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

Akutan

5
30
+ 1
0
Apache Flink

401
625
+ 1
35
Add tool

Beam vs Apache Flink: What are the differences?

Developers describe Beam as "A Distributed Knowledge Graph Store". 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. On the other hand, Apache Flink is detailed as "Fast and reliable large-scale data processing engine". 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.

Beam belongs to "Graph Databases" category of the tech stack, while Apache Flink can be primarily classified under "Big Data Tools".

Beam and Apache Flink are both open source tools. It seems that Apache Flink with 9.36K GitHub stars and 5.01K forks on GitHub has more adoption than Beam with 1.37K GitHub stars and 64 GitHub forks.

Advice on Akutan and Apache Flink
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 219K 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
Elasticsearch

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 · 121.9K views
Recommends
Apache 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 Private StackShare. Sign up for Private StackShare.
Learn More
Pros of Akutan
Pros of Apache Flink
    Be the first to leave a pro
    • 15
      Unified batch and stream processing
    • 8
      Easy to use streaming apis
    • 8
      Out-of-the box connector to kinesis,s3,hdfs
    • 3
      Open Source
    • 1
      Low latency

    Sign up to add or upvote prosMake informed product decisions

    Sign up to add or upvote consMake informed product decisions

    What is 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.

    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.

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

    What companies use Akutan?
    What companies use Apache Flink?
      No companies found
      See which teams inside your own company are using Akutan or Apache Flink.
      Sign up for Private StackShareLearn More

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

      What tools integrate with Akutan?
      What tools integrate with Apache Flink?

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

      Blog Posts

      What are some alternatives to Akutan and Apache Flink?
      Apache Beam
      It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
      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.
      Arc
      Arc is designed for exploratory programming: the kind where you decide what to write by writing it. A good medium for exploratory programming is one that makes programs brief and malleable, so that's what we've aimed for. This is a medium for sketching software.
      Neo4j
      Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
      Dgraph
      Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.
      See all alternatives