Getting involved

Tuplex is an ideal project if you’d like to learn more about data analytics, compilers and how they work, and enjoy writing C/C++ and Python code.

If you’re still reading and this sounds like a project you’d love to get involved in, please email us directly (Leonhard or Malte) and fill out the following form so we can get you access to the source code and computing resources.

Starter projects

Before attempting any project on your own, please familiarize yourself with the core ideas of the Tuplex project (e.g., by reading the preprint) and install Tuplex on your machine (build instructions). Then, try out one of the examples in the examples/ folder. Tuplex only works on Mac OS X or a Linux distribution currently. If your machine runs Windows, you might want to consider using either Docker or a Virtual Machine. When attempting one of these starter projects, use the master to setup your own development branch.

git clone https://github.com/LeonhardFS/Tuplex.git
git checkout -b tplx-<your branchname>

Then, please create a pull request on Github when you’re done or need help!

Starter project I: Job Metrics

Tuplex currently prints a good amount of information out while running a job. However, these numbers are not really exposed to the user yet. The only metric that users can retrieve is the exception count:

import tuplex
c = Context()
ds = c.parallelize([1, 2, 0, 4, 5]).map(lambda x: 2 / x)
res = ds.collect()

print('Exceptions in map operator: {}'.format(ds.exception_counts))
The purpose of this project is to expose additional metrics. The C++ class where metrics are stored is JobMetrics (JobMetrics.h). Extend the class to record following times (you may use the Timer class in Timer.cc):
  1. logical optimization time (see LogicalOptimizer.cc, LogicalOptimize::optimize);

  2. compilation times via LLVM (see TransformStage.cc, TransformStage::compile);

  3. total compilation time for everything; and

  4. time for using LLVM optimization passes.

Then, expose the numbers via the Context class in context.py. To do so, create a new class called Metrics in Python (save to metrics.py). The following should expose the metrics class in Python:

c.metrics # get a new Metrics object back

To get the numbers from C++ back to Python, create a new class in the Boost Python module folder (python/ folder) and expose it. Consult PythonBindings.cc/PythonContext.cc and context.py to see how this is done.

Starter project II: More convenient aggregates

Tuplex allows for simple aggregations: e.g., in order to count the number of rows you can use the following code snippet:

import tuplex
c = Context()
c.parallelize([1, 2, 3, 4, 5]).aggregate(lambda a, b: a + b, lambda a, x: a + 1, 0).collect()

The way this works is basically as follows. A new aggregate is initialized with a value 0. Then, for each row (represented by x) the aggregate a is increased by 1 in the lambda a, x: a + 1 function. Two aggregates a, b are then combined via lambda a, b: a + b. However, having to always write all this boilerplate code just to count rows is cumbersome. The goal of this project is to allow a more convenient API, such as count(), to have a shorter API that simply returns the count of rows as a single integer.

In dataset.py, implement a new function count() which returns the count of rows. In other words, the following code snippet should work:

import tuplex
c = Context()
cnt = c.parallelize([1, 2, 3, 4, 5]).count()
assert cnt == 5, 'wrong count delivered…'

To make sure your code works, add a new file python/tests/test_aggregates.py and write some tests to check your count function works. Make sure to think of possible edge cases! To copy your test file during the build in Tuplex, edit the FILE(COPY …) command in python/CMakeLists.txt.

In a second step, it would be also interesting to get the mean and variance via functions .mean(), or .var(). Implement mean/var, but note that these functions are only meaningful for numeric data! Make sure to throw an exception if the user calls mean() or var() over non-numeric data. You can use the .types property of the dataset class in dataset.py for this. To compute mean/var, use the online version for each formula. Note that an aggregate can be a tuple of any size! You can use that to store multiple variables to compute the mean/variance.

When you have variance implemented, add a function std by using the fact that std = sqrt(var).

Starter project III: Add a new string built-in function

Tuplex already supports many built-in string functions. However, not all functions are supported. In this project, the goal is to add support for str.swapcase() (https://docs.python.org/3.7/library/stdtypes.html#str.swapcase). First, read the documentation for swapcase. To add support, first create a C++ test case in RuntimeTest.cc. Then, implement a C function strSwapcase in runtime/src/StringFunctions.cc. In your C++ test case, test and make sure your strSwapcase function works. Next, we need to hook up the C function with Tuplex’s Python compiler. For this, we need to register the function: go to SymbolTableVisitor.cc and, in SymbolTableBuilder::addBuiltIns(), add swapcase similar to lower/upper/… via table->addBuiltinTypeAttribute. This requires the correct typing for the function. In this case, it’s a function which maps str -> str. Next, we need to add an LLVM call to the function: Go to FunctionRegistry.cc and study how FunctionRegistry::createLowerCall works. Similarly to it, add a new function createSwapcaseCall and implement it. Once you’re done with that, it’s time to test that swapcase works: go to test/core/StringFunctions.cc and add a test case similar to the others in the file to test your swapcase function.

Last, add a python test case: go to python/tests/test_strings.py and add a new test case for the swapcase function. Congrats, Tuplex now supports swapcase!

Starter project IV: Adding support for the is keyword in Python

Many static analysis tools (e.g., the ones used in Jetbrains’ IDEs like PyCharm, CLion) recommend for checks to write x is not None instead of x != None. The goal of this project is to support the is keyword but only allow it to be used with None, True, False. Else, a warning should be displayed and the user prevented from submitting code containing is in other places. As a start, read up on the is keyword in the Python language specification: https://docs.python.org/3.7/reference/expressions.html#comparisons. For an explanation why the is usage should be restricted, cf. https://stackoverflow.com/questions/2987958/how-is-the-is-keyword-implemented-in-python. Therefore, Tuplex should allow expressions like x is None but prevent problematic ones like 3 * 'a' is 'aaa'. A good first test case should be:

import tuplex
c = tuplex.Context()
c.parallelize([1, 2, 3, None, 4]).map(lambda x: x is None).collect()

First, make sure you understand the different stages of compiler (lexing, parsing, code generation) and how the visitor pattern works (https://www.cse.wustl.edu/~cytron/cacweb/Tutorial/Visitor/). Luckily, for is lexing and parsing is already done via ANTLR4. In ASTNodes.h you can find a class NCompare which represents a comparison expression. grammar/Python3.g4 is the ANTLR4 grammar we use to generate a lexer and parser. It’s always helpful to take a first look there to see how a rule is implemented. The class ASTBuilderVisitor converts the parse tree provided by ANTLR4 into an abstract syntax tree (AST). As the is keyword is part of a compare expression, in the first step support needs to be added in the antlrcpp::Any ASTBuilderVisitor::visitComparison(Python3Parser::ComparisonContext *ctx) function to process is and is not. For this, emit two new TokenType entries: TokenType::IS and TokenType::ISNOT. You need to edit TokenType.h for this and update the stringToToken conversion function. As a comparison yields a boolean as type, you don’t need to work with the TypeAnnotatorVisitor class, as this is already handled. However, support for is should be added to the TracingVisitor class which performs the tracing of the sample if necessary. I.e., in void TraceVisitor::visit(NCompare *node) add support for your new TokenType::IS, TokenType::ISNOT tokens. Make sure to write a test for this e.g. in test/core/PythonTracer.cc.

Next, after having done the prerequisites actual code generation support needs to be added.

Go to BlockGeneratorVisitor.cc and edit the

llvm::Value *
BlockGeneratorVisitor::compareInst(llvm::IRBuilder<>& builder, llvm::Value *L, const python::Type &leftType, const TokenType &tt,
                                       llvm::Value *R, const python::Type &rightType)

function to add support for the is tokens you added. You can use error(...) to fail on bad comparison expressions involving is as discussed above.

In a final step, add 1. C++ tests 2. Python tests for the is functionality. For 1. C++ tests, create a new file under test/core and make sure to cover several edge cases. You should use the TEST_F(...) pattern as used in the other tests. For 2. Python tests, add in python/tests a new test file test_is.py. Again, write here a test to make sure the C++/Python integration works. You can confer the other tests. Important: You need to edit python/CMakeLists.txt to copy over the new test file.

  • Difficulty: Medium-Hard

  • Skills required: C++, Python, LLVM, rough knowledge of how a compiler works

Starter project V: Ctrl-C support for parallelize(…) functions

Users sometimes execute code, especially in a jupyter notebook or the Tuplex interactive shell which might take longer than they expected. Thus, they want to stop execution by sending SIGINT (e.g. via Ctrl-C). In Tuplex, currently some functions can be interrupted via SIGINT. However, this does not work for tuplex.Context().parallelize(...) yet. This project should make parallelize(...) interruptible. In python/src/PythonContext.cc you can find the Boost-Python bindings for parallelize, i.e. the C++ backend interface which is called from context.py. The function check_and_forward_signals in Signal.h provides an easy way to detect whether SIGINT was caught.

In this project please add support for allowing SIGINT to be handled within the various parallelize... functions in PythonContext.cc.

The questions to keep in mind thereby are: What could be a meaningful return value? How can we assure that the state of the CPython interpreter is not being corrupted? Can you think of a way to test this? (cf. e.g. test/core/SignalTest.cc) If so, please add a C++ or Python test.

  • Difficulty: Medium

  • Skills required: C++, Python, CPython interpreter, signals

Starter project VI: Jemalloc instead of libc malloc?

In this project, we want to explore experimentally whether an alternative memory allocator like jemalloc (http://jemalloc.net/) could provide benefits to Tuplex. For this, in a first step edit the top-level CMakeLists.txt file to add support for using jemalloc. You may want to read up how other projects usually include jemalloc, e.g. https://github.com/apache/arrow/blob/master/cpp/CMakeLists.txt and add an option USE_JEMALLOC to the cmake setup for Tuplex. E.g., to compile with jemalloc support the following command should work

cmake -DCMAKE_BUILD_TYPE=Release -DUSE_JEMALLOC=ON .. && make -j$(nproc) && ctest

Then, in a next step compile Tuplex with and without jemalloc. Use the zillow and flights benchmark in benchmarks/ to produce a first result.

Does jemalloc provide a benefit?

Next, perform a follow-up experiment. Use different data input sizes for zillow and flights to see whether the impact of jemalloc is the same for each datasize. Is there any correlation to data size or is the impact (in %) independent of the data size?

After you’ve verified your experimental setup works locally, please reach out to Leonhard (lspiegel@cs.brown.edu) to get access to a performance benchmarking machine. Then, rerun your experiments on the benchmarking machine.

  • Difficulty: Medium

  • Skills required: Bash, CMake, python data science libs for analysis