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Hadoop vs Redis: What are the differences?

Introduction

In this article, we will discuss the key differences between Hadoop and Redis. Hadoop and Redis are two popular technologies used in the field of data storage and processing. While both serve different purposes and have their own strengths and weaknesses, it is important to understand their differences in order to choose the right technology for a particular use case.

  1. Scalability: One of the key differences between Hadoop and Redis is in terms of scalability. Hadoop is designed to deal with large-scale data processing and storage. It can easily handle petabytes of data and can be distributed across multiple machines, making it highly scalable. On the other hand, Redis is an in-memory data structure store that is primarily used for caching and real-time data processing. While Redis can also be distributed across multiple machines, its scalability is limited compared to Hadoop.

  2. Data Persistence: Another significant difference between Hadoop and Redis is in terms of data persistence. Hadoop is designed to store and process data in a distributed file system called HDFS (Hadoop Distributed File System). This allows data to be stored persistently even if a node fails. Redis, on the other hand, is an in-memory database, which means that the data is stored in memory and can be lost in case of a system failure unless a backup mechanism is implemented.

  3. Data Processing Paradigm: Hadoop and Redis also differ in terms of their data processing paradigms. Hadoop follows the MapReduce paradigm, where data is divided into chunks and processed in parallel across multiple nodes. This makes it suitable for batch processing and analyzing large volumes of structured and unstructured data. On the other hand, Redis supports various data structures and provides a rich set of operations to manipulate data in real-time. It is commonly used for caching, real-time analytics, and message queuing.

  4. Data Access Patterns: Hadoop and Redis also differ in terms of their data access patterns. Hadoop is optimized for reading large volumes of data sequentially, making it suitable for analytical queries. Redis, on the other hand, is optimized for low-latency data access, making it suitable for applications that require real-time responses. Redis excels in use cases where fast read and write operations are required, such as caching and session management.

  5. Fault Tolerance: Fault tolerance is another important difference between Hadoop and Redis. Hadoop is designed to handle node failures and provides built-in fault tolerance mechanisms. If a node fails, Hadoop can automatically recover and redistribute the data to other healthy nodes, ensuring high availability and fault resilience. Redis, on the other hand, does not provide built-in fault tolerance. It relies on external mechanisms such as replication and backups to ensure data durability and availability in case of failures.

  6. Data Durability: Hadoop and Redis also differ in terms of data durability. Hadoop's distributed file system (HDFS) replicates the data across multiple machines, ensuring high data durability. In the event of a node failure, the data can be recovered from other replicated copies. Redis, being an in-memory database, relies on persistence mechanisms such as snapshots and append-only logs (AOF) to ensure data durability. These mechanisms can be configured to periodically save the data on disk, minimizing the risk of data loss.

In summary, Hadoop and Redis differ in terms of scalability, data persistence, data processing paradigms, data access patterns, fault tolerance, and data durability. Understanding these differences is crucial for selecting the most appropriate technology for specific use cases.

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KafkaKafka

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

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DruidDruid

Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.

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Pros of Hadoop
Pros of Redis
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 886
    Performance
  • 542
    Super fast
  • 513
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
  • 194
    Open source
  • 182
    Easy to deploy
  • 164
    Stable
  • 155
    Free
  • 121
    Fast
  • 42
    High-Performance
  • 40
    High Availability
  • 35
    Data Structures
  • 32
    Very Scalable
  • 24
    Replication
  • 22
    Great community
  • 22
    Pub/Sub
  • 19
    "NoSQL" key-value data store
  • 16
    Hashes
  • 13
    Sets
  • 11
    Sorted Sets
  • 10
    NoSQL
  • 10
    Lists
  • 9
    Async replication
  • 9
    BSD licensed
  • 8
    Bitmaps
  • 8
    Integrates super easy with Sidekiq for Rails background
  • 7
    Keys with a limited time-to-live
  • 7
    Open Source
  • 6
    Lua scripting
  • 6
    Strings
  • 5
    Awesomeness for Free
  • 5
    Hyperloglogs
  • 4
    Transactions
  • 4
    Outstanding performance
  • 4
    Runs server side LUA
  • 4
    LRU eviction of keys
  • 4
    Feature Rich
  • 4
    Written in ANSI C
  • 4
    Networked
  • 3
    Data structure server
  • 3
    Performance & ease of use
  • 2
    Dont save data if no subscribers are found
  • 2
    Automatic failover
  • 2
    Easy to use
  • 2
    Temporarily kept on disk
  • 2
    Scalable
  • 2
    Existing Laravel Integration
  • 2
    Channels concept
  • 2
    Object [key/value] size each 500 MB
  • 2
    Simple

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Cons of Hadoop
Cons of Redis
    Be the first to leave a con
    • 15
      Cannot query objects directly
    • 3
      No secondary indexes for non-numeric data types
    • 1
      No WAL

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    What is Hadoop?

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

    What is 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.

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    What companies use Hadoop?
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    What are some alternatives to Hadoop and Redis?
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    Snowflake
    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
    See all alternatives