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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Apache Flink vs Hazelcast

Apache Flink vs Hazelcast

OverviewDecisionsComparisonAlternatives

Overview

Hazelcast
Hazelcast
Stacks427
Followers474
Votes59
GitHub Stars6.4K
Forks1.9K
Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K

Apache Flink vs Hazelcast: What are the differences?

Introduction

Apache Flink and Hazelcast are both distributed data processing frameworks that provide scalable and fault-tolerant processing of large datasets. However, they have key differences in terms of their programming models, data processing capabilities, and fault tolerance mechanisms.

  1. Programming Model: Apache Flink uses a unified programming model called the DataStream API and the DataSet API to process unbounded and bounded data respectively. It offers a high-level API that allows developers to express complex data processing tasks, including event time processing and stateful operations, in a concise and declarative manner. On the other hand, Hazelcast provides a distributed computing platform that is primarily focused on in-memory data grids and distributed computing. It offers an intuitive Java-based programming interface for distributed data structures and distributed computing tasks.

  2. Processing Capabilities: Apache Flink supports a wide range of data processing operations including batch processing, stream processing, and graph processing. It provides built-in support for windowing, event time processing, and stateful computations, making it suitable for real-time analytics and complex data processing scenarios. Hazelcast, on the other hand, is primarily designed for in-memory data storage and distributed caching. While it provides some basic data processing capabilities like querying and aggregation, it may not be as feature-rich as Apache Flink in terms of advanced data processing functionalities.

  3. Fault Tolerance: Apache Flink provides strong fault tolerance guarantees by leveraging its distributed snapshotting and checkpointing mechanism. It allows for exactly-once state consistency, ensuring that computations can be recovered reliably in the event of failures. Hazelcast also offers fault tolerance by replicating data across multiple nodes in its distributed data grid. However, it may not provide the same level of fault tolerance guarantees as Apache Flink, especially in scenarios involving complex stateful computations and event time processing.

  4. Deployment Options: Apache Flink can be deployed on various cluster managers like Apache Mesos, Kubernetes, and Hadoop YARN. It also provides built-in support for local development and testing. Hazelcast, on the other hand, can be deployed as a standalone server or embedded within other applications. It offers flexible deployment options that cater to different use cases, including cloud environments, on-premise deployments, and hybrid setups.

  5. Integration Ecosystem: Apache Flink has a rich ecosystem of connectors and libraries that enable seamless integration with various data sources and system. It provides connectors for popular message queues like Apache Kafka and Apache Pulsar, as well as integration with Apache Hive and Apache HBase. Hazelcast also offers connectors for popular data sources like Apache Kafka and Apache Ignite, and it provides integration with Spring Framework and Java EE technologies.

  6. Use Cases: Apache Flink is well-suited for use cases that require real-time analytics, event-driven applications, and complex data processing tasks. It is commonly used in industries like banking, e-commerce, and telecommunications for fraud detection, recommendation systems, and predictive analytics. Hazelcast, on the other hand, is often used for in-memory caching, distributed computing, and low-latency data access use cases. It is commonly used in industries like finance, manufacturing, and e-commerce for improving application performance and scalability.

In Summary, Apache Flink and Hazelcast are distributed data processing frameworks that have different programming models, processing capabilities, fault tolerance mechanisms, deployment options, integration ecosystems, and use cases. While Apache Flink is more focused on complex data processing and real-time analytics, Hazelcast is more oriented towards in-memory data storage and distributed caching.

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Advice on Hazelcast, Apache Flink

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

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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Comments

Detailed Comparison

Hazelcast
Hazelcast
Apache Flink
Apache Flink

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.

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.

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
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars
6.4K
GitHub Stars
25.4K
GitHub Forks
1.9K
GitHub Forks
13.7K
Stacks
427
Stacks
534
Followers
474
Followers
879
Votes
59
Votes
38
Pros & Cons
Pros
  • 11
    High Availibility
  • 6
    Distributed compute
  • 6
    Distributed Locking
  • 5
    Sharding
  • 4
    Load balancing
Cons
  • 4
    License needed for SSL
Pros
  • 16
    Unified batch and stream processing
  • 8
    Easy to use streaming apis
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 4
    Open Source
  • 2
    Low latency
Integrations
Java
Java
Spring
Spring
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka

What are some alternatives to Hazelcast, Apache Flink?

Redis

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.

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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

lakeFS

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

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

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.

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