Merge branch 'Eventual-Inc:main' into uc-write-deltalake

Author: anilmenon14

.github/workflows/pr-labeller.yml

@@ -16,5 +16,5 @@ jobs:
     - name: PR Conventional Commit Validation
       uses: ytanikin/[email protected]
       with:
-        task_types: '["feat","fix","docs","test","ci","refactor","perf","chore","revert"]'
+        task_types: '["feat","fix","docs","test","ci","refactor","perf","chore","revert","build"]'
         add_label: 'true'

.gitignore

@@ -16,6 +16,7 @@ build
 
 **/node_modules/**
 data/**
+benchmarking/tpcds/data
 *.so
 *.whl
 log/

Cargo.lock

@@ -56,11 +56,11 @@ hooks: .venv
 
 .PHONY: build
 build: check-toolchain .venv  ## Compile and install Daft for development
-	@unset CONDA_PREFIX && PYO3_PYTHON=$(VENV_BIN)/python $(VENV_BIN)/maturin develop --extras=all
+	@unset CONDA_PREFIX && PYO3_PYTHON=$(VENV_BIN)/python $(VENV_BIN)/maturin develop --extras=all --uv
 
 .PHONY: build-release
 build-release: check-toolchain .venv  ## Compile and install a faster Daft binary
-	@unset CONDA_PREFIX && PYO3_PYTHON=$(VENV_BIN)/python $(VENV_BIN)/maturin develop --release
+	@unset CONDA_PREFIX && PYO3_PYTHON=$(VENV_BIN)/python $(VENV_BIN)/maturin develop --release --uv
 
 .PHONY: test
 test: .venv build  ## Run tests

Makefile

@@ -0,0 +1,189 @@
+import argparse
+import logging
+import typing
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Optional
+
+import ray
+
+import daft
+
+from ..tpch import __main__ as tpch
+from ..tpch import ray_job_runner
+from . import datagen, helpers
+
+logger = logging.getLogger(__name__)
+
+SQL_QUERIES_PATH = Path(__file__).parent / "queries"
+
+
+@dataclass
+class ParsedArgs:
+    tpcds_gen_folder: Path
+    scale_factor: float
+    questions: str
+    ray_address: Optional[str]
+    dry_run: bool
+
+
+@dataclass
+class RunArgs:
+    scaled_tpcds_gen_folder: Path
+    query_indices: list[int]
+    ray_address: Optional[str]
+    dry_run: bool
+
+
+@dataclass
+class Result:
+    index: int
+    duration: Optional[timedelta]
+    error_msg: Optional[str]
+
+    def __repr__(self) -> str:
+        if self.duration and self.error_msg:
+            typing.assert_never("Both duration and error_msg are not None")
+        elif self.duration:
+            return f"(Q{self.index} SUCCESS - duration: {self.duration})"
+        elif self.error_msg:
+            return f"(Q{self.index} FAILURE - error msg: {self.error_msg})"
+        else:
+            typing.assert_never("Both duration and error_msg are None")
+
+
+def run_query_on_ray(
+    run_args: RunArgs,
+) -> list[Result]:
+    ray.init(address=run_args.ray_address if run_args.ray_address else None)
+    results = []
+
+    for query_index in run_args.query_indices:
+        working_dir = Path("benchmarking") / "tpcds"
+        ray_entrypoint_script = "ray_entrypoint.py"
+        duration = None
+        error_msg = None
+        try:
+            start = datetime.now()
+            ray_job_runner.run_on_ray(
+                run_args.ray_address,
+                {
+                    "entrypoint": f"python {ray_entrypoint_script} --tpcds-gen-folder 'data/0.01' --question {query_index} {'--dry-run' if run_args.dry_run else ''}",
+                    "runtime_env": {
+                        "working_dir": working_dir,
+                    },
+                },
+            )
+            end = datetime.now()
+            duration = end - start
+        except Exception as e:
+            error_msg = str(e)
+
+        results.append(Result(index=query_index, duration=duration, error_msg=error_msg))
+
+    return results
+
+
+def run_query_on_local(
+    run_args: RunArgs,
+) -> list[Result]:
+    catalog = helpers.generate_catalog(run_args.scaled_tpcds_gen_folder)
+    results = []
+
+    for query_index in run_args.query_indices:
+        query_file = SQL_QUERIES_PATH / f"{query_index:02}.sql"
+        with open(query_file) as f:
+            query = f.read()
+
+        start = datetime.now()
+
+        duration = None
+        error_msg = None
+        try:
+            daft.sql(query, catalog=catalog).explain(show_all=True)
+            if not run_args.dry_run:
+                daft.sql(query, catalog=catalog).collect()
+
+            end = datetime.now()
+            duration = end - start
+        except Exception as e:
+            error_msg = str(e)
+
+        results.append(Result(index=query_index, duration=duration, error_msg=error_msg))
+
+    return results
+
+
+def run_benchmarks(
+    run_args: RunArgs,
+) -> list[Result]:
+    logger.info(
+        "Running the following questions: %s",
+        run_args.query_indices,
+    )
+
+    runner = tpch.get_daft_benchmark_runner_name()
+
+    logger.info(
+        "Running on the following runner: %s",
+        runner,
+    )
+
+    if runner == "ray":
+        return run_query_on_ray(run_args)
+    elif runner == "py" or runner == "native":
+        return run_query_on_local(run_args)
+    else:
+        typing.assert_never(runner)
+
+
+def main(args: ParsedArgs):
+    scaled_tpcds_gen_folder = args.tpcds_gen_folder / str(args.scale_factor)
+    datagen.gen_tpcds(scaled_tpcds_gen_folder, args.scale_factor)
+    query_indices = helpers.parse_questions_str(args.questions)
+    results = run_benchmarks(
+        RunArgs(
+            scaled_tpcds_gen_folder=scaled_tpcds_gen_folder,
+            query_indices=query_indices,
+            ray_address=args.ray_address,
+            dry_run=args.dry_run,
+        )
+    )
+
+    # TODO(ronnie): improve visualization of results; simply printing them to console is not the best way...
+    print(f"{results=}")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level="INFO")
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--tpcds-gen-folder",
+        default="benchmarking/tpcds/data",
+        type=Path,
+        help="Path to the folder containing the TPC-DS dsdgen tool and generated data",
+    )
+    parser.add_argument("--scale-factor", default=0.01, type=float, help="Scale factor to run on in GB")
+    parser.add_argument("--questions", default="*", type=str, help="The questions to run")
+    parser.add_argument("--ray-address", type=str, help="The address of the head node of the ray cluster")
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Whether to run in dry-run mode; if true, only the plan will be printed, but no query will be executed",
+    )
+    args = parser.parse_args()
+
+    tpcds_gen_folder: Path = args.tpcds_gen_folder
+    assert args.scale_factor > 0
+
+    main(
+        ParsedArgs(
+            tpcds_gen_folder=tpcds_gen_folder,
+            scale_factor=args.scale_factor,
+            questions=args.questions,
+            ray_address=args.ray_address,
+            dry_run=args.dry_run,
+        )
+    )

benchmarking/tpcds/__init__.py

@@ -1,39 +1,50 @@
 import argparse
 import logging
-import os
+from pathlib import Path
 
 import duckdb
 
 logger = logging.getLogger(__name__)
 
 
-def gen_tpcds(basedir: str, scale_factor: float):
-    if not os.path.exists(basedir):
-        os.makedirs(basedir)
-    db = duckdb.connect(f"{basedir}/tpcds.db")
+def gen_tpcds(dir: Path, scale_factor: float):
+    if dir.exists():
+        assert dir.is_dir(), "The location in which to generate the data must be a directory"
+        logger.info(
+            "The directory %s already exists; doing nothing",
+            dir,
+        )
+        return
+
+    dir.mkdir(parents=True, exist_ok=True)
+    db = duckdb.connect(database=dir / "tpcds.db")
     db.sql(f"call dsdgen(sf = {scale_factor})")
     for item in db.sql("show tables").fetchall():
         tbl = item[0]
-        print(f"Exporting {tbl} to {basedir}/{tbl}.parquet")
-        db.sql(f"COPY {tbl} TO '{basedir}/{tbl}.parquet'")
+        parquet_file = dir / f"{tbl}.parquet"
+        print(f"Exporting {tbl} to {parquet_file}")
+        db.sql(f"COPY {tbl} TO '{parquet_file}'")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--tpcds-gen-folder",
         default="data/tpcds-dbgen",
+        type=Path,
         help="Path to the folder containing the TPC-DS dsdgen tool and generated data",
     )
     parser.add_argument("--scale-factor", default=0.01, help="Scale factor to run on in GB", type=float)
-
     args = parser.parse_args()
-    num_parts = args.scale_factor
+
+    tpcds_gen_folder: Path = args.tpcds_gen_folder
+    assert args.scale_factor > 0
 
     logger.info(
         "Generating data at %s with: scale_factor=%s",
-        args.tpcds_gen_folder,
+        tpcds_gen_folder,
         args.scale_factor,
     )
 
-    gen_tpcds(basedir=args.tpcds_gen_folder, scale_factor=args.scale_factor)
+    scaled_tpcds_gen_folder = tpcds_gen_folder / str(args.scale_factor)
+    gen_tpcds(dir=scaled_tpcds_gen_folder, scale_factor=args.scale_factor)

benchmarking/tpcds/__main__.py

@@ -0,0 +1,43 @@
+from pathlib import Path
+
+import daft
+from daft.sql.sql import SQLCatalog
+
+
+def generate_catalog(dir: Path):
+    if not dir.exists():
+        raise RuntimeError(f"Directory not found: {dir}")
+    return SQLCatalog(
+        tables={
+            file.stem: daft.read_parquet(path=str(file))
+            for file in dir.iterdir()
+            if file.is_file() and file.suffix == ".parquet"
+        }
+    )
+
+
+def parse_questions_str(questions: str) -> list[int]:
+    if questions == "*":
+        return list(range(1, 100))
+
+    nums = set()
+    for split in filter(lambda str: str, questions.split(",")):
+        try:
+            num = int(split)
+            nums.add(num)
+        except ValueError:
+            ints = split.split("-")
+            assert (
+                len(ints) == 2
+            ), f"A range must include two numbers split by a dash (i.e., '-'); instead got '{split}'"
+            [lower, upper] = ints
+            try:
+                lower = int(lower)
+                upper = int(upper)
+                assert lower <= upper
+                for index in range(lower, upper + 1):
+                    nums.add(index)
+            except ValueError:
+                raise ValueError(f"Invalid range: {split}")
+
+    return nums

benchmarking/tpcds/datagen.py

@@ -0,0 +1,53 @@
+import argparse
+import sys
+from pathlib import Path
+
+import helpers
+
+import daft
+
+
+def run(
+    parquet_folder: Path,
+    question: int,
+    dry_run: bool,
+):
+    catalog = helpers.generate_catalog(parquet_folder)
+    query_file = Path(__file__).parent / "queries" / f"{question:02}.sql"
+    with open(query_file) as f:
+        query = f.read()
+
+    try:
+        daft.sql(query, catalog=catalog).explain(show_all=True)
+        if not dry_run:
+            daft.sql(query, catalog=catalog).collect()
+    except Exception as e:
+        print(str(e), file=sys.stderr)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--tpcds-gen-folder",
+        required=True,
+        type=Path,
+        help="Path to the TPC-DS data generation folder",
+    )
+    parser.add_argument(
+        "--question",
+        required=True,
+        type=int,
+        help="The TPC-DS question index to run",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Whether or not to run the query in dry-run mode; if true, only the plan will be printed out",
+    )
+    args = parser.parse_args()
+
+    tpcds_gen_folder: Path = args.tpcds_gen_folder
+    assert tpcds_gen_folder.exists()
+    assert args.question in range(1, 100)
+
+    run(args.tpcds_gen_folder, args.question, args.dry_run)