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InfluxDB vs RethinkDB: What are the differences?
Introduction
In this Markdown code, we will explore the key differences between InfluxDB and RethinkDB. Both databases are popular choices for different use cases and have distinct features and functionalities. Below, we will outline six key differences between these two databases.
Data Model: InfluxDB is a time series database designed specifically for handling time-stamped data and time series analysis. It provides efficient storage, retrieval, and analysis of time-based data, making it ideal for monitoring systems, IoT applications, and real-time analytics. On the other hand, RethinkDB is a distributed document-oriented database that focuses on real-time updates and scalability. It offers a flexible JSON-like data model and supports ad-hoc queries and indexing, making it well-suited for applications that require real-time data synchronization and high availability.
Query Language: InfluxDB uses an SQL-like query language called InfluxQL, which is optimized for querying time series data. It includes specialized functions and operators for filtering, aggregating, and transforming time series. RethinkDB, on the other hand, uses ReQL (RethinkDB Query Language), which provides a fluent and composable API for querying and manipulating JSON-like documents. ReQL offers powerful filtering, grouping, and aggregation capabilities, making it suitable for a wide range of use cases.
Replication and High Availability: InfluxDB supports high availability through clustering and replication. It offers various replication configurations, including synchronous and asynchronous replication, and supports automatic failover and data synchronization. RethinkDB, on the other hand, uses a distributed consensus algorithm called Raft to ensure data consistency and high availability across a cluster of nodes. It provides automatic replication and failover, allowing applications to seamlessly handle node failures and maintain data durability.
Scaling: InfluxDB provides horizontal scalability through sharding, which allows data to be distributed across multiple nodes. It supports both auto-sharding and user-defined sharding strategies, enabling efficient distribution and load balancing of time series data. RethinkDB also supports horizontal scalability through sharding, allowing data to be divided into smaller subsets and distributed across a cluster of nodes. It automatically rebalances data and provides built-in support for distributing queries across shards.
Integration and Ecosystem: InfluxDB has a wide range of integrations and a vibrant ecosystem. It provides native support for popular programming languages, such as Go, Python, and JavaScript, and offers integrations with various visualization tools, including Grafana and Chronograf. InfluxDB also integrates well with other data processing frameworks and technologies, such as Apache Kafka and Apache Spark. RethinkDB, on the other hand, has a smaller ecosystem but provides integrations with popular programming languages and frameworks, such as Node.js and Ruby.
Durability: InfluxDB ensures data durability through its replication mechanisms and by leveraging data compression and compaction techniques. It provides various durability levels, allowing users to balance data durability and storage efficiency. RethinkDB also ensures data durability by automatically replicating data across multiple nodes. It provides configurable durability options, allowing users to trade off durability for performance or storage efficiency.
In summary, InfluxDB and RethinkDB differ in their data models, query languages, replication and high availability mechanisms, scaling capabilities, integration ecosystems, and durability options. These differences make them suitable for different use cases and highlight their unique strengths in handling time series data and real-time synchronization, respectively.
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.
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.
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
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 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.
Druid 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.
if 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’m newbie I was developing a pouchdb and couchdb app cause if the sync. Lots of learning very little code available. I dropped the project cause it consumed my life. Yeats later I’m back into it. I researched other db and came across rethinkdb and mongo for the subscription features. With socketio I should be able to create and similar sync feature. Attempted to use mongo. I attempted to use rethink. Rethink for the win. Super clear l. I had it running in minutes on my local machine and I believe it’s supposed to scale easy. Mongo wasn’t as easy and there free online db is so slow what’s the point. Very easy to find mongo code examples and use rethink code in its place. I wish I went this route years ago. All that corporate google Amazon crap get bent. The reason they have so much power in the world is cause you guys are giving it to them.
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 analysis59
- Easy setup, no dependencies30
- Fast, scalable & open source24
- Open source21
- Real-time analytics20
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
Pros of RethinkDB
- Powerful query language48
- Excellent dashboard46
- JSON42
- Distributed database41
- Open source38
- Reactive25
- Atomic updates16
- Joins15
- MVCC concurrency9
- Hadoop-style map/reduce9
- Geospatial support4
- Real-time, open-source, scalable4
- YC Company2
- A NoSQL DB with joins2
- Great Admin UI2
- Changefeeds: no polling needed to get updates2
- Fast, easily scalable, great customer support2
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Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of RethinkDB
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What is InfluxDB?
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