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Hadoop vs Redis: What are the differences?
What is Hadoop? Open-source software for reliable, scalable, distributed computing. 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? An in-memory database that persists on disk. Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
Hadoop and Redis are primarily classified as "Databases" and "In-Memory Databases" tools respectively.
"Great ecosystem" is the primary reason why developers consider Hadoop over the competitors, whereas "Performance" was stated as the key factor in picking Redis.
Hadoop and Redis are both open source tools. It seems that Redis with 37.1K GitHub stars and 14.3K forks on GitHub has more adoption than Hadoop with 9.18K GitHub stars and 5.74K GitHub forks.
reddit, Instacart, and Slack are some of the popular companies that use Redis, whereas Hadoop is used by Slack, Shopify, and SendGrid. Redis has a broader approval, being mentioned in 3239 company stacks & 1732 developers stacks; compared to Hadoop, which is listed in 237 company stacks and 116 developer stacks.
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.
DruidDruid 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.
Pros of Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
Pros of Redis
- Performance882
- Super fast540
- Ease of use510
- In-memory cache441
- Advanced key-value cache321
- Open source190
- Easy to deploy180
- Stable163
- Free153
- Fast120
- High-Performance40
- High Availability39
- Data Structures34
- Very Scalable31
- Replication23
- Great community21
- Pub/Sub21
- "NoSQL" key-value data store17
- Hashes14
- Sets12
- Sorted Sets10
- Lists9
- BSD licensed8
- NoSQL8
- Integrates super easy with Sidekiq for Rails background7
- Async replication7
- Bitmaps7
- Keys with a limited time-to-live6
- Open Source6
- Strings5
- Lua scripting5
- Hyperloglogs4
- Awesomeness for Free!4
- Transactions3
- Runs server side LUA3
- outstanding performance3
- Networked3
- LRU eviction of keys3
- Written in ANSI C3
- Feature Rich3
- Performance & ease of use2
- Data structure server2
- Simple1
- Channels concept1
- Scalable1
- Temporarily kept on disk1
- Dont save data if no subscribers are found1
- Automatic failover1
- Easy to use1
- Existing Laravel Integration1
- Object [key/value] size each 500 MB1
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Cons of Hadoop
Cons of Redis
- Cannot query objects directly15
- No secondary indexes for non-numeric data types3
- No WAL1
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