Apache Spark vs Redis

Overview

Redis

Stacks61.9K

Followers46.5K

Votes3.9K

GitHub Stars42

Forks6

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Redis: What are the differences?

Introduction

Apache Spark and Redis are two popular technologies used in the field of distributed computing. While both are designed to handle large-scale data processing, they have key differences in terms of their architecture, functionality, and use cases.

Architecture: Apache Spark is a distributed computing framework that runs on a cluster of computers, allowing for parallel processing and data storage across multiple nodes. Redis, on the other hand, is an in-memory data structure store that can be used as a database, cache, or message broker. It operates as a single server or a cluster of servers, with data stored primarily in memory for high-speed access.
Data Processing: Apache Spark is primarily designed for big data processing and analytics. It provides a high-level API for distributed data processing, making it easier to write complex data transformations and analytics algorithms. Redis, on the other hand, excels at rapid data access and manipulation. It provides a rich set of data types and operations, allowing for efficient handling of structured and unstructured data in real-time.
Scalability: Apache Spark is highly scalable and can handle large-scale data processing tasks by distributing the workload across multiple nodes in a cluster. It can efficiently process and analyze huge volumes of data in parallel. Redis, on the other hand, is designed for high-speed data access and can handle millions of concurrent operations. It can be scaled horizontally by adding more servers to the cluster to handle increasing data loads.
Persistence: Apache Spark can persist data to disk or external storage systems, allowing for fault tolerance and data recovery in case of failures. It can also cache intermediate results in memory for faster processing. Redis, on the other hand, primarily stores data in memory for high-speed access but can also persist data to disk for durability. It offers different persistence options, including snapshots and append-only files.
Data Manipulation: Apache Spark provides a wide range of data manipulation and analytics capabilities, including SQL queries, data streaming, machine learning, and graph processing. It allows for complex data transformations and analysis within a single framework. Redis, on the other hand, offers a rich set of data manipulation operations for structured and unstructured data, including sorting, set operations, pub/sub messaging, and geospatial support.
Use Cases: Apache Spark is commonly used for big data processing, analytics, and machine learning. It is suitable for batch processing, real-time streaming, and interactive data analysis. Redis, on the other hand, is widely used as a high-performance cache, message broker, and session store. It is commonly used in web applications, real-time analytics, and high-speed data processing.

In summary, Apache Spark is a distributed computing framework for big data processing and analytics, while Redis is an in-memory data structure store for rapid data access and manipulation. They differ in terms of their architecture, data processing capabilities, scalability, persistence options, data manipulation operations, and use cases.

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Advice on Redis, 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

Redis	Apache Spark
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.	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.
-	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 42	GitHub Stars 42.2K
GitHub Forks 6	GitHub Forks 28.9K
Stacks 61.9K	Stacks 3.1K
Followers 46.5K	Followers 3.5K
Votes 3.9K	Votes 140
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 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Redis, Apache Spark?

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

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 Redis: What are the differences?

Introduction

Architecture: Apache Spark is a distributed computing framework that runs on a cluster of computers, allowing for parallel processing and data storage across multiple nodes. Redis, on the other hand, is an in-memory data structure store that can be used as a database, cache, or message broker. It operates as a single server or a cluster of servers, with data stored primarily in memory for high-speed access.
Data Processing: Apache Spark is primarily designed for big data processing and analytics. It provides a high-level API for distributed data processing, making it easier to write complex data transformations and analytics algorithms. Redis, on the other hand, excels at rapid data access and manipulation. It provides a rich set of data types and operations, allowing for efficient handling of structured and unstructured data in real-time.
Scalability: Apache Spark is highly scalable and can handle large-scale data processing tasks by distributing the workload across multiple nodes in a cluster. It can efficiently process and analyze huge volumes of data in parallel. Redis, on the other hand, is designed for high-speed data access and can handle millions of concurrent operations. It can be scaled horizontally by adding more servers to the cluster to handle increasing data loads.
Persistence: Apache Spark can persist data to disk or external storage systems, allowing for fault tolerance and data recovery in case of failures. It can also cache intermediate results in memory for faster processing. Redis, on the other hand, primarily stores data in memory for high-speed access but can also persist data to disk for durability. It offers different persistence options, including snapshots and append-only files.
Data Manipulation: Apache Spark provides a wide range of data manipulation and analytics capabilities, including SQL queries, data streaming, machine learning, and graph processing. It allows for complex data transformations and analysis within a single framework. Redis, on the other hand, offers a rich set of data manipulation operations for structured and unstructured data, including sorting, set operations, pub/sub messaging, and geospatial support.
Use Cases: Apache Spark is commonly used for big data processing, analytics, and machine learning. It is suitable for batch processing, real-time streaming, and interactive data analysis. Redis, on the other hand, is widely used as a high-performance cache, message broker, and session store. It is commonly used in web applications, real-time analytics, and high-speed data processing.

Apache Spark vs Redis

Overview

Apache Spark vs Redis: What are the differences?