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

May 11, 2023
Sergio Ferragut

Apache Druid – Using Range Partitioning for Speed

One of Apache Druid’s key characteristics is its ability to partition ingested data so that it can be queried in parallel and pruned, processing only the required Segments for a given request at query time. This enables distributed processing, scalability and efficiency that drive performance and support higher concurrency.

The primary partitioning schema is, almost always, on the time dimension. Ingested rows have a primary timestamp, and are split into equally sized time intervals, sometimes referred to as “time chunks”. It is good practice to write queries that use a condition on a subset of the overall time horizon in order to take advantage of pruning. Pruning is the technique Druid uses to ignore segments that are unnecessary to process at query time given filter conditions of the query.

Druid also supports secondary partitioning schemes, allowing you to further subdivide into more segments.  Druid will parallelize processing of a query across  your new segments and, given a secondary partitioning scheme aligned with the filter criteria you’re using at query time, achieve a greater degree of  pruning. Secondary partitioning, then, is key to greater performance and greater efficiency.

The ideal size of a segment varies by implementation and query patterns, but the general recommendation is that they be somewhere in the vicinity of 5 million rows or 500-700MB in size. So if you have a dataset where the number of rows per time chunk is much higher, you’ll want to use secondary partitioning.

In this blog post, I’m going to focus on how to use secondary partitioning through SQL ingestion’s CLUSTERED BY clause which uses a Range Secondary Partitioning scheme. When you’re ready to learn more, check out the useful links in the “Learn more” section, which includes links to my video series on Partitioning.

SQL Ingestion and Partitioning

When you ingest data into Druid using SQL INSERT or REPLACE statements you must specify primary partitioning using the PARTITIONED BY clause which determines the size of the time chunks for the target table. With the CLUSTERED BY clause you can specify secondary ranged partitioning using one or more dimensions.

Let’s use the sample data in Apache Druid 24.0 (or newer) to do this. I’m using the quickstart micro instance for these examples.

Primary Partitioning

First let’s just use primary partitioning and see what the segments look like without using CLUSTERED BY. Use the “Connect external data” option in the Query view of the Druid Console:

Select “Example data”, check the radio button for “Wikipedia”, and click “Use Example”:

It will automatically parse a sample of the data, there’s no need to adjust it here, so just click “Done”:

The result is a prebuilt SQL REPLACE statement with the EXTERN table function to read the external file for “Wikipedia” data:

Notice that it contains the PARTITIONED BY DAY syntax at the end which sets the time chunk size to a day each. The Wikipedia data it rather small and contains only rows for a single day, so when you run this ingestion by clicking on the “Run” button, you get a single segment file, visible in the Datasources view:

For this tiny dataset we don’t really need more partitioning, even if each day contained up to a few million rows, there would be little benefit to secondary partitioning. If this is the rate of rows per day for a real-life dataset, then you’d likely benefit from using PARTITIONED BY MONTH or even coarser time partitioning in order to get a few million rows per time chunk.

But the size and set of dimensions in this data make it a good dataset to quickly experiment with, so for the remainder of this blog we will explore partitioning this dataset even though it is tiny and would normally not need it.

The target segment size is controlled by the Query Context parameter rowsPerSegment which you can add to through the “Edit Context” menu:

I set it to 8000, so that we can see multiple segment files and how CLUSTERED BY changes them:

I reran the ingestion “as-is” (without CLUSTERED BY) to see how the segment files changed. In the Datasources view, you can now see four segments:

Click on the “4 segments” link to see the detail:

You can see that the four segments correspond to the same time chunk; the “Start” and “End” columns correspond to the same timeframe. The shard type column says “numbered” meaning that the data is not organized in any particular way, it is just split evenly into enough segments such that none exceed the rowsPerSegment value we selected.

As you can see, even without specifying a CLUSTERED BY clause, the ingestion will create partitions of a time chunk when there are more than “rowsPerSegment” rows in that interval. But given that these segments use a numbered shard type, they will all need to be processed at query time when this time interval is requested.

Secondary Partitioning with CLUSTERED BY

Segment pruning improves query response time by removing unnecessary segments from consideration. It also improves overall system efficiency by using less resources to resolve queries.

You can improve pruning by using the CLUSTERED BY clause at ingestion. The best dimensions for this purpose are the ones that will most commonly be used to filter data at query time. Let’s say you want to enable analytics for each Channel, meaning your users will almost always include a filter on that dimension. In that case, clustering by channel would be a good choice.

Using the same ingestion SQL above I added  a CLUSTERED BY clause on the “channel” dimension:

The resulting segments are still evenly split but the rows are now organized:

The Shard type is “ranged” and the Shard Spec shows the boundary values for the channel dimension at which it is splitting the files.  Druid automatically added another column to the range partitioning thresholds besides “channel”; it shows up here as “d1”. It is an internally calculated field that uses a hash of all the columns to create additional granularity in the boundaries in order to automatically deal with skew in the selected dimension.

Skew is the imbalance in the distribution of values of a dimension. When some values appear much more than others in the data, splitting the data along values in that dimension alone could create some segments that are much larger than others. Response times for queries are affected by this because overall query performance will be throttled by the largest segment processed. The largest segments will take longer to process causing some threads to take longer than others, this is called hotspotting. Keeping segments evenly sized will reduce or eliminate hotspotting.

Let’s review how Druid automatically dealt with skew in this example. Take a look at the counts by channel to see how skewed the dimension actually is:

Using the ratio of the maximum row count over the average row count provides a measure of skew. In this case, it means that there is at least one value of channel that appears 13 times more often than the average. Apache Druid deals with skew by adding the hash dimension to the range partitioning dimensions in order to split the rows within the time interval evenly.

The boundaries between segments are determined within a time interval such that the generated segments each get an even number of rows.

Querying CLUSTERED Data

As stated before, using CLUSTERED BY at ingestion will improve pruning which in turn improves query performance and overall system efficiency. In this section I’ll review how pruning occurs with different query scenarios on the sample data.

At query time, if a filter on channel is used, pruning is applied such that segments without the values you are looking for are not processed. Here are a few examples of queries, and how pruning will work in each case because of the CLUSTERED BY operation above:

SELECT page, SUM(added) total_added, SUM(deleted) total_deleted

FROM wikipedia

WHERE channel = '#ca.wikipedia'

GROUP BY page

In this case only one segment will be used, since the value “#ca.wikipedia” only exists in one of the shard specs, this one:

Notice that a start of negative infinity just means that all values that are less than the end boundary are included in that segment.

It supports IN list conditions such as:

SELECT page, SUM(added) total_added, SUM(deleted) total_deleted

FROM wikipedia

WHERE channel in ('#ca.wikipedia', '#gl.wikipedia`)

GROUP BY page

Which will process Partitions 0 and 2 that contain those two values:

Note that filtering for values at the boundaries will use the two partitions that contain the boundary as either a Start on End value in the Shard Spec. So, querying for channel = ‘#en.wikipedia’ will require partitions 0 and 1:

It supports range conditions as well, by pruning segments that are completely outside the range filter:

SELECT page, SUM(added) total_added, SUM(deleted) total_deleted

FROM wikipedia

WHERE channel >= '#ca.wikipedia' and channel <='#de.wikipedia`

GROUP BY page

In this case, only Partition 0 is needed. The partitions that do not contain either the start or the end of the range will be pruned.

Conclusion

Using SQL to ingest data in Apache Druid is easy and powerful, it allows you to control time granularity using PARTITIONED BY, and control dimension based pruning by using the CLUSTERED BY clause. While this blog used a small dataset to show how range partitioning works, it becomes important when the number of rows within a time chunk in your dataset is larger than a few million rows (the default rowsPerSegment is set at 3 million). It makes a lot of sense to use clustering to achieve better performance of individual queries and it also drives more efficiency in the use of overall cluster resources. It is important to choose clustering dimension(s) that will be frequently used in query filter criteria such that Druid’s brokers can take the most advantage of pruning.

The fact that it deals with skew so elegantly is not just icing on the cake, although it is pretty sweet. It automatically deals with a big problem in distributed systems which frequently suffer from hotspotting when skew is not addressed. With SQL based ingestion, it is now very easy to load and organize data to achieve application analytical needs with great performance.

Learn more

Other blogs you might find interesting

No records found...
Feb 21, 2024

What’s new in Imply Polaris – January 2024

At Imply, we're excited to share the latest enhancements in Imply Polaris, our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®. Our commitment to refining your experience with Polaris...

Learn More
Feb 21, 2024

Introducing Apache Druid 29.0

Apache Druid® is an open-source distributed database designed for real-time analytics at scale. We are excited to announce the release of Apache Druid 29.0. This release contains over 350 commits & 67 contributors.

Learn More
Feb 14, 2024

Apache Druid vs. ClickHouse

If your project needs a real-time analytics database that provides subsecond performance at scale you should consider both Apache Druid and ClickHouse. Find out how to make an informed choice.

Learn More
Jan 23, 2024

Enhancing Data Security with Role-Based Access Control in Druid and Imply

Managing user access to relevant data is a crucial aspect of any data platform. In a typical Role Based Access Control (RBAC) setup, users are assigned roles that determine their access to relevant data. We...

Learn More
Jan 16, 2024

Comparing Data Formats for Analytics: Parquet, Iceberg, and Druid Segments

In this blog, I will give you a detailed overview of each choice. We will cover key features, benefits, defining characteristics, and provide a table comparing the file formats. Dive in and explore the characteristics...

Learn More
Jan 12, 2024

Scheduling batch ingestion with Apache Airflow

This guide is your map to navigating the confluence of Airflow and Druid for smooth batch ingestion. We'll get you started by showing you how to setup Airflow and the Druid Provider and use it to ingest some...

Learn More
Dec 29, 2023

A Buyer’s Guide to OLAP Tools

How do OLAP databases work—and which one is right for you? Read this blog post to learn more about which OLAP solutions are best for different use cases.

Learn More
Dec 26, 2023

What is IoT Analytics?

Because it deals with fast-moving, real-time data, IoT analytics is uniquely challenging. Learn how to overcome these challenges and how to extract (and act on) valuable insights from IoT data.

Learn More
Dec 19, 2023

OLTP and OLAP Databases: How They Differ and Where to Use Them

Learn about the differences between analytical and transactional databases—their strengths and weaknesses, what they’re used for, and which option to choose for your own use case.

Learn More
Dec 15, 2023

Query from deep storage: Introducing a new performance tier in Apache Druid

Now, Druid offers a simpler, cost-effective solution with its new feature, Query from Deep Storage. This feature enables you to query Druid’s deep storage layer directly without having to preload all of your...

Learn More
Dec 15, 2023

How KakaoBank Uses Imply for Financial Analysis

As a mobile-first digital platform, KakaoBank accumulates a substantial amount of data. Therefore, analysts need a solution that can effectively analyze and pre-process large quantities of data, visualize the...

Learn More
Dec 14, 2023

Joins, Multi-Stage Queries, and More: Relive the Excitement of Druid Summit 2023

Druid Summit kicked off its fourth year as a global gathering of minds passionate about real-time analytics and the power of Apache Druid. This year’s event revealed a common theme: the growing significance...

Learn More
Dec 13, 2023

An Introduction to Online Analytical Processing (OLAP)

Online analytical processing (OLAP) analyzes data at scale—and provides actionable insights to organizations. Learn about how OLAP works, what a data cube is, and which OLAP product to use.

Learn More
Dec 12, 2023

Real-Time Data: What it is, Why it Matters, and More

Real-time data travels directly from the source to end users, so that it can be processed and acted on instantly. Learn all about the challenges, benefits, and best practices for real-time data.

Learn More
Dec 08, 2023

Druid vs Pinot: Choosing the best database for Real-Time Analytics

Do you want fast analytics, with subsecond queries, high concurrency, and combination of streams and batch data? If so, you want real-time analytics, and you probably want to consider the two Apache Software...

Learn More
Dec 07, 2023

What’s new in Imply Polaris – October and November 2023

At Imply, our commitment to continually improving your experience with Imply Polaris—our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®—is evident in recent developments. Over...

Learn More
Nov 15, 2023

Introducing Apache Druid 28.0.0

Apache Druid 28.0, an open-source database for real-time analytics, introduces Async queries, UNION ALL support, SQL WINDOW functions, enhanced ingestion features, including multi-Kafka topic support, and...

Learn More
Oct 18, 2023

Migrating Data From S3 To Apache Druid

This blog covers the rationale, advantages, and step-by-step process for data transfer from AWS s3 to Apache Druid for faster real-time analytics and querying.

Learn More
Oct 12, 2023

What’s new in Imply Polaris, our real-time analytics DBaaS  – September 2023

Every week, we add new features and capabilities to Imply Polaris. Throughout September, we've focused on enhancing your experience as you explore trials, navigate data integration, oversee data management,...

Learn More
Sep 27, 2023

Introducing incremental encoding for Apache Druid dictionary encoded columns

In this blog post we deep dive on a recent engineering effort: incremental encoding of STRING columns. In preliminary testing, it has shown to be quite promising at significantly reducing the size of segment...

Learn More
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 Data 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 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 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

Let us help with your analytics apps

Request a Demo