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InfluxDB vs KairosDB: What are the differences?
Introduction
InfluxDB and KairosDB are both popular time series databases that are widely used for storing and analyzing time-stamped data. While they both serve the same purpose, there are several key differences between the two.
Data Model: InfluxDB adopts a tag-based data model, where data is organized into measurements, tags, and fields. Measurements represent a specific metric or data value, tags provide metadata for filtering and grouping, and fields store the actual data values. On the other hand, KairosDB uses a key-value pair data model, where the data is stored as a set of key-value pairs without the concept of tags or fields.
Query Language: InfluxDB provides a powerful query language called InfluxQL, which allows users to perform complex queries and aggregations on the time series data. InfluxQL supports various functions, joins, and group by operations. In contrast, KairosDB primarily relies on a simple JSON-based query language, which doesn't offer the same level of flexibility and functionality as InfluxQL.
Scalability: InfluxDB is designed to be highly scalable and can handle large volumes of data and high write and read throughput. InfluxDB achieves scalability through its clustered architecture and the ability to distribute data across multiple nodes. On the other hand, KairosDB doesn't have built-in clustering capabilities and may require external solutions for achieving scalability in highly demanding environments.
Ecosystem and Integrations: InfluxDB has a vibrant community and a rich ecosystem of integrations, libraries, and tools. It provides various client libraries and plugins for different programming languages and supports popular data visualization and monitoring tools. KairosDB, while being a capable time series database, has a smaller community and a less extensive ecosystem compared to InfluxDB.
Data Retention and Downsampling: InfluxDB offers built-in mechanisms for data retention and downsampling, which allow users to automatically expire or downsample older data to conserve storage space and optimize querying performance. KairosDB doesn't provide native support for data retention and downsampling, and users may need to implement custom solutions to achieve similar functionality.
Data Replication and High Availability: InfluxDB offers various replication options, including asynchronous and synchronous replication, to ensure data durability and high availability. It provides the ability to configure data replication across multiple clusters and data centers. KairosDB, on the other hand, doesn't have built-in replication mechanisms and may require additional setup and configuration for achieving data replication and high availability.
In summary, InfluxDB and KairosDB differ in their data models, query languages, scalability capabilities, ecosystems, data retention and downsampling mechanisms, and data replication and high availability features. These differences should be considered when choosing the appropriate time series database for a specific use case.
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 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 KairosDB
- As fast as your cassandra/scylla cluster go1
- Time-Series data analysis1
- Easy setup1
- Easy Rest API1
- Open source1
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Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of KairosDB
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