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Apache Oozie

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Apache Oozie vs Apache Spark: What are the differences?

Developers describe Apache Oozie as "An open-source workflow scheduling system *". It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path. On the other hand, *Apache Spark** is detailed as "Fast and general engine for large-scale data processing". 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 Oozie and Apache Spark are primarily classified as "Workflow Manager" and "Big Data" tools respectively.

Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.

According to the StackShare community, Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Apache Oozie, which is listed in 8 company stacks and 5 developer stacks.

Advice on Apache Oozie and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 299.3K 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.

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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.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 190.2K views
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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"

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Pros of Apache Oozie
Pros of Apache Spark
    Be the first to leave a pro
    • 59
      Open-source
    • 48
      Fast and Flexible
    • 8
      One platform for every big data problem
    • 7
      Great for distributed SQL like applications
    • 6
      Easy to install and to use
    • 3
      Works well for most Datascience usecases
    • 2
      Interactive Query
    • 2
      In memory Computation
    • 2
      Machine learning libratimery, Streaming in real

    Sign up to add or upvote prosMake informed product decisions

    Cons of Apache Oozie
    Cons of Apache Spark
      Be the first to leave a con
      • 3
        Speed

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is Apache Oozie?

      It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path.

      What is 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.

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

      What companies use Apache Oozie?
      What companies use Apache Spark?
      See which teams inside your own company are using Apache Oozie or Apache Spark.
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      What tools integrate with Apache Oozie?
      What tools integrate with Apache Spark?
        No integrations found

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        Blog Posts

        Mar 24 2021 at 12:57PM

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        What are some alternatives to Apache Oozie and Apache Spark?
        Airflow
        Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
        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.
        Yarn
        Yarn caches every package it downloads so it never needs to again. It also parallelizes operations to maximize resource utilization so install times are faster than ever.
        Zookeeper
        A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications.
        Apache Beam
        It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
        See all alternatives