Multi-dimensional range partitioning

Feb 04, 2022
Kashif Faraz

Contributing authors: Kashif Faraz and Eric Tschetter, design partner for multi-dimension range partitioning
Reviewer: Abhishek Agarwal

Here at Imply, we run Druid clusters which churn hundreds of million rows of data every hour. And that’s only the metric data generated to monitor the actual analytical data! 

For a complex system like Druid that handles enormous data loads, every component must be robust and reliable. At the same time, the system must be ever evolving.

Recently, we implemented a feature that decreased storage size by 40% while improving query speeds by 75% with absolutely no loss in data fidelity. Here we discuss the twists and tweaks of an integral cog of the Druid machinery which made these improvements possible: partitioning. 


(TL;DR: If you are a person of few words, please refer to the table at the end to choose the best partitioning scheme for your use cases.)

The partitioning motivation

Anyone who works with data is already familiar with techniques such as partitioning, sharding and bucketing as a means to improve performance. But for the joy of listing things, let’s rehash some of the potential benefits partitioning could offer in Druid:

  • Parallelism: Multiple processes can operate on different portions of data simultaneously.
  • Distributed storage: Data can be spread across several data servers thus allowing even commodity hardware to meet memory and disk requirements.
  • Improved I/O management: Reasonably sized partitions make reading from/writing to disk easier and transmission over network less prone to failure.
  • Granular replication: With the same factor, say 2, replication at the partition level allows better fault tolerance than replication of unpartitioned data as a whole.

Perhaps we needed that refresher after all. It reminds us that partitioning is not a luxury, but rather a necessity for a scalable database system. Now let’s take a moment to consider what kind of a partitioning scheme would actually meet our needs.

The paradoxical partition

‘Spread out’ for Write

Streaming ingestion is a popular method of loading data into Druid. To ensure rapid event processing and to avoid lag, the streaming pipeline should organise data in a write-optimized manner that minimizes hot-spots. This would mean spreading out the incoming data across several partitions to evenly distribute the ingestion workload among multiple processes and servers.

‘Group up’ for Read

Druid loads the absolute minimum amount of data necessary for a query to optimize performance. It uses indexes and other metadata to aggressively prune out rows which are not needed for a query.

For example, imagine we have data for a whole year with 365 partitions, one for each day. When querying for the first week of January, we need to look at only 7 partitions and ignore the remaining 358. This would help us save significantly on compute resources and/or query time. But if the data for a single day were spread across many partitions, this strategy would be less effective. Simply put, we must partition by time to actually benefit time-based queries.

The practice of storing similar data together is referred to as ‘data locality’. The similarity can be on the basis of the values of one or more columns (typically dimensions). Along with query performance, locality also helps in reducing disk footprint as co-locating similar data allows for better compression.

Hot-spot or not?

It is evident that both ingestion and queries can benefit through partitioning but they have contradictory requirements. While the former requires eliminating hot-spots by spreading data out, the latter benefits from similar data being stored together, effectively creating hot-spots.

A timely solution

Despite the differences between read and write operations, the time dimension plays a key role in both of them. This is only to be expected since all event-oriented data and the insights drawn from it are more meaningful when considered over a time duration. Even simple questions like “What is the total number of users?”, “What is the increase in sales?” are more meaningful when you add the words “today”, “since last month”, or “over all time” to them.

Thus, Druid always partitions data by the timestamp dimension at a chosen segment granularity. For example, choosing a granularity of ‘DAY’ would divide the data timeline into several chunks, each containing the whole data for a day.

Such a time-based partitioning certainly benefits queries with a time filter. However, it might not work as well for write-time parallelism. This is because consecutive events in the input data stream, with adjacent timestamps, would be written to the same partition. Another concern is that as data grows and you start seeing billions of events an hour, even the finest time granularities will not yield partitions of a manageable size.

The solution is secondary partitioning to further divide each of the time chunks into smaller partitions (aka segments). This enables us to retain write-time parallelism and create appropriately-sized partitions while continuing to benefit time-based queries. Secondary partitioning can also create more opportunities for pruning out unnecessary rows based on column filters in a query.

Re-organization by auto-compaction

Partitioning by time takes us only half of the way because we are still left with the question, how should the secondary partitioning work? Should it spread out incoming data in favour of write-time speed or should it group similar data together to improve read-time performances? Unfortunately, these requirements are mutually exclusive and we cannot have a single data layout that satisfies both of them.

The good news is that Druid is a WORM (write once read many) system. We need to optimize for write only during ingestion as all later operations will only be read. This means that data can be ingested from the stream in a write-optimized manner which spreads it out in favour of parallelism. After the data is old enough to have no more writes, Druid can reorganize it in favor of data locality.

This convenient process of rearranging data in Druid is known as ‘compaction’, so called because the resulting column-based partitioning allows for rollup as well as greater compression. You can schedule compaction to run periodically with configurations defined to attain the best data layout for your querying use cases.

Row distribution

Now that compaction allows us to choose different partitioning schemes for ingest-time and query-time, let’s find the best candidates for the two. Until recently, Druid supported the following secondary partitioning schemes:

  • Dynamic: The default scheme, which creates a new partition after every N rows. It provides no data locality but creates uniformly sized partitions.
  • Hashed: A dimension-based scheme, which creates partitions based on the hashed value of chosen partition dimensions. It offers low data locality and creates more or less uniform partitions.
  • Single-Dim: A dimension-based scheme, which uses the range of a single dimension to create partitions. While it boasts of a high data locality, it can often create partitions that are much larger than the target size.

Let’s have a closer look at each scheme by applying it to the following sample dataset from a typical e-commerce website.

idcategorysub_categorynum_itemscheckout_price
1electronicstablet1107
2apparelkids3316
3electronicsphone1405
4furnituretable1126
5apparelshirts3352
6electronicslaptop1700
7kitchencutlery10525
8electronicslaptop1950

Dynamic Partitioning

With a target partition size of 2 rows each, dynamic partitioning of this dataset looks something like this:

idcategorysub_categorypartition number
1electronicstablet0
2apparelkids0
3electronicsphone1
4furnituretable1
5apparelshirts2
6electronicslaptop2
7kitchencutlery3
8electronicslaptop3

By definition, the dynamic scheme creates partitions of size as close as possible to the target size. It is best suited for write-time operations as incoming data is written to uniformly sized partitions without the need to shuffle rows. The primary drawback is that dynamic partitioning doesn’t care about data organization at all. Note that each of the 4 partitions has a row for  ‘electronics’. There is no data locality because the column values play no role in determining the partitions.

Hashed Partitioning

Hashed partitioning allows us to partition on any number of dimensions, so let’s use both category and sub_category. The number of partitions is determined by the given target size.

Num Partitions = Num Rows / Target Size = 8 / 2 = 4
Possible partition numbers = {0, 1, 2, 3}

We need a hashing function to map the dimension values to a valid partition number. While the function used in practice is a 32-bit Murmur 3 Hash, let’s define a simpler one here that we can actually follow:

Partition Number = (length(category) + length(sub_category)) % 4

i.e. the partition number is the total length of category and sub_category columns taken as a modulo of 4.

idcategorysub_categorytotal lengthpartition mumber
1electronicstablet171
2apparelkids113
3electronicsphone160
4furnituretable142
5apparelshirts131
6electronicslaptop171
7kitchencutlery142
8electronicslaptop171

The distribution above demonstrates the following:

  • There is some data locality because all rows for (‘electronics’, ‘laptop’) are assigned to partition 1. With hashed partitioning, rows with the same combination of dimension values always end up in the same partition. This implies that if a query were only looking for rows with category=‘electronics’ AND sub_category=‘laptop’, we could safely ignore all the other partitions.
  • The data locality is not perfect because rows for other ‘electronics’ items have ended up in other partitions.
  • The distribution is not as uniform as dynamic partitioning: one segment has 4 rows and one has only a single row
  • It is rather difficult to predict the final layout due to the complexity introduced by the hashing function (even a simple one).

Single-Dim Range Partitioning

Let’s partition our sample data on the dimension category with the same target partition size of 2 rows each. To determine the distribution, we must first identify the range of the partitioning dimension and then create boundaries at appropriate intervals. The distribution looks something like this:

idcategory (sorted)sub_categorypartition number
2apparelkids0
5apparelshirts0
1electronicstablet1 (boundary)
3electronicsphone1
6electronicslaptop1
8electronicslaptop1
4furnituretable2 (boundary)
7kitchencutlery2

The above layout demonstrates great locality because all the rows for ‘electronics’ are in partition 1. But at the same time, that partition has 4 rows: twice the target size! This is because with single-dim, all rows for a given value of the partitioning dimension (here category) always go to the same partition. So while querying for rows with say category=‘furniture’ or category=‘kitchen’, we would need to look only at partition 2.

Multi-dimension generalization

Single-dim partitioning offers a high data locality which benefits both query performance and storage efficiency. But there are two primary challenges with using single-dim:

  • Only one partition dimension: It is difficult to choose a single dimension that is likely to satisfy all the requirements of partitioning, i.e. improve locality, boost query perf, etc.
  • Uneven partition sizes: In the example above, we saw that partitioning on a single dimension can result in uneven partitions as real-world data is often skewed. 

If we could somehow mitigate these drawbacks, single-dim partitioning could very well become the best read-time strategy. Driven by this lucrative possibility, we began to investigate the effects of including more dimensions in range partitioning. For example, if we were to partition on the ranges of the tuple (category, sub_category) instead of just category, the distribution for the above dataset would be as follows:

idcategory (sorted)sub_category (sorted with category)partition number
2apparelkids0
5apparelshirts0
6electronicslaptop1 (boundary)
8electronicslaptop1
3electronicsphone2 (boundary)
1electronicstablet2
4furnituretable3 (boundary)
7kitchencutlery3

We can see that the partitions are more evenly sized as compared to single-dim. Adding sub_category allows the ranges of both the dimensions to help determine the partition boundaries. So the rows for ‘electronics’ can now be split across two partitions without compromising on data locality.

Safe experimentation

Encouraged by these observations and some proof of concept work, we ventured a little deeper into the world of multiple dimensions – a Druid multiverse if you will. We implemented the prototype of a partitioning scheme that could work across any number of dimensions, thus creating multi-dimension range partitioning aka ‘multi-dim’.

Imply Clarity is an APM product which allows users to monitor their Druid clusters’ operational health. It is powered by a large Druid deployment itself and provides deep visibility into query performances and data flow. Built specifically for Druid, Clarity makes it very easy to pin-point performance bottlenecks and hot-spots on Druid clusters.

Given the huge data load churned by the Druid cluster for Clarity, we found it to be the perfect avenue for further experimentation. What better way to validate our theories than to test them on production data (with supervision, of course)? With a solid 75% decrease in query times and a 40% decrease in storage size over un-compacted data, multi-dim proved itself to be the best read-time partitioning scheme for most, if not all use cases.

A comparative conclusion

Druid always partitions data by the timestamp dimension to benefit time-based analytical queries. A secondary partitioning is needed to further break down the time chunks into manageable partition sizes.

Based on the observations above, we can all agree that ‘dynamic’ is the best scheme for write-time partitioning, especially with streaming ingestion. A periodic auto-compaction can then reorganize the data with a suitable read-time partitioning. 

To help you choose the best scheme for read-time, we have compiled a summary of all the options now available in Druid. Here’s a hint, go for the green one!

Too good to be kept a secret, multi-dim will soon be available in Apache Druid 0.23 for everyone to use. In the meantime, it already comes packaged with Imply 2021.12 (refer to docs here). Feel free to try it out on any dataset of your choice (even prod). Stay tuned for our upcoming blog where we take you on the journey of our own multidimensional adventures!

Other blogs you might find interesting

No records found...
Sep 21, 2023

Migrate Analytics Data from MongoDB to Apache Druid

This blog presents a concise guide on migrating data from MongoDB to Druid. It includes Python scripts to extract data from MongoDB, save it as CSV, and then ingest it into Druid. It also touches on maintaining...

Learn More
Sep 21, 2023

How Druid Facilitates Real-Time Analytics for Mass Transit

Mass transit plays a key role in reimagining life in a warmer, more densely populated world. Learn how Apache Druid helps power data and analytics for mass transit.

Learn More
Sep 19, 2023

Migrate Analytics Data from Snowflake to Apache Druid

This blog outlines the steps needed to migrate data from Snowflake to Apache Druid, a platform designed for high-performance analytical queries. The article covers the migration process, including Python scripts...

Learn More
Sep 15, 2023

Apache Kafka, Flink, and Druid: Open Source Essentials for Real-Time Applications

Apache Kafka, Flink, and Druid, when used together, create a real-time data architecture that eliminates all these wait states. In this blog post, we’ll explore how the combination of these tools enables...

Learn More
Sep 11, 2023

Visualizing Data in Apache Druid with the Plotly Python Library

In today's data-driven world, making sense of vast datasets can be a daunting task. Visualizing this data can transform complicated patterns into actionable insights. This blog delves into the utilization of...

Learn More
Sep 05, 2023

Bringing Real-Time Data to Solar Power with Apache Druid

In a rapidly warming world, solar power is critical for decarbonization. Learn how Apache Druid empowers a solar equipment manufacturer to provide real-time data to users, from utility plant operators to homeowners

Learn More
Sep 05, 2023

When to Build (Versus Buy) an Observability Application

Observability is the key to software reliability. Here’s how to decide whether to build or buy your own solution—and why Apache Druid is a popular database for real-time observability

Learn More
Aug 29, 2023

How Innowatts Simplifies Utility Management with Apache Druid

Data is a key driver of progress and innovation in all aspects of our society and economy. By bringing digital data to physical hardware, the Internet of Things (IoT) bridges the gap between the online and...

Learn More
Aug 14, 2023

Three Ways to Use Apache Druid for Machine Learning Workflows

An excellent addition to any machine learning environment, Apache Druid® can facilitate analytics, streamline monitoring, and add real-time data to operations and training

Learn More
Aug 11, 2023

Introducing Apache Druid 27.0.0

Apache Druid® is an open-source distributed database designed for real-time analytics at scale. Apache Druid 27.0 contains over 350 commits & 46 contributors. This release's focus is on stability and scaling...

Learn More
Aug 10, 2023

Unleashing Real-Time Analytics in APJ: Introducing Imply Polaris on AWS AP-South-1

Imply, the company founded by the original creators of Apache Druid, has exciting news for developers in India seeking to build real-time analytics applications. Introducing Imply Polaris, a powerful database-as-a-Service...

Learn More
Aug 03, 2023

Embedding Visualizations using React and Express

In this guide, we will walk you through creating a very simple web app that shows a different embedded chart for each user selected from a drop-down. While this example is simple it highlights the possibilities...

Learn More
Jul 25, 2023

Apache Druid: Making 1000+ QPS for Analytics Look Easy

This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid

Learn More
Jul 25, 2023

Things to Consider When Scaling Analytics for High QPS

This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid

Learn More
Jul 20, 2023

Automate Streaming Data Ingestion with Kafka and Druid

In this blog post, we explore the integration of Kafka and Druid for data stream management and analysis, emphasizing automatic topic detection and ingestion. We delve into the creation of 'Ingestion Spec',...

Learn More
Jul 12, 2023

Schema Auto-Discovery with Apache Druid

This guide explores configuring Apache Druid to receive Kafka streaming messages. To demonstrate Druid's game-changing automatic schema discovery. Using a real-world scenario where data changes are handled...

Learn More
Jul 11, 2023

What’s new in Imply Polaris – Q2 2023

Imply Polaris, our ever-evolving Database-as-a-Service, recently focused on global expansion, enhanced security, and improved data handling and visualization. This fully managed cloud service, based on Apache...

Learn More
Jun 06, 2023

Introducing hands-on developer tutorials for Apache Druid

The objective of this blog is to introduce the new set of interactive tutorials focused on the Druid API fundamentals. These tutorials are available as Jupyter Notebooks and can be downloaded as a Docker container.

Learn More
Jun 01, 2023

Introducing Schema Auto-Discovery in Apache Druid

In this blog article I’ll unpack schema auto-discovery, a new feature now available in Druid 26.0, that enables Druid to automatically discover data fields and data types and update tables to match changing...

Learn More
May 30, 2023

Exploring Unnest in Druid

Druid now has a new function, Unnest. Unnest explodes an array into individual elements. This blog contains design methodology and examples for this new Unnest function both from native and SQL binding perspectives.

Learn More
May 28, 2023

What’s new in Imply Polaris – Our Real-Time Analytics DBaaS

Every week we add new features and capabilities to Imply Polaris. This month, we’ve expanded security capabilities, added new query functionality, and made it easier to monitor your service with your preferred...

Learn More
May 24, 2023

Introducing Apache Druid 26.0

Apache Druid® 26.0, an open-source distributed database for real-time analytics, has seen significant improvements with 411 new commits, a 40% increase from version 25.0. The expanded contributor base of 60...

Learn More
May 22, 2023

ACID and Apache Druid

ACID and Druid, an interesting dive into some of the Druid capabilities in the light of ACID compliance

Learn More
May 21, 2023

How to Build a Sentiment Analysis Application with ChatGPT and Druid

Leveraging ChatGPT for sentiment analysis, when combined with Apache Druid, offers results from large data volumes. This integration is easily achievable, revealing valuable insights and trends for businesses...

Learn More
May 21, 2023

Snowflake and Apache Druid

In this blog, we will compare Snowflake and Druid. It is important to note that reporting data warehouses and real-time analytics databases are different domains. Choosing the right tool for your specific requirements...

Learn More
May 20, 2023

Learn how to achieve sub-second responses with Apache Druid

Learn how to achieve sub-second responses with Apache Druid. This article is an in-depth look at how Druid resolves queries and describes data modeling techniques that improve performance.

Learn More
May 19, 2023

Apache Druid – Recovering Dropped Segments

Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make...

Learn More
May 18, 2023

Real-Time Analytics: Building Blocks and Architecture

This blog identifies the key technical considerations for real-time analytics. It answers what is the right data architecture and why. It spotlights the technologies used at Confluent, Reddit, Target and 1000s...

Learn More
May 17, 2023

Transactions Come and Go, but Events are Forever

For decades, analytics has focused on Transactions. While Transactions are still important, the future of analytics is understanding Events.

Learn More
May 16, 2023

What’s new in Imply Polaris – Our Real-Time Analytics DBaaS

This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability...

Learn More
May 15, 2023

Elasticsearch and Druid

This blog will help you understand what Elasticsearch and Druid do well and will help you decide whether you need one or both to reach your goals

Learn More
May 14, 2023

Wow, that was easy – Up and running with Apache Druid

The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.

Learn More
May 13, 2023

Top 7 Questions about Kafka and Druid

Read on to learn more about common questions and answers about using Kafka with Druid.

Learn More
May 12, 2023

Tales at Scale Podcast Kicks off with the Apache Druid Origin Story

Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key...

Learn More
May 11, 2023

Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high...

Learn More
May 10, 2023

Easily embed analytics into your own apps with Imply’s DBaaS

This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming...

Learn More
May 09, 2023

Building an Event Analytics Pipeline with Confluent Cloud and Imply’s real time DBaaS, Polaris

Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed...

Learn More
May 08, 2023

Real time DBaaS comes to Europe

We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service...

Learn More
May 07, 2023

Stream big, think bigger—Analyze streaming data at scale in 2023

Imply is predicting the next "big thing" in 2023 will be analyzing streaming data in real time (and Druid is built for just that!)

Learn More
May 07, 2023

Should You Build or Buy Security Analytics for SecOps?

When should you build—or buy—a security analytics platform for your environment? Here are some common considerations—and how Apache Druid is the ideal foundation for any in-house security solution.

Learn More
May 05, 2023

Introducing Apache Druid 25.0

Apache Druid 25.0 contains over 293 updates from over 56 contributors.

Learn More
May 03, 2023

Druid and SQL syntax

This is a technical blog, which summarises the process of extending the Druid's SQL grammar for ingestion and delves into the nitty gritty of Calcite.

Learn More
May 02, 2023

Native support for semi-structured data in Apache Druid

Describes a new feature- ingest complex data as is into Druid- massive improvement in developer productivity

Learn More
May 01, 2023

Real-Time Analytics with Imply Polaris: From Setup to Visualization

Imply Polaris offers reduced operational overhead and elastic scaling for efficient real-time analytics that helps you unlock your data's potential.

Learn More
May 01, 2023

Datanami Award

Apache Druid won Datanami's 2022 Readers’ and Editors’ Choice Awards for Reader's Choice "Best Data and AI Product or Technology: Analytics Database".

Learn More
Apr 30, 2023

Alerting and Security Features in Polaris

Describes new features - alerts and some security features- and how Imply customers can leverage it

Learn More
Apr 29, 2023

Ingestion from Amazon Kinesis and S3 into Imply Polaris

Imply Polaris now supports data ingestion from Amazon Kinesis and Amazon S3

Learn More
Apr 27, 2023

Getting the Most Out of your Data

Ingesting data from one table to another is easy and fast in Imply Polaris!

Learn More
Apr 26, 2023

Combating financial fraud and money laundering at scale with Apache Druid

Learn how Apache Druid enables financial services firms and FinTech companies to get immediate insights from petabytes-plus data volumes for anti-fraud and anti-money laundering compliance.

Learn More
Apr 26, 2023

What’s new in Imply – December 2022

This is a what's new to Imply in Dec 2022. We’ve added two new features to Imply Polaris to make it easier for your end users to take advantage of real-time insights.

Learn More
Apr 25, 2023

What’s New in Imply Polaris – November 2022

This blog provides an overview for the new features, functionality, and connectivity to Imply Polaris for November 2022.

Learn More
Apr 24, 2023

Imply Pivot delivers the final mile for modern analytics applications

This blog is focused on how Imply Pivot delivers the final mile for building an anlaytics app. It showcases two customer examples - Twitch and ironsource.

Learn More
Apr 23, 2023

Why Analytics Need More than a Data Warehouse

For decades, analytics has been defined by the standard reporting and BI workflow, supported by the data warehouse. Now, 1000s of companies are realizing an expansion of analytics beyond reporting, which requires...

Learn More
Apr 21, 2023

Why Open Source Matters for Databases

Apache Druid is at the heart of Imply. We’re an open source business, and that’s why we’re committed to making Druid the best open source database for modern analytics applications

Learn More
Apr 20, 2023

Ingestion from Confluent Cloud and Kafka in Polaris

How to ingest data into Imply Polaris from Confluent Cloud and from Apache Kafka

Learn More
Apr 18, 2023

What Makes a Database Built for Streaming Data?

For an analytics app to handle real-time, streaming sources, it must be built for streaming data. Druid has 3 essential features for stream data.

Learn More
Oct 12, 2022

SQL-based Transformations and JSON Columns in Imply Polaris

You can easily do data transformations and manage JSON data with Imply Polaris, both using SQL.

Learn More
Oct 06, 2022

Approximate Distinct Counts in Imply Polaris

When it comes to modern data analytics applications, speed is of the utmost importance. In this blog we discuss two approximation algorithms which can be used to greatly enhance speed with only a slight reduction...

Learn More
Sep 20, 2022

The next chapter for Imply Polaris: celebrating 250+ accounts, continued innovation

Today we announced the next iteration of Imply Polaris, the fully managed Database-as-a-Service that helps you build modern analytics applications faster, cheaper, and with less effort. Since its launch in...

Learn More
Sep 20, 2022

Introducing Imply’s Total Value Guarantee for Apache Druid

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

Learn More
Sep 16, 2022

Introducing Apache Druid 24.0

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

Learn More
Aug 16, 2022

Using Imply Pivot with Druid to Deduplicate Timeseries Data

Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.

Learn More
Jul 21, 2022

A Look Under the Surface at Polaris Security

We have taken a security-first approach in building the easiest real-time database for modern analytics applications.

Learn More
Jul 14, 2022

Upserts and Data Deduplication with Druid

A look at what can be done with Druid for upserts and data deduplication.

Learn More
Jul 01, 2022

What Developers Can Build with Apache Druid

We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out

Learn More
Jun 29, 2022

When Streaming Analytics… Isn’t

Nearly all databases are designed for batch processing, which leaves three options for stream analytics.

Learn More
Jun 29, 2022

Apache Druid vs. Snowflake

Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.

Learn More
Jun 22, 2022

Druid 0.23 – Features And Capabilities For Advanced Scenarios

Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for...

Learn More
Jun 22, 2022

Introducing Apache Druid 0.23

Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.

Learn More
Jun 20, 2022

An Opinionated Guide to Component APIs

We have collected a number of guidelines for React component APIs that make components more predictable in terms of behavior and performance.

Learn More
Jun 10, 2022

Druid Architecture & Concepts

In a world full of databases, learn how Apache Druid makes real-time analytics apps a reality in this Whitepaper from Imply

Learn More
May 25, 2022

3 decisions that shaped the Polaris UI

Imply Polaris is a fully managed database-as-a-service for building realtime analytics applications. John is the tech lead for the Polaris UI, known internally as the Unified App. It began with a profound question:...

Learn More
May 19, 2022

How Imply Polaris takes a security-first approach

A primer for developers on security tools and controls available in Imply Polaris

Learn More
May 17, 2022

Imply Raises $100MM in Series D funding

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

Learn More
May 11, 2022

Imply Named “Cool Database Vendor” by CRN

There can’t be one database good at everything. When it comes to real-time analytics, you need a database built for it.

Learn More
May 11, 2022

Living the Stream

We are in the early stages of a stream revolution, as developers build modern transactional and analytic applications that use real-time data continuously delivered.

Learn More
May 02, 2022

Migrating Data from ClickHouse to Imply Polaris

In this blog, we’ll review the simple steps to export data from ClickHouse in a format that is easy to ingest into Polaris.

Learn More
Apr 18, 2022

Apache Druid vs. ClickHouse

Developers and architects must look beyond query performance to understand the operational realities of growing and managing a high performance database and if it will consume their valuable time.

Learn More
Apr 06, 2022

Java Keytool, TLS, and Zookeeper Security

Lean the basics of Public Key Infrastructure (PKI) as it relates to Druid and Zookeeper security.

Learn More
Apr 01, 2022

Building high performance logging analytics with Polaris and Logstash

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Apr 01, 2022

For April 1st: a New Description of Apache Druid from Our Youngest Technical Architect

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

Learn More
Mar 24, 2022

Distributed by Nature: Druid at Scale

Horizontal scaling is the key to performance at scale, which is why every database claims this. You should investigate, though, to see how much effort it takes, especially compared to Apache Druid.

Learn More
Mar 23, 2022

Atomic Replace in Polaris

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Mar 22, 2022

Announcing Imply Polaris

Today, we're excited to announce a major leap forward in ease-of-use with the introduction of Imply Polaris, our fully-managed, database-as-a-service.

Learn More
Mar 22, 2022

Building Analytics for External Users is a Whole Different Animal

Analytics aren’t just for internal stakeholders anymore. If you’re building an analytics application for customers, then you’re probably wondering…what’s the right database backend? 

Learn More
Mar 07, 2022

Clustered Apache Druid® on your Laptop – Easy!

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

Learn More
Mar 01, 2022

Why Data Needs More than CRUD

After over 30 years of working with data analytics, we’ve been witness (and sometimes participant) to three major shifts in how we find insights from data - and now we’re looking at the fourth.

Learn More
Mar 01, 2022

The Rise of a New Analytics Hero in 2022

Every year industry pundits predict data and analytics becoming more valuable the following year. But this doesn’t take a crystal ball to predict. There’s instead something much more interesting happening...

Learn More
Mar 01, 2022

A new shape for Apache Druid

Today, I'm prepared to share our progress on this effort and some of our plans for the future. But before diving further into that, let's take a closer look at how Druid's core query engine executes queries,...

Learn More
Feb 11, 2022

Product Update: SSO, Cluster level authorization, OAuth 2.0 and more security features

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Dec 12, 2021

Log4Shell Vulnerability and Mitigation

A critical vulnerability has recently been discovered in Apache Log4j, a popular logging library for Java projects.

Learn More
Nov 22, 2021

Druid Nails Cost Efficiency Challenge Against ClickHouse & Rockset

To make a long story short, we were pleased to confirm that Druid is 2 times faster than ClickHouse and 8 times faster than Rockset with fewer hardware resources!.

Learn More
Nov 09, 2021

Unveiling Project Shapeshift Nov. 9th at Druid Summit 2021

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

Learn More
Oct 25, 2021

How we made long-running queries work in Apache Druid

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Oct 20, 2021

Auto Scaling real-time Kafka Ingestion FTW!

Uneven traffic flow in streaming pipelines is a common problem. Providing the right level of resources to keep up with spikes in demand is a requirement in order to deliver timely analytics.

Learn More
Oct 04, 2021

Community Discoveries: multi-value dimensions in Apache Druid

Hellmar Becker is an Imply solutions engineer based in Germany, where he has been delving into the nooks-and-crannies of multi-valued dimension support in Druid. In this interview, Hellmar explains why...

Learn More
Sep 28, 2021

Community Spotlight: Apache Pulsar and Apache Druid get close…

The community team at Imply spoke with an Apache Pulsar community member, Giannis Polyzos, about how collaboration between open source communities generates great things, and more specifically, about how...

Learn More
Sep 27, 2021

Meet the team: Abhishek Agarwal, engineering lead in India

Abhishek is Imply’s first engineer in India. We spoke to him about setting up our operations in Bangalore and asked what kind of local talent the company is looking for.

Learn More
Sep 27, 2021

Meet the team: Jihoon Son, Software Engineer

Jihoon Son is a software engineer at Imply who works on Apache Druid®. He explains what drew him to Imply five years ago and why he’s even more inspired by the company today.

Learn More

Let us help with your analytics apps

Request a Demo