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A Query Model for Ad Hoc Queries using a Scanning Architecture 
 
             Erik Freed           Brian Anderson 
         Flurry/Yahoo           Flurry/Yahoo 
  erikfreed@yahoo­inc.com  briananderson@yahoo­inc.com 
 
Abstract 
Systems like Hadoop, Hbase and Hive allowed the world to take huge strides in managing and analyzing large                                   
amounts of data. Products like Flurry analytics make efficient use of large amounts of hardware using these tools to                                     
build statistics for hundreds of thousands of applications. However, these tools require the end user to first set up                                     
relevant analytics queries and then wait days for the results. If the results prompt new questions or the original                                     
query is not quite right, the user must rerun and wait  again for the results. 
We present the Burst system developed at Flurry to support low­latency single pass queries over very large and                                   
complex mobile application streams. We have created a data schema and query model that can answer very                                 
complex ad­hoc queries over data, and is highly parallelizable while maintaining low­latency. We implement these                             
scans so that they are time and space efficient using the advanced disk scanning techniques provided by the                                   
underlying operating system. 
1. Introduction 
Flurry gathers mobile application analytics for over 500,000 applications on hundreds of millions of devices.                             
Currently we have accumulated petabytes of metrics across a 2000 node hbase cluster. 1000 node hadoop jobs run                                   
throughout the day to calculate data for graphs and displays that appear in the Flurry Developer Portal. Metrics and                                     
graphs have been previously specified by the user of the developer portal. If the developer wants to explore the data                                       
with new graphs or metrics, they must change the definitions and wait days for the next job run. 
Many of these metrics are based on a series of time­ordered dependent events. For example, funnels define a cohort                                     
[COHORT] event that partitions the application’s users into groups. Afterwards, funnel events track significant                           
events performed by users in that cohort. These metrics are not traditional associative and commutative aggregation                               
functions but instead need finite state machine functions to calculate. 
We developed Explorer as an ad­hoc query product that allows the developer to interactively explore their                               
application metrics and get graphs and charts in sub­second time. This allows the user to do iterative deep­dives                                   
into their application statistics in order to increase the retention and revenue of their application. The Burst system                                   
is the backend storage and query system that supports the Explorer product. 
This paper will discuss how Burst has chosen to focus on a scan­only architecture for processing very large amounts                                     
of data. We will cover the Burst data and query execution model. The underlying architecture and implementation                                 
of Burst is covered in more detail elsewhere [BURST]. 
2. Background 
An ad­hoc query is one where the execution engine cannot predict what form the question will take.  In the world of 
mobile analytics, developers are constantly asking iterative questions about their users and their usage of an 
application so they can improve adoption, retention and ultimately increase revenue. The answer from one query 
drives the next, so the turnaround for results must be sub­second.  While the developer is sifting through the 
time­ordered record of events performed by a user in one or more of their applications, they are doing 
multidimensional aggregates as well as temporal and causal analysis in the form of cohort and funnel analysis 
[COHORT]. Flurry provides analytics as a service to hundreds of thousands of applications so the Burst system 
supports hundreds of simultaneous queries by developers analyzing their event data. 
 
page 1 of 6 
 
A Model for Ad Hoc Queries over Large Datasets using a Scan Optimized Architecture 
Ad­hoc queries with medium size datasets are traditionally being executed  using a relational tuple model [CODD] 
with the declarative query language SQL implemented on a medium to large single machine.  As the size of the 
dataset grows, processing is distributed across multiple machines to increase parallelism.  
In order to make such an ad­hoc query system perform well, the backend system devises complex query plans to 
reduce table scans, find the smallest join cardinalities and utilize data indexes. Data is organized on disk for easy 
scans and index lookup.  The OS helps out with low­level APIs to load the contents of the disk directly into memory 
as well as pre­caching to anticipate reads.  For distributed query engines, the cost of shuffling data between nodes 
must be included while planning the query [DATE][HIVE]. 
However as the size of data or the complexity of the query grows, query performance degrades until usable 
interactive ad­hoc queries are no longer possible. The database expert is called in to denormalize the tables and 
precompute results to restore performance at the expense of query flexibility.  In spite of this effort, the causal 
analysis of cohort analysis can become too challenging for the traditional index and join models of databases. At this 
point, the developer may have to look to alternative implementation strategies to regain performance, such as using 
column oriented storage structures [DRUID] [REDSHIFT], time series databases [TSDB] or data warehouses 
[HIVE], but with the result of giving up query flexibility or complexity, especially in the area of cohort analysis. 
3. Burst 
In developing the Explorer product, we decided to take the approach of assuming that a full scan of the data is 
required and focused on building the burst engine to do this as fast and efficiently as possible.  Once we assumed 
that we were always going to scan everything, we simplified the execution model so we could accomplish 
everything we needed in a ​single​ scan.  We restricted the use of join as well as its more generalized cousin, the sort, 
to create a single pass execution model that scales linearly.  As the dataset sizes grow, we can bring more hardware 
online to maintain low­latency query performance. 
3.1. The Data Model 
The flurry SDK continuously records events produced by an application.  Ultimately, all this data is gathered into a 
user object with references and collections of child objects chronicling those events the user performed: 
Burst supports metadata defined data schemas that are a generalized from the type of analytics data the Flurry SDK 
gathers from mobile applications. The data model has the following characteristics: 
● A ​dataset ​is a homogenous collection of items. 
● Each ​item ​is a rooted hierarchical object consisting of ​scalar value​ fields, ​vector value​ fields, ​scalar 
reference​ fields, ​vector reference​ fields, and value to value ​map​ fields.  Values can be one of the following 
types:  byte, short, integer, long, double, boolean, and string.  Vectors are collections of scalars or 
references to items that can optionally be ordered on some key. 
page 2 of 6 
 
A Model for Ad Hoc Queries over Large Datasets using a Scan Optimized Architecture 
● References point to other items, but an item can only be referenced once:  either as the member of a dataset 
or in a scalar reference field or a scalar vector field.  1
● An item is ​versioned ​so that the data schema can evolve over time.  A dataset as well as scalar and vector 
reference fields can contain items at different versions. 
3.2. Query Model 
Use cases for mobile analytics are inherently unbounded, personalized, and constantly evolving. Queries range from 
simple counting aggregations, multidimensional aggregations, up to complex time sequence conditionals.  Some 
examples include: 
● Count the number of users by day with sessions where they spent more than $5 on in­app purchases 
● Count the number of users by day with sessions where they made a bet and then made an in­app purchase 
to buy more gold before betting again.  2
The low level Burst query execution engine scans the dataset and produces a collection of tuples for each item.  A 
tuple consists of a number of fields:  
● An ​aggregation​ field is one of the following functions:  ​count, sum, , top​K​
, max, min ​.  These functions are 3
associative and commutative so they can be applied in any order. 
● A ​dimension​ field is a scalar value that can partition data using group functions:  ​enum, splits, month, day, 
year, ​and a menagerie of other time partitioning functions. 
As a tuple is created during the scan,  it is combined with any existing tuple with matching values in all the 
dimension fields.  There is no ordering of the scan of the items in dataset, and with the assurance of the associativity 
and commutativity restrictions,  the scan can be done in any order and split up into any number of arbitrary streams 
that are merged together into a final set of tuples.  This ​result set​ of tuples is the query result.  
Each item is ​evaluated​ by traversing it in a depth first search manner starting at the root item and visiting each 
referenced item in a scalar or vector reference field.  The evaluation is done by a single thread and is guaranteed to 
traverse​ the items by following the item relationships, as defined by the scalar and vector reference fields of an 
1
 A tree graph. 
2
 This filter on a time­ordered series of events would require one or more sub­queries in a relational system and can 
typically be unimplementable in time series databases. 
3
 This is an approximated single­pass top​K​
. 
page 3 of 6 
 
A Model for Ad Hoc Queries over Large Datasets using a Scan Optimized Architecture 
item, in DFS order. During the traversal, tuple fields are assigned to build partial results for this item. However, 
while the traversal can be short­circuited it cannot back track to reexamine items already visited. 
During evaluation, the query has two temporary data structures to help it keep state information about the item: 
● A ​global register​ can store a single scalar value.  It can be set and/or reference at any point in the traversal. 
● A​ route ​is defined by a number of ​steps​ as well as the valid ​transitions​ from one step to another (with 
optional time constraints). The route has at least one ​starting​ step with no transitions into it, and one 
terminating​ step with exit transitions . The route​ ​is finite state machine which logs any valid step transition 4
along with a timestamp.  One can ​assert​ an step occurrence to the route any time,  but the route object will 
only record the assertion if a transition from the current route state is allowed. As the route records step 
transitions, it cuts the log into ​paths​ that always begin with a starting step and finish with a terminating 
step.  At any time in the traversal the route can be queried for any currently recorded steps or completed 
paths. 
An evaluation can use multiple instances of each type which are valid within the scope of a single item evaluation. 
They allow the engine to record enough state to evaluate complex event queries in a single pass. 
Evaluation is defined so that it always advancing through an item.  The underlying storage layout of items in Burst 
can take advantage of this property in order to make evaluation very fast and efficient  [BURST].  Some important 
sources of speedup are: 
1. memory cache line prefetching in high end CPUs 
2. disk head read­head in the disk controller 
3. disk to memory prefetching in the disk controller and OS 
4. single copy memory mapping support in the OS 
Imperative Query Plans 
 
The Burst execution engine scans a dataset using a ​GIST​ plan.  The GIST plan is imperative execution plan that 
specifies the schema of the result tuples and what actions to perform during an item evaluation.  Just to give the 
reader a sense of a GIST plan, the following example calculates the total the length of all sessions for all users in 
flurry’s usual metric event schema: 
Gist(Over(1L,512,"America/Los_Angeles"), NoOptions,
Declare(
Gather("user",
NoDimensions,
Aggregations(
Sum[Long]("totalsessiontime")
)
)),
VisitReferenceVector("user.sessions",
pre=NoPre,
post=Post{
s⇒
if(!s.fldIsNull("user.sessions","duration")){s.aggLongWr("totalsessiontime",
s.fldScalarLong("user.sessions","duration"))}
}))
This doesn’t come close to showing the full power of GIST, but a more meaningful analytics query would be quite 
large.  Notice, the plan  consists of a number of ​gather​ clauses each with a ​path ​identifying a location in the  data 
schema plus a number of optional aggregate and dimension fields. There is always one root gather at the top of the 
schema as well as optional nested gathers.  A gather defines a join point where results are built from partial results 
of the children.  A gather also has a number of ​visit​ declarations each with a path and a ​closure​. The closure is 
4
 They may even be the same node. 
page 4 of 6 
 
A Model for Ad Hoc Queries over Large Datasets using a Scan Optimized Architecture 
executed when the traversal visits any item residing at that point in the schema.  A closure can record an aggregation 5
or dimension defined in the enclosing gather  or record information in a global register.  A closure at some node 
only has visibility to the current visited item instance and all parent instances on the path; it can never access child 
items directly. 
 
At every gather in the execution,  the executor collects all the partial rows for created by its visit closures as well  as 
partial rows from child gathers.  When the gather finishes,  the partial rows are cross joined together to produce new 
partial tuples composed of all the fields.  Partial results are created at the visits and then composed into larger results 
at the gathers.  At the root, we end up with the total set of complete tuples for the item.   
 
A GIST plan can also use a route structure in a gather to help evaluate causal and temporal data filters.   A visit 
closure will​ ​assert the occurrence of an step based on some test of the current item’s field values,  but the route will 
only record an step assertion if a transition for the current route state is allowed.  A closure can also test for the 
occurrence of previous successful steps.  For example, in order to find users that made a bet and then purchased 
something in a single session, the GIST plan will declare a route with a simple linear progression of three steps.  At 
a session item visit, the closure asserts the first step.  At the event item level the closure asserts step two if it sees a 
bet type event or step three if it sees an in­app purchase. Finally, when the root item is visited, if the route has at 
least one path, then this user satisfies the filter. Some of the cohort analysis queries in the Explorer product use two 
interacting funnels for their filtering. 
 
Declarative Queries 
 
Burst provides a high­level declarative query language for users and applications using the Burst system:  SILQ. 
SILQ queries insulate the user from much of complexities inherent in writing GIST plans.   
The SILQ compiler takes this text and builds a GIST plan for it.  The SILQ equivalent to the previous GIST 
example totalling the durations of all sessions for an applications is : 6
over("quo",1)
aggregate(
"totalsessiontime"assum(user.sessions.duration);
)
 
SILQ supports all of the features of our scan query engine that GIST does, including funnels, but in a declarative 
form.  The compiler builds the GIST plan by: 
● choosing the best gather nesting and field layout 
● generating visit closures for condition tests 
● use registers to support complex tests of collection conditions such as “where sum(events.duration) > 1” 
4. Conclusions 
The Burst system supports the constantly changing, complex causal based queries needed for modern application 
analytics. We use a unique single pass scan model in order to satisfy these requirements, remain low­latency, and 
have a system that scales with the amount of data.  
 
The current Burst system, is the backend for Flurry’s Explorer system which is running in production.  The system 
does sub­second ad­hoc queries over 60G datasets including complex funnel and user retention calculations that take 
hours in Flurry’s traditional hadoop infrastructure. The first major release of the back end query engine engineered 
to fully support this type of exploration was developed in the Flurry Yahoo Mobile Analytics group and released in 
Feb 2015 to a beta customer group. The next major release of this back end system being implemented will improve 
the SILQ and GIST languages by adding new features as well as usability improvements.  
5
 This will probably remind many users of the execution model for the SAX XML parser. 
6
 In fact, the previous GIST was directly generated by the SILQ compiler from this example. 
page 5 of 6 
 
A Model for Ad Hoc Queries over Large Datasets using a Scan Optimized Architecture 
References 
● [BURST] Erik Freed and Brian Anderson, “A General Purpose Extensible Scanning Query Architecture for                           
Ad Hoc Analytics” 
● [BLINK] AmpLab, “Queries with Bounded Errors and Bounded Response Times on Very Large Data”:                           
http://blinkdb.org/ 
● [CODD] E.F. Codd, “A Relational Model of Data for Large Shared Data Banks”, Communications of the                               
ACM, 1970: ​http://www.seas.upenn.edu/~zives/03f/cis550/codd.pdf 
● [COHORTS] Wikipedia, “Cohort Analysis”:​ https://en.wikipedia.org/wiki/Cohort_analysis 
● [DRUID] Druid, “Open Source Data Store for Interactive Analytics at Scale”:  ​http://druid.io/ 
● [DATE] C. J. Date. An Introduction to Database Systems. O’Reilly, 7 edition, 2000 
● [DRILL] MAPR, “Industry's First Schema­Free SQL Engine for Big Data”:                   
https://www.mapr.com/products/apache­drill 
● [DREMEL] Sergey Melnik and Andrey Gubarev and Jing Jing Long and Geoffrey Romer and Shiva                             
Shivakumar and Matt Tolton and Theo Vassilakis, “Dremel: Interactive Analysis of Web­Scale Datasets”,                         
Proc. of the 36th Int'l Conf on Very Large Data Bases: ​http://research.google.com/pubs/pub36632.html 
● [HIVE] Apache Software Foundation, “Hive: A ​data warehouse infrastructure built on top of ​Hadoop for                             
providing data summarization, query, and analysis“: ​https://hive.apache.org/ 
● [TSDB] OpenTSDB, “The Scalable Time Series Database”: ​http://opentsdb.net/ 
● [REDHIFT] Amazon Web Services,  “Amazon Redshift”: ​http://aws.amazon.com/redshift/ 
page 6 of 6 

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