feat: PubSub Lite Spark Connector

.gitignore

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+# Compiled class file
+*.class
+
+# Log file
+*.log
+
+# BlueJ files
+*.ctxt
+
+# Mobile Tools for Java (J2ME)
+.mtj.tmp/
+
+# Package Files #
+*.jar
+*.war
+*.nar
+*.ear
+*.zip
+*.tar.gz
+*.rar
+
+# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
+hs_err_pid*
+
+# intellij project folder
+.idea/
+*.iml
+
+# maven build directory
+target/
+
+.flattened-pom.xml

.readme-partials.yaml

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+custom_content: |
+  ## Requirements
+
+  ### Enable the PubSub Lite API
+
+                  Follow [these instructions](https://cloud.google.com/pubsub/lite/docs/quickstart#before-you-begin).
+
+  ### Create a new subscription or use existing subscription
+
+                  Follow [the instruction](https://cloud.google.com/pubsub/lite/docs/quickstart#create_a_lite_subscription) to create a new
+                  subscription or use existing subscription. If using existing subscription, the connector will read message from the
+                  oldest unacknowledged.
+
+  ### Create a Google Cloud Dataproc cluster (Optional)
+
+                  If you do not have an Apache Spark environment you can create a Cloud Dataproc cluster with pre-configured auth. The following examples assume you are using Cloud Dataproc, but you can use `spark-submit` on any cluster.
+
+                  ```
+                  MY_CLUSTER=...
+                  gcloud dataproc clusters create "$MY_CLUSTER"
+                  ```
+
+  ## Downloading and Using the Connector
+
+  <!--- TODO(jiangmichael): Add jar link for spark-pubsublite-latest.jar -->
+    The latest version connector of the connector (Scala 2.11) is publicly available in
+    gs://spark-lib/pubsublite/spark-pubsublite-latest.jar.
+
+  <!--- TODO(jiangmichael): Release on Maven Central and add Maven Central link -->
+                              The connector is also available from the Maven Central
+                              repository. It can be used using the `--packages` option or the
+                              `spark.jars.packages` configuration property. Use the following value
+
+                              | Scala version | Connector Artifact |
+                              | --- | --- |
+                              | Scala 2.11 | `com.google.cloud.pubsublite.spark:pubsublite-spark-sql-streaming-with-dependencies_2.11:0.1.0` |
+
+  <!--- TODO(jiangmichael): Add exmaple code and brief description here -->
+
+    ## Usage
+
+    ### Reading data from PubSub Lite
+
+    ```
+    df = spark.readStream \
+    .option("pubsublite.subscription", "projects/123456789/locations/us-central1-a/subscriptions/test-spark-subscription")
+    .format("pubsublite") \
+    .load
+    ```
+
+    Note that the connector supports both MicroBatch Processing and [Continuous Processing](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#continuous-processing).
+
+  ### Properties
+
+  The connector supports a number of options to configure the read:
+
+    | Option | Type | Required | Meaning |
+    | ------ | ---- | -------- | ------- |
+    | pubsublite.subscription | String | Y | Full subscription path that the connector will read from. |
+    | pubsublite.flowcontrol.byteoutstandingperpartition | Long | N | Max number of bytes per partition that will be cached in workers before Spark processes the messages. Default to 50000000 bytes. |
+    | pubsublite.flowcontrol.messageoutstandingperpartition | Long | N | Max number of messages per partition that will be cached in workers before Spark processes the messages. Default to Long.MAX_VALUE. |
+    | gcp.credentials.key | String | N | Service account JSON in base64. Default to [Application Default Credentials](https://cloud.google.com/docs/authentication/production#automatically). |
+
+  ### Data Schema
+
+  The connector has fixed data schema as follows:
+
+    | Data Field | Spark Data Type | Notes |
+    | ---------- | --------------- | ----- |
+    | subscription | StringType | Full subscription path |
+    | partition | LongType | |
+    | offset | LongType | |
+    | key | BinaryType | |
+    | data | BinaryType | |
+    | attributes | MapType\[StringType, ArrayType\[BinaryType\]\] | |
+    | publish_timestamp | TimestampType | |
+    | event_timestamp | TimestampType | Nullable |
+
+  ## Compiling with the connector
+
+  To include the connector in your project:
+
+    ### Maven
+
+    ```xml
+    <dependency>
+    <groupId>com.google.cloud.pubsublite.spark</groupId>
+    <artifactId>pubsublite-spark-sql-streaming-with-dependencies_2.11</artifactId>
+    <version>0.1.0</version>
+    </dependency>
+    ```
+
+    ### SBT
+
+    ```sbt
+    libraryDependencies += "com.google.cloud.pubsublite.spark" %% "pubsublite-spark-sql-streaming-with-dependencies_2.11" % "0.1.0"
+    ```
+
+  ## Building the Connector
+
+  The connector is built using Maven. Following command creates a jar with shaded dependencies:
+
+    ```
+    mvn package
+    ```
+
+    ## FAQ
+
+    ### What is the Pricing for the PubSub Lite?
+
+    See the [PubSub Lite pricing documentation](https://cloud.google.com/pubsub/lite/pricing).
+
+    ### Can I configure the number of spark partitions?
+
+    No, the number of spark partitions is set to be the number of PubSub Lite partitions of the topic that the supplied subscription is for.
+
+    ### How do I authenticate outside GCE / Dataproc?
+
+    Use a service account JSON key and `GOOGLE_APPLICATION_CREDENTIALS` as described [here](https://cloud.google.com/docs/authentication/getting-started).
+
+    Credentials can be provided with `gcp.credentials.key` option, it needs be passed in as a base64-encoded string directly.
+
+  Example:
+    ```
+    spark.readStream.format("pubsublite").option("gcp.credentials.key", "<SERVICE_ACCOUNT_JSON_IN_BASE64>")
+    ```