[FSTORE-1008] enable interacting with java client to hopsworks

docs/user_guides/fs/compute_engines.md

@@ -4,12 +4,13 @@ In order to execute a feature pipeline to write to the Feature Store, as well as
 Hopsworks Feature Store APIs are built around dataframes, that means feature data is inserted into the Feature Store from a Dataframe and likewise when reading data from the Feature Store, it is returned
 as a Dataframe.
 
-As such, Hopsworks supports three computational engines:
+As such, Hopsworks supports five computational engines:
 
 1. [Apache Spark](https://spark.apache.org): Spark Dataframes and Spark Structured Streaming Dataframes are supported, both from Python environments (PySpark) and from Scala environments.
 2. [Python](https://www.python.org/): For pure Python environments without dependencies on Spark, Hopsworks supports [Pandas Dataframes](https://pandas.pydata.org/) and [Polars Dataframes](https://pola.rs/).
 3. [Apache Flink](https://flink.apache.org): Flink Data Streams are currently supported as an experimental feature from Java/Scala environments.
-3. [Apache Beam](https://beam.apache.org/) *experimental*: Beam Data Streams are currently supported as an experimental feature from Java/Scala environments.
+4. [Apache Beam](https://beam.apache.org/) *experimental*: Beam Data Streams are currently supported as an experimental feature from Java/Scala environments.
+5. [Java](https://www.java.com): For pure Java environments without dependencies on Spark, Hopsworks supports writing using List of POJO Objects. 
 
 Hopsworks supports running [compute on the platform itself](../../concepts/dev/inside.md) in the form of [Jobs](../projects/jobs/pyspark_job.md) or in [Jupyter Notebooks](../projects/jupyter/python_notebook.md).
 Alternatively, you can also connect to Hopsworks using Python or Spark from [external environments](../../concepts/dev/outside.md), given that there is network connectivity.
@@ -18,17 +19,16 @@ Alternatively, you can also connect to Hopsworks using Python or Spark from [ext
 
 Hopsworks is aiming to provide functional parity between the computational engines, however, there are certain Hopsworks functionalities which are exclusive to the engines.
 
-| Functionality                                                     | Method | Spark | Python | Flink  | Beam   | Comment |
-| ----------------------------------------------------------------- | ------ | ----- | ------ | ------ | ------ | ------- |
-| Feature Group Creation from dataframes                            | [`FeatureGroup.create_feature_group()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#create_feature_group)  | :white_check_mark: | :white_check_mark: | - | - | Currently Flink/Beam doesn't support registering feature group metadata. Thus it needs to be pre-registered before you can write real time features computed by Flink/Beam.|
-| Training Dataset Creation from dataframes                         | [`TrainingDataset.save()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/training_dataset_api/#save)  | :white_check_mark: | - | - | - | Functionality was deprecated in version 3.0 |
-| Data validation using Great Expectations for streaming dataframes | [`FeatureGroup.validate()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#validate) [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | - | - | - | - | `insert_stream` does not perform any data validation even when a expectation suite is attached. |
-| Stream ingestion    | [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | :white_check_mark: | - | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming. |
-| Stream ingestion    | [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | :white_check_mark: | - | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming. |
-| Reading from Streaming Storage Connectors | [`KafkaConnector.read_stream()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/storage_connector_api/#read_stream) | :white_check_mark: | - | - | - | Python/Pandas/Polars has currently no notion of streaming. For Flink/Beam only write operations are supported |
-| Reading training data from external storage other than S3 | [`FeatureView.get_training_data()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_view_api/#get_training_data) | :white_check_mark: | - | - | - | Reading training data that was written to external storage using a Storage Connector other than S3 can currently not be read using HSFS APIs, instead you will have to use the storage's native client. |
-| Reading External Feature Groups into Dataframe | [`ExternalFeatureGroup.read()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/external_feature_group_api/#read) | :white_check_mark: | - | - | - | Reading an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the [Query API](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/query_api/) to create Feature Views/Training Data containing External Feature Groups. |
-| Read Queries containing External Feature Groups into Dataframe | [`Query.read()`](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/query_api/#read) | :white_check_mark: | - | - | - | Reading a Query containing an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the Query to create Feature Views/Training Data and write the data to a Storage Connector, from where you can read up the data into a Pandas/Polars Dataframe. |
+| Functionality                                                     | Method                                                                                                                                                                          | Spark              | Python             | Flink                  | Beam               | Java               | Comment                                                                                                                                                                                                                                                                                                    |
+| ----------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | ------------------ | ---------------------- | ------------------ | ------------------ |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Feature Group Creation from dataframes                            | [`FeatureGroup.create_feature_group()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#create_feature_group)            | :white_check_mark: | :white_check_mark: | -                      | -                  | -                  | Currently Flink/Beam/Java doesn't support registering feature group metadata. Thus it needs to be pre-registered before you can write real time features computed by Flink/Beam.                                                                                                                           |
+| Training Dataset Creation from dataframes                         | [`TrainingDataset.save()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/training_dataset_api/#save)                                     | :white_check_mark: | -                  | -                      | -                  | -                  | Functionality was deprecated in version 3.0                                                                                                                                                                                                                                                                |
+| Data validation using Great Expectations for streaming dataframes | [`FeatureGroup.validate()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#validate) <br/> [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | -                  | -                  | -                      | -                  | -                  | `insert_stream` does not perform any data validation even when a expectation suite is attached.                                                                                  |
+| Stream ingestion                                                  | [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream)                          | :white_check_mark: | -                  | :white_check_mark:     | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming.                                                                                                                                                                                                                                                 |
+| Reading from Streaming Storage Connectors                         | [`KafkaConnector.read_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/storage_connector_api/#read_stream)                        | :white_check_mark: | -                  | -                      | -                  | -                  | Python/Pandas/Polars has currently no notion of streaming. For Flink/Beam/Java only write operations are supported                                                                                                                                                                                         |
+| Reading training data from external storage other than S3         | [`FeatureView.get_training_data()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_view_api/#get_training_data)                    | :white_check_mark: | -                  | -                      | -                  | -                  | Reading training data that was written to external storage using a Storage Connector other than S3 can currently not be read using HSFS APIs, instead you will have to use the storage's native client.                                                                                                    |
+| Reading External Feature Groups into Dataframe                    | [`ExternalFeatureGroup.read()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/external_feature_group_api/#read)                           | :white_check_mark: | -                  | -                      | -                  | -                  | Reading an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the [Query API](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/query_api/) to create Feature Views/Training Data containing External Feature Groups. |
+| Read Queries containing External Feature Groups into Dataframe    | [`Query.read()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/query_api/#read)                                                           | :white_check_mark: | -                  | -                      | -                  | -                  | Reading a Query containing an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the Query to create Feature Views/Training Data and write the data to a Storage Connector, from where you can read up the data into a Pandas/Polars Dataframe.         |
 
 ## Python
 
@@ -77,3 +77,7 @@ Apache Beam integration with Hopsworks feature store was only tested using Dataf
 
 For more details head over to the [Getting Started Guide](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/java/beam).
 
+## Java
+It is also possible to interact to Hopsworks feature store using  pure Java environments without dependencies on Spark, Flink or Beam.
+
+For more details head over to the [Getting Started Guide](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/java).

docs/user_guides/integrations/index.md

@@ -3,6 +3,7 @@
 Hopsworks is an open platform aiming to be accessible from a variety of tools. Learn in this section how to connect to Hopsworks from
 
 - [Python, AWS SageMaker, Google Colab, Kubeflow](python)
+- [Java](java)
 - [Databricks](databricks/networking)
 - [AWS EMR](emr/emr_configuration)
 - [Azure HDInsight](hdinsight)

docs/user_guides/integrations/java.md

@@ -0,0 +1,51 @@
+---
+description: Documentation on how to connect to Hopsworks from a Java client.
+---
+
+# Java client
+
+This guide explains step by step how to connect to Hopsworks from a Java client.
+
+
+## Generate an API key
+
+For instructions on how to generate an API key follow this [user guide](../projects/api_key/create_api_key.md). For the Java client to work correctly make sure you add the following scopes to your API key:
+
+  1. featurestore
+  2. project
+  3. job
+  4. kafka
+
+## Connecting to the Feature Store
+
+You are now ready to connect to the Hopsworks Feature Store from a Java client:
+
+```Java
+//Import necessary classes
+import com.logicalclocks.hsfs.FeatureStore;
+import com.logicalclocks.hsfs.FeatureView;
+import com.logicalclocks.hsfs.HopsworksConnection;
+
+//Establish connection with Hopsworks.
+HopsworksConnection hopsworksConnection = HopsworksConnection.builder()
+  .host("my_instance")                      // DNS of your Feature Store instance
+  .port(443)                                // Port to reach your Hopsworks instance, defaults to 443
+  .project("my_project")                    // Name of your Hopsworks Feature Store project 
+  .apiKeyValue("api_key")                   // The API key to authenticate with the feature store
+  .hostnameVerification(false)               // Disable for self-signed certificates
+  .build();
+
+//get feature store handle
+FeatureStore fs = hopsworksConnection.getFeatureStore();
+
+//get feature view handle
+FeatureView fv = fs.getFeatureView(fvName, fvVersion);
+
+// get feature vector
+List<Object> singleVector = fv.getFeatureVector(new HashMap<String, Object>() {{
+        put("id", 100);
+        }});
+```
+
+## Next Steps
+For more information how to interact from Java client with the Hopsworks Feature store follow this [tutorial](https://github.com/logicalclocks/hopsworks-tutorials/tree/java_engine/java).