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Apache Spark vs Kestrel: What are the differences?
Apache Spark and Kestrel are two popular technologies in the field of big data processing. Below are the key differences between Apache Spark and Kestrel:
1. **Processing model**: Apache Spark is designed for in-memory processing, making it faster for iterative workloads, while Kestrel focuses on real-time message queuing and processing for handling large volumes of data at scale.
2. **Ease of use**: Apache Spark provides a high-level API which allows users to easily write complex data processing logic, while Kestrel is more low-level and requires more manual configuration and management.
3. **Fault tolerance**: Apache Spark comes with built-in mechanisms for fault tolerance through its resilient distributed datasets (RDDs) and lineage tracking, whereas Kestrel relies on external tools or custom implementations for fault tolerance.
4. **Supported languages**: Apache Spark supports multiple programming languages like Scala, Java, and Python, making it versatile for various use cases, while Kestrel is mainly focused on supporting Scala and Java, limiting its flexibility in multi-language environments.
5. **Use cases**: Apache Spark is suited for batch processing, iterative algorithms, real-time stream processing, and machine learning applications, while Kestrel is best used for building real-time data pipelines, message queuing, and event-driven architectures.
6. **Resource management**: Apache Spark comes with its own resource manager (like YARN or Mesos) for resource allocation and job scheduling, whereas Kestrel relies on external tools for resource management and coordination.
In Summary, Apache Spark and Kestrel differ in their processing model, ease of use, fault tolerance mechanisms, supported languages, use cases, and resource management approaches. Each technology caters to different needs in the big data processing landscape.
Advice on Kestrel and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 541.8K views
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.
Replies (2)
lvhuyen
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.
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 381.9K views
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"
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Learn MorePros of Kestrel
Pros of Apache Spark
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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 Kestrel
Cons of Apache Spark
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Cons of Apache Spark
- Speed4
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What is Kestrel?
Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM.
What is Apache Spark?
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.
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What companies use Kestrel?
What companies use Apache Spark?
What companies use Kestrel?
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What tools integrate with Kestrel?
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