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| CSE 662 - Languages and Runtimes for Big Data - Fall 2016 |
CSE 662 - Fall 2017
Addressing the challenges of big data requires a combination of human intuition and automation. Rather than tackling these challenges head-on with build-from-scratch solutions, or through general-purpose database systems, developer and analyst communities are turning to building blocks: Specialized languages, runtimes, data-structures, services, compilers, and frameworks that simplify the task of creating a systems that are powerful enough to handle terabytes of data or more or efficient enough to run on your smartphone. In this class, we will explore these fundamental building blocks and how they relate to the constraints imposed by workloads and the platforms they run on.
Coursework consists of lectures and a multi-stage final project. Students are expected to attend all lectures. Projects may be performed individually or in groups. Projects will be evaluated in three stages through code deliverables, reports, and group meetings with either or both of the instructors. During these meetings, instructors will question the entire group extensively about the group's report, deliverables, and any related tools and technology.
- At initial stage, students are expected to demonstrate a high level of proficiency with the tools, techniques, data structures, algorithms and source code that will form the basis of their project. The group is expected to submit and defend a roughly 5-page report surveying the space in which their project will be performed. This report and presentation constitute 15% of the final grade.
- At the second stage, students are expected to provide a detailed design for their final project. A roughly 5-page report should outline the group’s proposed design, any algorithms or data structures being introduced, as well as a strategy for evaluating the resulting project against the current state of the art. This report and presentation constitute 35% of the final grade.
- At the final stage, students are expected to provide a roughly 5-page report detailing their project, any algorithms or data structures developed, and evaluating their project against any comparable state of the art systems and techniques. Groups will also be expected to demon- strate their project and present their findings in-class, or in a meeting with both instructors if necessitated by time constraints. This report and presentation constitute 50% of the final grade.
Information
- Instructors
- Oliver Kennedy (Davis 338H; Office Hours Weds 1:00-3:00)
- Time: MWF 12:00-12:50
- Location: Knox 04
Course Objectives
After the taking the course, students should be able to:
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Design domain specific query languages, by first developing an understanding the common tropes of a target domain, exploring ways of allowing users to efficiently express those tropes, and developing ways of mapping the resulting programs to an efficient evaluation strategy.
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Identify concurrency challenges in data-intensive computing tasks, and address them through locking, code associativity, and correctness analysis.
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Understand a variety of index data structures, as well as their application and use in data management systems for high velocity, volume, veracity, and/or variety data.
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Understand query and program compilation techniques, including the design of intermediate representations, subexpression equivalence, cost estimation, and the construction of target-representation code.
Course Schedule
- Aug. 28 : Introduction ( group formation | slides )
- Aug. 30 : Project Seeds - Mimir
- Sept. 01 : Project Seeds - JITDs & PocketData
- Sept. 04 : Database Cracking ( Cracking )
- Sept. 06 : Functional Data Structures
- Sept. 12 : Just-in-Time Data Structures ( JITDs )
- Sept. 8 : Incomplete Databases 1
- Sept. 11 : Incomplete Databases 2
- Sept. 13 : Incomplete Databases 3
- Sept. 15 : Mimir ( Mimir )
- Sept. 18 : MayBMS ( MayBMS )
- Sept. 20 : Sampling From Probabilistic Queries ( MCDB )
- Sept. 22 : Probabilistic Constraint Repair ( Sampling from Repairs )
- Sept. 25 : R-Trees and Multidimensional Indexing
- Sept. 27 - Sept. 29 : Student Project Presentations
- Oct. 2 : BloomL ( Bloom/Bud, BloomL )
- Oct. 4 - Oct. 6 : Oliver Away (Content TBD)
- Oct. 9 : NoDB ( NoDB )
- Oct. 11 - Oct. 13 : Student Project Presentations
- Oct. 16 : Lazy Transactions ( Stickies )
- Oct. 18 : Streaming ( Cayuga )
- Oct. 20 : Scan Sharing ( Crescando )
- Oct. 23 - Oct. 27 : Checkpoint 2 Reviews
- Oct. 30 : Declarative Games ( SGL )
- Nov. 1 - Nov. 3 : Student Project Presentations
- Nov. 6 - Nov. 10 : Oliver Away (Content TBD)
- Nov. 13 : Buffer
- Nov. 15 - Nov. 17 : Student Project Presentations
- Nov. 20 : Buffer
- Nov. 22 - Nov. 24 : Student Project Presentations
- Nov. 27 : Buffer
- Nov. 29 - Dec. 1 : Student Project Presentations
- Dec. 4 - Dec. 8 : Checkpoint 3 Reviews
Project Seeds
Deferred Constraint-Based Data Validation
There are a number of reasons that data might go bad: sensor errors, data entry errors, lag spikes, filesystem corruption, and more. One thing is certain though: you don't want to make decisions based on bad data. What people will often do is do basic sanity checks. For example, if we have a record of someone checking out of a hospital, we should have a record of them checking in at some point earlier. Declaring these sanity checks is hard, but fixing violations is even harder. In this project, you will explore ways to safely defer repairing the data. Challenges include:
- Deciding what types of sanity constraints you want to be able to support.
- Interfacing with an existing database (Spark, SQLite, or Oracle) to identify sets of tuples that come together to violate a sanity constraint.
- Using Mimir to warning users when a query result depends on a tuple that participates in a violation
- Suggesting and ranking modifications that repair violations
Background Material:
Query Sampling Optimizer
Most probabilistic database systems aim to produce all possible results. A few, most notably MCDB, instead generate samples of possible results. The basic idea is to split the database into a fixed number (N) of possible worlds, and run the query on all N possible worlds in parallel. There are actually a few different ways to do this. Three relatively common examples include:
- Naive: Literally run N copies of the query and union the results at the end.
- Interleave: Tag each tuple with the possible world that it comes from, and then just run one query. Make sure the query ensures that tuples from different possible worlds can't interact (i.e., Joins always happen between tuples from the same world and the world becomes another group-by column)
- Tuple Bundle: Create mega-tuples, that represent alternative versions of the same tuple in different possible worlds. If an attribute value is the same in all possible worlds store only one copy of it. (See MCDB)
Perhaps counterintuitively, our preliminary implementations of the Interleave and Tuple Bundle algorithms suggest that none of these approaches will be the best in all cases. For example, in a simple select-aggregate query, tuple-bundles are the most efficient. Conversely, if you're joining on an attribute with different values in each possible world, interleave will be faster. We suspect that there are some cases where Naive will win out as well. The aim of this project is to implement a query optimizer for sampling-based probabilistic database queries. If I hand you a query, you tell me which strategy is fastest for that query. As an optional extension, you may be able to interleave different strategies, each evaluating a different part of the query.
Background Material:
Explaining Offset-Outliers
When looking at queries, a common question is "why is this result the way it is?" While a broad question, database researchers have been hard at work isolating and addressing specific cases. For this particular project, we'd like to explore one specific category of explanation, where users have provided us with points of stability: Group-by aggregates that are supposed to remain stable over time. For example, consider
SELECT author, STDDEV(cnt) FROM (
SELECT author, year, COUNT(*) AS cnt FROM Publications
);
This query gives the variation per user in terms of number of publications per year. We might use a query like this to define a constraint that says "For any author, the number of publications per year stays roughly constant". Constraints like this help can us to explain aggregate values. For example let's say you run the following query and the result is lower than expected.
SELECT COUNT(*) FROM Publications WHERE author = 'Alice' AND venue = 'ICDE' AND year = 2017;
If you ask "Why is this result so low", the system can look at the above constraint and figure out that there's another aggregate query that is higher than usual (to preserve the stability of the publications/year constraint defined above)
SELECT COUNT(*) FROM Publications WHERE author = 'Alice' AND venue = 'ICDE' AND year = 2017;
The aim of this project would be to implement a simple frontend to an existing database system (Spark, SQLite, or Oracle) that accepts a set of constrants and answers questions like this.
(This project is part of ongoing joint work with Boris Glavic and Sudeepa Roy)
Background Material:
Adaptive Multidimensional Indexing
Indexes work by reducing the effort required to locate specific data records. For example, in a tree index, if the range of records in a given subtree doesn't overlap with the query, the entire subtree can be ruled out (or ruled in). Not surprisingly, this means that data partitioning plays a large role in how effective the index is. The fewer partitions lie on query boundaries, the less work is required to respond to those queries.
Partitioning is especially a problem in 2-dimensional (and 3-, 4-, etc... dimensional) indexes, where there are always two entirely orthogonal dimensions to partition on. Accordingly, there's a wide range of techniques for organizing 2-dimensional data, including a family of indexes based on R-Trees. The aim of this project is to develop a "dynamic" r-like tree structure that adaptively partitions its contents, and (if time permits) that adapts its partition boundaries to changing workloads.
Background Material:
- Database Cracking
- The R*-tree: an efficient and robust access method for points and rectangles
- The Big Red Data Spatial Indexing Project
Mimir on SparkSQL
(Summary In Progress)
Garbage Collection in Embedded Databases
(Summary In Progress)
Academic Content
The course will involve lectures and readings drawn from an assortment of academic papers selected by the instructors. There is no textbook for the course.
Academic Integrity
Students may discuss and advise one another on their lab projects, but groups are expected to turn in their own work. Cheating on any course deliverable will result in automatic failure of the course. The University policy on academic integrity can be reviewed at: http://academicintegrity.buffalo.edu/policies/index.php
Accessibility Resources
If you have a diagnosed disability (physical, learning, or psychological) that will make it difficult for you to carry out the course work as outlined, or that requires accommodations such as recruiting note-takers, readers, or extended time on exams or assignments, please advise the instructor during the first two weeks of the course so that we may review possible arrangements for reasonable accommodations. In addition, if you have not yet done so, contact the Office of Accessibility Resources (formerly the Office of Disability Services).