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# External System Integration
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Integration examples for Kafka, Twitter, and other external data sources with fault-tolerant connectors. Demonstrates real-world stream processing with external system integration patterns.
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## Capabilities
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### Kafka Integration
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#### ReadFromKafka
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Read strings from Kafka topics and process them in Flink streaming jobs.
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```java { .api }
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/**
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 * Read strings from Kafka and print to standard output
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 * Demonstrates Kafka consumer integration with checkpointing
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 * @param args Command line arguments (--topic, --bootstrap.servers, --zookeeper.connect, --group.id)
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 */
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public class ReadFromKafka {
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    public static void main(String[] args) throws Exception;
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}
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```
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**Usage Example:**
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```bash
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# Run Kafka consumer example
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java -cp flink-examples-streaming_2.10-1.3.3.jar \
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  org.apache.flink.streaming.examples.kafka.ReadFromKafka \
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  --topic test-topic \
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  --bootstrap.servers localhost:9092 \
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  --zookeeper.connect localhost:2181 \
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  --group.id flink-consumer-group
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```
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#### WriteIntoKafka
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Write processed data to Kafka topics with exactly-once semantics and continuous data generation.
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```java { .api }
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/**
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 * Write data into Kafka topics with continuous data generation
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 * Demonstrates Kafka producer integration with fault tolerance
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 * @param args Command line arguments (--topic, --bootstrap.servers)
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 */
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public class WriteIntoKafka {
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    public static void main(String[] args) throws Exception;
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}
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```
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**Usage Example:**
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```bash
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# Run Kafka producer example
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java -cp flink-examples-streaming_2.10-1.3.3.jar \
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  org.apache.flink.streaming.examples.kafka.WriteIntoKafka \
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  --topic test-topic \
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  --bootstrap.servers localhost:9092
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```
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### Twitter Integration
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#### TwitterExample
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Real-time Twitter stream processing and analysis with trending hashtag detection.
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```java { .api }
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/**
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 * Real-time Twitter stream processing
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 * Analyzes tweet streams for trending hashtags and user activity
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 * @param args Command line arguments (Twitter API credentials)
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 */
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public class TwitterExample {
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    public static void main(String[] args) throws Exception;
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}
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```
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**Usage Example:**
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```bash
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# Run Twitter streaming example (requires Twitter API credentials)
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java -cp flink-examples-streaming_2.10-1.3.3.jar \
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  org.apache.flink.streaming.examples.twitter.TwitterExample \
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  --twitter-source.consumerKey YOUR_CONSUMER_KEY \
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  --twitter-source.consumerSecret YOUR_CONSUMER_SECRET \
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  --twitter-source.token YOUR_ACCESS_TOKEN \
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  --twitter-source.tokenSecret YOUR_ACCESS_TOKEN_SECRET
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```
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## Kafka Integration Patterns
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### Consumer Configuration
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```java
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// Parameter validation for Kafka consumer
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final ParameterTool parameterTool = ParameterTool.fromArgs(args);
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if (parameterTool.getNumberOfParameters() < 4) {
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    System.out.println("Missing parameters! Usage: Kafka --topic <topic> " +
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        "--bootstrap.servers <kafka brokers> " +
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        "--zookeeper.connect <zk quorum> " +
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        "--group.id <consumer group>");
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    return;
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}
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// Configure execution environment
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StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
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env.getConfig().disableSysoutLogging();
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env.getConfig().setRestartStrategy(RestartStrategies.fixedDelayRestart(4, 10000));
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env.enableCheckpointing(5000); // Checkpoint every 5 seconds
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env.getConfig().setGlobalJobParameters(parameterTool);
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```
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### Kafka Source Setup
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```java
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// Create Kafka consumer source
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DataStream<String> messageStream = env.addSource(
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    new FlinkKafkaConsumer08<>(
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        parameterTool.getRequired("topic"),           // Topic name
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        new SimpleStringSchema(),                     // Deserialization schema
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        parameterTool.getProperties()                 // Kafka properties
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    )
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);
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// Process the stream
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messageStream.print();
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```
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### Data Generation for Kafka Producer
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```java
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// Simple sequential data generator
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DataStream<String> messageStream = env.addSource(new SourceFunction<String>() {
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    public boolean running = true;
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    @Override
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    public void run(SourceContext<String> ctx) throws Exception {
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        int counter = 0;
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        while (running) {
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            Thread.sleep(500); // 500ms intervals
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            ctx.collect("Element - " + counter);
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            counter++;
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        }
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    }
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    @Override
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    public void cancel() {
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        running = false;
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    }
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});
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// Send generated data to Kafka
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messageStream.addSink(new FlinkKafkaProducer08<>(
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    parameterTool.getRequired("topic"), 
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    new SimpleStringSchema(), 
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    parameterTool.getProperties()));
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```
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### Kafka Producer Configuration
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```java
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Properties kafkaProps = new Properties();
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kafkaProps.setProperty("bootstrap.servers", bootstrapServers);
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kafkaProps.setProperty("group.id", consumerGroup);
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// Configure producer for exactly-once semantics
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FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>(
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    outputTopic,                    // Target topic
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    new SimpleStringSchema(),       // Serialization schema
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    kafkaProps,                     // Producer properties
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    FlinkKafkaProducer.Semantic.EXACTLY_ONCE // Delivery guarantee
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);
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// Add producer to stream
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processedStream.addSink(kafkaProducer);
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```
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## Twitter Integration Patterns
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### Twitter Source Configuration
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```java
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// Twitter API credentials from parameters
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Properties twitterProps = new Properties();
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twitterProps.setProperty(TwitterSource.CONSUMER_KEY, params.get("twitter-source.consumerKey"));
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twitterProps.setProperty(TwitterSource.CONSUMER_SECRET, params.get("twitter-source.consumerSecret"));
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twitterProps.setProperty(TwitterSource.TOKEN, params.get("twitter-source.token"));
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twitterProps.setProperty(TwitterSource.TOKEN_SECRET, params.get("twitter-source.tokenSecret"));
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// Create Twitter source
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DataStream<String> twitterStream = env.addSource(new TwitterSource(twitterProps));
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```
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### Tweet Processing Pipeline
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```java
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// Parse JSON tweets and extract hashtags
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DataStream<Tuple2<String, Integer>> hashtagCounts = twitterStream
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    .flatMap(new HashtagExtractor())
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    .keyBy(0)
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    .timeWindow(Time.minutes(1))
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    .sum(1);
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// Extract trending hashtags
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DataStream<String> trendingHashtags = hashtagCounts
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    .timeWindowAll(Time.minutes(5))
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    .apply(new TopHashtagsFunction(10)); // Top 10 hashtags
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```
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### JSON Processing
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```java
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private static class HashtagExtractor implements FlatMapFunction<String, Tuple2<String, Integer>> {
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    @Override
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    public void flatMap(String tweetJson, Collector<Tuple2<String, Integer>> out) throws Exception {
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        try {
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            // Parse tweet JSON
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            ObjectMapper mapper = new ObjectMapper();
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            JsonNode tweet = mapper.readTree(tweetJson);
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            JsonNode entities = tweet.get("entities");
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            if (entities != null) {
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                JsonNode hashtags = entities.get("hashtags");
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                if (hashtags != null && hashtags.isArray()) {
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                    for (JsonNode hashtag : hashtags) {
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                        String tag = hashtag.get("text").asText();
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                        out.collect(new Tuple2<>("#" + tag.toLowerCase(), 1));
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                    }
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                }
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            }
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        } catch (Exception e) {
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            // Skip malformed tweets
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        }
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    }
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}
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```
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## Error Handling and Fault Tolerance
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### Restart Strategies
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```java
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// Fixed delay restart strategy
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env.getConfig().setRestartStrategy(
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    RestartStrategies.fixedDelayRestart(
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        4,      // Number of restart attempts
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        10000   // Delay between restarts (ms)
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    )
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);
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// Exponential backoff restart strategy
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env.getConfig().setRestartStrategy(
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    RestartStrategies.exponentialDelayRestart(
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        Time.milliseconds(1000),  // Initial delay
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        Time.milliseconds(60000), // Max delay
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        1.2                       // Backoff multiplier
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    )
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);
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```
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### Checkpointing Configuration
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```java
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// Enable checkpointing for fault tolerance
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env.enableCheckpointing(5000, CheckpointingMode.EXACTLY_ONCE);
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// Checkpoint configuration
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CheckpointConfig checkpointConfig = env.getCheckpointConfig();
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checkpointConfig.setMinPauseBetweenCheckpoints(1000);
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checkpointConfig.setCheckpointTimeout(60000);
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checkpointConfig.setMaxConcurrentCheckpoints(1);
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checkpointConfig.enableExternalizedCheckpoints(
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    CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
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```
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### Connection Resilience
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```java
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// Configure connector resilience
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Properties connectorProps = new Properties();
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connectorProps.setProperty("flink.partition-discovery.interval-millis", "30000");
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connectorProps.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
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connectorProps.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
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connectorProps.setProperty("auto.offset.reset", "latest");
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connectorProps.setProperty("enable.auto.commit", "false");
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```
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## Data Serialization
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### Simple String Schema
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```java
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// Basic string serialization/deserialization
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SimpleStringSchema stringSchema = new SimpleStringSchema();
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// For Kafka string messages
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new FlinkKafkaConsumer08<>(topic, stringSchema, properties);
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new FlinkKafkaProducer<>(topic, stringSchema, properties);
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```
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### JSON Schema
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```java
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// Custom JSON schema for complex objects
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public class TweetSchema implements DeserializationSchema<Tweet> {
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    private ObjectMapper objectMapper = new ObjectMapper();
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    @Override
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    public Tweet deserialize(byte[] message) throws IOException {
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        return objectMapper.readValue(message, Tweet.class);
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    }
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    @Override
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    public boolean isEndOfStream(Tweet nextElement) {
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        return false;
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    }
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    @Override
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    public TypeInformation<Tweet> getProducedType() {
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        return TypeInformation.of(Tweet.class);
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    }
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}
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```
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## Performance Tuning
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### Parallelism Configuration
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```java
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// Set source parallelism
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twitterStream.setParallelism(1); // Single Twitter connection
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// Set processing parallelism  
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processedStream.setParallelism(4); // Parallel processing
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// Set sink parallelism
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kafkaSink.setParallelism(2); // Multiple Kafka producers
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```
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### Backpressure Handling
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```java
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// Configure buffering and batching
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env.setBufferTimeout(100); // 100ms buffer timeout
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// Kafka producer batching
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kafkaProps.setProperty("batch.size", "16384");
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kafkaProps.setProperty("linger.ms", "10");
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kafkaProps.setProperty("buffer.memory", "33554432");
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```
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## External System Requirements
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### Kafka Setup
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- Apache Kafka 0.8+ cluster
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- Zookeeper ensemble  
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- Configured topics with appropriate partitioning
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- Network connectivity from Flink cluster
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### Twitter API Setup
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- Twitter Developer Account
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- API credentials (Consumer Key/Secret, Access Token/Secret)
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- Rate limit considerations (Twitter API limits)
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## Dependencies
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```xml
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<!-- Kafka Connector -->
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<dependency>
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    <groupId>org.apache.flink</groupId>
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    <artifactId>flink-connector-kafka-0.8_2.10</artifactId>
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    <version>1.3.3</version>
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</dependency>
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<!-- Twitter Connector -->
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<dependency>
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    <groupId>org.apache.flink</groupId>
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    <artifactId>flink-connector-twitter_2.10</artifactId>
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    <version>1.3.3</version>
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</dependency>
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<!-- JSON Processing -->
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<dependency>
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    <groupId>com.fasterxml.jackson.core</groupId>
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    <artifactId>jackson-databind</artifactId>
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    <version>2.8.8</version>
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</dependency>
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```
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## Required Imports
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### Kafka Integration
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```java
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import org.apache.flink.api.common.restartstrategy.RestartStrategies;
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import org.apache.flink.streaming.api.datastream.DataStream;
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import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer08;
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import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
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import org.apache.flink.streaming.util.serialization.SimpleStringSchema;
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```
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### Twitter Integration  
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```java
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import org.apache.flink.streaming.connectors.twitter.TwitterSource;
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import org.apache.flink.api.common.functions.FlatMapFunction;
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import org.apache.flink.streaming.api.windowing.time.Time;
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import com.fasterxml.jackson.databind.JsonNode;
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import com.fasterxml.jackson.databind.ObjectMapper;
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```