Need advice about which tool to choose?Ask the StackShare community!

Apache Flink

519
860
+ 1
38
Samza

24
63
+ 1
0
Add tool

Apache Flink vs Samza: What are the differences?

Introduction

Apache Flink and Samza are both stream processing systems that provide support for real-time data processing. While they share similarities in terms of their purpose, there are several key differences between the two.

  1. Integration with Ecosystem: Apache Flink has a broader integration with various data sources and sinks, including Hadoop Distributed File System (HDFS), Apache Kafka, and others. Samza, on the other hand, has a more specific focus on integrating with Apache Kafka, making it a suitable choice for Kafka-based architectures.

  2. Processing Model: Flink supports both batch processing and stream processing, offering a unified processing model. It provides a rich set of operators and an event time processing model, allowing for complex event-driven data processing. Samza, on the contrary, is primarily designed for stream processing and does not inherently support batch processing.

  3. State Management: Flink provides built-in support for maintaining and managing state in stream processing applications. It includes features like stateful stream processing, fault-tolerant state checkpoints, and state recovery. Samza, on the other hand, does not have built-in state management capabilities and relies on external systems like Apache Kafka or Apache HBase for storing and managing the state.

  4. Fault Tolerance: Flink offers robust fault-tolerance mechanisms, including exactly-once processing guarantees. It achieves this by maintaining consistent checkpoints of the operator states and providing recovery mechanisms in case of failures. Samza, on the other hand, focuses on at-least-once processing guarantees. It relies on Apache Kafka's offset-tracking mechanism for handling failures and ensuring data integrity.

  5. Programming Model: Flink provides a high-level programming model with a SQL-like language called Flink SQL, as well as APIs in Java and Scala. It also supports complex event processing using CEP libraries and graph-based data processing using the Gelly library. Samza, on the other hand, primarily emphasizes a simple and lightweight programming model using the Apache Kafka Streams API.

  6. Community and Maturity: Flink has a larger and more active community compared to Samza, resulting in a wider range of documentation, community support, and ecosystem integrations. Flink is also more mature and has been widely adopted in various industries. Samza, although still actively maintained, has a smaller community and is relatively less mature.

In summary, Apache Flink offers broader ecosystem integration, support for batch processing, built-in state management, and exactly-once processing guarantees. On the other hand, Samza focuses on integration with Apache Kafka, provides a lightweight programming model, relies on external systems for state management, and offers at-least-once processing guarantees.

Advice on Apache Flink and Samza
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 511.9K views

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.

See more
Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

See more
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 357.5K views
Recommends
on
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Apache Flink
Pros of Samza
  • 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
    Be the first to leave a pro

    Sign up to add or upvote prosMake informed product decisions

    What is Apache Flink?

    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.

    What is Samza?

    It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Apache Flink?
    What companies use Samza?
    See which teams inside your own company are using Apache Flink or Samza.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Apache Flink?
    What tools integrate with Samza?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    Mar 24 2021 at 12:57PM

    Pinterest

    GitJenkinsKafka+7
    3
    2124
    What are some alternatives to Apache Flink and Samza?
    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.
    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.
    Akutan
    A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
    Apache Flume
    It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    See all alternatives