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Druid vs InfluxDB: What are the differences?
Introduction
Markdown code formatting has been applied to the text below to be used on a website. The key differences between Druid and InfluxDB have been identified, highlighting six specific distinctions. The generic and declarative sentences have been extracted and removed for better clarity.
Data Model and Query Language: Druid is column-oriented and supports a multi-dimensional data model, while InfluxDB is a time series database that organizes data in fields, tags, and time. Druid uses a SQL-like query language called Druid Query Language (DQL), whereas InfluxDB uses its own query language called InfluxQL.
Scalability and Performance: Druid is designed for high performance and scalability, with the ability to handle large-scale data sets and complex queries. InfluxDB also offers scalability but is optimized for time series data specifically. Druid achieves better performance for multi-dimensional queries, while InfluxDB excels in querying time series data efficiently.
Storage Format: Druid stores data in compressed, immutable segments that allow for efficient query processing and storage optimization. InfluxDB utilizes a compact binary storage format called the Time-Structured Merge Tree (TSM), which is tailored to optimize time series data storage and retrieval.
Real-time Ingestion: Both Druid and InfluxDB support real-time data ingestion. Druid processes real-time data by ingesting it into real-time indexing tasks, while InfluxDB provides a continuous query capability that allows for real-time data ingestion and processing.
Data Retention Policy: Druid offers native support for both time-based and non-time-based data retention policies. It allows defining data retention rules at the ingestion level. InfluxDB provides data retention policies based on time durations, allowing users to specify the duration for which data should be retained.
Ecosystem and Integrations: Druid has a rich ecosystem with integrations with various data ingestion frameworks, such as Apache Kafka and Apache Samza. It also has integrations with popular BI tools and SQL-on-Hadoop systems. InfluxDB has a growing ecosystem with integrations for data collection, visualization, and alerting tools like Telegraf, Grafana, and Kapacitor.
Summary
In summary, Druid and InfluxDB differ in their data models and query languages, scalability and performance characteristics, storage formats, real-time ingestion approaches, data retention policies, and ecosystem integrations.
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 Druid
- Real Time Aggregations15
- Batch and Real-Time Ingestion6
- OLAP5
- OLAP + OLTP3
- Combining stream and historical analytics2
- OLTP1
Pros of InfluxDB
- Time-series data analysis58
- 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
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Cons of Druid
- Limited sql support3
- Joins are not supported well2
- Complexity1
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
