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Druid

289
605
+ 1
27
InfluxDB

831
852
+ 1
163
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Druid vs InfluxDB: What are the differences?

What is Druid? Fast column-oriented distributed data store. Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

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..

Druid can be classified as a tool in the "Big Data Tools" category, while InfluxDB is grouped under "Databases".

"Real Time Aggregations" is the primary reason why developers consider Druid over the competitors, whereas "Time-series data analysis" was stated as the key factor in picking InfluxDB.

Druid and InfluxDB are both open source tools. InfluxDB with 16.7K GitHub stars and 2.38K forks on GitHub appears to be more popular than Druid with 8.31K GitHub stars and 2.08K GitHub forks.

trivago, Redox Engine, and Thumbtack are some of the popular companies that use InfluxDB, whereas Druid is used by Airbnb, Instacart, and Dial Once. InfluxDB has a broader approval, being mentioned in 119 company stacks & 39 developers stacks; compared to Druid, which is listed in 24 company stacks and 12 developer stacks.

Advice on Druid and InfluxDB
Needs advice
on
Kafka
InfluxDB
and
Hadoop

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.

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Replies (1)
Recommends
Druid

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.

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Needs advice
on
TimescaleDB
MongoDB
and
InfluxDB

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

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Replies (3)
Yaron Lavi
Recommends
PostgreSQL

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.

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Recommends
Druid

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.

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Ankit Malik
Software Developer at CloudCover | 3 upvotes 路 35.9K views
Recommends
Google BigQuery

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.

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Decisions about Druid and InfluxDB
Benoit Larroque
Principal Engineer at Sqreen | 2 upvotes 路 42.9K views

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

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Pros of Druid
Pros of InfluxDB
  • 13
    Real Time Aggregations
  • 4
    OLAP
  • 4
    Batch and Real-Time Ingestion
  • 3
    OLAP + OLTP
  • 2
    Combining stream and historical analytics
  • 1
    OLTP
  • 51
    Time-series data analysis
  • 28
    Easy setup, no dependencies
  • 24
    Fast, scalable & open source
  • 21
    Open source
  • 18
    Real-time analytics
  • 6
    Continuous Query support
  • 5
    Easy Query Language
  • 4
    HTTP API
  • 4
    Out-of-the-box, automatic Retention Policy
  • 1
    Offers Enterprise version
  • 1
    Free Open Source version

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Cons of Druid
Cons of InfluxDB
  • 2
    Limited sql support
  • 1
    Complexity
  • 1
    Joins are not supported well
  • 4
    Instability
  • 1
    HA or Clustering is only in paid version

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What companies use Druid?
What companies use InfluxDB?
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What tools integrate with Druid?
What tools integrate with InfluxDB?

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What are some alternatives to Druid and InfluxDB?
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Prometheus
Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
See all alternatives