Add object store support

Cargo.toml

@@ -52,6 +52,7 @@ object_store = { version = "0.11.0", features = [
 ] }
 parking_lot = { version = "0.12", features = ["deadlock_detection"] }
 prost = "0.13"
+protobuf-src = "2.1"
 pyo3 = { version = "0.23", features = [
   "extension-module",
   "abi3",
@@ -85,7 +86,6 @@ tonic-build = { version = "0.8", default-features = false, features = [
   "prost",
 ] }
 url = "2"
-protobuf-src = "2.1"
 
 [dev-dependencies]
 tempfile = "3.17"

README.md

@@ -51,12 +51,20 @@ Once installed, you can run queries using DataFusion's familiar API while levera
 capabilities of Ray.
 
 ```python
+# from example in ./examples/http_csv.py
 import ray
 from datafusion_ray import DFRayContext, df_ray_runtime_env
 
 ray.init(runtime_env=df_ray_runtime_env)
-session = DFRayContext()
-df = session.sql("SELECT * FROM my_table WHERE value > 100")
+
+ctx = DFRayContext()
+ctx.register_csv(
+    "aggregate_test_100",
+    "https://github.com/apache/arrow-testing/raw/master/data/csv/aggregate_test_100.csv",
+)
+
+df = ctx.sql("SELECT c1,c2,c3 FROM aggregate_test_100 LIMIT 5")
+
 df.show()
 ```
 

datafusion_ray/core.py

@@ -86,9 +86,7 @@ async def wait_for(coros, name=""):
     # wrap the coro in a task to work with python 3.10 and 3.11+ where asyncio.wait semantics
     # changed to not accept any awaitable
     start = time.time()
-    done, _ = await asyncio.wait(
-        [asyncio.create_task(_ensure_coro(c)) for c in coros]
-    )
+    done, _ = await asyncio.wait([asyncio.create_task(_ensure_coro(c)) for c in coros])
     end = time.time()
     log.info(f"waiting for {name} took {end - start}s")
     for d in done:
@@ -166,9 +164,7 @@ async def acquire(self, need=1):
         need_to_make = need - have
 
         if need_to_make > can_make:
-            raise Exception(
-                f"Cannot allocate workers above {self.max_workers}"
-            )
+            raise Exception(f"Cannot allocate workers above {self.max_workers}")
 
         if need_to_make > 0:
             log.debug(f"creating {need_to_make} additional processors")
@@ -197,9 +193,9 @@ def _new_processor(self):
         self.processors_ready.clear()
         processor_key = new_friendly_name()
         log.debug(f"starting processor: {processor_key}")
-        processor = DFRayProcessor.options(
-            name=f"Processor : {processor_key}"
-        ).remote(processor_key)
+        processor = DFRayProcessor.options(name=f"Processor : {processor_key}").remote(
+            processor_key
+        )
         self.pool[processor_key] = processor
         self.processors_started.add(processor.start_up.remote())
         self.available.add(processor_key)
@@ -248,9 +244,7 @@ async def _wait_for_serve(self):
 
     async def all_done(self):
         log.info("calling processor all done")
-        refs = [
-            processor.all_done.remote() for processor in self.pool.values()
-        ]
+        refs = [processor.all_done.remote() for processor in self.pool.values()]
         await wait_for(refs, "processors to be all done")
         log.info("all processors shutdown")
 
@@ -293,9 +287,7 @@ async def update_plan(
         )
 
     async def serve(self):
-        log.info(
-            f"[{self.processor_key}] serving on {self.processor_service.addr()}"
-        )
+        log.info(f"[{self.processor_key}] serving on {self.processor_service.addr()}")
         await self.processor_service.serve()
         log.info(f"[{self.processor_key}] done serving")
 
@@ -332,9 +324,7 @@ def __init__(
         worker_pool_min: int,
         worker_pool_max: int,
     ) -> None:
-        log.info(
-            f"Creating DFRayContextSupervisor worker_pool_min: {worker_pool_min}"
-        )
+        log.info(f"Creating DFRayContextSupervisor worker_pool_min: {worker_pool_min}")
         self.pool = DFRayProcessorPool(worker_pool_min, worker_pool_max)
         self.stages: dict[str, InternalStageData] = {}
         log.info("Created DFRayContextSupervisor")
@@ -347,9 +337,7 @@ async def wait_for_ready(self):
 
     async def get_stage_addrs(self, stage_id: int):
         addrs = [
-            sd.remote_addr
-            for sd in self.stages.values()
-            if sd.stage_id == stage_id
+            sd.remote_addr for sd in self.stages.values() if sd.stage_id == stage_id
         ]
         return addrs
 
@@ -399,10 +387,7 @@ async def new_query(
             refs.append(
                 isd.remote_processor.update_plan.remote(
                     isd.stage_id,
-                    {
-                        stage_id: val["child_addrs"]
-                        for (stage_id, val) in kid.items()
-                    },
+                    {stage_id: val["child_addrs"] for (stage_id, val) in kid.items()},
                     isd.partition_group,
                     isd.plan_bytes,
                 )
@@ -434,9 +419,7 @@ async def sort_out_addresses(self):
                 ]
 
                 # sanity check
-                assert all(
-                    [op == output_partitions[0] for op in output_partitions]
-                )
+                assert all([op == output_partitions[0] for op in output_partitions])
                 output_partitions = output_partitions[0]
 
                 for child_stage_isd in child_stage_datas:
@@ -520,9 +503,7 @@ def collect(self) -> list[pa.RecordBatch]:
             )
             log.debug(f"last stage addrs {last_stage_addrs}")
 
-            reader = self.df.read_final_stage(
-                last_stage_id, last_stage_addrs[0]
-            )
+            reader = self.df.read_final_stage(last_stage_id, last_stage_addrs[0])
             log.debug("got reader")
             self._batches = list(reader)
         return self._batches
@@ -589,11 +570,55 @@ def __init__(
         )
 
     def register_parquet(self, name: str, path: str):
+        """
+        Register a Parquet file with the given name and path.
+        The path can be a local filesystem path, absolute filesystem path, or a url.
+
+        If the path is a object store url, the appropriate object store will be registered.
+        Configuration of the object store will be gathered from the environment.
+
+        For example for s3:// urls, credentials will be looked for by the AWS SDK,
+        which will check environment variables, credential files, etc
+
+        Parameters:
+        path (str): The file path to the Parquet file.
+        name (str): The name to register the Parquet file under.
+        """
         self.ctx.register_parquet(name, path)
 
-    def register_listing_table(
-        self, name: str, path: str, file_extention="parquet"
-    ):
+    def register_csv(self, name: str, path: str):
+        """
+        Register a csvfile with the given name and path.
+        The path can be a local filesystem path, absolute filesystem path, or a url.
+
+        If the path is a object store url, the appropriate object store will be registered.
+        Configuration of the object store will be gathered from the environment.
+
+        For example for s3:// urls, credentials will be looked for by the AWS SDK,
+        which will check environment variables, credential files, etc
+
+        Parameters:
+        path (str): The file path to the csv file.
+        name (str): The name to register the Parquet file under.
+        """
+        self.ctx.register_csv(name, path)
+
+    def register_listing_table(self, name: str, path: str, file_extention="parquet"):
+        """
+        Register a directory of parquet files with the given name.
+        The path can be a local filesystem path, absolute filesystem path, or a url.
+
+        If the path is a object store url, the appropriate object store will be registered.
+        Configuration of the object store will be gathered from the environment.
+
+        For example for s3:// urls, credentials will be looked for by the AWS SDK,
+        which will check environment variables, credential files, etc
+
+        Parameters:
+        path (str): The file path to the Parquet file directory
+        name (str): The name to register the Parquet file under.
+        """
+
         self.ctx.register_listing_table(name, path, file_extention)
 
     def sql(self, query: str) -> DFRayDataFrame:

examples/http_csv.py

@@ -0,0 +1,40 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# this is a port of the example at
+# https://github.com/apache/datafusion/blob/45.0.0/datafusion-examples/examples/query-http-csv.rs
+
+import ray
+
+from datafusion_ray import DFRayContext, df_ray_runtime_env
+
+
+def main():
+    ctx = DFRayContext()
+    ctx.register_csv(
+        "aggregate_test_100",
+        "https://github.com/apache/arrow-testing/raw/master/data/csv/aggregate_test_100.csv",
+    )
+
+    df = ctx.sql("SELECT c1,c2,c3 FROM aggregate_test_100 LIMIT 5")
+
+    df.show()
+
+
+if __name__ == __name__:
+    ray.init(namespace="http_csv", runtime_env=df_ray_runtime_env)
+    main()

examples/tips.py

@@ -16,40 +16,27 @@
 # under the License.
 
 import argparse
-import datafusion
+import os
 import ray
 
-from datafusion_ray import DFRayContext
+from datafusion_ray import DFRayContext, df_ray_runtime_env
 
 
 def go(data_dir: str):
     ctx = DFRayContext()
-    # we could set this value to however many CPUs we plan to give each
-    # ray task
-    ctx.set("datafusion.execution.target_partitions", "1")
-    ctx.set("datafusion.optimizer.enable_round_robin_repartition", "false")
 
-    ctx.register_parquet("tips", f"{data_dir}/tips*.parquet")
+    ctx.register_parquet("tips", os.path.join(data_dir, "tips.parquet"))
 
     df = ctx.sql(
         "select sex, smoker, avg(tip/total_bill) as tip_pct from tips group by sex, smoker order by sex, smoker"
     )
     df.show()
 
-    print("no ray result:")
-
-    # compare to non ray version
-    ctx = datafusion.SessionContext()
-    ctx.register_parquet("tips", f"{data_dir}/tips*.parquet")
-    ctx.sql(
-        "select sex, smoker, avg(tip/total_bill) as tip_pct from tips group by sex, smoker order by sex, smoker"
-    ).show()
-
 
 if __name__ == "__main__":
-    ray.init(namespace="tips")
+    ray.init(namespace="tips", runtime_env=df_ray_runtime_env)
     parser = argparse.ArgumentParser()
-    parser.add_argument("--data-dir", required=True, help="path to tips*.parquet files")
+    parser.add_argument("--data-dir", required=True, help="path to tips.parquet files")
     args = parser.parse_args()
 
     go(args.data_dir)

src/context.rs

@@ -16,17 +16,17 @@
 // under the License.
 
 use datafusion::datasource::file_format::parquet::ParquetFormat;
-use datafusion::datasource::listing::ListingOptions;
-use datafusion::{execution::SessionStateBuilder, prelude::*};
+use datafusion::datasource::listing::{ListingOptions, ListingTableUrl};
+use datafusion::execution::SessionStateBuilder;
+use datafusion::prelude::{CsvReadOptions, ParquetReadOptions, SessionConfig, SessionContext};
 use datafusion_python::utils::wait_for_future;
-use object_store::aws::AmazonS3Builder;
+use log::debug;
 use pyo3::prelude::*;
 use std::sync::Arc;
 
 use crate::dataframe::DFRayDataFrame;
 use crate::physical::RayStageOptimizerRule;
-use crate::util::ResultExt;
-use url::Url;
+use crate::util::{maybe_register_object_store, ResultExt};
 
 /// Internal Session Context object for the python class DFRayContext
 #[pyclass]
@@ -54,23 +54,27 @@ impl DFRayContext {
         Ok(Self { ctx })
     }
 
-    pub fn register_s3(&self, bucket_name: String) -> PyResult<()> {
-        let s3 = AmazonS3Builder::from_env()
-            .with_bucket_name(&bucket_name)
-            .build()
-            .to_py_err()?;
+    pub fn register_parquet(&self, py: Python, name: String, path: String) -> PyResult<()> {
+        let options = ParquetReadOptions::default();
+
+        let url = ListingTableUrl::parse(&path).to_py_err()?;
 
-        let path = format!("s3://{bucket_name}");
-        let s3_url = Url::parse(&path).to_py_err()?;
-        let arc_s3 = Arc::new(s3);
-        self.ctx.register_object_store(&s3_url, arc_s3.clone());
+        maybe_register_object_store(&self.ctx, url.as_ref()).to_py_err()?;
+        debug!("register_parquet: registering table {} at {}", name, path);
+
+        wait_for_future(py, self.ctx.register_parquet(&name, &path, options.clone()))?;
         Ok(())
     }
 
-    pub fn register_parquet(&self, py: Python, name: String, path: String) -> PyResult<()> {
-        let options = ParquetReadOptions::default();
+    pub fn register_csv(&self, py: Python, name: String, path: String) -> PyResult<()> {
+        let options = CsvReadOptions::default();
 
-        wait_for_future(py, self.ctx.register_parquet(&name, &path, options.clone()))?;
+        let url = ListingTableUrl::parse(&path).to_py_err()?;
+
+        maybe_register_object_store(&self.ctx, url.as_ref()).to_py_err()?;
+        debug!("register_csv: registering table {} at {}", name, path);
+
+        wait_for_future(py, self.ctx.register_csv(&name, &path, options.clone()))?;
         Ok(())
     }
 
@@ -85,6 +89,15 @@ impl DFRayContext {
         let options =
             ListingOptions::new(Arc::new(ParquetFormat::new())).with_file_extension(file_extension);
 
+        let path = format!("{path}/");
+        let url = ListingTableUrl::parse(&path).to_py_err()?;
+
+        maybe_register_object_store(&self.ctx, url.as_ref()).to_py_err()?;
+
+        debug!(
+            "register_listing_table: registering table {} at {}",
+            name, path
+        );
         wait_for_future(
             py,
             self.ctx