Apache Flink vs Redis

Overview

Redis

Stacks61.9K

Followers46.5K

Votes3.9K

GitHub Stars42

Forks6

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Redis: What are the differences?

Key Differences between Apache Flink and Redis

Apache Flink and Redis are both popular technologies used in the field of data processing and storage. While they serve different purposes, there are several key differences between the two:

Data Processing vs Data Storage: Apache Flink is a distributed processing framework that focuses on data processing and analysis in real-time or batch mode. It provides powerful stream processing capabilities and supports fault-tolerant, scalable data processing pipelines. On the other hand, Redis is an in-memory data structure store that primarily focuses on data storage and caching. It provides fast read and write operations by keeping data in-memory.
Data Model: Apache Flink operates on a flexible and powerful data model that supports both structured and unstructured data. It provides various APIs and libraries for processing and analyzing data at large scale. Redis, on the other hand, uses a simple key-value data model where data is stored and accessed using keys and values. It also supports additional data structures such as lists, sets, and hashes.
Processing Paradigm: Apache Flink supports both batch and stream processing paradigms, allowing users to process both historical and real-time data. It provides built-in support for event time and out-of-order processing. Redis, on the other hand, is primarily focused on real-time data processing and storage. While it has some support for pub/sub messaging, it is not a dedicated stream processing engine like Apache Flink.
Scalability and Fault Tolerance: Apache Flink is designed to scale horizontally and handle large volumes of data by distributing the processing across multiple machines. It provides fault-tolerance mechanisms like checkpointing and exactly-once semantics for data processing. Redis, on the other hand, can be deployed in a cluster mode to achieve high scalability and availability. It supports replication and sharding to distribute data across multiple nodes.
Persistence: Apache Flink is primarily an in-memory processing engine, but it also provides support for various persistent storages like Apache Hadoop Distributed File System (HDFS) and cloud-based object stores. It allows users to store and retrieve data for both batch and stream processing. Redis, on the other hand, is an in-memory data store that can optionally persist data to disk. It provides mechanisms like snapshots and persistence modes to ensure data durability.
Use cases: Apache Flink is commonly used for real-time analytics, stream processing, and complex event processing. It finds applications in areas like fraud detection, machine learning, and real-time monitoring. On the other hand, Redis is often used for caching, session storage, message queues, and building real-time applications that require high-speed data access.

In summary, Apache Flink and Redis differ in their focus and capabilities. Apache Flink is a scalable data processing framework for both batch and stream processing, while Redis is an in-memory data storage and caching solution.

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Advice on Redis, 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.

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Detailed Comparison

Redis	Apache Flink
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 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.
-	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 42	GitHub Stars 25.4K
GitHub Forks 6	GitHub Forks 13.7K
Stacks 61.9K	Stacks 534
Followers 46.5K	Followers 879
Votes 3.9K	Votes 38
Pros & Cons
Pros 888 Performance 542 Super fast 514 Ease of use 444 In-memory cache 324 Advanced key-value cache Cons 15 Cannot query objects directly 3 No secondary indexes for non-numeric data types 1 No WAL	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 Redis, 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

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.

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

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 Flink vs Redis: What are the differences?

Key Differences between Apache Flink and Redis

Apache Flink and Redis are both popular technologies used in the field of data processing and storage. While they serve different purposes, there are several key differences between the two:

Data Processing vs Data Storage: Apache Flink is a distributed processing framework that focuses on data processing and analysis in real-time or batch mode. It provides powerful stream processing capabilities and supports fault-tolerant, scalable data processing pipelines. On the other hand, Redis is an in-memory data structure store that primarily focuses on data storage and caching. It provides fast read and write operations by keeping data in-memory.
Data Model: Apache Flink operates on a flexible and powerful data model that supports both structured and unstructured data. It provides various APIs and libraries for processing and analyzing data at large scale. Redis, on the other hand, uses a simple key-value data model where data is stored and accessed using keys and values. It also supports additional data structures such as lists, sets, and hashes.
Processing Paradigm: Apache Flink supports both batch and stream processing paradigms, allowing users to process both historical and real-time data. It provides built-in support for event time and out-of-order processing. Redis, on the other hand, is primarily focused on real-time data processing and storage. While it has some support for pub/sub messaging, it is not a dedicated stream processing engine like Apache Flink.
Scalability and Fault Tolerance: Apache Flink is designed to scale horizontally and handle large volumes of data by distributing the processing across multiple machines. It provides fault-tolerance mechanisms like checkpointing and exactly-once semantics for data processing. Redis, on the other hand, can be deployed in a cluster mode to achieve high scalability and availability. It supports replication and sharding to distribute data across multiple nodes.
Persistence: Apache Flink is primarily an in-memory processing engine, but it also provides support for various persistent storages like Apache Hadoop Distributed File System (HDFS) and cloud-based object stores. It allows users to store and retrieve data for both batch and stream processing. Redis, on the other hand, is an in-memory data store that can optionally persist data to disk. It provides mechanisms like snapshots and persistence modes to ensure data durability.
Use cases: Apache Flink is commonly used for real-time analytics, stream processing, and complex event processing. It finds applications in areas like fraud detection, machine learning, and real-time monitoring. On the other hand, Redis is often used for caching, session storage, message queues, and building real-time applications that require high-speed data access.

Apache Flink vs Redis

Overview

Apache Flink vs Redis: What are the differences?