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  1. Stackups
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  4. Big Data Tools
  5. Apache Flink vs Heron

Apache Flink vs Heron

OverviewDecisionsComparisonAlternatives

Overview

Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
Heron
Heron
Stacks22
Followers63
Votes4

Apache Flink vs Heron: What are the differences?

# Introduction
This markdown highlights the key differences between Apache Flink and Heron.

# 1. Scalability:
Apache Flink is designed with a focus on horizontal scalability, allowing users to scale their processing capabilities by adding more machines to the cluster. Heron, on the other hand, is geared towards vertical scalability, optimizing resource utilization within individual machines rather than across a cluster.

# 2. Fault Tolerance:
Apache Flink relies on a mechanism called checkpoints to provide fault tolerance, where the system periodically saves the state of data streams to recover from failures. In contrast, Heron employs a fine-grained process-level fault tolerance approach, allowing for better isolation and recovery at the individual task level.

# 3. Processing Model:
Apache Flink follows a dataflow programming model, where operations are represented as vertices in a directed graph, enabling efficient parallel execution. Heron, however, adopts a streaming-first processing model, which is designed to handle real-time data processing scenarios with low latency and high throughput requirements.

# 4. Language Support:
Apache Flink has native support for Java and Scala, making it a preferred choice for developers familiar with these languages. Heron, on the other hand, offers support for multiple programming languages, including Java, Python, and C++, providing more flexibility to users in selecting their preferred language for stream processing.

# 5. Ecosystem Integration:
Apache Flink has seamless integration with popular big data ecosystems like Apache Hadoop and Apache Kafka, enabling users to leverage a diverse set of tools for data processing. Heron, while being compatible with Apache Storm topologies, lacks the same level of ecosystem integration as Apache Flink, limiting its interoperability with other big data platforms.

# 6. Stream Processing Guarantees:
Apache Flink provides configurable stream processing guarantees, such as at-most-once, at-least-once, and exactly-once, catering to different reliability requirements of streaming applications. Heron supports at-most-once and at-least-once processing guarantees out of the box, with limited support for exactly-once semantics, which may be a deciding factor for applications with stringent data consistency needs.

In Summary, the key differences between Apache Flink and Heron lie in their scalability approach, fault tolerance mechanisms, processing models, language support, ecosystem integration, and stream processing guarantees.

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Advice on Apache Flink, Heron

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Flink
Apache Flink
Heron
Heron

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.

Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
-
Statistics
GitHub Stars
25.4K
GitHub Stars
-
GitHub Forks
13.7K
GitHub Forks
-
Stacks
534
Stacks
22
Followers
879
Followers
63
Votes
38
Votes
4
Pros & Cons
Pros
  • 16
    Unified batch and stream processing
  • 8
    Easy to use streaming apis
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 4
    Open Source
  • 2
    Low latency
Pros
  • 1
    Highly Customizable
  • 1
    Support most popular container environment
  • 1
    Operation friendly
  • 1
    Realtime Stream Processing
Integrations
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka
No integrations available

What are some alternatives to Apache Flink, Heron?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Storm

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

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