[BEAM-294] Rename dataflow references to beam

runners/spark/pom.xml

@@ -334,7 +334,7 @@
               </goals>
               <configuration>
                 <relocations>
-                  <!-- relocate Guava used by Dataflow (v18) since it conflicts with 
+                  <!-- relocate Guava used by Beam (v18) since it conflicts with
                     version used by Hadoop (v11) -->
                   <relocation>
                     <pattern>com.google.common</pattern>

runners/spark/src/main/java/org/apache/beam/runners/spark/SparkRunner.java

@@ -50,7 +50,7 @@
 /**
  * The SparkRunner translate operations defined on a pipeline to a representation
  * executable by Spark, and then submitting the job to Spark to be executed. If we wanted to run
- * a dataflow pipeline with the default options of a single threaded spark instance in local mode,
+ * a Beam pipeline with the default options of a single threaded spark instance in local mode,
  * we would do the following:
  *
  * {@code

runners/spark/src/main/java/org/apache/beam/runners/spark/TestSparkRunner.java

@@ -28,7 +28,7 @@
 
 /**
  * The SparkRunner translate operations defined on a pipeline to a representation executable
- * by Spark, and then submitting the job to Spark to be executed. If we wanted to run a dataflow
+ * by Spark, and then submitting the job to Spark to be executed. If we wanted to run a Beam
  * pipeline with the default options of a single threaded spark instance in local mode, we would do
  * the following:
  *

runners/spark/src/main/java/org/apache/beam/runners/spark/aggregators/NamedAggregators.java

@@ -32,7 +32,7 @@
 
 /**
  * This class wraps a map of named aggregators. Spark expects that all accumulators be declared
- * before a job is launched. Dataflow allows aggregators to be used and incremented on the fly.
+ * before a job is launched. Beam allows aggregators to be used and incremented on the fly.
  * We create a map of named aggregators and instantiate in the the spark context before the job
  * is launched. We can then add aggregators on the fly in Spark.
  */

runners/spark/src/main/java/org/apache/beam/runners/spark/examples/WordCount.java

@@ -110,7 +110,7 @@ public PCollection<KV<String, Long>> apply(PCollection<String> lines) {
    */
   public static interface WordCountOptions extends PipelineOptions {
     @Description("Path of the file to read from")
-    @Default.String("gs://dataflow-samples/shakespeare/kinglear.txt")
+    @Default.String("gs://beam-samples/shakespeare/kinglear.txt")
     String getInputFile();
     void setInputFile(String value);
 

runners/spark/src/main/java/org/apache/beam/runners/spark/io/hadoop/ShardNameTemplateHelper.java

@@ -36,9 +36,9 @@ public final class ShardNameTemplateHelper {
 
   private static final Logger LOG = LoggerFactory.getLogger(ShardNameTemplateHelper.class);
 
-  public static final String OUTPUT_FILE_PREFIX = "spark.dataflow.fileoutputformat.prefix";
-  public static final String OUTPUT_FILE_TEMPLATE = "spark.dataflow.fileoutputformat.template";
-  public static final String OUTPUT_FILE_SUFFIX = "spark.dataflow.fileoutputformat.suffix";
+  public static final String OUTPUT_FILE_PREFIX = "spark.beam.fileoutputformat.prefix";
+  public static final String OUTPUT_FILE_TEMPLATE = "spark.beam.fileoutputformat.template";
+  public static final String OUTPUT_FILE_SUFFIX = "spark.beam.fileoutputformat.suffix";
 
   private ShardNameTemplateHelper() {
   }

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/DoFnFunction.java

@@ -31,7 +31,7 @@
 import org.slf4j.LoggerFactory;
 
 /**
- * Dataflow's Do functions correspond to Spark's FlatMap functions.
+ * Beam's Do functions correspond to Spark's FlatMap functions.
  *
  * @param <InputT> Input element type.
  * @param <OutputT> Output element type.

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkPipelineTranslator.java

@@ -20,7 +20,7 @@
 import org.apache.beam.sdk.transforms.PTransform;
 
 /**
- * Translator to support translation between Dataflow transformations and Spark transformations.
+ * Translator to support translation between Beam transformations and Spark transformations.
  */
 public interface SparkPipelineTranslator {
 

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkRuntimeContext.java

@@ -57,7 +57,7 @@ public class SparkRuntimeContext implements Serializable {
   private final String serializedPipelineOptions;
 
   /**
-   * Map fo names to dataflow aggregators.
+   * Map fo names to Beam aggregators.
    */
   private final Map<String, Aggregator<?, ?>> aggregators = new HashMap<>();
   private transient CoderRegistry coderRegistry;

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java

@@ -94,7 +94,7 @@
 import scala.Tuple2;
 
 /**
- * Supports translation between a DataFlow transform, and Spark's operations on RDDs.
+ * Supports translation between a Beam transform, and Spark's operations on RDDs.
  */
 public final class TransformTranslator {
 
@@ -895,7 +895,7 @@ private static Map<TupleTag<?>, BroadcastHelper<?>> getSideInputs(
   }
 
   /**
-   * Translator matches Dataflow transformation with the appropriate evaluator.
+   * Translator matches Beam transformation with the appropriate evaluator.
    */
   public static class Translator implements SparkPipelineTranslator {
 

runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StreamingTransformTranslator.java

@@ -68,7 +68,7 @@
 
 
 /**
- * Supports translation between a DataFlow transform, and Spark's operations on DStreams.
+ * Supports translation between a Beam transform, and Spark's operations on DStreams.
  */
 public final class StreamingTransformTranslator {
 
@@ -349,13 +349,13 @@ public void evaluate(Window.Bound<T> transform, EvaluationContext context) {
         (TransformEvaluator<TransformT>) EVALUATORS.get(clazz);
     if (transform == null) {
       if (UNSUPPORTED_EVALUATORS.contains(clazz)) {
-        throw new UnsupportedOperationException("Dataflow transformation " + clazz
+        throw new UnsupportedOperationException("Beam transformation " + clazz
           .getCanonicalName()
           + " is currently unsupported by the Spark streaming pipeline");
       }
       // DStream transformations will transform an RDD into another RDD
       // Actions will create output
-      // In Dataflow it depends on the PTransform's Input and Output class
+      // In Beam it depends on the PTransform's Input and Output class
       Class<?> pTOutputClazz = getPTransformOutputClazz(clazz);
       if (PDone.class.equals(pTOutputClazz)) {
         return foreachRDD(rddTranslator);
@@ -373,7 +373,7 @@ public void evaluate(Window.Bound<T> transform, EvaluationContext context) {
   }
 
   /**
-   * Translator matches Dataflow transformation with the appropriate Spark streaming evaluator.
+   * Translator matches Beam transformation with the appropriate Spark streaming evaluator.
    * rddTranslator uses Spark evaluators in transform/foreachRDD to evaluate the transformation
    */
   public static class Translator implements SparkPipelineTranslator {

runners/spark/src/main/java/org/apache/beam/runners/spark/util/BroadcastHelper.java

@@ -34,12 +34,12 @@
 public abstract class BroadcastHelper<T> implements Serializable {
 
   /**
-   * If the property {@code dataflow.spark.directBroadcast} is set to
+   * If the property {@code beam.spark.directBroadcast} is set to
    * {@code true} then Spark serialization (Kryo) will be used to broadcast values
    * in View objects. By default this property is not set, and values are coded using
    * the appropriate {@link Coder}.
    */
-  public static final String DIRECT_BROADCAST = "dataflow.spark.directBroadcast";
+  public static final String DIRECT_BROADCAST = "beam.spark.directBroadcast";
 
   private static final Logger LOG = LoggerFactory.getLogger(BroadcastHelper.class);
 

runners/spark/src/test/java/org/apache/beam/runners/spark/translation/TransformTranslatorTest.java

@@ -56,7 +56,7 @@ public class TransformTranslatorTest {
 
   /**
    * Builds a simple pipeline with TextIO.Read and TextIO.Write, runs the pipeline
-   * in DirectRunner and on SparkRunner, with the mapped dataflow-to-spark
+   * in DirectRunner and on SparkRunner, with the mapped beam-to-spark
    * transforms. Finally it makes sure that the results are the same for both runs.
    */
   @Test

runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/KafkaStreamingTest.java

@@ -56,7 +56,7 @@ public class KafkaStreamingTest {
           new EmbeddedKafkaCluster.EmbeddedZookeeper();
   private static final EmbeddedKafkaCluster EMBEDDED_KAFKA_CLUSTER =
           new EmbeddedKafkaCluster(EMBEDDED_ZOOKEEPER.getConnection(), new Properties());
-  private static final String TOPIC = "kafka_dataflow_test_topic";
+  private static final String TOPIC = "kafka_beam_test_topic";
   private static final Map<String, String> KAFKA_MESSAGES = ImmutableMap.of(
       "k1", "v1", "k2", "v2", "k3", "v3", "k4", "v4"
   );