Merge remote-tracking branch 'origin/develop'

.coveragerc

@@ -0,0 +1,3 @@
+[run]
+concurrency = thread,multiprocessing
+parallel = true

.github/workflows/continuous_integration.yml

@@ -57,6 +57,8 @@ jobs:
       # Upload coverage report
       # https://github.com/codecov/codecov-action
       - name: Upload coverage report
-        uses: codecov/codecov-action@v1
+        uses: codecov/codecov-action@v4
         with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          slug: eecs485staff/madoop
           fail_ci_if_error: true

.gitignore

@@ -24,6 +24,8 @@ build/
 /.tox/
 /.coverage*
 *,cover
+# Make an exception for .coveragerc, which should be checked into version control.
+!.coveragerc
 
 # Text editors and IDEs
 *~

CONTRIBUTING.md

@@ -6,7 +6,7 @@ Set up a development virtual environment.
 ```console
 $ python3 -m venv .venv
 $ source .venv/bin/activate
-$ pip install --editable .[dev,test]
+$ pip install --editable .[dev]
 ```
 
 A `madoop` entry point script is installed in your virtual environment.

MANIFEST.in

@@ -9,6 +9,7 @@ graft madoop/example
 
 # Avoid dev and and binary files
 exclude tox.ini
+exclude .coveragerc
 exclude .editorconfig
 global-exclude *.pyc
 global-exclude __pycache__

README_Hadoop_Streaming.md

@@ -1,7 +1,7 @@
 Hadoop Streaming in Python
 ===========================
 
-This tutorial shows how to write MapReduce programs in Python that are compatible with [Hadoop Streaming](https://hadoop.apache.org/docs/r1.2.1/streaming.html).  We'll use Python's `itertools.groupby()` function to simplify our code.
+This tutorial shows how to write MapReduce programs in Python that are compatible with [Hadoop Streaming](https://hadoop.apache.org/docs/current/hadoop-streaming/HadoopStreaming.html).  We'll use Python's `itertools.groupby()` function to simplify our code.
 
 Install Madoop, a light weight MapReduce framework for education. Madoop implements the Hadoop Streaming interface.
 ```console

madoop/mapreduce.py

@@ -7,25 +7,24 @@
 import collections
 import hashlib
 import logging
-import math
 import pathlib
 import shutil
 import subprocess
 import tempfile
+import multiprocessing
+import concurrent.futures
 from .exceptions import MadoopError
 
 
 # Large input files are automatically split
-MAX_INPUT_SPLIT_SIZE = 2**21  # 2 MB
-
-# The number of reducers is dynamically determined by the number of unique keys
-# but will not be more than num_reducers
+MAX_INPUT_SPLIT_SIZE = 10 * 1024 * 1024  # 10 MB
 
 # Madoop logger
 LOGGER = logging.getLogger("madoop")
 
 
 def mapreduce(
+    *,
     input_path,
     output_dir,
     map_exe,
@@ -51,18 +50,15 @@ def mapreduce(
         LOGGER.debug("tmpdir=%s", tmpdir)
 
         # Create stage input and output directory
-        map_input_dir = tmpdir/'input'
         map_output_dir = tmpdir/'mapper-output'
         reduce_input_dir = tmpdir/'reducer-input'
         reduce_output_dir = tmpdir/'output'
-        map_input_dir.mkdir()
         map_output_dir.mkdir()
         reduce_input_dir.mkdir()
         reduce_output_dir.mkdir()
 
         # Copy and rename input files: part-00000, part-00001, etc.
         input_path = pathlib.Path(input_path)
-        prepare_input_files(input_path, map_input_dir)
 
         # Executables must be absolute paths
         map_exe = pathlib.Path(map_exe).resolve()
@@ -72,7 +68,7 @@ def mapreduce(
         LOGGER.info("Starting map stage")
         map_stage(
             exe=map_exe,
-            input_dir=map_input_dir,
+            input_dir=input_path,
             output_dir=map_output_dir,
         )
 
@@ -98,7 +94,7 @@ def mapreduce(
         for filename in sorted(reduce_output_dir.glob("*")):
             st_size = filename.stat().st_size
             total_size += st_size
-            shutil.copy(filename, output_dir)
+            shutil.move(filename, output_dir)
             output_path = output_dir.parent/last_two(filename)
             LOGGER.debug("%s size=%sB", output_path, st_size)
 
@@ -107,52 +103,36 @@ def mapreduce(
     LOGGER.info("Output directory: %s", output_dir)
 
 
-def prepare_input_files(input_path, output_dir):
-    """Copy and split input files.  Rename to part-00000, part-00001, etc.
+def split_file(input_filename, max_chunksize):
+    """Iterate over the data in a file one chunk at a time."""
+    with open(input_filename, "rb") as input_file:
+        buffer = b""
 
-    The input_path can be a file or a directory of files.  If a file is smaller
-    than MAX_INPUT_SPLIT_SIZE, then copy it to output_dir.  For larger files,
-    split into blocks of MAX_INPUT_SPLIT_SIZE bytes and write block to
-    output_dir. Input files will never be combined.
+        while True:
+            chunk = input_file.read(max_chunksize)
+            # Break if no more data remains.
+            if not chunk:
+                break
 
-    The number of files created will be the number of mappers since we will
-    assume that the number of tasks per mapper is 1.  Apache Hadoop has a
-    configurable number of tasks per mapper, however for both simplicity and
-    because our use case has smaller inputs we use 1.
+            # Add the chunk to the buffer.
+            buffer += chunk
 
-    """
-    part_num = 0
-    total_size = 0
-    for inpath in normalize_input_paths(input_path):
-        assert inpath.is_file()
-
-        # Compute output filenames
-        st_size = inpath.stat().st_size
-        total_size += st_size
-        n_splits = math.ceil(st_size / MAX_INPUT_SPLIT_SIZE)
-        n_splits = 1 if not n_splits else n_splits  # Handle empty input file
-        LOGGER.debug(
-            "input %s size=%sB partitions=%s", inpath, st_size, n_splits
-        )
-        outpaths = [
-            output_dir/part_filename(part_num + i) for i in range(n_splits)
-        ]
-        part_num += n_splits
-
-        # Copy to new output files
-        with contextlib.ExitStack() as stack:
-            outfiles = [stack.enter_context(i.open('w')) for i in outpaths]
-            infile = stack.enter_context(inpath.open(encoding="utf-8"))
-            outparent = outpaths[0].parent
-            assert all(i.parent == outparent for i in outpaths)
-            outnames = [i.name for i in outpaths]
-            logging.debug(
-                "partition %s >> %s/{%s}",
-                last_two(inpath), outparent.name, ",".join(outnames),
-            )
-            for i, line in enumerate(infile):
-                outfiles[i % n_splits].write(line)
-    LOGGER.debug("total input size=%sB", total_size)
+            # Find the last newline character in the buffer. We don't want to
+            # yield a chunk that ends in the middle of a line; we have to
+            # respect line boundaries or we'll corrupt the input.
+            last_newline = buffer.rfind(b"\n")
+            if last_newline != -1:
+                # Yield the content up to the last newline, saving the rest
+                # for the next chunk.
+                yield buffer[:last_newline + 1]
+
+                # Remove processed data from the buffer. The next chunk will
+                # start with whatever data came after the last newline.
+                buffer = buffer[last_newline + 1:]
+
+        # Yield any remaining data.
+        if buffer:
+            yield buffer
 
 
 def normalize_input_paths(input_path):
@@ -208,30 +188,51 @@ def part_filename(num):
     return f"part-{num:05d}"
 
 
+def map_single_chunk(exe, input_path, output_path, chunk):
+    """Execute mapper on a single chunk."""
+    with output_path.open("w") as outfile:
+        try:
+            subprocess.run(
+                str(exe),
+                shell=False,
+                check=True,
+                input=chunk,
+                stdout=outfile,
+            )
+        except subprocess.CalledProcessError as err:
+            raise MadoopError(
+                f"Command returned non-zero: "
+                f"{exe} < {input_path} > {output_path}"
+            ) from err
+
+
 def map_stage(exe, input_dir, output_dir):
     """Execute mappers."""
-    i = 0
-    for i, input_path in enumerate(sorted(input_dir.iterdir()), 1):
-        output_path = output_dir/part_filename(i)
-        LOGGER.debug(
-            "%s < %s > %s",
-            exe.name, last_two(input_path), last_two(output_path),
-        )
-        with input_path.open() as infile, output_path.open('w') as outfile:
-            try:
-                subprocess.run(
-                    str(exe),
-                    shell=False,
-                    check=True,
-                    stdin=infile,
-                    stdout=outfile,
+    part_num = 0
+    futures = []
+    with concurrent.futures.ThreadPoolExecutor(
+        max_workers=multiprocessing.cpu_count()
+    ) as pool:
+        for input_path in normalize_input_paths(input_dir):
+            for chunk in split_file(input_path, MAX_INPUT_SPLIT_SIZE):
+                output_path = output_dir/part_filename(part_num)
+                LOGGER.debug(
+                    "%s < %s > %s",
+                    exe.name, last_two(input_path), last_two(output_path),
                 )
-            except subprocess.CalledProcessError as err:
-                raise MadoopError(
-                    f"Command returned non-zero: "
-                    f"{exe} < {input_path} > {output_path}"
-                ) from err
-    LOGGER.info("Finished map executions: %s", i)
+                futures.append(pool.submit(
+                    map_single_chunk,
+                    exe,
+                    input_path,
+                    output_path,
+                    chunk,
+                ))
+                part_num += 1
+    for future in concurrent.futures.as_completed(futures):
+        exception = future.exception()
+        if exception:
+            raise exception
+    LOGGER.info("Finished map executions: %s", part_num)
 
 
 def sort_file(path):
@@ -287,7 +288,9 @@ def partition_keys_custom(
     Update the data structures provided by the caller input_keys_stats and
     output_keys_stats.  Both map a filename to a set of of keys.
     """
-    # pylint: disable=too-many-arguments,too-many-locals
+    # pylint: disable=too-many-arguments
+    # pylint: disable=too-many-positional-arguments
+    # pylint: disable=too-many-locals
     assert len(outpaths) == num_reducers
     outparent = outpaths[0].parent
     assert all(i.parent == outparent for i in outpaths)
@@ -392,35 +395,61 @@ def group_stage(input_dir, output_dir, num_reducers, partitioner):
             path.unlink()
 
     # Sort output files
-    for path in sorted(output_dir.iterdir()):
-        sort_file(path)
+    try:
+        # Don't use a with statement here, because Coverage won't be able to
+        # detect code running in a subprocess if we do.
+        # https://pytest-cov.readthedocs.io/en/latest/subprocess-support.html
+        # pylint: disable=consider-using-with
+        pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
+        pool.map(sort_file, sorted(output_dir.iterdir()))
+    finally:
+        pool.close()
+        pool.join()
 
     log_output_key_stats(output_keys_stats, output_dir)
 
 
+def reduce_single_file(exe, input_path, output_path):
+    """Execute reducer on a single file."""
+    with input_path.open() as infile, output_path.open("w") as outfile:
+        try:
+            subprocess.run(
+                str(exe),
+                shell=False,
+                check=True,
+                stdin=infile,
+                stdout=outfile,
+            )
+        except subprocess.CalledProcessError as err:
+            raise MadoopError(
+                f"Command returned non-zero: "
+                f"{exe} < {input_path} > {output_path}"
+            ) from err
+
+
 def reduce_stage(exe, input_dir, output_dir):
     """Execute reducers."""
     i = 0
-    for i, input_path in enumerate(sorted(input_dir.iterdir())):
-        output_path = output_dir/part_filename(i)
-        LOGGER.debug(
-            "%s < %s > %s",
-            exe.name, last_two(input_path), last_two(output_path),
-        )
-        with input_path.open() as infile, output_path.open('w') as outfile:
-            try:
-                subprocess.run(
-                    str(exe),
-                    shell=False,
-                    check=True,
-                    stdin=infile,
-                    stdout=outfile,
-                )
-            except subprocess.CalledProcessError as err:
-                raise MadoopError(
-                    f"Command returned non-zero: "
-                    f"{exe} < {input_path} > {output_path}"
-                ) from err
+    futures = []
+    with concurrent.futures.ThreadPoolExecutor(
+        max_workers=multiprocessing.cpu_count()
+    ) as pool:
+        for i, input_path in enumerate(sorted(input_dir.iterdir())):
+            output_path = output_dir/part_filename(i)
+            LOGGER.debug(
+                "%s < %s > %s",
+                exe.name, last_two(input_path), last_two(output_path),
+            )
+            futures.append(pool.submit(
+                reduce_single_file,
+                exe,
+                input_path,
+                output_path,
+            ))
+    for future in concurrent.futures.as_completed(futures):
+        exception = future.exception()
+        if exception:
+            raise exception
     LOGGER.info("Finished reduce executions: %s", i+1)
 
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "madoop"
-version = "1.2.2"
+version = "1.3.0"
 description="A light weight MapReduce framework for education."
 license = {file = "LICENSE"}
 authors = [
@@ -26,15 +26,14 @@ madoop = "madoop.__main__:main"
 [project.optional-dependencies]
 dev = [
     "build",
-    "twine",
-    "tox",
     "check-manifest",
-    "freezegun",
     "pycodestyle",
     "pydocstyle",
     "pylint",
     "pytest",
     "pytest-cov",
+    "tox",
+    "twine",
 ]
 
 [tool.setuptools.packages.find]