureView, FeatureView
public abstract class FeatureViewBase<T extends FeatureViewBase,T3 extends FeatureStoreBase<T4>,T4 extends QueryBase,T5> extends Object

  • Field Details

  • Constructor Details

    • FeatureViewBase

      public FeatureViewBase()

  • Method Details

    • createTrainingData

      public Integer createTrainingData(String startTime, String endTime, String description, DataFormat dataFormat) throws IOException, FeatureStoreException, ParseException
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data can be retrieved by calling `feature_view.getTrainingData()`. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset
        String startTime = "20220101000000";
        String endTime = "20220606235959";
        String description = "demo training dataset":
        fv.createTrainingData(startTime, endTime, description, DataFormat.CSV);
      Parameters:
      startTime - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      endTime - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided `startTime`/`endTime` date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided `startTime`/`endTime` strings to date types.

    • createTrainingData

      @Deprecated public Integer createTrainingData(String startTime, String endTime, String description, DataFormat dataFormat, Boolean coalesce, StorageConnector storageConnector, String location, Long seed, StatisticsConfig statisticsConfig, Map<String,String> writeOptions, FilterLogic extraFilterLogic, Filter extraFilter) throws IOException, FeatureStoreException, ParseException
      Deprecated.
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data can be retrieved by calling `featureView.getTrainingData()`. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset
        String startTime = "20220101000000";
        String endTime = "20220606235959";
        String description = "demo training dataset":
        String location = "";
        StatisticsConfig statisticsConfig = new StatisticsConfig(true, true, true, true)
        fv.createTrainingData(startTime, endTime, description, DataFormat.CSV, true, location, statisticsConfig);
      Parameters:
      startTime - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      endTime - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      coalesce - If true the training dataset data will be coalesced into a single partition before writing. The resulting training dataset will be a single file per split.
      storageConnector - Storage connector defining the sink location for the training dataset. If `null` is provided and materializes training dataset on HopsFS.
      location - Path to complement the sink storage connector with, e.g if the storage connector points to an S3 bucket, this path can be used to define a sub-directory inside the bucket to place the training dataset. If empty string is provided `""`, saving the training dataset at the root defined by the storage connector.
      seed - Define a seed to create the random splits with, in order to guarantee reproducability,
      statisticsConfig - A configuration object, to generally enable descriptive statistics computation for this feature group, `"correlations`" to turn on feature correlation computation, `"histograms"` to compute feature value frequencies and `"exact_uniqueness"` to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass `statisticsConfig=null`.
      writeOptions - Additional write options as key-value pairs.
      extraFilterLogic - Additional filters (set of Filter objects) to be attached to the training dataset. The filters will be also applied in `getBatchData`.
      extraFilter - Additional filter to be attached to the training dataset. The filter will be also applied in `getBatchData`.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided `startTime`/`endTime` date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided `startTime`/`endTime` strings to date types.

    • createTrainingData

      public Integer createTrainingData(String startTime, String endTime, String description, DataFormat dataFormat, Boolean coalesce, DataSource dataSource, Long seed, StatisticsConfig statisticsConfig, Map<String,String> writeOptions, FilterLogic extraFilterLogic, Filter extraFilter) throws IOException, FeatureStoreException, ParseException
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data can be retrieved by calling `featureView.getTrainingData()`. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset
        String startTime = "20220101000000";
        String endTime = "20220606235959";
        String description = "demo training dataset";
        DataSource dataSource = fs.getDataSource("my_datasource");
        dataSource.setPath("test/path");
        StatisticsConfig statisticsConfig = new StatisticsConfig(true, true, true, true);
        fv.createTrainingData(startTime, endTime, description, DataFormat.CSV, true, dataSource,
        null, statisticsConfig, null, null, null);
      Parameters:
      startTime - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      endTime - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      coalesce - If true the training dataset data will be coalesced into a single partition before writing. The resulting training dataset will be a single file per split.
      dataSource - Data source defining the sink location for the training dataset. If `null` is provided and materializes training dataset on HopsFS.
      seed - Define a seed to create the random splits with, in order to guarantee reproducability,
      statisticsConfig - A configuration object, to generally enable descriptive statistics computation for this feature group, `"correlations`" to turn on feature correlation computation, `"histograms"` to compute feature value frequencies and `"exact_uniqueness"` to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass `statisticsConfig=null`.
      writeOptions - Additional write options as key-value pairs.
      extraFilterLogic - Additional filters (set of Filter objects) to be attached to the training dataset. The filters will be also applied in `getBatchData`.
      extraFilter - Additional filter to be attached to the training dataset. The filter will be also applied in `getBatchData`.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided `startTime`/`endTime` date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided `startTime`/`endTime` strings to date types.

    • createTrainTestSplit

      public Integer createTrainTestSplit(Float testSize, String trainStart, String trainEnd, String testStart, String testEnd, String description, DataFormat dataFormat) throws IOException, FeatureStoreException, ParseException
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data is split into train and test set at random or according to time ranges. The training data can be retrieved by calling `featureView.getTrainTestSplit` method. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset based on time split
        String trainStart = "20220101000000";
        String trainEnd = "20220630235959";
        String testStart = "20220701000000";
        String testEnd = "20220830235959";
        String description = "demo training dataset":
        fv.createTrainTestSplit(null, trainStart, trainEnd, testStart, testEnd, description, DataFormat.CSV);

        // or based on random split
        fv.createTrainTestSplit(30, null, null, null, null, description, DataFormat.CSV);
      Parameters:
      testSize - Size of test set.
      trainStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      trainEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      Returns:
      Integer Training dataset version
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided date strings to date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided date strings to date types.

    • createTrainTestSplit

      @Deprecated public Integer createTrainTestSplit(Float testSize, String trainStart, String trainEnd, String testStart, String testEnd, String description, DataFormat dataFormat, Boolean coalesce, StorageConnector storageConnector, String location, Long seed, StatisticsConfig statisticsConfig, Map<String,String> writeOptions, FilterLogic extraFilterLogic, Filter extraFilter) throws IOException, FeatureStoreException, ParseException
      Deprecated.
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data is split into train and test set at random or according to time ranges. The training data can be retrieved by calling `featureView.getTrainTestSplit` method. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset based on time split
        String trainStart = "20220101000000";
        String trainEnd = "20220630235959";
        String testStart = "20220701000000";
        String testEnd = "20220830235959";
        String description = "demo training dataset":
        StatisticsConfig statisticsConfig = new StatisticsConfig(true, true, true, true)
        Map<String, String> writeOptions = new HashMap<String, String>() {{
                           put("header", "true");
                           put("delimiter", ",")}
                           };
        // define extra filters
        Filter leftFtFilter = new Filter();
        leftFtFilter.setFeature(new Feature("left_ft_name"));
        leftFtFilter.setValue("400");
        leftFtFilter.setCondition(SqlFilterCondition.EQUALS);
        Filter rightFtFilter = new Filter();
        rightFtFilter.setFeature(new Feature("right_ft_name"));
        rightFtFilter.setValue("50");
        rightFtFilter.setCondition(SqlFilterCondition.EQUALS);
        FilterLogic extraFilterLogic = new FilterLogic(SqlFilterLogic.AND, leftFtFilter, rightFtFilter);
        Filter extraFilter = new Filter();
        extraFilter.setFeature(new Feature("ft_name"));
        extraFilter.setValue("100");
        extraFilter.setCondition(SqlFilterCondition.GREATER_THAN);

        // create training data
        fv.createTrainTestSplit(null, null, trainStart, trainEnd, testStart,
        testEnd,  description, DataFormat.CSV, coalesce, storageConnector, location, seed, statisticsConfig,
        writeOptions, extraFilterLogic, extraFilter);

        // or based on random split
        fv.createTrainTestSplit(20, 10, null, null,  null, null, description, DataFormat.CSV, coalesce,
        storageConnector, location, seed, statisticsConfig, writeOptions, extraFilterLogic, extraFilter);
      Parameters:
      testSize - Size of test set.
      trainStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      trainEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      coalesce - If true the training dataset data will be coalesced into a single partition before writing. The resulting training dataset will be a single file per split.
      storageConnector - Storage connector defining the sink location for the training dataset. If `null` is provided and materializes training dataset on HopsFS.
      location - Path to complement the sink storage connector with, e.g if the storage connector points to an S3 bucket, this path can be used to define a sub-directory inside the bucket to place the training dataset. If empty string is provided `""`, saving the training dataset at the root defined by the storage connector.
      seed - Define a seed to create the random splits with, in order to guarantee reproducability,
      statisticsConfig - A configuration object, to generally enable descriptive statistics computation for this feature group, `"correlations`" to turn on feature correlation computation, `"histograms"` to compute feature value frequencies and `"exact_uniqueness"` to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass `statisticsConfig=null`.
      writeOptions - Additional write options as key-value pairs.
      extraFilterLogic - Additional filters (set of Filter objects) to be attached to the training dataset. The filters will be also applied in `getBatchData`.
      extraFilter - Additional filter to be attached to the training dataset. The filter will be also applied in `getBatchData`.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided date strings to date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided date strings to date types.

    • createTrainTestSplit

      public Integer createTrainTestSplit(Float testSize, String trainStart, String trainEnd, String testStart, String testEnd, String description, DataFormat dataFormat, Boolean coalesce, DataSource dataSource, Long seed, StatisticsConfig statisticsConfig, Map<String,String> writeOptions, FilterLogic extraFilterLogic, Filter extraFilter) throws IOException, FeatureStoreException, ParseException
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data is split into train and test set at random or according to time ranges. The training data can be retrieved by calling `featureView.getTrainTestSplit` method. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset based on time split
        String trainStart = "20220101000000";
        String trainEnd = "20220630235959";
        String testStart = "20220701000000";
        String testEnd = "20220830235959";
        String description = "demo training dataset":
        StatisticsConfig statisticsConfig = new StatisticsConfig(true, true, true, true)
        Map<String, String> writeOptions = new HashMap<String, String>() {{
                           put("header", "true");
                           put("delimiter", ",")}
                           };
        // define extra filters
        Filter leftFtFilter = new Filter();
        leftFtFilter.setFeature(new Feature("left_ft_name"));
        leftFtFilter.setValue("400");
        leftFtFilter.setCondition(SqlFilterCondition.EQUALS);
        Filter rightFtFilter = new Filter();
        rightFtFilter.setFeature(new Feature("right_ft_name"));
        rightFtFilter.setValue("50");
        rightFtFilter.setCondition(SqlFilterCondition.EQUALS);
        FilterLogic extraFilterLogic = new FilterLogic(SqlFilterLogic.AND, leftFtFilter, rightFtFilter);
        Filter extraFilter = new Filter();
        extraFilter.setFeature(new Feature("ft_name"));
        extraFilter.setValue("100");
        extraFilter.setCondition(SqlFilterCondition.GREATER_THAN);

        // create training data
        fv.createTrainTestSplit(null, null, trainStart, trainEnd, testStart,
        testEnd,  description, DataFormat.CSV, coalesce, dataSource, seed, statisticsConfig,
        writeOptions, extraFilterLogic, extraFilter);

        // or based on random split
        fv.createTrainTestSplit(20, 10, null, null,  null, null, description, DataFormat.CSV, coalesce,
        dataSource, seed, statisticsConfig, writeOptions, extraFilterLogic, extraFilter);
      Parameters:
      testSize - Size of test set.
      trainStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      trainEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      coalesce - If true the training dataset data will be coalesced into a single partition before writing. The resulting training dataset will be a single file per split.
      dataSource - Data source defining the sink location for the training dataset. If `null` is provided and materializes training dataset on HopsFS.
      seed - Define a seed to create the random splits with, in order to guarantee reproducability,
      statisticsConfig - A configuration object, to generally enable descriptive statistics computation for this feature group, `"correlations`" to turn on feature correlation computation, `"histograms"` to compute feature value frequencies and `"exact_uniqueness"` to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass `statisticsConfig=null`.
      writeOptions - Additional write options as key-value pairs.
      extraFilterLogic - Additional filters (set of Filter objects) to be attached to the training dataset. The filters will be also applied in `getBatchData`.
      extraFilter - Additional filter to be attached to the training dataset. The filter will be also applied in `getBatchData`.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided date strings to date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided date strings to date types.

    • createTrainValidationTestSplit

      public Integer createTrainValidationTestSplit(Float validationSize, Float testSize, String trainStart, String trainEnd, String validationStart, String validationEnd, String testStart, String testEnd, String description, DataFormat dataFormat) throws IOException, FeatureStoreException, ParseException
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data is split into train, validation, and test set at random or according to time range. The training data can be retrieved by calling `featureView.getTrainValidationTestSplit`. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset based on time split
        String trainStart = "20220101000000";
        String trainEnd = "20220630235959";
        String validationStart = "20220701000000";
        String validationEnd = "20220830235959";
        String testStart = "20220901000000";
        String testEnd = "20220931235959";
        String description = "demo training dataset":
        fv.createTrainTestSplit(null, null, trainStart, trainEnd, validationStart, validationEnd, testStart,
        testEnd, description, DataFormat.CSV);

        // or based on random split
        fv.createTrainTestSplit(20, 10, null, null, null, null, null, null, description, DataFormat.CSV);
      Parameters:
      validationSize - Size of validation set.
      testSize - Size of test set.
      trainStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      trainEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      validationStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      validationEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided date strings to date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided date strings to date types.

    • createTrainValidationTestSplit

      @Deprecated public Integer createTrainValidationTestSplit(Float validationSize, Float testSize, String trainStart, String trainEnd, String validationStart, String validationEnd, String testStart, String testEnd, String description, DataFormat dataFormat, Boolean coalesce, StorageConnector storageConnector, String location, Long seed, StatisticsConfig statisticsConfig, Map<String,String> writeOptions, FilterLogic extraFilterLogic, Filter extraFilter) throws IOException, FeatureStoreException, ParseException
      Deprecated.
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data is split into train, validation, and test set at random or according to time range. The training data can be retrieved by calling `feature_view.getTrainValidationTestSplit`. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset based on time split
        String trainStart = "20220101000000";
        String trainEnd = "20220630235959";
        String validationStart = "20220701000000";
        String validationEnd = "20220830235959";
        String testStart = "20220901000000";
        String testEnd = "20220931235959";
        String description = "demo training dataset":
        StorageConnector.S3Connector storageConnector = fs.getS3Connector("s3Connector");
        String location = "";
        Long seed = 1234L;
        Boolean coalesce = true;
        StatisticsConfig statisticsConfig = new StatisticsConfig(true, true, true, true)
        Map<String, String> writeOptions = new HashMap<String, String>() {{
                           put("header", "true");
                           put("delimiter", ",")}
                           };
        // define extra filters
        Filter leftFtFilter = new Filter();
        leftFtFilter.setFeature(new Feature("left_ft_name"));
        leftFtFilter.setValue("400");
        leftFtFilter.setCondition(SqlFilterCondition.EQUALS);
        Filter rightFtFilter = new Filter();
        rightFtFilter.setFeature(new Feature("right_ft_name"));
        rightFtFilter.setValue("50");
        rightFtFilter.setCondition(SqlFilterCondition.EQUALS);
        FilterLogic extraFilterLogic = new FilterLogic(SqlFilterLogic.AND, leftFtFilter, rightFtFilter);
        Filter extraFilter = new Filter();
        extraFilter.setFeature(new Feature("ft_name"));
        extraFilter.setValue("100");
        extraFilter.setCondition(SqlFilterCondition.GREATER_THAN);
        // create training data
        fv.createTrainTestSplit(null, null, trainStart, trainEnd, validationStart, validationEnd, testStart,
        testEnd,  description, DataFormat.CSV, coalesce, storageConnector, location, seed, statisticsConfig,
        writeOptions, extraFilterLogic, extraFilter);

        // or based on random split
        fv.createTrainTestSplit(20, 10, null, null, null, null, null, null, description, DataFormat.CSV, coalesce,
        storageConnector, location, seed, statisticsConfig, writeOptions, extraFilterLogic, extraFilter);
      Parameters:
      validationSize - Size of validation set.
      testSize - Size of test set.
      trainStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      trainEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      validationStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      validationEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      coalesce - If true the training dataset data will be coalesced into a single partition before writing. The resulting training dataset will be a single file per split.
      storageConnector - Storage connector defining the sink location for the training dataset. If `null` is provided and materializes training dataset on HopsFS.
      location - Path to complement the sink storage connector with, e.g if the storage connector points to an S3 bucket, this path can be used to define a sub-directory inside the bucket to place the training dataset. If empty string is provided `""`, saving the training dataset at the root defined by the storage connector.
      seed - Define a seed to create the random splits with, in order to guarantee reproducability,
      statisticsConfig - A configuration object, to generally enable descriptive statistics computation for this feature group, `"correlations`" to turn on feature correlation computation, `"histograms"` to compute feature value frequencies and `"exact_uniqueness"` to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass `statisticsConfig=null`.
      writeOptions - Additional write options as key-value pairs.
      extraFilterLogic - Additional filters (set of Filter objects) to be attached to the training dataset. The filters will be also applied in `getBatchData`.
      extraFilter - Additional filter to be attached to the training dataset. The filter will be also applied in `getBatchData`.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided date strings to date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided date strings to date types.

    • createTrainValidationTestSplit

      public Integer createTrainValidationTestSplit(Float validationSize, Float testSize, String trainStart, String trainEnd, String validationStart, String validationEnd, String testStart, String testEnd, String description, DataFormat dataFormat, Boolean coalesce, DataSource dataSource, Long seed, StatisticsConfig statisticsConfig, Map<String,String> writeOptions, FilterLogic extraFilterLogic, Filter extraFilter) throws IOException, FeatureStoreException, ParseException
      Create the metadata for a training dataset and save the corresponding training data into `location`. The training data is split into train, validation, and test set at random or according to time range. The training data can be retrieved by calling `feature_view.getTrainValidationTestSplit`. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // create training dataset based on time split
        String trainStart = "20220101000000";
        String trainEnd = "20220630235959";
        String validationStart = "20220701000000";
        String validationEnd = "20220830235959";
        String testStart = "20220901000000";
        String testEnd = "20220931235959";
        String description = "demo training dataset";
        DataSource dataSource = fs.getDataSource("my_datasource");
        dataSource.setPath("test/path");
        Long seed = 1234L;
        Boolean coalesce = true;
        StatisticsConfig statisticsConfig = new StatisticsConfig(true, true, true, true)
        Map<String, String> writeOptions = new HashMap<String, String>() {{
                           put("header", "true");
                           put("delimiter", ",")}
                           };
        // define extra filters
        Filter leftFtFilter = new Filter();
        leftFtFilter.setFeature(new Feature("left_ft_name"));
        leftFtFilter.setValue("400");
        leftFtFilter.setCondition(SqlFilterCondition.EQUALS);
        Filter rightFtFilter = new Filter();
        rightFtFilter.setFeature(new Feature("right_ft_name"));
        rightFtFilter.setValue("50");
        rightFtFilter.setCondition(SqlFilterCondition.EQUALS);
        FilterLogic extraFilterLogic = new FilterLogic(SqlFilterLogic.AND, leftFtFilter, rightFtFilter);
        Filter extraFilter = new Filter();
        extraFilter.setFeature(new Feature("ft_name"));
        extraFilter.setValue("100");
        extraFilter.setCondition(SqlFilterCondition.GREATER_THAN);
        // create training data
        fv.createTrainTestSplit(null, null, trainStart, trainEnd, validationStart, validationEnd, testStart,
        testEnd,  description, DataFormat.CSV, coalesce, dataSource, seed, statisticsConfig,
        writeOptions, extraFilterLogic, extraFilter);

        // or based on random split
        fv.createTrainTestSplit(20, 10, null, null, null, null, null, null, description, DataFormat.CSV, coalesce,
        dataSource, seed, statisticsConfig, writeOptions, extraFilterLogic, extraFilter);
      Parameters:
      validationSize - Size of validation set.
      testSize - Size of test set.
      trainStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      trainEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      validationStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      validationEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testStart - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      testEnd - Datetime string. The String should be formatted in one of the following formats `yyyyMMdd`, `yyyyMMddHH`, `yyyyMMddHHmm`, or `yyyyMMddHHmmss`.
      description - A string describing the contents of the training dataset to improve discoverability for Data Scientists.
      dataFormat - The data format used to save the training dataset.
      coalesce - If true the training dataset data will be coalesced into a single partition before writing. The resulting training dataset will be a single file per split.
      dataSource - Data source defining the sink location for the training dataset. If `null` is provided and materializes training dataset on HopsFS.
      seed - Define a seed to create the random splits with, in order to guarantee reproducability,
      statisticsConfig - A configuration object, to generally enable descriptive statistics computation for this feature group, `"correlations`" to turn on feature correlation computation, `"histograms"` to compute feature value frequencies and `"exact_uniqueness"` to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass `statisticsConfig=null`.
      writeOptions - Additional write options as key-value pairs.
      extraFilterLogic - Additional filters (set of Filter objects) to be attached to the training dataset. The filters will be also applied in `getBatchData`.
      extraFilter - Additional filter to be attached to the training dataset. The filter will be also applied in `getBatchData`.
      Returns:
      Integer Training dataset version.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks and/or unable to identify format of the provided date strings to date formats.
      IOException - Generic IO exception.
      ParseException - In case it's unable to parse provided date strings to date types.

    • validateTrainTestSplit

      protected void validateTrainTestSplit(Float testSize, String trainEnd, String testStart) throws FeatureStoreException
      Throws:
      FeatureStoreException

    • validateTrainValidationTestSplit

      protected void validateTrainValidationTestSplit(Float validationSize, Float testSize, String trainEnd, String validationStart, String validationEnd, String testStart) throws FeatureStoreException
      Throws:
      FeatureStoreException

    • initServing

      public void initServing() throws FeatureStoreException, IOException, SQLException, ClassNotFoundException
      Initialise feature view to retrieve feature vector from online feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // Initialise feature view serving
        fv.initServing();
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      SQLException - In case there is online storage (RonDB) access error or other errors.
      ClassNotFoundException - In case class `com.mysql.jdbc.Driver` can not be found.

    • initServing

      public void initServing(Boolean batch) throws FeatureStoreException, IOException, SQLException, ClassNotFoundException
      Initialise feature view to retrieve feature vector from online feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // Initialise feature view batch serving
        fv.initServing(true);
      Parameters:
      batch - Whether to initialise feature view to retrieve feature vectors from the online feature store in batches.
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      SQLException - In case there is online storage (RonDB) access error or other errors.
      ClassNotFoundException - In case class `com.mysql.jdbc.Driver` can not be found.

    • initServing

      public void initServing(Boolean batch, Boolean external) throws FeatureStoreException, IOException, SQLException, ClassNotFoundException
      Initialise feature view to retrieve feature vector from online feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // Initialise feature view batch serving
        fv.initServing(true, false);
      Parameters:
      batch - Whether to initialise feature view to retrieve feature vectors from the online feature store in batches.
      external - If set to `true`, the connection to the online feature store is established using the same host as for the `host` parameter in the connection object. If set to False, the online feature store storage connector is used which relies on the private IP.
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      SQLException - In case there is online storage (RonDB) access error or other errors.
      ClassNotFoundException - In case class `com.mysql.jdbc.Driver` can not be found.

    • initBatchScoring

      public void initBatchScoring(Integer trainingDatasetVersion)
      Initialise feature view to retrieve feature vector from offline feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // Initialise feature view batch scoring
        fv.initBatchScoring(1);
      Parameters:
      trainingDatasetVersion - Version of training dataset to identify additional filters attached to the training dataset and statistics to use for transformation functions.

    • getFeatureVector

      public List<Object> getFeatureVector(Map<String,Object> entry) throws FeatureStoreException, IOException, ClassNotFoundException
      Returns assembled feature vector from online feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // define primary key values to fetch data from online feature store
        Map<String, Object> pkMap = new HashMap<String, Object>() {
               {put("customer_id", 1);
                put("contract_id" , 100);
                }
        };
        // get feature vector
        fv.getFeatureVector(entry);
      Parameters:
      entry - Fictionary of feature group primary key and values provided by serving application.
      Returns:
      List of feature values related to provided primary keys, ordered according to positions of the features in the feature view query.
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      ClassNotFoundException - In case class `com.mysql.jdbc.Driver` can not be found.

    • getFeatureVector

      public List<Object> getFeatureVector(Map<String,Object> entry, boolean external) throws FeatureStoreException, IOException, ClassNotFoundException
      Returns assembled feature vector from online feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // define primary key values to fetch data from online feature store
        Map<String, Object> pkMap = new HashMap<String, Object>() {
               {put("customer_id", 1);
                put("contract_id" , 100);
                }
        };
        // get feature vector
        fv.getFeatureVector(entry, false);
      Parameters:
      entry - Dictionary of feature group primary key and values provided by serving application.
      external - If set to true, the connection to the online feature store is established using the same host as for the `host` parameter in the connection object. If set to false, the online feature store storage connector is used which relies on the private IP. Defaults to True if connection to Hopsworks is established from external environment
      Returns:
      List of feature values related to provided primary keys, ordered according to positions of the features in the feature view query.
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      ClassNotFoundException - In case class `com.mysql.jdbc.Driver` can not be found.

    • getFeatureVectors

      public List<List<Object>> getFeatureVectors(Map<String,List<Object>> entry) throws SQLException, FeatureStoreException, IOException
      Returns assembled feature vectors in batches from online feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // define primary key values to fetch data from online feature store
        Map<String, List<Long>> entry = ...;
        // get feature vector
        fv.getFeatureVector(entry);
      Parameters:
      entry - A list of dictionaries of feature group primary key and values provided by serving application.
      Returns:
      List of lists of feature values related to provided primary keys, ordered according to positions of the features in the feature view query.
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      SQLException - In case there is online storage (RonDB) access error or other errors.

    • getFeatureVectors

      public List<List<Object>> getFeatureVectors(Map<String,List<Object>> entry, boolean external) throws SQLException, FeatureStoreException, IOException, ClassNotFoundException
      Returns assembled feature vectors in batches from online feature store. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // define primary key values to fetch data from online feature store
        Map<String, List<Long>> entry = ...;
        // get feature vector
        fv.getFeatureVectors(entry, false);
      Parameters:
      entry - A list of dictionaries of feature group primary key and values provided by serving application.
      external - If set to `true`, the connection to the online feature store is established using the same host as for the `host` parameter in the connection object. If set to False, the online feature store storage connector is used which relies on the private IP.
      Returns:
      List of lists of feature values related to provided primary keys, ordered according to positions of this features in the feature view query.
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      SQLException - In case there is online storage (RonDB) access error or other errors.
      ClassNotFoundException - In case class `com.mysql.jdbc.Driver` can not be found.

    • getFeatureVectorObject

      public <T> T getFeatureVectorObject(Map<String,Object> entry, boolean external, Class<T> returnType) throws FeatureStoreException, IOException, ClassNotFoundException, IllegalAccessException, InstantiationException
      Returns assembled feature vector from online feature store (as Object). 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // define primary key values to fetch data from online feature store
        Map<String, Object> pkMap = new HashMap<String, Object>() {
               {put("customer_id", 1);
                put("contract_id" , 100);
                }
        };
        // get feature vector
        fv.getFeatureVectorObject(entry, false, ReturnType.class);
      Parameters:
      entry - Dictionary of feature group primary key and values provided by serving application.
      external - If set to true, the connection to the online feature store is established using the same host as for the `host` parameter in the connection object. If set to false, the online feature store storage connector is used which relies on the private IP. Defaults to True if connection to Hopsworks is established from external environment
      returnType - The type of the returned object. Should match the expected structure of the feature vector. The class should also provide the necessary setter methods to set the values of the feature vector.
      Returns:
      an instance of type `returnType` containing the values of the requested feature vector
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.
      ClassNotFoundException - In case class `com.mysql.jdbc.Driver` can not be found.
      IllegalAccessException - If the object of type `returnType` cannot be instantiated
      InstantiationException - If the object of type `returnType` cannot be instantiated

    • getFeatureVectorObject

      public <T> T getFeatureVectorObject(Map<String,Object> entry, Class<T> returnType) throws FeatureStoreException, InstantiationException, IllegalAccessException
      Returns assembled feature vector from online feature store (as Object). 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // define primary key values to fetch data from online feature store
        Map<String, Object> pkMap = new HashMap<String, Object>() {
               {put("customer_id", 1);
                put("contract_id" , 100);
                }
        };
        // get feature vector
        fv.getFeatureVectorObject(entry, ReturnType.class);
      Parameters:
      entry - Dictionary of feature group primary key and values provided by serving application.
      returnType - The type of the returned object. Should match the expected structure of the feature vector. The class should also provide the necessary setter methods to set the values of the feature vector.
      Returns:
      an instance of type `returnType` containing the values of the requested feature vector
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IllegalAccessException - If the object of type `returnType` cannot be instantiated
      InstantiationException - If the object of type `returnType` cannot be instantiated

    • addTag

      public void addTag(String name, Object value) throws FeatureStoreException, IOException
      Add name/value tag to the feature view. A tag consists of a name and value pair. Tag names are unique identifiers across the whole cluster. The value of a tag can be any valid json - primitives, arrays or json objects. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // attach a tag to a feature view
        JSONObject value = ...;
        fv.addTag("tag_schema", value);
      Parameters:
      name - Name of the tag
      value - Value of the tag. The value of a tag can be any valid json - primitives, arrays or json objects
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • getTags

      public Map<String,Object> getTags() throws FeatureStoreException, IOException
      Get all tags of the feature view. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // get tags
        fv.getTags();
      Returns:
      Map<String, Object> a map of tag name and values. The value of a tag can be any valid json - primitives, arrays or json objects
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • getTag

      public Object getTag(String name) throws FeatureStoreException, IOException
      Get a single tag value of the feature view. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // get tag
        fv.getTag("tag_name");
      Parameters:
      name - name of the tag
      Returns:
      Object The value of a tag can be any valid json - primitives, arrays or json objects
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • deleteTag

      public void deleteTag(String name) throws FeatureStoreException, IOException
      Delete a tag of the feature view. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // delete tag
        fv.deleteTag("tag_name");
      Parameters:
      name - Name of the tag to be deleted.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • addTrainingDatasetTag

      public void addTrainingDatasetTag(Integer version, String name, Object value) throws FeatureStoreException, IOException
      Add name/value tag to the training dataset. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // add tag to datasets version 1 in this feature view.
        JSONObject json = ...;
        fv.addTrainingDatasetTag(1, "tag_name", json);
      Parameters:
      version - Training dataset version.
      name - Name of the tag.
      value - Value of the tag. The value of a tag can be any valid json - primitives, arrays or json objects.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • getTrainingDatasetTags

      public Map<String,Object> getTrainingDatasetTags(Integer version) throws FeatureStoreException, IOException
      Get all tags of the training dataset. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // get tags of training dataset version 1 in this feature view.
        fv.getTrainingDatasetTags(1);
      Parameters:
      version - Training dataset version.
      Returns:
      Map<String, Object> A map of tag name and values. The value of a tag can be any valid json - primitives, arrays or json objects
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • getTrainingDatasetTag

      public Object getTrainingDatasetTag(Integer version, String name) throws FeatureStoreException, IOException
      Get a single tag value of the training dataset. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // get tag with name `"demo_name"` of training dataset version 1 in this feature view.
        fv.getTrainingDatasetTags(1, "demo_name");
      Parameters:
      version - Training dataset version.
      name - Name of the tag.
      Returns:
      Object The value of a tag can be any valid json - primitives, arrays or json objects.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • deleteTrainingDatasetTag

      public void deleteTrainingDatasetTag(Integer version, String name) throws FeatureStoreException, IOException
      Delete a tag of the training dataset. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // delete tag with name `"demo_name"` of training dataset version 1 in this feature view.
        fv.deleteTrainingDatasetTag(1, "demo_name");
      Parameters:
      version - Tag version.
      name - Name of the tag to be deleted.
      Throws:
      FeatureStoreException - If Client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • delete

      public void delete() throws FeatureStoreException, IOException
      Delete current feature view, all associated metadata and training data. 
       
        // get feature store handle
        FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
        // get feature view handle
        FeatureView fv = fs.getFeatureView("fv_name", 1);
        // delete feature view
        fv.delete();
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • delete

      public void delete(Boolean force) throws FeatureStoreException, IOException
      Delete current feature view, all associated metadata and training data. By default, the feature view will not be deleted if there are models associated to it. If force is set to true, the feature view is deleted even if there are models associated to it. 
       
 // get feature store handle
 FeatureStore fs = HopsworksConnection.builder().build().getFeatureStore();
 // get feature view handle
 FeatureView fv = fs.getFeatureView("fv_name", 1);
 // delete feature view
 fv.delete();
 // or force delete
 fv.delete(true);
      Parameters:
      force - If set to true, forces deletion of the feature view even if there are models associated to it. Defaults to false, in which case the feature view will not be deleted if there are models associated to it and an exception will be raised instead.
      Throws:
      FeatureStoreException - In case client is not connected to Hopsworks.
      IOException - Generic IO exception.

    • getPrimaryKeys

      public HashSet<String> getPrimaryKeys() throws SQLException, IOException, FeatureStoreException, ClassNotFoundException
      Set of primary key names that is used as keys in input dict object for `get_serving_vector` method.
      Returns:
      Set of serving keys
      Throws:
      SQLException
      IOException
      FeatureStoreException
      ClassNotFoundException

    • closeVectorServer

      public void closeVectorServer()
      Closes the ExecutorService and JDBC DataSource used to retrieve feature vectors from the online feature store.