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Apache Spark vs Pig: What are the differences?
Introduction
Apache Spark and Pig are both big data processing frameworks used in the Hadoop ecosystem. They offer similar functionalities but also have some key differences. In this article, we will explore these differences in detail.
Execution Engine: Apache Spark uses a general-purpose cluster computing framework, whereas Pig uses a scripting language called Pig Latin that is executed using a two-step process - compilation and execution. Spark's execution engine is more optimized and faster compared to Pig's two-step process.
Data Processing Model: Spark provides a distributed computing model called Resilient Distributed Datasets (RDDs) that allows in-memory processing, making it significantly faster than Pig. Pig, on the other hand, uses a data flow model, which is easy to understand and write, but it doesn't optimize for in-memory processing like Spark.
Language: Spark supports multiple programming languages like Scala, Java, Python, and R, making it more flexible for developers. Pig, on the other hand, only supports its own scripting language called Pig Latin. This limitation can be a disadvantage if developers are not familiar with Pig Latin.
Ease of Use: Spark provides a high-level API that makes it easy to write complex data processing pipelines. It also has built-in libraries for machine learning and graph processing. Pig, on the other hand, requires writing scripts in Pig Latin, which can be more difficult for beginners or developers who are not familiar with the language.
Optimization: Spark has a built-in optimizer that automatically optimizes the execution plan based on the data and operations performed. Pig, on the other hand, relies on the Pig Latin compiler to optimize the execution plan. As a result, Spark tends to have better performance and faster execution time compared to Pig.
Integration: Spark integrates well with other big data technologies like Hadoop, Hive, and HBase. It can read and write data directly from/to these systems. Pig also integrates with Hadoop ecosystem components but requires additional steps like loading and storing data using scripts.
In summary, Apache Spark has a more optimized execution engine, supports multiple programming languages, offers a high-level API, and provides better performance compared to Pig. Pig, on the other hand, has a simpler data flow model and easier syntax for beginners.
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 Pig
- Finer-grained control on parallelization2
- Proven at Petabyte scale1
- Open-source1
- Join optimizations for highly skewed data1
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 Pig
Cons of Apache Spark
- Speed4
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What is Pig?
What is Apache Spark?
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