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Druid

312
683
+ 1
29
TimescaleDB

156
268
+ 1
41
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Druid vs TimescaleDB: What are the differences?

Developers describe Druid as "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. On the other hand, TimescaleDB is detailed as "Scalable time-series database optimized for fast ingest and complex queries. Purpose-built as a PostgreSQL extension". TimescaleDB is the only open-source time-series database that natively supports full-SQL at scale, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL databases.

Druid belongs to "Big Data Tools" category of the tech stack, while TimescaleDB can be primarily classified under "Databases".

Druid and TimescaleDB are both open source tools. It seems that Druid with 8.32K GitHub stars and 2.08K forks on GitHub has more adoption than TimescaleDB with 7.28K GitHub stars and 385 GitHub forks.

According to the StackShare community, Druid has a broader approval, being mentioned in 24 company stacks & 12 developers stacks; compared to TimescaleDB, which is listed in 15 company stacks and 3 developer stacks.

Advice on Druid and TimescaleDB
Needs advice
on
TimescaleDBTimescaleDBMongoDBMongoDB
and
InfluxDBInfluxDB

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
PostgreSQLPostgreSQL

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
DruidDruid

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 · 78.5K views
Recommends
Google BigQueryGoogle 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 TimescaleDB
Benoit Larroque
Principal Engineer at Sqreen · | 2 upvotes · 55.7K 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 TimescaleDB
  • 14
    Real Time Aggregations
  • 5
    Batch and Real-Time Ingestion
  • 4
    OLAP
  • 3
    OLAP + OLTP
  • 2
    Combining stream and historical analytics
  • 1
    OLTP
  • 8
    Open source
  • 7
    Easy Query Language
  • 6
    Time-series data analysis
  • 5
    Established postgresql API and support
  • 4
    Reliable
  • 2
    Paid support for automatic Retention Policy
  • 2
    Fast and scalable
  • 2
    Chunk-based compression
  • 2
    Postgres integration
  • 2
    High-performance
  • 1
    Case studies

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Cons of Druid
Cons of TimescaleDB
  • 3
    Limited sql support
  • 2
    Joins are not supported well
  • 1
    Complexity
  • 3
    Licensing issues when running on managed databases

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What is Druid?

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 TimescaleDB?

TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.

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What companies use Druid?
What companies use TimescaleDB?
See which teams inside your own company are using Druid or TimescaleDB.
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What tools integrate with Druid?
What tools integrate with TimescaleDB?

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What are some alternatives to Druid and TimescaleDB?
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