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InfluxDB vs Redis: What are the differences?
What is InfluxDB? An open-source distributed time series database with no external dependencies. InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out..
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.
InfluxDB belongs to "Databases" category of the tech stack, while Redis can be primarily classified under "In-Memory Databases".
"Time-series data analysis" is the top reason why over 35 developers like InfluxDB, while over 842 developers mention "Performance" as the leading cause for choosing Redis.
InfluxDB and Redis are both open source tools. Redis with 37.1K GitHub stars and 14.3K forks on GitHub appears to be more popular than InfluxDB with 16.6K GitHub stars and 2.37K GitHub forks.
reddit, Instacart, and Slack are some of the popular companies that use Redis, whereas InfluxDB is used by Impossible Software, SimpleCrypto, and capscale. Redis has a broader approval, being mentioned in 3239 company stacks & 1732 developers stacks; compared to InfluxDB, which is listed in 116 company stacks and 38 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.
We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.
So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily
PostgreSQLWe had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.
DruidDruid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.
Google BigQueryif you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.
I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.
The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)
We are combining this with Grafana for display and Telegraf for data collection
Pros of InfluxDB
- Time-series data analysis53
- Easy setup, no dependencies29
- Fast, scalable & open source24
- Open source21
- Real-time analytics19
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
Pros of Redis
- Performance879
- Super fast537
- Ease of use511
- In-memory cache441
- Advanced key-value cache321
- Open source190
- Easy to deploy179
- Stable163
- Free152
- Fast120
- High-Performance40
- High Availability39
- Data Structures34
- Very Scalable32
- Replication23
- Great community20
- Pub/Sub19
- "NoSQL" key-value data store17
- Hashes14
- Sets12
- Sorted Sets10
- Lists9
- BSD licensed8
- NoSQL8
- Async replication7
- Integrates super easy with Sidekiq for Rails background7
- Bitmaps7
- Open Source6
- Keys with a limited time-to-live6
- Strings5
- Lua scripting5
- Awesomeness for Free!4
- Hyperloglogs4
- outstanding performance3
- Runs server side LUA3
- Networked3
- LRU eviction of keys3
- Written in ANSI C3
- Feature Rich3
- Transactions3
- Data structure server2
- Performance & ease of use2
- Existing Laravel Integration1
- Automatic failover1
- Easy to use1
- Object [key/value] size each 500 MB1
- Simple1
- Channels concept1
- Scalable1
- Temporarily kept on disk1
- Dont save data if no subscribers are found1
- Jk0
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Cons of InfluxDB
- Instability4
- HA or Clustering is only in paid version1
Cons of Redis
- Cannot query objects directly14
- No secondary indexes for non-numeric data types2
- No WAL1
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