Apache Spark vs Hazelcast

Overview

Hazelcast

Stacks427

Followers474

Votes59

GitHub Stars6.4K

Forks1.9K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

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.

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Advice on Hazelcast, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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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Detailed Comparison

Hazelcast	Apache Spark
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.	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.
Distributed implementations of java.util.{Queue, Set, List, Map};Distributed implementation of java.util.concurrent.locks.Lock;Distributed implementation of java.util.concurrent.ExecutorService;Distributed MultiMap for one-to-many relationships;Distributed Topic for publish/subscribe messaging;Synchronous (write-through) and asynchronous (write-behind) persistence;Transaction support;Socket level encryption support for secure clusters;Second level cache provider for Hibernate;Monitoring and management of the cluster via JMX;Dynamic HTTP session clustering;Support for cluster info and membership events;Dynamic discovery, scaling, partitioning with backups and fail-over	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;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars 6.4K	GitHub Stars 42.2K
GitHub Forks 1.9K	GitHub Forks 28.9K
Stacks 427	Stacks 3.1K
Followers 474	Followers 3.5K
Votes 59	Votes 140
Pros & Cons
Pros 11 High Availibility 6 Distributed compute 6 Distributed Locking 5 Sharding 4 Load balancing Cons 4 License needed for SSL	Pros 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 Cons 4 Speed
Integrations
Java Spring	No integrations available

What are some alternatives to Hazelcast, 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.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

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

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

Related Comparisons

Apache Spark vs Hazelcast: What are the differences?

Introduction

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.

Apache Spark vs Hazelcast

Overview

Apache Spark vs Hazelcast: What are the differences?