Example usage for com.google.common.collect Lists newArrayList

Introduction

In this page you can find the example usage for com.google.common.collect Lists newArrayList.

Prototype

@GwtCompatible(serializable = true)
public static <E> ArrayList<E> newArrayList(Iterator<? extends E> elements) 

Source Link

Document

Creates a mutable ArrayList instance containing the given elements; a very thin shortcut for creating an empty list and then calling Iterators#addAll .

Usage

From source file:mobicloud.examples.streaming.JavaCustomReceiver.java

public static void main(String[] args) {
    if (args.length < 2) {
        System.err.println("Usage: JavaNetworkWordCount <hostname> <port>");
        System.exit(1);/*from ww  w .  jav  a  2 s.  c o  m*/
    }

    StreamingExamples.setStreamingLogLevels();

    // Create the context with a 1 second batch size
    SparkConf sparkConf = new SparkConf().setAppName("JavaCustomReceiver");
    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));

    // Create a input stream with the custom receiver on target ip:port and count the
    // words in input stream of \n delimited text (eg. generated by 'nc')
    JavaReceiverInputDStream<String> lines = ssc
            .receiverStream(new JavaCustomReceiver(args[1], Integer.parseInt(args[2])));
    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        @Override
        public Iterable<String> call(String x) {
            return Lists.newArrayList(SPACE.split(x));
        }
    });
    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
        @Override
        public Tuple2<String, Integer> call(String s) {
            return new Tuple2<String, Integer>(s, 1);
        }
    }).reduceByKey(new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
            return i1 + i2;
        }
    });

    wordCounts.print();
    ssc.start();
    ssc.awaitTermination();
}

---

From source file:com.naltel.spark.JavaDirectKafkaWordCount.java

public static void main(String[] args) {
    if (args.length < 2) {
        System.err.println("Usage: DirectKafkaWordCount <brokers> <topics>\n"
                + "  <brokers> is a list of one or more Kafka brokers\n"
                + "  <topics> is a list of one or more kafka topics to consume from\n\n");
        System.exit(1);// ww  w . j  a v  a  2 s  .c o  m
    }

    StreamingExamples.setStreamingLogLevels();

    String brokers = args[0];
    String topics = args[1];

    // Create context with 2 second batch interval
    SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount");
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2));

    HashSet<String> topicsSet = new HashSet<String>(Arrays.asList(topics.split(",")));
    HashMap<String, String> kafkaParams = new HashMap<String, String>();
    kafkaParams.put("metadata.broker.list", brokers);

    // Create direct kafka stream with brokers and topics
    JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(jssc, String.class,
            String.class, StringDecoder.class, StringDecoder.class, kafkaParams, topicsSet);

    // Get the lines, split them into words, count the words and print
    JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
        @Override
        public String call(Tuple2<String, String> tuple2) {
            return tuple2._2();
        }
    });
    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        @Override
        public Iterable<String> call(String x) {
            return Lists.newArrayList(SPACE.split(x));
        }
    });
    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
        @Override
        public Tuple2<String, Integer> call(String s) {
            return new Tuple2<String, Integer>(s, 1);
        }
    }).reduceByKey(new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
            return i1 + i2;
        }
    });
    wordCounts.print();

    // Start the computation
    jssc.start();
    jssc.awaitTermination();
}

---

From source file:iie.hadoop.spark.streaming.KafkaWordCount.java

public static void main(String[] args) {
    if (args.length < 4) {
        System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
        System.exit(1);//from  ww  w. ja v  a2 s .c  o  m
    }
    SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
    // Create the context with a 1 second batch size
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
    int numThreads = Integer.parseInt(args[3]);
    Map<String, Integer> topicMap = new HashMap<String, Integer>();
    String[] topics = args[2].split(",");
    for (String topic : topics) {
        topicMap.put(topic, numThreads);
    }
    JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1],
            topicMap);
    JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
        @Override
        public String call(Tuple2<String, String> tuple2) {
            return tuple2._2();
        }
    });
    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        @Override
        public Iterable<String> call(String x) {
            return Lists.newArrayList(SPACE.split(x));
        }
    });
    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
        @Override
        public Tuple2<String, Integer> call(String s) {
            return new Tuple2<String, Integer>(s, 1);
        }
    }).reduceByKey(new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
            return i1 + i2;
        }
    });
    wordCounts.print();
    jssc.start();
    jssc.awaitTermination();
}

---

From source file:org.apache.spark.examples.streaming.JavaCustomReceiver.java

public static void main(String[] args) {
    if (args.length < 3) {
        System.err.println("Usage: JavaNetworkWordCount <master> <hostname> <port>\n"
                + "In local mode, <master> should be 'local[n]' with n > 1");
        System.exit(1);/*from w w w  . ja va 2  s.co m*/
    }

    StreamingExamples.setStreamingLogLevels();

    // Create the context with a 1 second batch size
    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "JavaNetworkWordCount", new Duration(1000),
            System.getenv("SPARK_HOME"), JavaStreamingContext.jarOfClass(JavaNetworkWordCount.class));

    // Create a input stream with the custom receiver on target ip:port and count the
    // words in input stream of \n delimited text (eg. generated by 'nc')
    JavaReceiverInputDStream<String> lines = ssc
            .receiverStream(new JavaCustomReceiver(args[1], Integer.parseInt(args[2])));
    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        @Override
        public Iterable<String> call(String x) {
            return Lists.newArrayList(SPACE.split(x));
        }
    });
    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
        @Override
        public Tuple2<String, Integer> call(String s) {
            return new Tuple2<String, Integer>(s, 1);
        }
    }).reduceByKey(new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
            return i1 + i2;
        }
    });

    wordCounts.print();
    ssc.start();
    ssc.awaitTermination();
}

---

From source file:brooklyn.demo.ResilientMongoDbApp.java

public static void main(String[] argv) {
    List<String> args = Lists.newArrayList(argv);
    String port = CommandLineUtil.getCommandLineOption(args, "--port", "8081+");
    String location = CommandLineUtil.getCommandLineOption(args, "--location", DEFAULT_LOCATION);

    BrooklynLauncher launcher = BrooklynLauncher.newInstance().application(EntitySpec
            .create(StartableApplication.class, ResilientMongoDbApp.class).displayName("Resilient MongoDB"))
            .webconsolePort(port).location(location).start();

    Entities.dumpInfo(launcher.getApplications());
}

---

From source file:com.acceleratedio.pac_n_zoom.UploadVideo.java

/**
 * Upload the user-selected video to the user's YouTube channel. The code
 * looks for the video in the application's project folder and uses OAuth
 * 2.0 to authorize the API request.//from w  w w  .j  a v  a  2s.c o m
 *
 * @param args command line args (not used).
 */
public static void main(String[] args) {

    MakePostRequest get_video = new MakePostRequest();
    get_video.execute();

    // This OAuth 2.0 access scope allows an application to upload files
    // to the authenticated user's YouTube channel, but doesn't allow
    // other types of access.
    List<String> scopes = Lists.newArrayList("https://www.googleapis.com/auth/youtube.upload");

    String vidFileName = PickAnmActivity.fil_nams[position].replace('/', '?') + ".mp4";
    String httpAddrs = "http://www.pnzanimate.me/Droid/db_rd.php?";
    httpAddrs += vidFileName;

    try {
        // Authorize the request.
        Credential credential = Auth.authorize(scopes, "uploadvideo");

        // This object is used to make YouTube Data API requests.
        youtube = new YouTube.Builder(Auth.HTTP_TRANSPORT, Auth.JSON_FACTORY, credential)
                .setApplicationName("youtube-cmdline-uploadvideo-sample").build();

        System.out.println("Uploading: " + SAMPLE_VIDEO_FILENAME);

        // Add extra information to the video before uploading.
        Video videoObjectDefiningMetadata = new Video();

        // Set the video to be publicly visible. This is the default
        // setting. Other supporting settings are "unlisted" and "private."
        VideoStatus status = new VideoStatus();
        status.setPrivacyStatus("public");
        videoObjectDefiningMetadata.setStatus(status);

        // Most of the video's metadata is set on the VideoSnippet object.
        VideoSnippet snippet = new VideoSnippet();

        // This code uses a Calendar instance to create a unique name and
        // description for test purposes so that you can easily upload
        // multiple files. You should remove this code from your project
        // and use your own standard names instead.
        Calendar cal = Calendar.getInstance();
        snippet.setTitle("Test Upload via Java on " + cal.getTime());
        snippet.setDescription(
                "Video uploaded via YouTube Data API V3 using the Java library " + "on " + cal.getTime());

        // Set the keyword tags that you want to associate with the video.
        List<String> tags = new ArrayList<String>();
        tags.add("test");
        tags.add("example");
        tags.add("java");
        tags.add("YouTube Data API V3");
        tags.add("erase me");
        snippet.setTags(tags);

        // Add the completed snippet object to the video resource.
        videoObjectDefiningMetadata.setSnippet(snippet);

        InputStreamContent mediaContent = new InputStreamContent("mp4",
                UploadVideo.class.getResourceAsStream("/sample-video.mp4"));

        // Insert the video. The command sends three arguments. The first
        // specifies which information the API request is setting and which
        // information the API response should return. The second argument
        // is the video resource that contains metadata about the new video.
        // The third argument is the actual video content.
        YouTube.Videos.Insert videoInsert = youtube.videos().insert("snippet,statistics,status",
                videoObjectDefiningMetadata, mediaContent);

        // Set the upload type and add an event listener.
        MediaHttpUploader uploader = videoInsert.getMediaHttpUploader();

        // Indicate whether direct media upload is enabled. A value of
        // "True" indicates that direct media upload is enabled and that
        // the entire media content will be uploaded in a single request.
        // A value of "False," which is the default, indicates that the
        // request will use the resumable media upload protocol, which
        // supports the ability to resume an upload operation after a
        // network interruption or other transmission failure, saving
        // time and bandwidth in the event of network failures.
        uploader.setDirectUploadEnabled(false);

        MediaHttpUploaderProgressListener progressListener = new MediaHttpUploaderProgressListener() {
            public void progressChanged(MediaHttpUploader uploader) throws IOException {
                switch (uploader.getUploadState()) {
                case INITIATION_STARTED:
                    System.out.println("Initiation Started");
                    break;
                case INITIATION_COMPLETE:
                    System.out.println("Initiation Completed");
                    break;
                case MEDIA_IN_PROGRESS:
                    System.out.println("Upload in progress");
                    System.out.println("Upload percentage: " + uploader.getProgress());
                    break;
                case MEDIA_COMPLETE:
                    System.out.println("Upload Completed!");
                    break;
                case NOT_STARTED:
                    System.out.println("Upload Not Started!");
                    break;
                }
            }
        };
        uploader.setProgressListener(progressListener);

        // Call the API and upload the video.
        Video returnedVideo = videoInsert.execute();

        // Print data about the newly inserted video from the API response.
        System.out.println("\n================== Returned Video ==================\n");
        System.out.println("  - Id: " + returnedVideo.getId());
        System.out.println("  - Title: " + returnedVideo.getSnippet().getTitle());
        System.out.println("  - Tags: " + returnedVideo.getSnippet().getTags());
        System.out.println("  - Privacy Status: " + returnedVideo.getStatus().getPrivacyStatus());
        System.out.println("  - Video Count: " + returnedVideo.getStatistics().getViewCount());

    } catch (GoogleJsonResponseException e) {
        System.err.println("GoogleJsonResponseException code: " + e.getDetails().getCode() + " : "
                + e.getDetails().getMessage());
        e.printStackTrace();
    } catch (IOException e) {
        System.err.println("IOException: " + e.getMessage());
        e.printStackTrace();
    } catch (Throwable t) {
        System.err.println("Throwable: " + t.getMessage());
        t.printStackTrace();
    }
}

---

From source file:idv.kyle.practice.spark.streaming.JavaKafkaWordCount.java

public static void main(String[] args) {
    if (args.length < 4) {
        System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
        System.exit(1);// w  w  w .ja va2 s .  c o m
    }

    SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
    // Create the context with a 1 second batch size
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));

    int numThreads = Integer.parseInt(args[3]);
    Map<String, Integer> topicMap = new HashMap<String, Integer>();
    String[] topics = args[2].split(",");
    for (String topic : topics) {
        topicMap.put(topic, numThreads);
    }

    JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1],
            topicMap);

    JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
        @Override
        public String call(Tuple2<String, String> tuple2) {
            return tuple2._2();
        }
    });

    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        @Override
        public Iterable<String> call(String x) {
            return Lists.newArrayList(SPACE.split(x));
        }
    });

    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
        @Override
        public Tuple2<String, Integer> call(String s) {
            return new Tuple2<String, Integer>(s, 1);
        }
    }).reduceByKey(new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
            return i1 + i2;
        }
    });

    wordCounts.print();
    jssc.start();
    jssc.awaitTermination();
}

---

From source file:brooklyn.qa.longevity.webcluster.WebClusterApp.java

public static void main(String[] argv) {
    List<String> args = Lists.newArrayList(argv);
    String port = CommandLineUtil.getCommandLineOption(args, "--port", "8081+");
    String location = CommandLineUtil.getCommandLineOption(args, "--location", "localhost");

    BrooklynLauncher launcher = BrooklynLauncher.newInstance()
            .application(EntitySpec.create(StartableApplication.class, WebClusterApp.class)
                    .displayName("Brooklyn WebApp Cluster example"))
            .webconsolePort(port).location(location).start();

    Entities.dumpInfo(launcher.getApplications());
}

---

From source file:sagar.spark.streaming.example.JavaKafkaWordCount.java

public static void main(String[] args) {
    if (args.length < 4) {
        System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
        System.exit(1);/*from   ww w  . j ava 2 s.  c  o m*/
    }

    // StreamingExamples.setStreamingLogLevels();
    SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
    // Create the context with a 1 second batch size
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));

    int numThreads = Integer.parseInt(args[3]);
    Map<String, Integer> topicMap = new HashMap<String, Integer>();
    String[] topics = args[2].split(",");
    for (String topic : topics) {
        topicMap.put(topic, numThreads);
    }

    JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1],
            topicMap);

    JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
        public String call(Tuple2<String, String> tuple2) {
            return tuple2._2();
        }
    });

    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        public Iterable<String> call(String x) {
            return Lists.newArrayList(SPACE.split(x));
        }
    });

    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
        public Tuple2<String, Integer> call(String s) {
            return new Tuple2<String, Integer>(s, 1);
        }
    }).reduceByKey(new Function2<Integer, Integer, Integer>() {
        public Integer call(Integer i1, Integer i2) {
            return i1 + i2;
        }

    });

    wordCounts.print();
    jssc.start();
    jssc.awaitTermination();
}

---

From source file:org.sparkexample.JavaKafkaWordCount.java

public static void main(String[] args) {
    if (args.length < 4) {
        System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
        System.exit(1);/*from   w w  w  .  j ava 2s .com*/
    }

    // StreamingExamples.setStreamingLogLevels();
    SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
    // Create the context with a 1 second batch size
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));

    int numThreads = Integer.parseInt(args[3]);
    Map<String, Integer> topicMap = new HashMap<String, Integer>();
    String[] topics = args[2].split(",");
    for (String topic : topics) {
        topicMap.put(topic, numThreads);
    }

    JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1],
            topicMap);

    JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
        @Override
        public String call(Tuple2<String, String> tuple2) {
            return tuple2._2();
        }
    });

    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        @Override
        public Iterable<String> call(String x) {
            return Lists.newArrayList(SPACE.split(x));
        }
    });

    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(new PairFunction<String, String, Integer>() {
        @Override
        public Tuple2<String, Integer> call(String s) {
            return new Tuple2<String, Integer>(s, 1);
        }
    }).reduceByKey(new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
            return i1 + i2;
        }
    });

    wordCounts.print();
    jssc.start();
    jssc.awaitTermination();
}

---

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