Assignment: Polyglot Persistence with NoSQL Systems
In this assignment, you will show that you can work with different NoSQL systems for
data persistence and understand the strength and weakness of each system with respect to
certain query workload features. You are asked to work with a Question and Answer data
set and a list of target queries. The target queries include very basic OLTP type queries and
analytic queries. You will design data schema based on the data set feature and the given
query workload for MongoDB and Neo4j respectively. You will show that your design can
support the target queries by loading the data in each system following your schema and
by running queries against the data.
The data that you will use is the latest dump (publication date: 2017-08-31) of the Open
Data Stack Exchange question and answer site (https://opendata.stackexchange.com/).
The dump is released and maintained by stackexchange: https://archive.org/details/
stackexchange. The original dump contains many files in XML format. The assignment
uses a subset of the data stored in four csv files. The data files and the description
(readme.txt) can be downloaded from ELearning.
The assignment data set contains the following files:
_ Posts.csv stores information about post, each row represents a post, which could
be a question or an answer
_ Users.csv stores user’s profile, each row represents a user
_ Votes.csv stores detailed vote information about post, each row represents a vote,
including the vote type, the date this vote is made and a few other information
_ Tags.csv contains summary of tag usage in this site.
_ Find all users involved in a given question (identified by id) and their respective
profiles including the creationDate, DisplayName, upVote and DownVote.
_ Assuming each tag represents a topic, find the most viewed question in a certain
_ We want to discover the questions that are easy to answer. We measure easiness with
the time it took to receive an accepted answer. The shorter the time, the easier the
question. Given a list of topics, you are asked to list the question easiest to answer
in each topic.
_ We are interested in discovering trending topics in a certain period. An indicator of
trending could be the number of users who have participated in the discussion, by
posting either questions or answers, during the period. You are asked to list five such
hottest topics in a given period as well as the number of users in each.
_ We are also interested in discovering champion user in a certain topic. A champion
user has the most answers accepted in that topic. Given a topic, you are asked to list
the champion user and all questions the user has answers accepted in that topic.
_ Some questions may not have accepted answers. They are considered as unanswered
questions. We would like to recommend those questions to a group of potential
answerer. For any active user, if the user has n answers accepted in a certain topic
and n is greater than or equal to a threshold value _, we consider the user a potential
answerer of unanswered questions in that topic. You are asked to recommend up to
5 most recent unanswered questions to a given user.
_ We want to know if the acceptance of an answer would affect its upVote as well
as other answers’ upVote. We denote the day an answer is accepted as “decision
day”. You are asked to find out among all questions whoes total number of upVote is
greater than or equal to a certain threshold value _ :
– The question whose accepted answer has the highest percentage of upVote received
after the “decision day”
– The question whose other answers as a whole has the highest percentage of
upVote received after the “decision day”.
1For any query, if there are more results than the specified limit, e.g. two questions have the highest view
count but we only want one, you can return any of the valid results within the limit
_ Consider all users involved in a question as co-authors, for a given user, find out
this user’s top 5 co-authors based on the number of times they appear in the same
question either as originator or answerer.
Your tasks include:
_ Schema Design for each system
You should provide two schema design versions to support both the simple queries
and the analytic queries. For each schema version, make sure you utilize features
provided by the storage system such as indexing, aggregation, ordering, filtering and
so on. Please note that your schema may deviate a lot from the relational structure
of the original data set. You will not get mark if you present a schema that is an exact
copy of the relational structure in the original data set.
You can discard data that are not useful. You can duplicate data if you find that
helps with certain queries. You may preprocessing the data by adding fields that are
not in the data set. Such preprocessing should not be used to create and save any
query result directly in the database. You need to justify any preprocessing you have
employed. The justification should include benefits, potential performance cost as
well as how often the preprocessing should be carried out. Keep in mind that the
storage system is also used to support online live transaction. Any activity such as
posting or voting will send one or more write queries to the system. You should not
use preprocessing that would cause significant delay to regular queries.
_ Query Design and Implementation
Load the complete data on both system and set up proper indexes that will be used
by the target queries. Design and implement all target queries. You may implement
a query using the provided query command (e.g. MongoDB shell or Cypher query)
or as Python/Java program. In case that a programming language is used, make
sure that you do most processing at the server side. You may get significant mark
deduction if most of the analysis are implemented with programming language.
Deliverable and Submission Guideline
This is a group project, each group can have up to 2 students. Each group needs to produce
_ A Written Report .
The report should contain four sections. The first section is a brief introduction of
the project. Section two and three should cover a system each. Section four should
provide a comparison and summary.
There is no mark on section one. It is included to make the report complete. So
please keep it really short.
Section two and three should contain the following two sub sections
– Schema Design
In this section, describe the schema with respect to the particular system. Your
description should include information at “table” and “column” level as well
as possible primary keys/row keys and secondary indexes. You should show
sample data based on schema. For instance, you may show sample documents
of each MongoDB collection, a sample property graph involving all node types
and relationship types for Neo4j. Highlight the data that are different to the
given one and justify your decision. These would include data you decide not
to put in the database, data that are duplicated, or data that are results of
– Query Design and Execution
In this section, describe implementation of each target query. You may include
the entire shell query command, or show pseudo code. You should also run
sample queries and provide a brief performance analysis of each query by citing
actual query execution statistics if such statistics are available or by indicating
that certain indexing would be used.
In section four, compare the two systems with respect to ease of use, query performance
and schema differences. You can also describe problems encountered in
schema design or query design.
Submit a hard copy of your report together with a signed group assignment sheet in
Week 10 lab.
_ System Demo
Each group will demo in week 10 lab. You can run demo on your own machine, on
lab machine or on some cloud servers. Please make sure you prepare the data before
the demo. The marker does not need to see your data loading steps. The marker will
ask you to run a few randomly selected queries to get an overview of the data model
and query design. The actual marking will be done after the demo using submitted
_ Source Code/Script and soft copy of report
Submit a zip file through the eLearning site, before 2pm on Tuesday 10th of October,
2017 (week 10). The zip file should contain the following:
– a soft copy of the report
– query script or program code for each system
– data loading script or program for each system
– a Readme document for how to prepare the data and run the queries. The instruction
should be detailed enough for the markers to quickly prepare the data
and to run the queries. For instance, it is preferable that you prepare a single
script for data loading. You should also indicate where and how run-time
argument are supplied. If you used special features only available in a particular
version or environment, indicate that as well. You may not get mark if the
marker is not able to prepare the database and run your code.