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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on Hazelcast and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 527.1K 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.

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Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. 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:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. 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
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. 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.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 369.7K views
Recommends
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Apache SparkApache Spark

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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Pros of Hazelcast
Pros of Apache Spark
  • 11
    High Availibility
  • 6
    Distributed Locking
  • 6
    Distributed compute
  • 5
    Sharding
  • 4
    Load balancing
  • 3
    Map-reduce functionality
  • 3
    Simple-to-use
  • 3
    Written in java. runs on jvm
  • 3
    Publish-subscribe
  • 3
    Sql query support in cluster wide
  • 2
    Optimis locking for map
  • 2
    Performance
  • 2
    Multiple client language support
  • 2
    Rest interface
  • 1
    Admin Interface (Management Center)
  • 1
    Better Documentation
  • 1
    Easy to use
  • 1
    Super Fast
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation

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Cons of Hazelcast
Cons of Apache Spark
  • 4
    License needed for SSL
  • 4
    Speed

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What is Hazelcast?

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

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 Hazelcast?
What companies use Apache Spark?
See which teams inside your own company are using Hazelcast or Apache Spark.
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What tools integrate with Hazelcast?
What tools integrate with Apache Spark?

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What are some alternatives to Hazelcast and Apache Spark?
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Memcached
Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.
Apache Ignite
It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale
RabbitMQ
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