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Apache Spark vs Pachyderm: What are the differences?
Architecture: Apache Spark is designed for in-memory processing, making it faster for iterative algorithms and interactive data queries. On the other hand, Pachyderm is built on a content-addressed file system, enabling version control and tracking changes to data over time.
Use cases: Apache Spark is commonly used for processing large volumes of data using its distributed computing framework, making it suitable for data analytics and machine learning tasks. Pachyderm, on the other hand, is ideal for managing data pipelines, enabling data lineage tracking and reproducibility in data processing workflows.
Scalability: Apache Spark is known for its scalability and ability to handle large-scale data processing tasks by distributing computations across multiple nodes in a cluster. Pachyderm, on the other hand, focuses on scalability in terms of managing data pipelines and ensuring the reproducibility of data processing steps.
Data Processing Model: Apache Spark follows a batch and stream processing model, allowing for real-time data processing and analytics. Pachyderm, on the other hand, emphasizes a version-controlled data processing model, enabling users to track changes to data and reproduce results consistently.
Ease of Use: Apache Spark provides a user-friendly API for data processing tasks, making it easier for developers and data scientists to work with large datasets. Pachyderm, on the other hand, requires a steeper learning curve due to its emphasis on version control and managing data pipelines.
Community Support: Apache Spark has a large and active community of developers and contributors, ensuring continuous development and support for the platform. Pachyderm, being a newer tool, has a smaller community, which may impact the availability of resources and support for users.
In Summary, Apache Spark and Pachyderm differ in terms of their architecture, use cases, scalability, data processing model, ease of use, and community support.
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 Pachyderm
- Containers3
- Versioning1
- Can run on GCP or AWS1
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 Pachyderm
- Recently acquired by HPE, uncertain future.1
Cons of Apache Spark
- Speed4