Learn how to achieve sub-second responses with Apache Druid

May 20, 2023
Sergio Ferragut


Data-driven applications and dashboards place performance and scalability demands on databases. Just one poorly designed query can make or break the user’s experience, as can the high numbers of queries per second that internet-facing apps and dashboards generate.

Apache Druid® was built for just this scenario – with the promise that its data format, architecture, and massively-parallelised operations help us to deliver on the levels of performance needed for real-time exploration.

The late Carl Sagan tells us that, “if you want to make an apple pie from scratch, you must first invent the universe.” If you want to have a performant data application, you must first understand how the query engine powering your application works – from the ground up. Any discussion I’ve had about query performance in Apache Druid has inevitably led to a discussion about data modeling and layout. When I’ve seen people ingesting data just to get the job done (treating Druid like a data lake, for example) they ignore this important relationship about how these two things influence and affect each other.

In this article, I’ll examine this interplay between data modeling and query performance in Apache Druid, and cover the four go-to data modeling techniques that you can apply at ingestion time:

  • Rollup (GROUP BY)
  • Sorting
  • Indexing

Unlocking Druid Performance

Take this diagram of how clients submit queries to Druid.

At the top, clients submit queries to the Broker (1). From there, the queries fan-out (2), distributing the queries to data processes (Historicals and Indexers / Peons). Each one handles its piece of the query (3) and returns the results to the Broker via a fan-in (4). The Broker merges the results (5) and serves them to the user.

Examining this in greater detail will inform your data modeling decisions giving you the tools to understand and improve performance. This Query Stack diagram helps us understand how Druid returns results:

Queries on tables execute with a fan-out / fan-in approach which is also called a  scatter / gather approach. The bottom two layers of the diagram are data process activities, the top two are Broker activities. The fan-in mechanism happens in between these two halves, connecting the Broker and data servers.

The diagram is shaped this way because it illustrates the level of parallelism involved in each stage. The bottom layer is more scalable because it is where the highest parallelism is achieved. But as processing moves up through the layers, there is progressively less parallelism involved. The impact is that queries that use the fan-in / fan-out execution pattern perform best when the volume of data reduces as processing moves up through the stages.

With that in mind, let’s look at each of the stages and how the infrastructure we provide to Druid is utilized.

Submitting the Query

The first step in query processing happens on the Broker.

It can distribute queries into different priority queues, this is called Query Laning. It enables you to manage mixed workloads with Druid, with fast queries getting the resources they need to be fast while keeping longer-running, more resource intensive queries in check. Read all about that in Kyle Hoondert’s great article Query Prioritization in Apache Druid.

Query Planning and Fan-Out

The Broker parses the request and figures out which segments of data are needed to respond to the request.

This is why the WHERE clause __time condition in your query is so important. Druid maintains an in-memory index of the table timeline, and the Broker uses this to find which data processes contain copies of individual segments that cover the requested part of the timeline.

If the data source uses secondary partitioning on other dimensions and you include a filter on those dimensions in the WHERE clause, the Broker uses this information (which is also in the global index) to further prune the list of relevant segments to process.

Check out my video series on secondary partitioning where I describe what goes on at ingestion time to generate the partitions and how each partitioning strategy helps prune at query time.

Data Server Processing Threads

Once the Broker has identified the data it needs, it issues requests to the corresponding data processes that have those segments available. Each data process executes its portion of the query: streaming ingestion tasks (Peon / Indexer) process recently consumed data, Historicals process already ingested data.

Starting at the bottom layer of the diagram, we can now turn to the individual processing threads on those Historicals and Indexers / Peons involved in executing the query plan.


Each thread works on one segment at a time.

The thread evaluates the WHERE clause to apply filters, including trimming __time even further, and examines the filtering columns’ dictionaries and indexes to find the relevant rows within each segment.

For more complex WHERE clauses the thread uses bitmaps from multiple index entries across different columns and bitwise operations among them to find the right rows.

If the query uses GROUP BY, the thread then performs local aggregation on the selected rows.

Depending on the query pattern, the results may be ORDERed and LIMITed at this point.

Zoom out a little, and you can visualize how this is happening in parallel across multiple threads on multiple Historicals.  When a table is made up of too many small segments it can generate management overhead because it will use many more threads to process many more segment files. Conversely, tables that have segments that are too large – even just one or two – cause delays in single threads that impact the overall parallel operation.

Read more about Segment size optimization in the official documentation, where you’ll see it’s best practice to balance segment sizes, and to keep them large enough to be efficient (approximately 5 million rows per segment with sizes in the 500-700 MB range).

Streaming Ingestion Tasks

When the query’s __time filter covers time intervals that streaming ingestion tasks are working on, the Broker submits the request to all the tasks involved.

Each task is in charge of certain shards of the incoming stream (e.g. a set of partitions in Kafka). Each task holds the rows associated with its shards in memory until the buffer is deemed full, at which time it spills the rows to disk into files in what’s called an “intermediate persist”.

When a query request arrives, the task merges the in-memory row buffer with any intermediate persists it has accumulated and resolves its portion of the query on that data. Just like in Historicals, the task filters and aggregates its local data, and might apply ORDERs and LIMITs before finally sending its results to the Broker.

Zooming Out

You can now see that there’s a high degree of parallelism at this stage. This bottom processing layer is constrained only by the number of threads available. Increasing resources here will likely help in query performance and in dealing with higher concurrency. That means providing enough cores to handle enough segments to support the parallelisation – and making sure that your segments are the right size.

Data Server Jetty Thread

Moving up the Query Stack diagram, there’s a single Jetty thread per query in each data process that takes the results from the individual segments and merges them into a single result set that it can percolate up to the next level – the Broker.

The Jetty thread accumulates all the data from the segment processing threads and does the final merge, filter, aggregation, ORDER BY, and LIMIT as needed.

Here, the opportunity for parallelism is reduced. Only one thread for each query is activated to do this work in each data process: one per Historical, one per streaming ingestion task.

You need to consider how much data the segment-processing threads are returning to this merging thread. For example, a GROUP BY on a high-cardinality dimension can cause a bottleneck, as would a SELECT * with many columns or a query without enough filtering in the WHERE clause.

Broker Parallel Merge Threads

The next step sees the Broker merge the partial results from each of the data processes.

At this point, a Druid query does not scale out – it is located in one specific Broker. Imagine you have Brokers  that are 16 cores each, and you have 10 of them. Even with 160 processors at your disposal, only 16 can be used for this merge stage as that’s where this query is being governed.

Monitoring core utilization on individual Brokers when queries execute is key here. You should have enough capacity per Broker to merge results from across your data servers at query time for many concurrent requests.

Broker Server Jetty Thread

Finally, at the very top, the Broker server Jetty thread completes the operation that evaluates the final LIMIT, ORDER BY, or HAVING clauses on the merged result.

This query step is single threaded, so for best performance we don’t want to have too much data or too many operations taking place here as these will rapidly cause a bottleneck.

As an example of the complex interplay between data modeling and query performance, it is much more efficient to push the LIMIT and ORDER BY down to the data server processing threads where query performance can scale better. Druid does this automatically for LIMIT clauses that run with the native Scan or TopN query types, and for ORDER BY clauses that run with native GroupBy query types. Adding LIMIT and/or ORDER BY clauses that push down to data servers is a great technique to boost performance in Druid.

The Query Stack in Action

Let’s dig deeper into the bottom layer of the Query Stack to see how to split things up and optimize for performance.

One way to think about how Druid operates is that the query fans out to a bunch of segments, and then the response fans-in. This is significant because we see lots of examples where queries bottleneck at that bottom layer. It’s easy to see that this is what is happening when queries are running and data server CPUs are pegged at 100%. That’s a clear indicator that that bottom layer is where the action is happening, and this is a typical Druid experience. Of course, any layer could be a bottleneck; the best way to identify the bottleneck is with flame graphs.

At the bottom layer there’s a set amount of CPU processing (or CPU time) that needs to happen on each segment to execute the query, i.e. segment one is going to take a certain amount of time and segment two will take a certain amount of time. These times are fixed for a given data set and a given query. A query on segment number one might take 400 milliseconds of CPU time. It’s not possible to change how fast that runs, but it is possible to add more processors and increase parallelism. You can keep adding processors until the number of processors equals the number of segments, and at that point adding more doesn’t decrease the amount of time needed because we can’t split a segment between multiple processors.

Druid processes segments in parallel across the available CPUs / threads. The total processing time that the bottom layer takes to process a query is therefore roughly the total CPU time needed for all segments divided by the number of processors available.

This is the beginning of a formula that will help us understand query performance…

Performance Algebra

It’s possible to break down different factors that come into play in determining query performance. Here’s a model for overall query performance that considers most of the factors. After we define it, we’ll connect the various pieces to the data modeling and query techniques that can help improve each factor.

t is an estimate of the time a query takes to complete, it has three major components:

Rs/Pr = Total row level processing time

            R – (rows) total number of rows in the selected segments

            s – (%) index selectivity – percentage or rows filtered

            P – (CPUs) number of CPUs available for processing

            r – (rows / cpu second) row processing rate

tsS/P = Segment overhead

            ts – (cpu seconds) segment processing overhead

            S – (#) number of matching segments

            P – (# CPUs) number of CPUs available for processing

Rm /rm = Merging

            Rm – (rows) total number of rows to merge

            rm – (rows / cpu second) number of rows merged per second

Row by row processing and segment overhead take place in the bottom layer of the Query Stack, while merging takes place in the top three layers.

Focusing on row by row processing, one way to think about it is that Druid will need to use a certain amount of CPU time to complete the request, and dividing that by the number of available CPUs, we can estimate the duration of the bottom layer of the Query Stack.

The term Rs over r is the amount of CPU time needed to process all the segments. Dividing that by P given that processing occurs in parallel, results in the time estimate for the first phase.

s is the selectivity of the filters in the query which can have significant impact on overall query time. If a query WHERE clause returns half the rows, index selectivity is one-half (0.5). Alternatively, if a query is not filtered, then index selectivity is equal to one (1.0). Reducing the number of rows to process will have a direct effect on performance.

Let’s work through an example: say we have 2 years of sales transactions for a chain of 1000 stores which on average have 100k transactions each per day. If we create daily segments that each have about 5 million rows, we will end up with about 1000 * 100k / 5 million rows per segment = 20 segments per day.

If we query one week of data, the total number of rows within matching segments would be R = 700 million rows split over 140 segments. When filtering on a single store, index selectivity will be 1/1000 = 0.001 resulting in only 700k rows.

If the row processing rate is 10 million rows per cpu second then total CPU time will be:

  • Without filter:  700 m / 10 m = 70 cpu seconds
  • With single store filter: 700 k / 10 m = 0.070 cpu seconds

Both queries can be fast given enough CPUs. With P=70 cpus, the first query can be done in 1 second and given that we are dealing with 140 segments, each CPU can process two segments in parallel. We could make it even faster with up to P=140 CPUs to resolve the query in 0.5 seconds. More CPUs than P=140 CPUs would not make the query any faster, given that each segment is processed by one thread which runs on one CPU.

With the filtered query, there is a lot less data to process, so the query will be faster. Using filters is a great way of speeding up queries in Druid. But keep in mind that the query will still need to process 140 segments, and that’s where the next term of the equation comes into play.

The segment overhead per segment ts is a result of how the data server processes the query, where each thread must process a segment at a time. Per segment processing, ts is multiplied by the number of segments S that hold the data for the query giving a total cpu seconds needed, divided by the number of CPUs P. However, ts and P can only be affected by changing the composition of the cluster with faster CPUs and more CPUs. You can see why we love compaction in Druid. Compaction can reduce the number of segments to make this term smaller. More generally speaking, segment size optimization is a best practice that will help optimize this term.

The last portion of the equation is merging, where the number of resulting rows from the row processing phase Rm will need to be merged. Notice that this is only true in an aggregation query, where row merging is necessary to calculate final grouped results. We determine the row merge rate rm mostly by the query server size; the query server is the one running the Broker service. The more powerful the server, the larger the row merge rate. Therefore the merging component of the equation has less impact. On the other hand, the number of rows to merge Rm is based on the cardinality of the grouping dimensions, with more rows to merge giving the merging component of the equation more impact.

Data Modelling and Performance

Now let’s consider the connection between data modeling and performance. Returning to our equation, note that some of the variables are now orange. The orange variables are ones that we can affect with data modeling.

The only things we can’t affect through modeling are the row merge rate, which we saw earlier is heavily dependent on query server size, and the number of CPUs. We can affect everything else in this performance equation by optimizing data modeling with Druid. There are four major data modeling techniques used to improve performance in Druid: rollup, partitioning, sorting and indexes.


Rollup in Druid is a process of summarization, an optional feature that serves to reduce the row count through pre-aggregation. Rollup at ingestion time, treats columns as either dimensions or metrics. It aggregates metrics by grouping on time and the unique combinations of dimension values. At query time, users may apply further aggregation along a subset of dimensions and do other metric calculations. The general idea is to do partial aggregation at ingestion to the level of granularity that is useful for querying. This is one of the ways that Druid accelerates query response times.

Rollup can be applied during both real-time ingestion or batch ingestion. For batch ingestion use SQL-based ingestion which allows you to specify the aggregation as a simple insert or replace statement with a SELECT using GROUP BY in the form:

SELECT FLOOR(“timestamp” TO HOUR) AS __time, # primary time column
      page, city,                            # dimensions 
      COUNT(*) as “count”,                   # metrics
      SUM( added) as “sum_added”,            # metrics
      SUM( deleted) as “sum_deleted”         # metrics
FROM TABLE (EXTERN ( … ) )                   # input source
GROUP BY  1,2,3
PARTITIONED BY DAY                      # time partitioning
CLUSTERED BY page                       # 2ndary partitioning

Truncating Time

Note that when applying rollup, it is common to truncate the primary time column to a level that is useful for users of this data. With native or real-time ingestion this is called the query granularity. When using SQL-based ingestion, it is a transformation of the input “timestamp” by truncating it to the desired time unit. Many times, hourly aggregation or minute by minute aggregation is more than enough granularity to achieve users’ goals.

Grouping Cardinality

Depending on the peculiarities of your data, rollup may do absolutely nothing, which may occur if there’s some combination of dimensions in your data set that is unique in some way. For example, this could happen with unique IDs in the data set, or an IoT data set that has only one data point per minute, and you’re grouping on minute and sensor. In either of these examples, the data set is such that no actual grouping occurs so the grouping does not improve performance. So, we see that rollup may result in no performance improvement or may yield great performance improvement, providing a 20 to 40 times reduction in data size and commensurate 20 to 40 times increase in performance. But even a bit of rollup at ingestion will help.

Rollup effectively decreases the number of rows R in the matching segment. It reduces the number of rows in the data source, and therefore the number of rows across all the segments that match a query.

Secondary Partitioning (CLUSTERED BY)

At query time, filtering on the columns used for secondary partitioning will likely reduce the number of segments Druid needs. Secondary partitioning is configured in the tuningConfig section of native batch inserts or compaction jobs. We can also specify secondary partitioning using the CLUSTERED BY clause in SQL Based Ingestion.

Partitioning matters most for situations where users consistently filter on the same selective column. A canonical example is a multi-tenant situation where there is a column for customer ID, and users are always filtering by customer ID so that the users only see their own data.

Partitioning can significantly improve Druid performance because the system can now process a subset of segments within the filtered time frame. Note that not all queries will improve in this fashion. Only those that filter on the partitioning dimension(s) will benefit.

Looking at our equation, partitioning and filtering on the partitioning column affects the number of rows in matching segments and the number of matching segments. Fewer segments will match the query filter, and less segments means less rows.


Every segment partition stores its row data pre-sorted. The sort order of the rows within a segment file will always follow the order in which the columns are ingested, sorting first on the primary __time dimension, followed by all columns as specified either in the SQL-based ingestion SELECT query, or in the dimensionSpec when using native ingestion.

Given the same values of __time, secondary sorting will follow with the next dimensions in the list. If you use queryGranularity in native batch ingestion or truncate time using an expression (i.e. FLOOR( “input_timestamp” TO MINUTE) ) in SQL-based ingestion, you can increase the locality of values in the dimensions that follow __time because there will likely be more rows with the same time value. This improves compression which in turn improves I/O performance.

Given that partitioning already organizes segments into ranges of values for a given dimension, placing that dimension first after __time is likely to increase locality of the same values. This is why we recommend listing the partitioning/clustering dimensions first which will drive primary sorting of the segment.

Let’s look at our equation to see why sorting helps.

Sorting improves the row process rate so we can process rows faster. It’s much harder to scan through jumbled chunks, decompressing and compressing them, because they’re out of order. The combination of partitioning and sorting is very valuable towards improving Druid query performance. Partitioning speeds up three variables in our performance equation and sorting adds another variable to improve. Together, partitioning and sorting optimize the bulk of our performance equation.


Today, Druid indexes string columns and numeric fields within Nested Columns. The Apache Druid project is adding first level numeric column indexes soon. As we saw when discussing index selectivity, filtering on columns with indexes helps query performance. This means that we may further optimize performance by ingesting first level numeric columns as strings. This is a little bit counterintuitive because usually with data systems for best performance it’s best to model things in their most natural type. This happens to be an exception, and it is only temporary.

Indexes work by first looking up the filter value in the column’s dictionary, which is always sorted, and from there looking at the same position in the index where both  reverse lookup and bitmaps are stored. Reverse lookup provides the row numbers within the segment that contain the value. If the filter condition is on multiple columns, bitmaps are used with bitwise operations to find the rows of interest.

Imagine that your data set includes a field for a number that users always filter on, e.g., customer ID. Users are always filtering on customer ID, and never running any aggregations that would require the field to be numerical. In this case, it’s beneficial to store the customer ID field as a string so it gets an index.

In the future, Druid will get indexes on numeric columns, but until then, you can help boost query performance with this powerful technique. In our performance equation, we reduce index selectivity s.

A Few More Thoughts on Modeling with Druid

We need to provide enough flexibility in our model such that Druid can calculate the metrics and statistics at the level of granularity that users need. But having a bias toward aggregation at ingestion and toward solving queries with more aggregate tables will serve us well. Overall, while more tables may use more storage and more CPU at ingestion, it will reduce the CPU needed to process queries. Ingestion occurs once, queries will occur many more times on the same data. Reducing the CPU cost of the query side will use resources more efficiently and enable higher concurrency.

Some very successful implementations of Druid ingest from a single stream of events into multiple tables. They use tables with different time granularities and varying levels of aggregation. We need to think in terms of what analytics are needed and along which dimensions.

We may want to keep a detailed data table, but we should design the applications that query it to use enough filtering to limit the Rows and Segments per query. It’ll be good to have the detailed data around as a source for backfill when developing new functionality or changing aggregation and partitioning strategies. The detail table should be partitioned on the dimensions that are used for filtering those detailed queries to reduce the segment count needed to get the results.

We should use other tables with more aggregation for subsets of functionality when the query workload and its performance expectations warrant it. We should look for opportunities to use approximation functions to remove high cardinality items. Take a user_id column from event data on a mobile app used by millions of users. Perhaps all you need are distinct counts and / or quantile statistics on user activity of the distinct values of user_id which has very high cardinality. Removing user_id in a rollup ingestion will help reduce R. Evaluate whether an approximation with enough accuracy is sufficient for the use case and, if so, replace the column with the appropriate approximation function.

We’ve talked about how secondary partitioning in combination with filters on the dimensions will reduce R and S. Also consider that with secondary partitioning you’ll reduce Rm when grouping on the partitioning dimensions. By partitioning on the dimensions that are commonly grouped on, you pre-organize the data so that it is more likely that the aggregate result for a given grouping value is within one or just a few segments. This improves processing efficiency. Consider a query with GROUP BY that includes the partitioning dimensions. The partial aggregation at the bottom layer of the Query Stack where there is higher parallelism will produce less results, effectively doing more merging sooner. The results are naturally reduced as rows with the same value for the grouping dimension will already be together within the segments. The resulting rows to merge per group as we move up the Query Stack will be reduced because they come from fewer segments.

Remember that for real-time ingestion, we should always use compaction to reduce the number of segments S. Streaming ingestion is optimized for writing, so no secondary partitioning occurs and data values can be fragmented across stream partitions. To mitigate this, the use of hash key distribution of data on the stream will help provide locality at ingestion and produce better organized segments. We should set up compaction to do secondary partitioning that corresponds to the query patterns the table is meant to support.

Druid is a powerful tool for fast analytics on both real-time and historical data, data modeling based on its strengths makes sense from a performance and cost perspective. Investing the time to do modeling based on user needs and an eye toward overall efficiency will render dividends.

Practice Makes Perfect

Druid is designed to be fast and scalable. This article showed you how Druid processes queries and provided four techniques for improving query response time:

  • Rollup – aggregate data as much as reasonable during ingestion, create other rollup tables for subsets of functionality that don’t need the details and can use more speed and less CPU.
  • Secondary Partitioning – optimize pruning when filtering on partition / CLUSTERED BY columns, also consider partitioning based on common grouping columns as this will reduce the amount of merging that needs to be done on the Broker.
  • Sorting – list partitioning columns right after the __time column in order to improve data locality and compression.
  • Indexes – most indexing in Druid is automatic. Take advantage of Nested Columns to automate indexing on all nested fields. Use string data type for numeric columns that are meant to be used for filters instead of metric calculations.

Download Druid and get started on the path to blazing fast data-driven applications.

Here’s a set of resources that will help:

SQL query translation · Apache Druid

Query execution · Apache Druid

Free Training on Apache Druid

You can test with a few readily available data sets directly in the Apache Druid UI. From the Query view, select “Connect external data” and choose “Example Data”:

Tell us about your tests and get help from the Apache Druid Community by joining our dedicated slack space. See you there.

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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...

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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

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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...

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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...

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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...

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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

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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

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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',...

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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...

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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...

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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.

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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...

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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.

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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...

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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...

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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

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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...

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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...

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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...

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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...

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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.

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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...

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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

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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.

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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.

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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...

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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...

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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...

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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...

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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...

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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!)

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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.

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May 05, 2023

Introducing Apache Druid 25.0

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

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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.

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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

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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.

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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".

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Apr 30, 2023

Alerting and Security Features in Polaris

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

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Apr 29, 2023

Ingestion from Amazon Kinesis and S3 into Imply Polaris

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

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Apr 27, 2023

Getting the Most Out of your Data

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

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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.

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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.

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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.

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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.

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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...

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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

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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

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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.

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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.

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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...

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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...

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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

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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

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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.

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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.

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Jul 14, 2022

Upserts and Data Deduplication with Druid

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

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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

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Jun 29, 2022

When Streaming Analytics… Isn’t

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

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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.

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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...

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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.

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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.

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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

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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:...

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May 19, 2022

How Imply Polaris takes a security-first approach

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

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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...

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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.

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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.

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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.

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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.

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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...

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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.

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