Apache Flink vs Apache Storm

Overview

Apache Storm

Stacks207

Followers282

Votes25

GitHub Stars6.7K

Forks4.1K

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Apache Storm: What are the differences?

Apache Flink and Apache Storm are both popular distributed stream processing systems used for real-time analytics. While they have some similarities, there are several key differences that distinguish them from each other.

Processing Model: Apache Flink uses a dataflow-based processing model, where the computation is represented as a directed acyclic graph (DAG) of operators. This allows for complex processing pipelines and supports both batch and stream processing. On the other hand, Apache Storm follows a micro-batching model, where data is processed in small batches and the processing logic is expressed as a series of spouts and bolts. This makes it more suitable for high-throughput stream processing.
Fault-tolerance: Apache Flink provides exactly-once processing semantics out of the box, ensuring that every event is processed exactly once, even in the presence of failures. It achieves this by using checkpoints to save the state of the computation and guarantees consistency. Apache Storm, on the other hand, provides at-least-once processing semantics by default and requires additional configuration and coordination to achieve exactly-once processing. This makes Flink better suited for applications where data accuracy is critical.
Latency: Apache Storm has lower latency compared to Apache Flink. Storm's micro-batching model can achieve sub-millisecond latencies, making it suitable for low-latency use cases such as real-time monitoring and alerting. Flink, on the other hand, has slightly higher latencies due to its dataflow model and the need for buffering and coordination between operators. However, Flink's latency is still in the order of milliseconds, making it suitable for most real-time applications.
Ease of Use: Apache Storm has a simpler programming model compared to Apache Flink. The spout-bolt paradigm in Storm makes it easier to understand and write stream processing code, especially for developers who are new to distributed stream processing. Flink, on the other hand, provides a more expressive API and has a steeper learning curve. It offers advanced features like event time processing and stateful stream processing, but these features require additional understanding and setup.
Memory Management: Apache Flink has a more efficient memory management system compared to Apache Storm. Flink manages memory explicitly and provides fine-grained control over memory usage, allowing users to optimize their applications for better performance. Storm, on the other hand, relies on the underlying JVM's garbage collection for memory management, which can lead to higher memory usage and overhead.
Data Processing Scale: Apache Flink is designed for both small-scale and large-scale data processing. It provides efficient and scalable fault-tolerant state management and can handle massive streams of data. On the other hand, Apache Storm is better suited for processing large volumes of data, especially in scenarios where low-latency processing is required.

In Summary, Apache Flink and Apache Storm differ in their processing models, fault-tolerance mechanisms, latency, ease of use, memory management, and scalability. Flink offers a more expressive programming model, exactly-once processing semantics, and efficient memory management, while Storm provides lower latency, simplicity, and high throughput for large-scale data processing.

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Advice on Apache Storm, Apache Flink

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

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Detailed Comparison

Apache Storm	Apache Flink
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 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.
Storm integrates with the queueing and database technologies you already use;Simple API;Scalable;Fault tolerant;Guarantees data processing;Use with any language;Easy to deploy and operate;Free and open source	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 6.7K	GitHub Stars 25.4K
GitHub Forks 4.1K	GitHub Forks 13.7K
Stacks 207	Stacks 534
Followers 282	Followers 879
Votes 25	Votes 38
Pros & Cons
Pros 10 Flexible 6 Easy setup 4 Event Processing 3 Clojure 2 Real Time	Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency
Integrations
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Apache Storm, Apache Flink?

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

Distributed SQL Query Engine for Big Data

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 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

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 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 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

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

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.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Apache Flink vs Apache Storm: What are the differences?

Processing Model: Apache Flink uses a dataflow-based processing model, where the computation is represented as a directed acyclic graph (DAG) of operators. This allows for complex processing pipelines and supports both batch and stream processing. On the other hand, Apache Storm follows a micro-batching model, where data is processed in small batches and the processing logic is expressed as a series of spouts and bolts. This makes it more suitable for high-throughput stream processing.
Fault-tolerance: Apache Flink provides exactly-once processing semantics out of the box, ensuring that every event is processed exactly once, even in the presence of failures. It achieves this by using checkpoints to save the state of the computation and guarantees consistency. Apache Storm, on the other hand, provides at-least-once processing semantics by default and requires additional configuration and coordination to achieve exactly-once processing. This makes Flink better suited for applications where data accuracy is critical.
Latency: Apache Storm has lower latency compared to Apache Flink. Storm's micro-batching model can achieve sub-millisecond latencies, making it suitable for low-latency use cases such as real-time monitoring and alerting. Flink, on the other hand, has slightly higher latencies due to its dataflow model and the need for buffering and coordination between operators. However, Flink's latency is still in the order of milliseconds, making it suitable for most real-time applications.
Ease of Use: Apache Storm has a simpler programming model compared to Apache Flink. The spout-bolt paradigm in Storm makes it easier to understand and write stream processing code, especially for developers who are new to distributed stream processing. Flink, on the other hand, provides a more expressive API and has a steeper learning curve. It offers advanced features like event time processing and stateful stream processing, but these features require additional understanding and setup.
Memory Management: Apache Flink has a more efficient memory management system compared to Apache Storm. Flink manages memory explicitly and provides fine-grained control over memory usage, allowing users to optimize their applications for better performance. Storm, on the other hand, relies on the underlying JVM's garbage collection for memory management, which can lead to higher memory usage and overhead.
Data Processing Scale: Apache Flink is designed for both small-scale and large-scale data processing. It provides efficient and scalable fault-tolerant state management and can handle massive streams of data. On the other hand, Apache Storm is better suited for processing large volumes of data, especially in scenarios where low-latency processing is required.

Apache Flink vs Apache Storm

Overview