Need advice about which tool to choose?Ask the StackShare community!
Apache Oozie vs Apache Spark: What are the differences?
Introduction
In this article, we will compare Apache Oozie and Apache Spark, two popular frameworks used in big data processing. We will highlight the key differences between these two frameworks.
Architecture: Apache Oozie is a workflow scheduler system used for managing Hadoop jobs. It provides a platform to define and execute workflows, which are sequences of Hadoop jobs. On the other hand, Apache Spark is an open-source, distributed computing system that provides a unified analytics engine for big data processing. It offers an in-memory computing model, allowing for faster data processing and iterative computations.
Data Processing: Oozie focuses primarily on batch processing and is designed for large-scale data processing workflows. It excels in handling complex workflows involving multiple Hadoop jobs. Spark, on the other hand, can handle both batch and real-time data processing. It provides an interactive shell for real-time data exploration and supports stream processing through its Spark Streaming module.
Programming Model: Oozie uses XML-based workflow definition language and requires developers to write code in various scripting languages such as Pig Latin, HiveQL, and MapReduce. It requires setting up and configuring multiple components for job execution. In contrast, Spark provides a rich set of APIs in Scala, Java, Python, and R, making it more developer-friendly. Spark programs are written in a unified API, providing a higher-level abstraction for building applications.
Performance: Oozie workflows execute Hadoop jobs using MapReduce, which can be slower for iterative algorithms or interactive data exploration. Spark, on the other hand, uses its in-memory computing capability and DAG (Directed Acyclic Graph) execution model to achieve faster performance. It can cache intermediate data in memory, reducing the need for disk I/O and improving overall processing speed.
Fault Tolerance: Oozie provides fault tolerance by tracking the execution status of individual workflow tasks and retrying failed tasks. It also supports job recovery in case of failures. Spark, on the other hand, offers fault tolerance through its Resilient Distributed Dataset (RDD) abstraction. RDDs automatically recover from node failures, allowing for reliable distributed processing.
Ecosystem Integration: Oozie is tightly integrated with the Apache Hadoop ecosystem and provides support for various Hadoop components like MapReduce, Pig, Hive, and Sqoop. It can orchestrate complex workflows involving these components. Spark, on the other hand, has a broader ecosystem integration and supports integration with Hadoop, Hive, HBase, Cassandra, and other popular data sources. It can also be used in combination with other frameworks like Apache Kafka for real-time data processing.
In summary, Apache Oozie is a workflow scheduler system primarily focused on batch processing, while Apache Spark is a powerful analytics engine that supports batch and real-time data processing. Oozie relies on XML-based workflows and Hadoop jobs, whereas Spark offers a unified programming model and in-memory computing capabilities for faster performance.
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.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- 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:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- 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
- When the Timer fires, read the 1st record from the State and send out as the output record.
- 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.
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"
Pros of Apache Oozie
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
Sign up to add or upvote prosMake informed product decisions
Cons of Apache Oozie
Cons of Apache Spark
- Speed4
Sign up to add or upvote consMake informed product decisions
What is Apache Oozie?
What is Apache Spark?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Apache Oozie?
What companies use Apache Spark?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Apache Oozie?
What tools integrate with Apache Spark?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+4
+7+8
+4
+11
+6
+16
+37