incubator-heron

Pros of incubator-heron

• Efficient and scalable distributed stream processing system
• Low latency and high throughput for real-time analytics
• Compatibility with Apache Storm topologies

Cons of incubator-heron

• Steeper learning curve compared to some other stream processing systems
• Limited ecosystem and third-party integrations

Code Comparison

Both repositories contain the same codebase for Apache Heron, so there isn't a direct code comparison to make. However, here's a sample of Heron's topology definition:

TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("word", new TestWordSpout(), 5);
builder.setBolt("exclaim1", new ExclamationBolt(), 4)
        .shuffleGrouping("word");
builder.setBolt("exclaim2", new ExclamationBolt(), 4)
        .shuffleGrouping("exclaim1");

This code demonstrates how to create a simple topology in Heron, which is similar to Apache Storm's API.

Summary

incubator-heron is a powerful distributed stream processing system that offers high performance and compatibility with Apache Storm. While it may have a steeper learning curve and a smaller ecosystem, its efficiency and scalability make it a strong choice for real-time analytics applications.

Pros of Storm

• More mature and widely adopted in production environments
• Extensive ecosystem with a large community and numerous connectors
• Supports multiple programming languages (Java, Python, etc.)

Cons of Storm

• Higher latency compared to Heron
• Less efficient resource utilization
• More complex configuration and tuning process

Code Comparison

Storm topology definition:

TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("spout", new RandomSentenceSpout(), 5);
builder.setBolt("split", new SplitSentenceBolt(), 8).shuffleGrouping("spout");
builder.setBolt("count", new WordCountBolt(), 12).fieldsGrouping("split", new Fields("word"));

Heron topology definition:

TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("spout", new RandomSentenceSpout(), 5);
builder.setBolt("split", new SplitSentenceBolt(), 8).shuffleGrouping("spout");
builder.setBolt("count", new WordCountBolt(), 12).fieldsGrouping("split", "word");

The code structure for defining topologies is similar between Storm and Heron, making it easier for developers to migrate between the two systems. However, Heron offers improved performance and resource efficiency while maintaining API compatibility with Storm.

Pros of Flink

• More mature and widely adopted in production environments
• Extensive ecosystem with a wide range of connectors and libraries
• Strong support for both stream and batch processing

Cons of Flink

• Steeper learning curve due to its comprehensive feature set
• Higher resource consumption, especially for smaller workloads
• More complex configuration and deployment process

Code Comparison

Flink:

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<String> text = env.socketTextStream("localhost", 9999);
DataStream<Tuple2<String, Integer>> counts = text
    .flatMap(new Tokenizer())
    .keyBy(0)
    .sum(1);
counts.print();

Heron:

Config conf = Config.newBuilder().build();
TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("word", new TestWordSpout(), 2);
builder.setBolt("count", new WordCountBolt(), 2)
       .fieldsGrouping("word", new Fields("word"));

Summary

Flink is a more established and feature-rich stream processing framework, offering a comprehensive ecosystem and strong support for both stream and batch processing. However, it comes with a steeper learning curve and higher resource requirements. Heron, being a newer project, focuses on simplicity and efficiency, making it easier to learn and deploy, especially for smaller-scale applications. The choice between the two depends on specific project requirements, scale, and team expertise.

Pros of Spark

• Mature ecosystem with extensive libraries and integrations
• Supports both batch and stream processing
• Highly scalable and efficient for large-scale data processing

Cons of Spark

• Higher memory consumption
• Steeper learning curve for beginners
• Can be slower for real-time processing compared to dedicated streaming systems

Code Comparison

Spark (Scala):

val lines = spark.readStream.format("kafka").option("kafka.bootstrap.servers", "host1:port1,host2:port2").option("subscribe", "topic1").load()
val words = lines.as[String].flatMap(_.split(" "))
val wordCounts = words.groupBy("value").count()

Heron (Java):

TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("word-spout", new WordSpout(), 2);
builder.setBolt("count-bolt", new CountBolt(), 4).fieldsGrouping("word-spout", new Fields("word"));
Config conf = new Config();
StormSubmitter.submitTopology("word-count-topology", conf, builder.createTopology());

Key Differences

• Spark offers a unified engine for various data processing tasks, while Heron focuses on real-time stream processing
• Heron provides lower latency for streaming applications
• Spark has a larger community and more extensive documentation
• Heron offers better resource isolation and easier debugging capabilities

Pros of Beam

• Broader ecosystem support with runners for multiple processing engines (Flink, Spark, Dataflow, etc.)
• More mature and widely adopted in production environments
• Unified programming model for batch and streaming processing

Cons of Beam

• Steeper learning curve due to more complex abstractions
• Can be overkill for simpler streaming use cases
• Potentially higher latency compared to Heron for certain streaming scenarios

Code Comparison

Heron (Java):

TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("word", new TestWordSpout(), 2);
builder.setBolt("count", new TestWordCounter(), 4)
        .shuffleGrouping("word");

Beam (Java):

Pipeline p = Pipeline.create();
p.apply(TextIO.read().from("input.txt"))
 .apply(FlatMapElements.into(TypeDescriptors.strings())
        .via((String line) -> Arrays.asList(line.split("\\s+"))))
 .apply(Count.<String>perElement())
 .apply(MapElements.into(TypeDescriptors.strings())
        .via((KV<String, Long> wordCount) ->
            wordCount.getKey() + ": " + wordCount.getValue()))
 .apply(TextIO.write().to("output.txt"));