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Apache Spark vs Celery: What are the differences?
Introduction
Apache Spark and Celery are both distributed computing frameworks used for processing large amounts of data. While they have some similarities, there are several key differences between them that make them suited for different use cases.
Execution Model: Apache Spark uses a distributed computing model where data is processed in parallel across a cluster of machines. It provides a high-level API and supports various programming languages. On the other hand, Celery is a task queue system that allows you to distribute tasks across worker nodes. It follows a message-passing model where tasks are executed asynchronously.
Data Processing Paradigm: Spark is designed for big data processing and provides a rich set of built-in libraries for batch processing, streaming, machine learning, and graph processing. It supports in-memory data processing and can handle large-scale data processing efficiently. In contrast, Celery is more focused on task scheduling and message passing. It does not provide built-in tools for complex data processing tasks like Spark.
Fault Tolerance: Spark provides fault tolerance through its Resilient Distributed Dataset (RDD) abstraction. RDDs are fault-tolerant data structures that can be stored in memory and recalculated if a node fails. This allows Spark to recover from failures and continue processing without losing data. Celery, on the other hand, does not provide built-in fault tolerance mechanisms. If a worker fails, the task may need to be rescheduled or reprocessed manually.
Integration with Ecosystem: Apache Spark is part of a larger ecosystem known as the Apache Big Data Stack. It can integrate with other Apache projects like Hadoop, Hive, and HBase, making it suitable for building end-to-end big data solutions. Celery, on the other hand, is a standalone task queue system and does not have built-in integrations with big data technologies.
Concurrency Model: Spark uses a master-worker architecture where a central driver program coordinates the execution of tasks on worker nodes. This allows Spark to leverage parallelism and distribute work efficiently. Celery, on the other hand, uses a decentralized architecture where tasks are executed independently by worker nodes. This makes Celery more scalable and flexible for task distribution.
Community and Documentation: Apache Spark has a large and active community with extensive documentation, tutorials, and resources available. It is widely adopted in industry and has a mature ecosystem. Celery also has a community and documentation, but it is not as extensive or mature as Spark's community.
In summary, Apache Spark and Celery are both distributed computing frameworks, but they have key differences in their execution model, data processing paradigm, fault tolerance, integration with the ecosystem, concurrency model, and community/documentation. Spark is more suited for big data processing with its rich set of libraries and integration with big data technologies, while Celery focuses on task scheduling and message passing.
I am just a beginner at these two technologies.
Problem statement: I am getting lakh of users from the sequel server for whom I need to create caches in MongoDB by making different REST API requests.
Here these users can be treated as messages. Each REST API request is a task.
I am confused about whether I should go for RabbitMQ alone or Celery.
If I have to go with RabbitMQ, I prefer to use python with Pika module. But the challenge with Pika is, it is not thread-safe. So I am not finding a way to execute a lakh of API requests in parallel using multiple threads using Pika.
If I have to go with Celery, I don't know how I can achieve better scalability in executing these API requests in parallel.
For large amounts of small tasks and caches I have had good luck with Redis and RQ. I have not personally used celery but I am fairly sure it would scale well, and I have not used RabbitMQ for anything besides communication between services. If you prefer python my suggestions should feel comfortable.
Sorry I do not have a more information
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 Celery
- Task queue99
- Python integration63
- Django integration40
- Scheduled Task30
- Publish/subsribe19
- Various backend broker8
- Easy to use6
- Great community5
- Workflow5
- Free4
- Dynamic1
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
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Cons of Celery
- Sometimes loses tasks4
- Depends on broker1
Cons of Apache Spark
- Speed4