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Apache Spark vs Hazelcast: What are the differences?
Introduction
Apache Spark and Hazelcast are two popular distributed computing frameworks used for processing big data. While they have some similarities, they also have key differences that set them apart in terms of features, architecture, and use cases.
Data Processing Model: Apache Spark uses a Resilient Distributed Dataset (RDD) as its core data abstraction, which allows for efficient in-memory processing and supports both batch and real-time processing. On the other hand, Hazelcast provides an in-memory data grid (IMDG) that allows for distributed caching and computing, making it suitable for applications requiring low-latency processing and high availability.
Ease of Use and Deployment: Spark provides a user-friendly API that abstracts away the complexities of distributed computing, making it easier for developers to write and deploy applications. It also offers built-in support for various programming languages such as Scala, Java, Python, and R. In contrast, Hazelcast provides a more lightweight and simpler programming model, making it easier to get started with. It also integrates well with Java-based applications.
Fault Tolerance and High Availability: Apache Spark provides fault tolerance through lineage information, which allows it to recover lost data and continue processing in case of failures. It achieves high availability by performing automatic recovery and minimizing data loss. In comparison, Hazelcast offers high availability through its built-in distributed architecture, allowing the data grid to scale and replicate data across nodes, ensuring no single point of failure.
Data Partitioning and Distribution: Spark partitions data across a cluster and executes tasks on these partitions in parallel, providing high scalability and distributed computing capabilities. It also supports data locality optimization, which minimizes data shuffling and improves performance. Hazelcast, on the other hand, allows data to be partitioned and distributed across nodes in a grid-like fashion. This enables parallel processing and efficient data access across the cluster.
Advanced Analytics and Machine Learning: Apache Spark provides a rich set of libraries and APIs for advanced analytics and machine learning, including Spark SQL, MLlib, and GraphX. These libraries offer a wide range of algorithms and tools for data analytics, data processing, and machine learning tasks. In contrast, Hazelcast focuses more on distributed caching and transactional data processing, lacking the advanced analytics capabilities provided by Spark.
Ecosystem and Integration: Spark has a vibrant ecosystem with a wide range of third-party integrations and tools, including connectors for various data sources, data lakes, and data processing frameworks. It also integrates well with popular big data technologies like Hadoop, Hive, and HBase. On the other hand, Hazelcast offers integrations with Java-based frameworks and technologies, making it suitable for Java-centric environments.
In Summary, Apache Spark and Hazelcast are both powerful distributed computing frameworks, but they have distinct differences in terms of their data processing models, ease of use, fault tolerance, data partitioning, advanced analytics capabilities, and ecosystem integrations. The choice between the two depends on specific requirements, use cases, and the development environment.
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 Hazelcast
- High Availibility11
- Distributed Locking6
- Distributed compute6
- Sharding5
- Load balancing4
- Map-reduce functionality3
- Simple-to-use3
- Written in java. runs on jvm3
- Publish-subscribe3
- Sql query support in cluster wide3
- Optimis locking for map2
- Performance2
- Multiple client language support2
- Rest interface2
- Admin Interface (Management Center)1
- Better Documentation1
- Easy to use1
- Super Fast1
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 Hazelcast
- License needed for SSL4
Cons of Apache Spark
- Speed4
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