Apache Flink vs Neo4j

Overview

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Neo4j: What are the differences?

1. Integration and Use Case: Apache Flink is a powerful open-source framework for big data processing and stream processing, while Neo4j is a highly scalable graph database. Flink focuses on processing large volumes of data in real-time, while Neo4j is designed to store, manage, and query connected data in graph form. Flink is commonly used in data analytics and machine learning applications, while Neo4j is widely used in social networking, recommendations, and fraud detection.
2. Data Model: Flink has a flexible and generic data model that can handle both structured and unstructured data, including batch and stream processing. Neo4j, on the other hand, is specifically built for graph data and provides specialized data structures and algorithms to efficiently query and analyze graph relationships. Neo4j's data model is based on nodes, relationships, and properties, enabling complex and efficient graph traversal operations.
3. Query Language: Flink uses Java and SQL-like programming interfaces for expressing data processing tasks. It also supports the CEP (Complex Event Processing) library for handling event streams. On the other hand, Neo4j uses a declarative query language called Cypher, which is specifically designed for graph pattern matching and traversal. Cypher makes it easier to express complex graph queries and perform graph analytics efficiently.
4. Scalability and Fault Tolerance: Flink is designed to handle large-scale data processing and can scale horizontally across multiple machines. It provides fault tolerance through distributed processing and state management mechanisms. Neo4j, on the other hand, is optimized for scalability within a single machine or a cluster of machines. It achieves fault tolerance through replication and data redundancy techniques.
5. Use of Graph Algorithms: Flink provides a wide range of general-purpose data processing and machine learning libraries, but it does not have built-in support for graph algorithms. On the other hand, Neo4j offers a rich set of graph algorithms and graph-specific optimizations that can be directly applied to analyze and traverse graph data. This makes it more suitable for graph-based analytics and recommendation systems.
6. Ecosystem and Community: Flink has a vibrant open-source community and a growing ecosystem of integrations and tools. It has strong support for various data sources and sinks, including popular big data frameworks like Hadoop and Kafka. Neo4j also has a thriving community and a rich ecosystem of graph-focused tools and libraries. It integrates well with popular data processing frameworks like Spark, enabling seamless integration between graph and non-graph data processing.

In Summary, Apache Flink and Neo4j are both powerful frameworks for data processing, but with key differences in data model, query language, scalability, graph algorithm support, and ecosystem. Flink is more focused on big data processing and stream processing, while Neo4j is specifically designed to handle graph data and complex graph analytics.

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Advice on Neo4j, Apache Flink

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.

576k views576k

Comments

Jaime

none at none

Aug 31, 2020

Needs advice

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

56.4k views56.4k

Comments

Detailed Comparison

Neo4j	Apache Flink
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.	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.
intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API	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 15.3K	GitHub Stars 25.4K
GitHub Forks 2.5K	GitHub Forks 13.7K
Stacks 1.2K	Stacks 534
Followers 1.4K	Followers 879
Votes 351	Votes 38
Pros & Cons
Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost	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
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Neo4j, Apache Flink?

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

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.

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.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Flink vs Neo4j: What are the differences?

1. Integration and Use Case: Apache Flink is a powerful open-source framework for big data processing and stream processing, while Neo4j is a highly scalable graph database. Flink focuses on processing large volumes of data in real-time, while Neo4j is designed to store, manage, and query connected data in graph form. Flink is commonly used in data analytics and machine learning applications, while Neo4j is widely used in social networking, recommendations, and fraud detection.
2. Data Model: Flink has a flexible and generic data model that can handle both structured and unstructured data, including batch and stream processing. Neo4j, on the other hand, is specifically built for graph data and provides specialized data structures and algorithms to efficiently query and analyze graph relationships. Neo4j's data model is based on nodes, relationships, and properties, enabling complex and efficient graph traversal operations.
3. Query Language: Flink uses Java and SQL-like programming interfaces for expressing data processing tasks. It also supports the CEP (Complex Event Processing) library for handling event streams. On the other hand, Neo4j uses a declarative query language called Cypher, which is specifically designed for graph pattern matching and traversal. Cypher makes it easier to express complex graph queries and perform graph analytics efficiently.
4. Scalability and Fault Tolerance: Flink is designed to handle large-scale data processing and can scale horizontally across multiple machines. It provides fault tolerance through distributed processing and state management mechanisms. Neo4j, on the other hand, is optimized for scalability within a single machine or a cluster of machines. It achieves fault tolerance through replication and data redundancy techniques.
5. Use of Graph Algorithms: Flink provides a wide range of general-purpose data processing and machine learning libraries, but it does not have built-in support for graph algorithms. On the other hand, Neo4j offers a rich set of graph algorithms and graph-specific optimizations that can be directly applied to analyze and traverse graph data. This makes it more suitable for graph-based analytics and recommendation systems.
6. Ecosystem and Community: Flink has a vibrant open-source community and a growing ecosystem of integrations and tools. It has strong support for various data sources and sinks, including popular big data frameworks like Hadoop and Kafka. Neo4j also has a thriving community and a rich ecosystem of graph-focused tools and libraries. It integrates well with popular data processing frameworks like Spark, enabling seamless integration between graph and non-graph data processing.

Apache Flink vs Neo4j

Overview