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Javalin vs Apache Spark: What are the differences?
What is Javalin? Simple REST APIs for Java and Kotlin. Javalin started as a fork of the Spark framework but quickly turned into a ground-up rewrite influenced by express.js. Both of these web frameworks are inspired by the modern micro web framework grandfather: Sinatra, so if you’re coming from Ruby then Javalin shouldn’t feel too unfamiliar.
What is Apache Spark? 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.
Javalin can be classified as a tool in the "Microframeworks (Backend)" category, while Apache Spark is grouped under "Big Data Tools".
Javalin and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than Javalin with 3.06K GitHub stars and 257 GitHub forks.
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"
I developed Hexagon heavily inspired in these great tools because of the following reasons:
- Take full advantage of the Kotlin programming language without any strings attached to Java (as a language).
- I wanted to be able to replace the HTTP server library used with different adapters (Jetty, Netty, etc.) and though right now there is only one, more are coming.
- Have a complete tool to do full applications, though you can use other libraries, Hexagon comes with a dependency injection helper, settings loading from different sources and HTTP Client, so it comes with (batteries included).
Right now I'm using it for my pet projects, and I'm happy with it.
Pros of Javalin
- Lightweight1
- Rich support of template engines1
- Does not require IDEA plugins1
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 Javalin
Cons of Apache Spark
- Speed4
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