If you thought you had perfect rollups before, you might have been wrong!

Apr 29, 2021
Maytas Monsereenusorn

The Druid engineering team at Imply recently made some changes to how rollup works with sparse data. This blog explains how rollups work, and the impact of the changes that we made. Interested? Of course you are! These changes make Druid an even faster and more storage-efficient database!

In Apache Druid, you can roll up duplicate rows into a single row to optimize storage and improve query performance. Rollup pre-aggregates data at ingestion time, which reduces the amount of data the query engine needs to process when the user makes a query. A rollup includes rows with the same timestamp and dimension values. Depending on the type of partitioning in the ingestionSpec, a rollup can be either “perfect” or “best effort”. A perfect rollup guarantees that all duplicate rows are perfectly aggregated at ingestion time. This post explains how perfect rollup works, and a bug with perfect rollup that we recently fixed when we ingest sparse data. If you thought you had perfect rollups before, you might have been wrong! Keep reading to understand why.

In dimensionsSpec, you can specify dimensions for your input data with any ordering that you want. The ordering of the dimensions is important: It can affect both how the data is compressed and the ultimate size of Druid segments. In the past, ordering your dimensions non-lexicographically (or, non-alphabetically) potentially posed problems for perfect rollups during ingestion. The bug resulted in perfect rollups not working in cases with a non-lexicographical dimension ordering specified in the dimensionsSpec when at least one of the dimensions contained sparse data. Sparse data means that a large proportion of the data within a given dimension is null. To put it another way, ingested data for which perfect rollups were enabled could still have resulted in the data having duplicate rows with the same timestamp and values for the roll up dimensions. This bug did not produce any error logs or user-facing failure messages.

The next section explains what was going wrong, and also how we fixed it.

Why sparse data impeded perfect rollups

When a batch task ingests data, row by row, it creates intermediate Druid segments that are persisted onto disk when the task runs out of in-memory storage. This method allows the task to free up in-memory storage and continue ingesting more data. These intermediate segments basically contain a subset of the entire input data that is read by the batch task and ingested into Druid with rows ordered based on the dimension ordering specified in dimensionsSpec. After the task finishes going through the assigned input data, it merges all the intermediate Druid segments that it generated into a new segment. The merge guarantees that duplicate rows across all intermediate Druid segments are perfectly rolled up, and the rows of the merged segment are still ordered based on the specified dimension ordering. When these intermediate segments contain sparse data, the bug caused the merged segments still to have duplicate rows that did not adhere to the specified dimension ordering.

A problem with merging intermediate Druid segments

When Druid merges all the intermediate segments, it first determines a shared dimension ordering across all the intermediate segments. If there isn’t one, then Druid falls back to using a lexicographic dimension ordering. This fallback used to happen with sparse data and is the root cause of the bug.

To illustrate what had been going wrong using an example, let’s say we ordered three dimensions in the dimensionsSpec as follows: dimB, dimA, dimC.

Let’s imagine that the rows of data below are data that the task received. The rows in bold are duplicates: They have the same timestamp, dimensions, and dimension values. Hence, we expect the task to roll up all the duplicate rows perfectly:

{"time":"2015-09-12T00:46:58.771Z","dimA":"C","dimB:"F"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"C","dimB":"J"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"H","dimB":"X"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"Z","dimB":"S"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"H","dimB":"X"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"H","dimB":"Z"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"J","dimB":"R"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"H","dimB":"T"}
{"time":"2015-09-12T00:46:58.771Z","dimA":"H","dimB":"X"}
{"time":"2015-09-12T00:46:58.771Z","dimC":"A","dimB":"X"}

To demonstrate when the bug happened, let’s assume that Druid can process three rows of input data in memory before having to persist data onto disk as intermediate segments. These intermediate segments are shown below. Notice that each intermediate segment has the rows ordered by the specified dimension ordering: dimB, dimA, dimC. You can tell because the rows in each segment are ordered alphabetically according to the values of dimB (dimB is evaluated first since it is the first dimension in the ordering).

Segment 1: {"time":"2015-09-12T00:46:58.771Z","dimB":"F","dimA":"C"}
Segment 1: {"time":"2015-09-12T00:46:58.771Z","dimB":"J","dimA":"C"}
Segment 1: {"time":"2015-09-12T00:46:58.771Z","dimB":"X","dimA":"H"}
Segment 2: {"time":"2015-09-12T00:46:58.771Z","dimB":"S","dimA":"Z"} Segment 2: {"time":"2015-09-12T00:46:58.771Z","dimB":"X","dimA":"H"} Segment 2: {"time":"2015-09-12T00:46:58.771Z","dimB":"Z","dimA":"H"}
Segment 3: {"time":"2015-09-12T00:46:58.771Z","dimB":"R","dimA":"J"} Segment 3: {"time":"2015-09-12T00:46:58.771Z","dimB":"T","dimA":"H"} Segment 3: {"time":"2015-09-12T00:46:58.771Z","dimB":"X","dimA":"H"}
Segment 4: {"time":"2015-09-12T00:46:58.771Z","dimB":"X","dimC":"A"}

Notice that no single intermediate segment has all three dimensions (dimB, dimA, dimC), because dimA and dimC are sparse. Specifically, rows in the first three segments do not contain dimC, and the rows in the fourth segment do not contain dimA. The dimensions of each intermediate segment are shown below:

Intermediate segment 1: dimB, dimA
Intermediate segment 2: dimB, dimA
Intermediate segment 3: dimB, dimA
Intermediate segment 4: dimB, dimC

Since there is not a common dimension ordering across all four intermediate segments, merging them used to fall back to a lexicographic ordering: dimA, dimB, dimC.

The process of merging intermediate segments begins by finding the smallest (timestamp, rollup dimensions) tuple value across the first row of every intermediate segment. Since the timestamps in this example are the same, the process begins by finding the smallest value for the first listed dimension: dimA, in the lexicographically reordered case. Therefore, the smallest value for dimA is null in Segment Four: {“dimB”:”X”, “dimC”:”A”}. After this row, the candidates for the next iteration of merging are the first rows of the three remaining intermediate segments.

By contrast, with the original ordering from the dimensionsSpec (dimB, dim A, dimC), the merging process would have begun by looking for the smallest value of dimB. The smallest value for dimB is a completely different row—namely, the first row in Segment One: {"dimA":"C", "dimB":"F"}. After this row, the candidates for the next iteration are the first rows of Segment Two, Segment Three, Segment Four as well as the second row of Segment One.

Let’s compare how the merging process would iterate through all ten rows in this example with the dimensionsSpec dimension ordering versus the lexicographical reordering:

Merging intermediate segments with
intended dimensionsSpec ordering

Merging intermediate segments with lexicographical ordering

Seg 1: {"dimB":"F", "dimA":"C"}       Seg 4: {"dimB":"X", "dimC":"A"}
Seg 1: {"dimB":"J", "dimA":"C"}     Seg 1: {"dimA":"C", "dimB":"F"} 
Seg 3: {"dimB":"R", "dimA":"J"}     Seg 1: {"dimA":"C", "dimB":"J"}
Seg 2: {"dimB":"S", "dimA":"Z"}     Seg 1: {"dimA":"H", "dimB":"X"}
Seg 3: {"dimB":"T", "dimA":"H"}     Seg 3: {"dimA":"J", "dimB":"R"}
Seg 4: {"dimB":"X", "dimC":"A"}     Seg 3: {"dimA":"H", "dimB":"T"} 
Seg 1: {"dimB":"X", "dimA":"H"}     Seg 3: {"dimA":"H", "dimB":"X"}
Seg 2: {"dimB":"X", "dimA":"H"}     Seg 2: {"dimA":"Z", "dimB":"S"}
Seg 3: {"dimB":"X", "dimA":"H"}     Seg 2: {"dimA":"H", "dimB":"X"}
Seg 2: {"dimB":"Z", "dimA":"H"}     Seg 2: {"dimA":"H", "dimB":"Z"}

The problem with the lexicographical ordering on the right is that rollup can only happen when the duplicate rows are returned in consecutive iterations—a rollup cannot happen when non-duplicate rows merge in between the duplicates. Merging the intermediate segments uses an n-way merge algorithm where each intermediate segment is already sorted. Basically, Druid finds every row for a given (time, rollup dimensions) tuple across all intermediate segments and rolls them up into a single row before moving to a new (time, rollup dimensions) tuple value. When Druid moves to a new tuple value, Druid no longer keeps track of the rows for the previous (time, rollup dimensions) tuple value. Therefore, even if Druid encounters a row with the previous tuple value, it cannot roll the row up, even though it ideally should. This is exactly what happens with the lexicographical reordering on the right. In the fourth iteration, Druid encountered {"dimA":"H", "dimB":"X"}. But in the fifth iteration, Druid moved to a new tuple value:{"dimA":"J", "dimB":"R"}. Therefore, when Druid encountered the duplicate {"dimA":"H", "dimB":"X"} rows later, Druid could no longer roll up these duplicate rows.

Fixing getLongestSharedDimOrder() for perfect rollups

The problem outlined above begins with a method called getLongestSharedDimOrder. This method used to return null when intermediate segments did not share a common dimension ordering. The returned null value triggered the change from the dimension ordering specified in the dimensionsSpec file to a lexicographical dimension ordering for the merging of the intermediate segments. (Note that if the dimension ordering in the dimensionsSpec is lexicographical, then this problem would not happen, since the change to lexicographical dimension ordering by getLongestSharedDimOrder resulted in the same ordering as the one specified in the dimensionsSpec.)

To preserve a non-lexicographical dimensionsSpec dimension ordering, we now pass this ordering as a new argument into the getLongestSharedDimOrder method.

In getLongestSharedDimOrder, we first try to find a common dimension ordering as before. But if the intermediate segments do not share a dimension ordering, we now try an extra step before reverting to lexicographical ordering. This new step uses the dimension ordering from the dimensionsSpec, and checks whether following conditions are met:

  • The dimension ordering in the dimensionsSpec is not null and not empty.
  • The dimension ordering in the dimensionsSpec does not have extra or missing columns compared to the set of dimension names from all intermediate segments.
  • The dimensions in all intermediate segments follow the same ordering as in the dimensionsSpec. For example, if the dimensionsSpec is dimB, dimC, dimA and one of the intermediate segment’s dimension ordering is dimB, dimA then this ordering is valid. But if a segment’s ordering is dimA, dimB, then it is invalid.

For batch ingestion with a non-lexicographical dimension specified, all three of these conditions should always hold. (There is a concept in Druid for schema-less dimensions, which can still fall back to lexicographic ordering, but this is out of scope here.) Now that the getLongestSharedDimOrder method correctly returns the same, non-lexicographical dimension ordering that was used to create intermediate segments, perfect rollups are possible—even with sparse data.

Conclusion

Since this bug didn’t produce any error messages, you might have thought you were creating perfect rollups when, in fact, you were not. With the dimension ordering specified in dimensionsSpec passed into getLongestSharedDimOrder, perfect rollups happen even with a non-lexicographical ordering and sparse data. While the fix is simple, the impact is huge. This change not only improves storage optimization but also improves query performance —two of the most important metrics for using Druid—for users who specify a non-lexicographical dimension ordering in dimensionsSpec when ingesting sparse data.

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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.

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Sep 06, 2021

Community Spotlight: Sparking that connection with Apache Druid

It’s been nearly 10 years now since Druid was open sourced “to help other organizations solve their real-time data analysis and processing needs”. This has happened not because of one person or one...

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Aug 18, 2021

Community Spotlight: Augmented analytics on business metrics by Cuebook with Apache Druid®

Cuebook is putting you, decision-maker, back in the driving seat, powered by Apache Druid®. In this interview with their founder and CEO, we learn their reason for being, their open source Cuelake tooling,...

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Jul 28, 2021

The Open Source Modern Analytics Stack

Empowering all types of users to analyze data incredibly quickly from wherever it sits provides huge value to organizations. Citizen data scientists and decision scientists are able to make empirically-backed,...

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Jun 16, 2021

Imply Raises $70MM in Series C funding

Our vision at Imply has always been to create a new category for data analytics, analytics-in-motion, and enable organizations to unlock workflows they’ve never been able to do before. With the most recent...

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May 25, 2021

Community Spotlight: Avesta powers next-generation applications with Apache Druid

When considering various real-time analytics solutions, Apache Druid quickly became the clear choice: Avesta uses only open-source products and libraries. And today, they’re using Druid as a central component...

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May 25, 2021

The Future of Analytics

The traditional BI workflow starts with a strategic question. Such a question is not too time-sensitive—days or weeks is okay—and the question is pretty complex to answer.

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May 18, 2021

How we enabled the “Go Fast” button on TopN queries: Hint: we used vectorized virtual columns (which is new in Apache Druid 0.20.0)

Apache Druid is a fast, modern analytics database designed for workflows where fast, ad-hoc analytics, instant data visibility, or supporting high concurrency is important. Multiple factors contribute to...

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May 17, 2021

How Sift is accurately identifying anomalies in real time by using Imply Druid

As the leader in Digital Trust & Safety and a pioneer in using machine learning to fight fraud, Sift regularly deploys new machine learning models into production. Sift’s customers use the scores generated...

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May 14, 2021

Making the impossible, possible: A GameAnalytics case study

We’ve had the pleasure of speaking with Ioana Hreninciuc, CEO of GameAnalytics, to learn just how they use Imply to make their next-generation data stack possible.

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May 11, 2021

Make your real-time dashboards blazing fast with per-segment caching

Imagine a scenario where Druid is collecting metrics about a huge microservices application —there’s a continuous stream of metrics coming in about the different services from this application.

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May 11, 2021

Community Spotlight: smart advertising from Sage+Archer + Apache Druid

Out-of-home advertising has changed. Gone are static, uncompromisingly homogenous posters, replaced instead with bright and fluid installations. Installations that make smart decisions about what and when...

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May 07, 2021

Data deletion in Apache Druid (part 2)

Some time ago, Dana Assa and I wrote a detailed blog post about Data retention and deletion in Apache Druid. Our intention was to help Druid database users and provide guidance on how to control the TTL...

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Apr 30, 2021

Data Revolution at Hawk powered by Imply

Hawk is the first independent European platform to offer a transparent and technological advertising experience across all screens: Desktop, Mobile, CTV, DOOH & Digital Audio.

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Apr 27, 2021

Imply’s real-time analytics maturity model to create better customer experiences

Imply’s real-time Druid database today powers the analytics needs of over 100 customers across industries such as Banking, Retail, Manufacturing, and Technology. We have observed that the majority of prospects...

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Apr 09, 2021

What I wish I knew about Imply when I was developing in-house analytics

Like a lot of engineers at Imply, I got my start here after having worked on an analytics solution for a previous employer. In my case, it was a large non-tech company going through a digital transformation.

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Mar 31, 2021

Kueez leverages Imply Cloud to reduce operational overhead & enable real-time analytics

Imply allows Kueez's data analysts, content editors, and growth teams to optimize their campaigns in real-time. With open-source Druid, they struggled to keep their system up and running, their queries were...

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