Need advice about which tool to choose?Ask the StackShare community!
Laravel Nova vs Apache Spark: What are the differences?
Developers describe Laravel Nova as "Beautifully-designed administration panel for Laravel". It is a beautifully designed administration panel for Laravel. Carefully crafted by the creators of Laravel to make you the most productive developer. It provides a full CRUD interface for your Eloquent models. Every type of Eloquent relationship is fully supported. On the other hand, Apache Spark is detailed as "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.
Laravel Nova can be classified as a tool in the "MVC Tools" category, while Apache Spark is grouped under "Big Data Tools".
Some of the features offered by Laravel Nova are:
- Code Driven Configuration
- full CRUD interface for your Eloquent models
- integrated with Laravel’s existing authorization policies
On the other hand, Apache Spark provides the following key features:
- Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
- Write applications quickly in Java, Scala or Python
- Combine SQL, streaming, and complex analytics
Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.
Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas Laravel Nova is used by cierra, Smart Send, and NeosIT GmbH. Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Laravel Nova, which is listed in 3 company stacks and 3 developer stacks.
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 Laravel Nova
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 Laravel Nova
Cons of Apache Spark
- Speed4
Sign up to add or upvote consMake informed product decisions
What is Laravel Nova?
What is Apache Spark?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Laravel Nova?
What companies use Apache Spark?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Laravel Nova?
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