diff --git a/python/ray/internal/internal_api.py b/python/ray/internal/internal_api.py index 67c1a9275f370..28252f9dbb6be 100644 --- a/python/ray/internal/internal_api.py +++ b/python/ray/internal/internal_api.py @@ -13,7 +13,9 @@ def global_gc(): worker.core_worker.global_gc() -def memory_summary(node_manager_address=None, node_manager_port=None): +def memory_summary(node_manager_address=None, + node_manager_port=None, + stats_only=False): """Returns a formatted string describing memory usage in the cluster.""" import grpc @@ -63,6 +65,13 @@ def memory_summary(node_manager_address=None, node_manager_port=None): reply.store_stats.restored_objects_total, int(reply.store_stats.restored_bytes_total / (1024 * 1024) / reply.store_stats.restore_time_total_s))) + if reply.store_stats.referenced_bytes > 0: + store_summary += ( + "Total bytes of plasma objects referenced by ray tasks " + "or ray.get: {} MiB.".format( + int(reply.store_stats.referenced_bytes / (1024 * 1024)))) + if stats_only: + return store_summary return reply.memory_summary + "\n" + store_summary diff --git a/python/ray/scripts/scripts.py b/python/ray/scripts/scripts.py index b61c6939984cb..5c3ce1012b179 100644 --- a/python/ray/scripts/scripts.py +++ b/python/ray/scripts/scripts.py @@ -1372,7 +1372,13 @@ def timeline(address): type=str, default=ray_constants.REDIS_DEFAULT_PASSWORD, help="Connect to ray with redis_password.") -def memory(address, redis_password): +@click.option( + "--stats-only", + is_flag=True, + type=bool, + default=False, + help="Connect to ray with redis_password.") +def memory(address, redis_password, stats_only): """Print object references held in a Ray cluster.""" if not address: address = services.get_ray_address_to_use_or_die() @@ -1381,7 +1387,8 @@ def memory(address, redis_password): raylet = state.node_table()[0] print( ray.internal.internal_api.memory_summary(raylet["NodeManagerAddress"], - raylet["NodeManagerPort"])) + raylet["NodeManagerPort"], + stats_only)) @cli.command() diff --git a/python/ray/tests/test_object_spilling.py b/python/ray/tests/test_object_spilling.py index 159e0aaf79b14..6cebeb0bf3730 100644 --- a/python/ray/tests/test_object_spilling.py +++ b/python/ray/tests/test_object_spilling.py @@ -88,6 +88,27 @@ def is_dir_empty(temp_folder, return num_files == 0 +def assert_no_thrashing(address): + state = ray.state.GlobalState() + state._initialize_global_state(address, + ray.ray_constants.REDIS_DEFAULT_PASSWORD) + raylet = state.node_table()[0] + memory_summary = ray.internal.internal_api.memory_summary( + raylet["NodeManagerAddress"], + raylet["NodeManagerPort"], + stats_only=True) + restored_bytes = 0 + referenced_bytes = 0 + + for line in memory_summary.split("\n"): + if "Restored" in line: + restored_bytes = int(line.split(" ")[1]) + if "referenced" in line: + referenced_bytes = int(line.split(" ")[-2]) + assert referenced_bytes >= restored_bytes, ( + f"referenced: {referenced_bytes}, restored: {restored_bytes}") + + def test_invalid_config_raises_exception(shutdown_only): # Make sure ray.init raises an exception before # it starts processes when invalid object spilling @@ -187,7 +208,7 @@ def test_spilling_not_done_for_pinned_object(object_spilling_config, shutdown_only): # Limit our object store to 75 MiB of memory. object_spilling_config, temp_folder = object_spilling_config - ray.init( + address = ray.init( object_store_memory=75 * 1024 * 1024, _system_config={ "max_io_workers": 4, @@ -203,6 +224,7 @@ def test_spilling_not_done_for_pinned_object(object_spilling_config, ref2 = ray.put(arr) # noqa wait_for_condition(lambda: is_dir_empty(temp_folder)) + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( @@ -249,6 +271,7 @@ def depends(arg): # Test passing the spilled object as an arg to another task. ray.get(depends.remote(ref)) + assert_no_thrashing(cluster.address) @pytest.mark.skipif( @@ -256,7 +279,7 @@ def depends(arg): def test_spill_objects_automatically(object_spilling_config, shutdown_only): # Limit our object store to 75 MiB of memory. object_spilling_config, _ = object_spilling_config - ray.init( + address = ray.init( num_cpus=1, object_store_memory=75 * 1024 * 1024, _system_config={ @@ -287,14 +310,15 @@ def test_spill_objects_automatically(object_spilling_config, shutdown_only): solution = solution_buffer[index] sample = ray.get(ref, timeout=0) assert np.array_equal(sample, solution) + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( - platform.system() in ["Darwin", "Windows"], reason="Failing on Windows.") + platform.system() in ["Windows", "Darwin"], reason="Failing on Windows.") def test_spill_stats(object_spilling_config, shutdown_only): # Limit our object store to 75 MiB of memory. object_spilling_config, _ = object_spilling_config - ray.init( + address = ray.init( num_cpus=1, object_store_memory=100 * 1024 * 1024, _system_config={ @@ -319,17 +343,31 @@ def f(): x_id = f.remote() # noqa ray.get(x_id) - s = memory_summary() + s = memory_summary(stats_only=True) assert "Plasma memory usage 50 MiB, 1 objects, 50.0% full" in s, s assert "Spilled 200 MiB, 4 objects" in s, s assert "Restored 150 MiB, 3 objects" in s, s + # Test if referenced bytes are correctly calculated. + obj = ray.put(np.zeros(30 * 1024 * 1024, dtype=np.uint8)) + + @ray.remote + def func_with_ref(obj): + return True + + ray.get(func_with_ref.remote(obj)) + + s = memory_summary(stats_only=True) + # 50MB * 5 references + 30MB used for task execution. + assert "ray.get: 280 MiB." in s, s + assert_no_thrashing(address["redis_address"]) + @pytest.mark.skipif( platform.system() == "Windows", reason="Failing on Windows.") def test_spill_during_get(object_spilling_config, shutdown_only): object_spilling_config, _ = object_spilling_config - ray.init( + address = ray.init( num_cpus=4, object_store_memory=100 * 1024 * 1024, _system_config={ @@ -355,6 +393,7 @@ def f(): # objects are being created. for x in ids: print(ray.get(x).shape) + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( @@ -362,7 +401,7 @@ def f(): def test_spill_deadlock(object_spilling_config, shutdown_only): object_spilling_config, _ = object_spilling_config # Limit our object store to 75 MiB of memory. - ray.init( + address = ray.init( object_store_memory=75 * 1024 * 1024, _system_config={ "max_io_workers": 1, @@ -386,6 +425,7 @@ def test_spill_deadlock(object_spilling_config, shutdown_only): ref = random.choice(replay_buffer) sample = ray.get(ref, timeout=0) assert np.array_equal(sample, arr) + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( @@ -394,7 +434,7 @@ def test_delete_objects(object_spilling_config, shutdown_only): # Limit our object store to 75 MiB of memory. object_spilling_config, temp_folder = object_spilling_config - ray.init( + address = ray.init( object_store_memory=75 * 1024 * 1024, _system_config={ "max_io_workers": 1, @@ -417,6 +457,7 @@ def test_delete_objects(object_spilling_config, shutdown_only): del replay_buffer del ref wait_for_condition(lambda: is_dir_empty(temp_folder)) + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( @@ -426,7 +467,7 @@ def test_delete_objects_delete_while_creating(object_spilling_config, # Limit our object store to 75 MiB of memory. object_spilling_config, temp_folder = object_spilling_config - ray.init( + address = ray.init( object_store_memory=75 * 1024 * 1024, _system_config={ "max_io_workers": 4, @@ -457,6 +498,7 @@ def test_delete_objects_delete_while_creating(object_spilling_config, del replay_buffer del ref wait_for_condition(lambda: is_dir_empty(temp_folder)) + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( @@ -466,7 +508,7 @@ def test_delete_objects_on_worker_failure(object_spilling_config, # Limit our object store to 75 MiB of memory. object_spilling_config, temp_folder = object_spilling_config - ray.init( + address = ray.init( object_store_memory=75 * 1024 * 1024, _system_config={ "max_io_workers": 4, @@ -518,6 +560,7 @@ def wait_until_actor_dead(): # After all, make sure all objects are deleted upon worker failures. wait_for_condition(lambda: is_dir_empty(temp_folder)) + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( @@ -539,10 +582,11 @@ def test_delete_objects_multi_node(multi_node_object_spilling_config, "object_store_full_delay_ms": 100, "object_spilling_config": object_spilling_config, }) + ray.init(address=cluster.address) # Add 2 worker nodes. for _ in range(2): cluster.add_node(num_cpus=1, object_store_memory=75 * 1024 * 1024) - ray.init(address=cluster.address) + cluster.wait_for_nodes() arr = np.random.rand(1024 * 1024) # 8 MB data @@ -565,9 +609,9 @@ def create_objects(self): self.replay_buffer.pop() # Do random sampling. - for _ in range(200): + for _ in range(50): ref = random.choice(self.replay_buffer) - sample = ray.get(ref, timeout=0) + sample = ray.get(ref, timeout=10) assert np.array_equal(sample, arr) actors = [Actor.remote() for _ in range(3)] @@ -586,6 +630,7 @@ def wait_until_actor_dead(actor): wait_for_condition(lambda: wait_until_actor_dead(actor)) # The multi node deletion should work. wait_for_condition(lambda: is_dir_empty(temp_folder)) + assert_no_thrashing(cluster.address) @pytest.mark.skipif(platform.system() == "Windows", reason="Flaky on Windows.") @@ -593,7 +638,7 @@ def test_fusion_objects(object_spilling_config, shutdown_only): # Limit our object store to 75 MiB of memory. object_spilling_config, temp_folder = object_spilling_config min_spilling_size = 10 * 1024 * 1024 - ray.init( + address = ray.init( object_store_memory=75 * 1024 * 1024, _system_config={ "max_io_workers": 3, @@ -637,12 +682,13 @@ def test_fusion_objects(object_spilling_config, shutdown_only): if file_size >= min_spilling_size: is_test_passing = True assert is_test_passing + assert_no_thrashing(address["redis_address"]) # https://github.com/ray-project/ray/issues/12912 def do_test_release_resource(object_spilling_config, expect_released): object_spilling_config, temp_folder = object_spilling_config - ray.init( + address = ray.init( num_cpus=1, object_store_memory=75 * 1024 * 1024, _system_config={ @@ -674,6 +720,7 @@ def f(dep): assert ready else: assert not ready + assert_no_thrashing(address["redis_address"]) @pytest.mark.skipif( @@ -745,6 +792,7 @@ def allocate(*args): # spilling. tasks = [foo.remote(*task_args) for task_args in args] ray.get(tasks) + assert_no_thrashing(cluster.address) @pytest.mark.skipif( @@ -801,14 +849,6 @@ def test_file_deleted_when_driver_exits(tmp_path, shutdown_only): driver.format(temp_dir=str(temp_folder), signum=2))) wait_for_condition(lambda: is_dir_empty(temp_folder, append_path="")) - # Q: Looks like Sigterm doesn't work with Ray? - # print("Sending sigterm...") - # # Run a driver with sigterm. - # with pytest.raises(subprocess.CalledProcessError): - # print(run_string_as_driver( - # driver.format(temp_dir=str(temp_folder), signum=15))) - # wait_for_condition(is_dir_empty, timeout=1000) - if __name__ == "__main__": sys.exit(pytest.main(["-sv", __file__])) diff --git a/python/ray/util/dask/scheduler.py b/python/ray/util/dask/scheduler.py index 0614d35641ec0..8737fc4913056 100644 --- a/python/ray/util/dask/scheduler.py +++ b/python/ray/util/dask/scheduler.py @@ -275,6 +275,8 @@ def _rayify_task( # Ray properly tracks the object dependencies between Ray tasks. object_refs, repack = unpack_object_refs(args, deps) # Submit the task using a wrapper function. + for ref in object_refs: + print(f"[GET] object_id:{ref.hex()} size:0 func:{key}") object_ref = dask_task_wrapper.options(name=f"dask:{key!s}").remote( func, repack, key, ray_pretask_cbs, ray_posttask_cbs, *object_refs) diff --git a/release/data_processing_tests/workloads/output.csv b/release/data_processing_tests/workloads/output.csv new file mode 100644 index 0000000000000..2fa7483a1b1dd --- /dev/null +++ b/release/data_processing_tests/workloads/output.csv @@ -0,0 +1,3 @@ +system,operation,num_nodes,nbytes,npartitions,duration +ray,sort,1,1000000000,50,38.00257587432861 +ray,sort,1,1000000000,100,65.93781399726868 diff --git a/release/data_processing_tests/workloads/test_dask.py b/release/data_processing_tests/workloads/test_dask.py new file mode 100644 index 0000000000000..a3b924ddcefbb --- /dev/null +++ b/release/data_processing_tests/workloads/test_dask.py @@ -0,0 +1,190 @@ +import glob +import ray +import dask +import dask.dataframe as dd +import json +import pandas as pd +import numpy as np +from ray.util.dask import ray_dask_get_sync +import os.path +import csv +import fastparquet + +from dask.distributed import Client +from dask.distributed import wait +import cProfile, pstats, io +from pstats import SortKey + +import time + +DATA_DIR = "~/dask-on-ray-data" + +# DATA_DIR = "/obj-data" + + +def load_dataset(nbytes, npartitions, sort): + num_bytes_per_partition = nbytes // npartitions + filenames = [] + + @ray.remote + def foo(i): + filename = "df-{}-{}-{}.parquet.gzip".format( + "sort" if sort else "groupby", num_bytes_per_partition, i) + filename = os.path.join(DATA_DIR, filename) + print("Partition file", filename) + if not os.path.exists(filename): + if sort: + nrows = num_bytes_per_partition // 8 + print("Allocating dataset with {} rows".format(nrows)) + dataset = pd.DataFrame( + np.random.randint( + 0, + np.iinfo(np.int64).max, + size=(nrows, 1), + dtype=np.int64), + columns=['a']) + else: + nrows = num_bytes_per_partition // (8 * 2) + print("Allocating dataset with {} rows".format(nrows)) + dataset = pd.DataFrame( + np.random.randint(0, 100, size=(nrows, 2), dtype=np.int64), + columns=['a', 'b']) + print("Done allocating") + dataset.to_parquet(filename, compression='gzip') + print("Done writing to disk") + return filename + + for i in range(npartitions): + filenames.append(foo.remote(i)) + filenames = ray.get(filenames) + + df = dd.read_parquet(filenames) + return df + + +def trial(nbytes, n_partitions, sort, generate_only): + df = load_dataset(nbytes, n_partitions, sort) + pr = cProfile.Profile() + if generate_only: + return + + times = [] + start = time.time() + for i in range(1): + print("Trial {} start".format(i)) + trial_start = time.time() + + if sort: + # pr.enable() + a = df.set_index('a', shuffle='tasks', max_branch=10**9) + # a.visualize(filename=f'a-{i}.svg') + a.head(10, npartitions=-1) + # pr.disable() + else: + df.groupby('b').a.mean().compute() + + trial_end = time.time() + duration = trial_end - trial_start + times.append(duration) + print("Trial {} done after {}".format(i, duration)) + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr).sort_stats(sortby) + # ps.dump_stats("ray_profile_data") + + if time.time() - start > 60 and i > 0: + break + return times + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + + parser.add_argument("--nbytes", type=int, default=1_000_000) + parser.add_argument("--npartitions", type=int, default=100, required=False) + # Max partition size is 1GB. + parser.add_argument( + "--max-partition-size", type=int, default=1000_000_000, required=False) + parser.add_argument("--num-nodes", type=int, default=1) + parser.add_argument("--sort", action="store_true") + parser.add_argument("--timeline", action="store_true") + parser.add_argument("--dask", action="store_true") + parser.add_argument("--ray", action="store_true") + parser.add_argument("--dask-tasks", action="store_true") + parser.add_argument("--generate-only", action="store_true") + parser.add_argument("--clear-old-data", action="store_true") + args = parser.parse_args() + + if args.clear_old_data: + print(f"Clearing old data from {DATA_DIR}.") + files = glob.glob(os.path.join(DATA_DIR, "*.parquet.gzip")) + for f in files: + os.remove(f) + + if args.ray: + args.dask_tasks = True + + if args.dask_tasks: + print("Using task-based Dask shuffle") + dask.config.set(shuffle='tasks') + else: + print("Using disk-based Dask shuffle") + + if args.dask: + client = Client('127.0.0.1:8786') + ray.init(address='auto') + if args.ray: + ray.init(address="auto") + # ray.init( + # num_cpus=16, + # _system_config={ + # "max_io_workers": 1, + # "object_spilling_config": json.dumps( + # { + # "type": "filesystem", + # "params": { + # "directory_path": "/tmp/spill" + # } + # }, + # separators=(",", ":")) + # }) + dask.config.set(scheduler=ray_dask_get_sync) + + system = "dask" if args.dask else "ray" + + # print(system, trial(1000, 10, args.sort, args.generate_only)) + print("WARMUP DONE") + + npartitions = args.npartitions + if args.nbytes // npartitions > args.max_partition_size: + npartitions = args.nbytes // args.max_partition_size + + output = trial(args.nbytes, npartitions, args.sort, args.generate_only) + print("{} mean over {} trials: {} +- {}".format(system, len(output), + np.mean(output), + np.std(output))) + + write_header = not os.path.exists("output.csv") or os.path.getsize( + "output.csv") == 0 + with open("output.csv", "a+") as csvfile: + fieldnames = [ + "system", "operation", "num_nodes", "nbytes", "npartitions", + "duration" + ] + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + if write_header: + writer.writeheader() + row = { + "operation": "sort" if args.sort else "groupby", + "num_nodes": args.num_nodes, + "nbytes": args.nbytes, + "npartitions": npartitions, + } + for output in output: + row["system"] = system + row["duration"] = output + writer.writerow(row) + + if args.timeline: + time.sleep(1) + ray.timeline(filename="dask.json") diff --git a/release/raydp_tests/dask_on_ray/a-0.svg b/release/raydp_tests/dask_on_ray/a-0.svg new file mode 100644 index 0000000000000..962e1773f794f --- /dev/null +++ b/release/raydp_tests/dask_on_ray/a-0.svg @@ -0,0 +1,82644 @@ + + + + + + + + + +5326898693109738271 + +assign + + + +-5552841363955063783 + +0 + + + +5326898693109738271->-5552841363955063783 + 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b/release/raydp_tests/dask_on_ray/analyze.py new file mode 100644 index 0000000000000..e3c71b88a30ff --- /dev/null +++ b/release/raydp_tests/dask_on_ray/analyze.py @@ -0,0 +1,60 @@ + +raylet = "/tmp/ray/session_latest/logs/raylet.err" +result = "result.txt" + +from collections import defaultdict + +def analyze(filename): + object_refs = defaultdict(int) + object_refs_referenced = defaultdict(int) + object_refs_funcs = defaultdict(set) + func_cnt = defaultdict(int) + total_sizes = 0 + total_sizes_referenced_more_than_once = 0 + with open(filename) as f: + for line in f.readlines(): + if "[GET]" in line: + tokens = line.strip("\n").split("[GET]")[1].split(" ") + object_id = tokens[1].split(":")[1] + size = tokens[2].split(":")[1] + if len(tokens) >= 4: + func = tokens[3].split(":")[1] + object_refs_funcs[object_id].add(func) + func_cnt[func] += 1 + object_refs[object_id] += int(size) + object_refs_referenced[object_id] += 1 + total_sizes += int(size) + if object_refs[object_id] > 1: + total_sizes_referenced_more_than_once += int(size) + # print(filename) + print(f"total object refs: {len(object_refs)}") + # for object_id in object_refs.keys(): + # cnt = object_refs_referenced[object_id] + # size = object_refs[object_id] + # if cnt > 1: + # print(f"{object_id} uses {int(size) /1024 /1024}MB, referenced: {cnt}") + # print(f"functions: {object_refs_funcs[object_id]}") + # print(f"total sizes: {total_sizes / 1024 / 1024} MB") + # print(f"total sizes referenced more than once: {total_sizes_referenced_more_than_once / 1024 / 1024} MB") + return object_refs, object_refs_referenced, object_refs_funcs, func_cnt + +sizes_dict, _, _, _ = analyze(raylet) +_, ref_count, funcs, func_cnt = analyze(result) + +c = 0 +for object_id in ref_count.keys(): + cnt = int(ref_count[object_id]) + size = sizes_dict.get(object_id, None) + if not size: + continue + + if int(size) > 100 * 1024: + c += 1 + print(f"{object_id} uses {int(size) /1024 /1024}MB, referenced: {cnt}") + print(f"functions: {funcs[object_id]}") +assert c == len(sizes_dict), len(sizes_dict) +from pprint import pprint +pprint(func_cnt) +# print(f"total sizes: {total_sizes / 1024 / 1024} MB") +# print(f"total sizes referenced more than once: {total_sizes_referenced_more_than_once / 1024 / 1024} MB") + diff --git a/release/raydp_tests/dask_on_ray/output.csv b/release/raydp_tests/dask_on_ray/output.csv new file mode 100644 index 0000000000000..baa6dcdd89364 --- /dev/null +++ b/release/raydp_tests/dask_on_ray/output.csv @@ -0,0 +1,45 @@ +system,operation,num_nodes,nbytes,npartitions,duration +ray,sort,1,1000000000,50,49.81460404396057 +ray,sort,1,100000000,50,26.4190411567688 +ray,sort,1,100000000,50,15.81236982345581 +ray,sort,1,100000000,50,37.54668378829956 +ray,sort,1,100000000,50,45.88498592376709 +ray,sort,1,100000000,2,6.961517095565796 +ray,sort,1,100000000,50,20.324124813079834 +ray,sort,1,100000000,50,39.60013222694397 +ray,sort,1,100000000,50,19.749282121658325 +ray,sort,1,100000000,50,14.275277853012085 +ray,sort,1,100000000,50,21.64378595352173 +ray,sort,1,100000000,50,6.137988805770874 +ray,sort,1,100000000,50,7.724100112915039 +ray,sort,1,100000000,50,16.97205114364624 +ray,sort,1,100000000,50,5.901505947113037 +ray,sort,1,100000000,50,7.561151027679443 +ray,sort,1,100000000,50,6.777873992919922 +ray,sort,1,100000000,50,22.736148834228516 +ray,sort,1,100000000,50,23.90089201927185 +ray,sort,1,100000000,50,47.67734980583191 +ray,sort,1,100000000,50,21.425940990447998 +ray,sort,1,100000000,50,28.597450971603394 +ray,sort,1,100000000,50,23.31282091140747 +ray,sort,1,100000000,50,18.240912914276123 +ray,sort,1,100000000,50,34.761038064956665 +ray,sort,1,100000000,50,23.420669317245483 +ray,sort,1,100000000,50,30.70834708213806 +ray,sort,1,100000000,50,25.204899072647095 +ray,sort,1,100000000,50,26.13987922668457 +ray,sort,1,100000000,50,24.668132066726685 +ray,sort,1,100000000,50,21.150716066360474 +ray,sort,1,100000000,50,24.247713088989258 +ray,sort,1,100000000,50,21.209686040878296 +ray,sort,1,100000000,50,18.561686038970947 +ray,sort,1,100000000,50,23.43537187576294 +ray,sort,1,100000000,50,17.797905206680298 +ray,sort,1,100000000,50,25.986549854278564 +ray,sort,1,100000000,50,18.29687786102295 +ray,sort,1,100000000,50,18.578434944152832 +ray,sort,1,100000000,50,17.5535569190979 +ray,sort,1,100000000,50,17.945733308792114 +ray,sort,1,100000000,50,17.20743417739868 +ray,sort,1,1000000000,20,41.81386184692383 +ray,sort,1,1000000000,20,76.37009787559509 diff --git a/release/raydp_tests/dask_on_ray/result.txt b/release/raydp_tests/dask_on_ray/result.txt new file mode 100644 index 0000000000000..e855de52c305c --- /dev/null +++ b/release/raydp_tests/dask_on_ray/result.txt @@ -0,0 +1,1248 @@ +Clearing old data from ~/dask-on-ray-data. +Using task-based Dask shuffle +Trial 0 start +[GET] object_id:34c9c2094e42fdbfffffffffffffffffffffffff0100000001000000 size:0 func:max-540008a5-f6fe-46e9-aec7-4b822bab42fb +[GET] object_id:34c9c2094e42fdbfffffffffffffffffffffffff0100000001000000 size:0 func:min-fcd63f92-28e4-4a6e-89f3-5dfd4162e36d +[GET] object_id:34c9c2094e42fdbfffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 0) +[GET] object_id:34c9c2094e42fdbfffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-0-76560f47b9510925df1e3e2bf421692e', 0) +[GET] object_id:44ed5e1383be6308ffffffffffffffffffffffff0100000001000000 size:0 func:max-cdfd1d82-8033-4557-8c42-bd249b8335d6 +[GET] object_id:44ed5e1383be6308ffffffffffffffffffffffff0100000001000000 size:0 func:min-de4a05be-2d3a-44ef-808e-b6d22f39be08 +[GET] object_id:44ed5e1383be6308ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 1) +[GET] object_id:4e2ab276f14c37c2ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 0) +[GET] object_id:139e431dd460af76ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 0) +[GET] object_id:acb2f4769a638593ffffffffffffffffffffffff0100000001000000 size:0 func:max-bb9c81aa-2cc1-4a8a-983f-194b813792b1 +[GET] object_id:acb2f4769a638593ffffffffffffffffffffffff0100000001000000 size:0 func:min-017f6dc4-2ef3-45fb-8c31-3267cf13321b +[GET] object_id:acb2f4769a638593ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 2) +[GET] object_id:d68fec326c8433c9ffffffffffffffffffffffff0100000001000000 size:0 func:max-37bb0297-0823-4764-9ed4-7f075658bfd6 +[GET] object_id:d68fec326c8433c9ffffffffffffffffffffffff0100000001000000 size:0 func:min-d9f40475-208f-4098-aa15-c7b6fc9edbf4 +[GET] object_id:d68fec326c8433c9ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 3) +[GET] object_id:909a212b104ea2f1ffffffffffffffffffffffff0100000001000000 size:0 func:max-239fa783-d16b-4bfb-aa6c-81eb999da9c9 +[GET] object_id:909a212b104ea2f1ffffffffffffffffffffffff0100000001000000 size:0 func:min-ab2ad07c-b9de-4e0d-869f-1684563daac4 +[GET] object_id:909a212b104ea2f1ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 4) +[GET] object_id:d5a75db31f99bd73ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 1) +[GET] object_id:aa64d94c3d0cf856ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 1) +[GET] object_id:5497aa04f981e4a1ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 1) +[GET] object_id:c96088b12950798bffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 1, 0) +[GET] object_id:4c37da832ee7bc31ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 1, 0) +[GET] object_id:a4b140629d390f70ffffffffffffffffffffffff0100000001000000 size:0 func:max-153ab69d-6e5a-483b-ae4b-ea353f48cac1 +[GET] object_id:a4b140629d390f70ffffffffffffffffffffffff0100000001000000 size:0 func:min-22e57d0c-227f-4b56-9a26-fd61bf800794 +[GET] object_id:a4b140629d390f70ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 7) +[GET] object_id:5e4556eab3523b9cffffffffffffffffffffffff0100000001000000 size:0 func:max-54b56d3e-e12f-4a44-a093-0471b01e5823 +[GET] object_id:5e4556eab3523b9cffffffffffffffffffffffff0100000001000000 size:0 func:min-f0cfbaf4-bbab-44ce-9e5b-9652a625f2de +[GET] object_id:5e4556eab3523b9cffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 8) +[GET] object_id:8d810c38302b25afffffffffffffffffffffffff0100000001000000 size:0 func:max-032cb695-d1b6-484f-ad8f-54ab388456b4 +[GET] object_id:8d810c38302b25afffffffffffffffffffffffff0100000001000000 size:0 func:min-f5467333-1fa1-4704-b656-cc0c95482466 +[GET] object_id:8d810c38302b25afffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 9) +[GET] object_id:21711c35be2858f6ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 3) +[GET] object_id:b4b63d32e46ad812ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 3) +[GET] object_id:e7b04d2c8c844064ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 3) +[GET] object_id:7d174d6864a3d2bbffffffffffffffffffffffff0100000001000000 size:0 func:max-b6bff76c-23ea-4e80-92af-a14ac7c787a1 +[GET] object_id:7d174d6864a3d2bbffffffffffffffffffffffff0100000001000000 size:0 func:min-53ef308e-a157-4728-9908-3dfda742c2fe +[GET] object_id:7d174d6864a3d2bbffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 5) +[GET] object_id:ff263408fac73fd7ffffffffffffffffffffffff0100000001000000 size:0 func:max-2c02c2e7-b1e0-4ae6-b355-813d9197c914 +[GET] object_id:ff263408fac73fd7ffffffffffffffffffffffff0100000001000000 size:0 func:min-7c1cc151-85ad-466e-bc65-b26d56c2e9d1 +[GET] object_id:ff263408fac73fd7ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 6) +[GET] object_id:19f1295112af6a56ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 2) +[GET] object_id:931690d20f999d2fffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 2) +[GET] object_id:964d85edc3bacb5bffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 1, 1) +[GET] object_id:d3ab1aaf09c00ee8ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 1, 1) +[GET] object_id:3f83646dddac6e52ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 2, 0) +[GET] object_id:b9679322487eb9c3ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 2, 0) +[GET] object_id:9d78bd90898368caffffffffffffffffffffffff0100000001000000 size:0 func:max-ef605214-688c-4895-8a6e-707d863786ac +[GET] object_id:9d78bd90898368caffffffffffffffffffffffff0100000001000000 size:0 func:min-8c5d25ac-0e13-4b85-a349-975dd9da01ac +[GET] object_id:9d78bd90898368caffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 14) +[GET] object_id:aae99729138a74d2ffffffffffffffffffffffff0100000001000000 size:0 func:max-29c4da43-7c74-4939-b8dc-3bb592cee9a7 +[GET] object_id:aae99729138a74d2ffffffffffffffffffffffff0100000001000000 size:0 func:min-dc3f5407-887f-4a14-b060-93498eb5ccb9 +[GET] object_id:aae99729138a74d2ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 15) +[GET] object_id:2b1d1545893d1daeffffffffffffffffffffffff0100000001000000 size:0 func:max-e4aef3b8-c423-4c78-8810-aa77c808bf74 +[GET] object_id:2b1d1545893d1daeffffffffffffffffffffffff0100000001000000 size:0 func:min-1d6b6f48-49b9-45b9-8229-192a3ba53a67 +[GET] object_id:2b1d1545893d1daeffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 16) +[GET] object_id:2638cdc57fda8d43ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 6) +[GET] object_id:9bb55bc9a880f4ceffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 6) +[GET] object_id:faf123d8b1b35293ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 6) +[GET] object_id:e07eebba574c5c52ffffffffffffffffffffffff0100000001000000 size:0 func:max-c9a8c9ce-3785-4fc5-8236-230facb08b1b +[GET] object_id:e07eebba574c5c52ffffffffffffffffffffffff0100000001000000 size:0 func:min-c1ebc0ca-caed-4aab-8611-99c6c20b2f95 +[GET] object_id:e07eebba574c5c52ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 17) +[GET] object_id:85186699e206774effffffffffffffffffffffff0100000001000000 size:0 func:max-1f338c52-8004-4662-a5d2-7601d797b061 +[GET] object_id:85186699e206774effffffffffffffffffffffff0100000001000000 size:0 func:min-583f0bf0-2c88-4544-b660-e0fced857a38 +[GET] object_id:85186699e206774effffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 18) +[GET] object_id:3f903472d63abe4effffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 7) +[GET] object_id:6c1e0b9bd7e2a3a6ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 7) +[GET] object_id:e707441f8900a635ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 1, 3) +[GET] object_id:02bed209b72c305affffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 1, 3) +[GET] object_id:ed64f45346849778ffffffffffffffffffffffff0100000001000000 size:0 func:max-2bc13a35-ed08-48bc-9f09-c731110d453e +[GET] object_id:ed64f45346849778ffffffffffffffffffffffff0100000001000000 size:0 func:min-8f574a88-df50-4939-aecb-8f9b76738b60 +[GET] object_id:ed64f45346849778ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 10) +[GET] object_id:671f1fd57bc3e36effffffffffffffffffffffff0100000001000000 size:0 func:max-498ea2b3-915d-4d10-bef2-56b92d307808 +[GET] object_id:671f1fd57bc3e36effffffffffffffffffffffff0100000001000000 size:0 func:min-e675a038-2871-40ed-b906-2168d4e76aed +[GET] object_id:671f1fd57bc3e36effffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-1-76560f47b9510925df1e3e2bf421692e', 11) +[GET] object_id:5b78680d95855782ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 4) +[GET] object_id:dc81b612c7b1ec30ffffffffffffffffffffffff0100000001000000 size:0 func:('re-quantiles-2-76560f47b9510925df1e3e2bf421692e', 0, 4) +[GET] object_id:6dbd075c28ae20d7ffffffffffffffffffffffff0100000001000000 size:0 func:max-c7645d11-5e0b-40b3-a23c-2459a53c79fb +[GET] object_id:6dbd075c28ae20d7ffffffffffffffffffffffff0100000001000000 size:0 func:min-bccdae83-da30-4bf6-a6b0-020c9f86a923 +[GET] 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+(pid=20860) +(pid=20860) +(pid=20860) +(pid=20863) +(pid=20869) +(pid=20861) +(pid=20871) +(pid=20854) +(pid=20866) +(pid=20863) +(pid=20871) +(pid=20864) +(pid=20874) +(pid=20869) +(pid=20873) +(pid=20867) +(pid=20872) +(pid=20861) +(pid=20854) +(pid=20866) +(pid=20862) +(pid=20864) +(pid=20874) +(pid=20873) +(pid=20867) +(pid=20872) +(pid=20871) +(pid=20874) +(pid=20872) +(pid=20862) +(pid=20870) +(pid=20866) +(pid=20873) +(pid=20867) +(pid=20862) +(pid=20870) +(pid=20867) +(pid=20870) +(pid=20866) +(pid=20873) +(pid=20867) +(pid=20866) +(pid=20863) +(pid=20863) +(pid=20869) +(pid=20863) +(pid=20869) +(pid=20863) +(pid=20854) +(pid=20860) +(pid=20874) +(pid=20872) +(pid=20868) +(pid=20868) +(pid=20868) +(pid=20862) +(pid=20937) +(pid=20959) +(pid=20961) +[GET] object_id:adfbf615e1b4bed2ffffffffffffffffffffffff0100000001000000 size:0 func:('simple-shuffle-split-ca93e1101d7c38c2d1778c94e785b328', 0, 0) +[GET] object_id:adfbf615e1b4bed2ffffffffffffffffffffffff0100000001000000 size:0 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func:('head-19-10-sort_index-ca66ea8c0355bd19d0e2a3558a5eb822', 0) +[GET] object_id:e8e0cbdf2460f7b2ffffffffffffffffffffffff0100000001000000 size:0 func:('head-19-10-sort_index-ca66ea8c0355bd19d0e2a3558a5eb822', 0) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20953) +(pid=20953) +(pid=20959) +(pid=20959) +(pid=20953) +(pid=20953) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20959) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20953) +(pid=20989) +(pid=20989) 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+(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20956) +(pid=20961) +(pid=20961) +(pid=20956) +(pid=20961) +(pid=20956) +(pid=20961) +(pid=20961) +(pid=20961) +(pid=20956) +(pid=20956) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=20961) +(pid=20961) +(pid=20956) +(pid=20956) +(pid=21013) +(pid=21013) +(pid=20961) +(pid=20961) +(pid=20956) +(pid=21013) +(pid=21013) +(pid=20961) +(pid=20961) +(pid=21013) +(pid=21013) +(pid=20961) +(pid=20961) +(pid=20961) +(pid=21013) +(pid=21013) +(pid=20961) +(pid=20961) +(pid=21013) +(pid=21013) +(pid=20961) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=20961) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20955) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=20954) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=21013) +(pid=21013) +(pid=20951) +(pid=20951) +(pid=21013) +(pid=21013) +(pid=20951) +(pid=21013) +(pid=21013) +(pid=21013) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20957) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +(pid=20951) +Trial 0 done after 76.37009787559509 +num obj ref bytes used: 4577.931343078613 +num objs: 400 +ray mean over 1 trials: 76.37009787559509 +- 0.0 diff --git a/release/raydp_tests/dask_on_ray/test_dask.py b/release/raydp_tests/dask_on_ray/test_dask.py new file mode 100644 index 0000000000000..7ab8648f3cf26 --- /dev/null +++ b/release/raydp_tests/dask_on_ray/test_dask.py @@ -0,0 +1,252 @@ +import glob +import ray +import dask +import dask.dataframe as dd +import json +import pandas as pd +import numpy as np +from ray.util.dask import ray_dask_get_sync +import os.path +import csv +import fastparquet +import sys + +from dask.distributed import Client +from dask.distributed import wait +import cProfile, pstats, io +from pstats import SortKey +from tqdm import tqdm + +import time + +DATA_DIR = "~/dask-on-ray-data" +# DATA_DIR = "/obj-data" + + +def load_dataset(nbytes, npartitions, sort): + num_bytes_per_partition = nbytes // npartitions + filenames = [] + + @ray.remote + def foo(i): + filename = "df-{}-{}-{}.parquet.gzip".format( + "sort" if sort else "groupby", num_bytes_per_partition, i) + filename = os.path.join(DATA_DIR, filename) + # print("Partition file", filename) + if not os.path.exists(filename): + if sort: + nrows = num_bytes_per_partition // 8 + # print("Allocating dataset with {} rows".format(nrows)) + dataset = pd.DataFrame( + np.random.randint( + 0, + np.iinfo(np.int64).max, + size=(nrows, 1), + dtype=np.int64), + columns=['a']) + else: + nrows = num_bytes_per_partition // (8 * 2) + # print("Allocating dataset with {} rows".format(nrows)) + dataset = pd.DataFrame( + np.random.randint(0, 100, size=(nrows, 2), dtype=np.int64), + columns=['a', 'b']) + # print("Done allocating") + dataset.to_parquet(filename, compression='gzip') + # print("Done writing to disk") + return filename + + for i in range(npartitions): + filenames.append(foo.remote(i)) + results = [] + pbar = tqdm(total=len(filenames)) + ready, unready = ray.wait(filenames) + while unready: + results.extend(ready) + ready, unready = ray.wait(unready) + pbar.update(len(ready)) + del filenames + filenames = ray.get(results) + + df = dd.read_parquet(filenames) + return df + + +def trial(nbytes, n_partitions, sort, generate_only): + from ray.util.dask import RayDaskCallback + + @ray.remote + class CounterActor: + def __init__(self): + self.cnt = 0 + self.object_num = 0 + def increment(self, n, object_num): + self.cnt += n + self.object_num += object_num + def get(self): + return self.cnt + def get_object_num(self): + return self.object_num + + c = CounterActor.remote() + + class CountRefs(RayDaskCallback): + def __init__(self, cnt_actor): + self.cnt_actor = cnt_actor + + def _ray_pretask(self, key, consumed_objs): + total = 0 + object_num = 0 + for obj in consumed_objs: + if sys.getsizeof(obj) > 1024 * 100: + print(type(obj)) + object_num += 1 + total += sys.getsizeof(obj) + self.cnt_actor.increment.remote(total, object_num) + + def _ray_postsubmit_all(self, object_refs, dsk): + pass + # print(f"dask DAG: {dsk.dicts}") + + def _ray_finish(self, result): + if isinstance(result, (list, tuple)): + result = [result] + total = 0 + object_num = 0 + for obj in result: + if sys.getsizeof(obj) > 1024 * 100: + object_num += 1 + total += sys.getsizeof(obj) + self.cnt_actor.increment.remote(total, object_num) + + + count = CountRefs(c) + df = load_dataset(nbytes, n_partitions, sort) + # pr = cProfile.Profile() + if generate_only: + return + + times = [] + # df.visualize(filename=f'a-{i}.svg') + start = time.time() + for i in range(1): + print("Trial {} start".format(i)) + trial_start = time.time() + + if sort: + # pr.enable() + with count: + a = df.set_index('a', shuffle='tasks', max_branch=10 ** 9) + a = a.head(10, npartitions=-1, compute=False) + # a.visualize(filename=f'a-{i}.svg') + a.compute() + # pr.disable() + else: + df.groupby('b').a.mean().compute() + + trial_end = time.time() + duration = trial_end - trial_start + times.append(duration) + print("Trial {} done after {}".format(i, duration)) + print(f"num obj ref bytes used: {ray.get(c.get.remote()) / 1024 / 1024}") + print(f"num objs: {ray.get(c.get_object_num.remote())}") + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr).sort_stats(sortby) + # ps.dump_stats("ray_profile_data") + + if time.time() - start > 60 and i > 0: + break + return times + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + + parser.add_argument("--nbytes", type=int, default=1_000_000) + parser.add_argument("--npartitions", type=int, default=100, required=False) + # Max partition size is 1GB. + parser.add_argument( + "--max-partition-size", type=int, default=1000_000_000, required=False) + parser.add_argument("--num-nodes", type=int, default=1) + parser.add_argument("--sort", action="store_true") + parser.add_argument("--timeline", action="store_true") + parser.add_argument("--dask", action="store_true") + parser.add_argument("--ray", action="store_true") + parser.add_argument("--dask-tasks", action="store_true") + parser.add_argument("--generate-only", action="store_true") + parser.add_argument("--clear-old-data", action="store_true") + args = parser.parse_args() + + if args.clear_old_data: + print(f"Clearing old data from {DATA_DIR}.") + files = glob.glob(os.path.join(DATA_DIR, "*.parquet.gzip")) + for f in files: + os.remove(f) + + if args.ray: + args.dask_tasks = True + + if args.dask_tasks: + print("Using task-based Dask shuffle") + dask.config.set(shuffle='tasks') + else: + print("Using disk-based Dask shuffle") + + if args.dask: + client = Client('127.0.0.1:8786') + ray.init(address='auto') + if args.ray: + ray.init(address="auto") + # ray.init( + # num_cpus=16, + # _system_config={ + # "max_io_workers": 1, + # "object_spilling_config": json.dumps( + # { + # "type": "filesystem", + # "params": { + # "directory_path": "/tmp/spill" + # } + # }, + # separators=(",", ":")) + # }) + dask.config.set(scheduler=ray_dask_get_sync) + + system = "dask" if args.dask else "ray" + + # print(system, trial(1000, 10, args.sort, args.generate_only)) + # print("WARMUP DONE") + + npartitions = args.npartitions + if args.nbytes // npartitions > args.max_partition_size: + npartitions = args.nbytes // args.max_partition_size + + output = trial(args.nbytes, npartitions, args.sort, args.generate_only) + print("{} mean over {} trials: {} +- {}".format(system, len(output), + np.mean(output), + np.std(output))) + + write_header = not os.path.exists("output.csv") or os.path.getsize( + "output.csv") == 0 + with open("output.csv", "a+") as csvfile: + fieldnames = [ + "system", "operation", "num_nodes", "nbytes", "npartitions", + "duration" + ] + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + if write_header: + writer.writeheader() + row = { + "operation": "sort" if args.sort else "groupby", + "num_nodes": args.num_nodes, + "nbytes": args.nbytes, + "npartitions": npartitions, + } + for output in output: + row["system"] = system + row["duration"] = output + writer.writerow(row) + + if args.timeline: + time.sleep(1) + ray.timeline(filename="dask.json") \ No newline at end of file diff --git a/src/ray/core_worker/core_worker.cc b/src/ray/core_worker/core_worker.cc index 1961406d8a8ab..3c9b101ceb6fb 100644 --- a/src/ray/core_worker/core_worker.cc +++ b/src/ray/core_worker/core_worker.cc @@ -566,6 +566,8 @@ CoreWorker::CoreWorker(const CoreWorkerOptions &options, const WorkerID &worker_ // NOTE: This also marks the worker as available in Raylet. We do this at the // very end in case there is a problem during construction. RAY_CHECK_OK(local_raylet_client_->AnnounceWorkerPort(core_worker_server_->GetPort())); + // Used to detect if the object is in the plasma store. + max_direct_call_object_size_ = RayConfig::instance().max_direct_call_object_size(); } void CoreWorker::Shutdown() { @@ -880,8 +882,7 @@ Status CoreWorker::Put(const RayObject &object, bool object_exists; if (options_.is_local_mode || (RayConfig::instance().put_small_object_in_memory_store() && - static_cast(object.GetSize()) < - RayConfig::instance().max_direct_call_object_size())) { + static_cast(object.GetSize()) < max_direct_call_object_size_)) { RAY_LOG(DEBUG) << "Put " << object_id << " in memory store"; RAY_CHECK(memory_store_->Put(object, object_id)); return Status::OK(); @@ -922,8 +923,7 @@ Status CoreWorker::CreateOwned(const std::shared_ptr &metadata, NodeID::FromBinary(rpc_address_.raylet_id())); if (options_.is_local_mode || (RayConfig::instance().put_small_object_in_memory_store() && - static_cast(data_size) < - RayConfig::instance().max_direct_call_object_size())) { + static_cast(data_size) < max_direct_call_object_size_)) { *data = std::make_shared(data_size); } else { auto status = @@ -1036,7 +1036,7 @@ Status CoreWorker::Get(const std::vector &ids, const int64_t timeout_m bool missing_result = false; bool will_throw_exception = false; for (size_t i = 0; i < ids.size(); i++) { - auto pair = result_map.find(ids[i]); + const auto pair = result_map.find(ids[i]); if (pair != result_map.end()) { (*results)[i] = pair->second; RAY_CHECK(!pair->second->IsInPlasmaError()); @@ -1777,8 +1777,7 @@ Status CoreWorker::AllocateReturnObjects( // Allocate a buffer for the return object. if (options_.is_local_mode || - static_cast(data_sizes[i]) < - RayConfig::instance().max_direct_call_object_size()) { + static_cast(data_sizes[i]) < max_direct_call_object_size_) { data_buffer = std::make_shared(data_sizes[i]); } else { RAY_RETURN_NOT_OK(CreateExisting(metadatas[i], data_sizes[i], object_ids[i], diff --git a/src/ray/core_worker/core_worker.h b/src/ray/core_worker/core_worker.h index 89331b5ce10fe..6fa24c29e94ec 100644 --- a/src/ray/core_worker/core_worker.h +++ b/src/ray/core_worker/core_worker.h @@ -1255,6 +1255,8 @@ class CoreWorker : public rpc::CoreWorkerServiceHandler { /// Whether we are shutting down and not running further tasks. bool exiting_ = false; + int64_t max_direct_call_object_size_; + friend class CoreWorkerTest; }; diff --git a/src/ray/core_worker/store_provider/plasma_store_provider.cc b/src/ray/core_worker/store_provider/plasma_store_provider.cc index b42c4b50941f2..f3b5f047c8fce 100644 --- a/src/ray/core_worker/store_provider/plasma_store_provider.cc +++ b/src/ray/core_worker/store_provider/plasma_store_provider.cc @@ -191,7 +191,8 @@ Status CoreWorkerPlasmaStoreProvider::FetchAndGetFromPlasmaStore( std::vector plasma_results; { std::lock_guard guard(store_client_mutex_); - RAY_RETURN_NOT_OK(store_client_.Get(batch_ids, timeout_ms, &plasma_results)); + RAY_RETURN_NOT_OK(store_client_.Get(batch_ids, timeout_ms, &plasma_results, + /*is_from_worker=*/true)); } // Add successfully retrieved objects to the result map and remove them from @@ -231,7 +232,9 @@ Status CoreWorkerPlasmaStoreProvider::GetIfLocal( std::vector plasma_results; { std::lock_guard guard(store_client_mutex_); - RAY_RETURN_NOT_OK(store_client_.Get(object_ids, /*timeout_ms=*/0, &plasma_results)); + // Since this path is used only for spilling, we should set is_from_worker: false. + RAY_RETURN_NOT_OK(store_client_.Get(object_ids, /*timeout_ms=*/0, &plasma_results, + /*is_from_worker=*/false)); } for (size_t i = 0; i < object_ids.size(); i++) { diff --git a/src/ray/object_manager/object_buffer_pool.cc b/src/ray/object_manager/object_buffer_pool.cc index 726a6fefca353..63dabcb419ef2 100644 --- a/src/ray/object_manager/object_buffer_pool.cc +++ b/src/ray/object_manager/object_buffer_pool.cc @@ -57,7 +57,8 @@ std::pair ObjectBufferPool::Ge std::lock_guard lock(pool_mutex_); if (get_buffer_state_.count(object_id) == 0) { plasma::ObjectBuffer object_buffer; - RAY_CHECK_OK(store_client_.Get(&object_id, 1, 0, &object_buffer)); + RAY_CHECK_OK( + store_client_.Get(&object_id, 1, 0, &object_buffer, /*is_from_worker=*/false)); if (object_buffer.data == nullptr) { RAY_LOG(INFO) << "Failed to get a chunk of the object: " << object_id diff --git a/src/ray/object_manager/object_manager.cc b/src/ray/object_manager/object_manager.cc index 448245e012ee0..fc768fb20a31c 100644 --- a/src/ray/object_manager/object_manager.cc +++ b/src/ray/object_manager/object_manager.cc @@ -834,6 +834,9 @@ void ObjectManager::FillObjectStoreStats(rpc::GetNodeStatsReply *reply) const { stats->set_object_store_bytes_used(used_memory_); stats->set_object_store_bytes_avail(config_.object_store_memory); stats->set_num_local_objects(local_objects_.size()); + if (plasma::plasma_store_runner) { + stats->set_referenced_bytes(plasma::plasma_store_runner->GetReferencedBytes()); + } } void ObjectManager::Tick(const boost::system::error_code &e) { diff --git a/src/ray/object_manager/plasma/client.cc b/src/ray/object_manager/plasma/client.cc index a5429d985f91b..9b9bb5408df4f 100644 --- a/src/ray/object_manager/plasma/client.cc +++ b/src/ray/object_manager/plasma/client.cc @@ -121,10 +121,10 @@ class PlasmaClient::Impl : public std::enable_shared_from_this *data, int device_num); Status Get(const std::vector &object_ids, int64_t timeout_ms, - std::vector *object_buffers); + std::vector *object_buffers, bool is_from_worker); Status Get(const ObjectID *object_ids, int64_t num_objects, int64_t timeout_ms, - ObjectBuffer *object_buffers); + ObjectBuffer *object_buffers, bool is_from_worker); Status Release(const ObjectID &object_id); @@ -172,7 +172,7 @@ class PlasmaClient::Impl : public std::enable_shared_from_this( const ObjectID &, const std::shared_ptr &)> &wrap_buffer, - ObjectBuffer *object_buffers); + ObjectBuffer *object_buffers, bool is_from_worker); uint8_t *LookupMmappedFile(MEMFD_TYPE store_fd_val); @@ -362,7 +362,7 @@ Status PlasmaClient::Impl::GetBuffers( const ObjectID *object_ids, int64_t num_objects, int64_t timeout_ms, const std::function( const ObjectID &, const std::shared_ptr &)> &wrap_buffer, - ObjectBuffer *object_buffers) { + ObjectBuffer *object_buffers, bool is_from_worker) { // Fill out the info for the objects that are already in use locally. bool all_present = true; for (int64_t i = 0; i < num_objects; ++i) { @@ -409,7 +409,8 @@ Status PlasmaClient::Impl::GetBuffers( // If we get here, then the objects aren't all currently in use by this // client, so we need to send a request to the plasma store. - RAY_RETURN_NOT_OK(SendGetRequest(store_conn_, &object_ids[0], num_objects, timeout_ms)); + RAY_RETURN_NOT_OK(SendGetRequest(store_conn_, &object_ids[0], num_objects, timeout_ms, + is_from_worker)); std::vector buffer; RAY_RETURN_NOT_OK(PlasmaReceive(store_conn_, MessageType::PlasmaGetReply, &buffer)); std::vector received_object_ids(num_objects); @@ -470,7 +471,8 @@ Status PlasmaClient::Impl::GetBuffers( } Status PlasmaClient::Impl::Get(const std::vector &object_ids, - int64_t timeout_ms, std::vector *out) { + int64_t timeout_ms, std::vector *out, + bool is_from_worker) { std::lock_guard guard(client_mutex_); const auto wrap_buffer = [=](const ObjectID &object_id, @@ -479,16 +481,19 @@ Status PlasmaClient::Impl::Get(const std::vector &object_ids, }; const size_t num_objects = object_ids.size(); *out = std::vector(num_objects); - return GetBuffers(&object_ids[0], num_objects, timeout_ms, wrap_buffer, &(*out)[0]); + return GetBuffers(&object_ids[0], num_objects, timeout_ms, wrap_buffer, &(*out)[0], + is_from_worker); } Status PlasmaClient::Impl::Get(const ObjectID *object_ids, int64_t num_objects, - int64_t timeout_ms, ObjectBuffer *out) { + int64_t timeout_ms, ObjectBuffer *out, + bool is_from_worker) { std::lock_guard guard(client_mutex_); const auto wrap_buffer = [](const ObjectID &object_id, const std::shared_ptr &buffer) { return buffer; }; - return GetBuffers(object_ids, num_objects, timeout_ms, wrap_buffer, out); + return GetBuffers(object_ids, num_objects, timeout_ms, wrap_buffer, out, + is_from_worker); } Status PlasmaClient::Impl::MarkObjectUnused(const ObjectID &object_id) { @@ -753,13 +758,14 @@ Status PlasmaClient::TryCreateImmediately(const ObjectID &object_id, } Status PlasmaClient::Get(const std::vector &object_ids, int64_t timeout_ms, - std::vector *object_buffers) { - return impl_->Get(object_ids, timeout_ms, object_buffers); + std::vector *object_buffers, bool is_from_worker) { + return impl_->Get(object_ids, timeout_ms, object_buffers, is_from_worker); } Status PlasmaClient::Get(const ObjectID *object_ids, int64_t num_objects, - int64_t timeout_ms, ObjectBuffer *object_buffers) { - return impl_->Get(object_ids, num_objects, timeout_ms, object_buffers); + int64_t timeout_ms, ObjectBuffer *object_buffers, + bool is_from_worker) { + return impl_->Get(object_ids, num_objects, timeout_ms, object_buffers, is_from_worker); } Status PlasmaClient::Release(const ObjectID &object_id) { diff --git a/src/ray/object_manager/plasma/client.h b/src/ray/object_manager/plasma/client.h index e88a9eb138a1c..703250bd23b06 100644 --- a/src/ray/object_manager/plasma/client.h +++ b/src/ray/object_manager/plasma/client.h @@ -161,9 +161,10 @@ class PlasmaClient { /// \param timeout_ms The amount of time in milliseconds to wait before this /// request times out. If this value is -1, then no timeout is set. /// \param[out] object_buffers The object results. + /// \param is_from_worker Whether or not if the Get request comes from a Ray workers. /// \return The return status. Status Get(const std::vector &object_ids, int64_t timeout_ms, - std::vector *object_buffers); + std::vector *object_buffers, bool is_from_worker); /// Deprecated variant of Get() that doesn't automatically release buffers /// when they get out of scope. @@ -173,12 +174,13 @@ class PlasmaClient { /// \param timeout_ms The amount of time in milliseconds to wait before this /// request times out. If this value is -1, then no timeout is set. /// \param object_buffers An array where the results will be stored. + /// \param is_from_worker Whether or not if the Get request comes from a Ray workers. /// \return The return status. /// /// The caller is responsible for releasing any retrieved objects, but it /// should not release objects that were not retrieved. Status Get(const ObjectID *object_ids, int64_t num_objects, int64_t timeout_ms, - ObjectBuffer *object_buffers); + ObjectBuffer *object_buffers, bool is_from_worker); /// Tell Plasma that the client no longer needs the object. This should be /// called after Get() or Create() when the client is done with the object. diff --git a/src/ray/object_manager/plasma/plasma.fbs b/src/ray/object_manager/plasma/plasma.fbs index 3816de79e842a..d8fda975b998b 100644 --- a/src/ray/object_manager/plasma/plasma.fbs +++ b/src/ray/object_manager/plasma/plasma.fbs @@ -210,6 +210,8 @@ table PlasmaGetRequest { object_ids: [string]; // The number of milliseconds before the request should timeout. timeout_ms: long; + // Whether or not the get request is from the core worker. It is used to record how many objects are referenced by core workers. + is_from_worker: bool; } table PlasmaGetReply { diff --git a/src/ray/object_manager/plasma/protocol.cc b/src/ray/object_manager/plasma/protocol.cc index 8c3164d6a7dfb..c3b5b55ee1d50 100644 --- a/src/ray/object_manager/plasma/protocol.cc +++ b/src/ray/object_manager/plasma/protocol.cc @@ -553,16 +553,16 @@ Status ReadEvictReply(uint8_t *data, size_t size, int64_t &num_bytes) { // Get messages. Status SendGetRequest(const std::shared_ptr &store_conn, - const ObjectID *object_ids, int64_t num_objects, - int64_t timeout_ms) { + const ObjectID *object_ids, int64_t num_objects, int64_t timeout_ms, + bool is_from_worker) { flatbuffers::FlatBufferBuilder fbb; auto message = fb::CreatePlasmaGetRequest( - fbb, ToFlatbuffer(&fbb, object_ids, num_objects), timeout_ms); + fbb, ToFlatbuffer(&fbb, object_ids, num_objects), timeout_ms, is_from_worker); return PlasmaSend(store_conn, MessageType::PlasmaGetRequest, &fbb, message); } Status ReadGetRequest(uint8_t *data, size_t size, std::vector &object_ids, - int64_t *timeout_ms) { + int64_t *timeout_ms, bool *is_from_worker) { RAY_DCHECK(data); auto message = flatbuffers::GetRoot(data); RAY_DCHECK(VerifyFlatbuffer(message, data, size)); @@ -571,6 +571,7 @@ Status ReadGetRequest(uint8_t *data, size_t size, std::vector &object_ object_ids.push_back(ObjectID::FromBinary(object_id)); } *timeout_ms = message->timeout_ms(); + *is_from_worker = message->is_from_worker(); return Status::OK(); } diff --git a/src/ray/object_manager/plasma/protocol.h b/src/ray/object_manager/plasma/protocol.h index a8ba71b4621f4..f5baf03ec9553 100644 --- a/src/ray/object_manager/plasma/protocol.h +++ b/src/ray/object_manager/plasma/protocol.h @@ -128,11 +128,11 @@ Status ReadSealReply(uint8_t *data, size_t size, ObjectID *object_id); /* Plasma Get message functions. */ Status SendGetRequest(const std::shared_ptr &store_conn, - const ObjectID *object_ids, int64_t num_objects, - int64_t timeout_ms); + const ObjectID *object_ids, int64_t num_objects, int64_t timeout_ms, + bool is_from_worker); Status ReadGetRequest(uint8_t *data, size_t size, std::vector &object_ids, - int64_t *timeout_ms); + int64_t *timeout_ms, bool *is_from_worker); Status SendGetReply(const std::shared_ptr &client, ObjectID object_ids[], std::unordered_map &plasma_objects, diff --git a/src/ray/object_manager/plasma/store.cc b/src/ray/object_manager/plasma/store.cc index e101c5a9b71a4..0c8eeebd1de13 100644 --- a/src/ray/object_manager/plasma/store.cc +++ b/src/ray/object_manager/plasma/store.cc @@ -69,7 +69,7 @@ namespace plasma { struct GetRequest { GetRequest(boost::asio::io_service &io_context, const std::shared_ptr &client, - const std::vector &object_ids); + const std::vector &object_ids, bool is_from_worker); /// The client that called get. std::shared_ptr client; /// The object IDs involved in this request. This is used in the reply. @@ -82,6 +82,9 @@ struct GetRequest { /// The number of object requests in this wait request that are already /// satisfied. int64_t num_satisfied; + /// Whether or not the request comes from the core worker. It is used to track the size + /// of total objects that are referenced by core worker. + bool is_from_worker; void AsyncWait(int64_t timeout_ms, std::function on_timeout) { @@ -100,11 +103,12 @@ struct GetRequest { GetRequest::GetRequest(boost::asio::io_service &io_context, const std::shared_ptr &client, - const std::vector &object_ids) + const std::vector &object_ids, bool is_from_worker) : client(client), object_ids(object_ids.begin(), object_ids.end()), objects(object_ids.size()), num_satisfied(0), + is_from_worker(is_from_worker), timer_(io_context) { std::unordered_set unique_ids(object_ids.begin(), object_ids.end()); num_objects_to_wait_for = unique_ids.size(); @@ -393,6 +397,10 @@ void PlasmaStore::ReturnFromGet(GetRequest *get_req) { fds_to_send.insert(fd); store_fds.push_back(fd); mmap_sizes.push_back(GetMmapSize(fd)); + if (get_req->is_from_worker) { + RAY_LOG(ERROR) << "[GET] object_id:" << object_id << " size:" << (object.data_size + object.metadata_size); + total_referenced_bytes_ += object.data_size + object.metadata_size; + } } } // Send the get reply to the client. @@ -465,9 +473,9 @@ void PlasmaStore::UpdateObjectGetRequests(const ObjectID &object_id) { void PlasmaStore::ProcessGetRequest(const std::shared_ptr &client, const std::vector &object_ids, - int64_t timeout_ms) { + int64_t timeout_ms, bool is_from_worker) { // Create a get request for this object. - auto get_req = new GetRequest(io_context_, client, object_ids); + auto get_req = new GetRequest(io_context_, client, object_ids, is_from_worker); for (auto object_id : object_ids) { // Check if this object is already present // locally. If so, record that the object is being used and mark it as accounted for. @@ -894,8 +902,10 @@ Status PlasmaStore::ProcessMessage(const std::shared_ptr &client, case fb::MessageType::PlasmaGetRequest: { std::vector object_ids_to_get; int64_t timeout_ms; - RAY_RETURN_NOT_OK(ReadGetRequest(input, input_size, object_ids_to_get, &timeout_ms)); - ProcessGetRequest(client, object_ids_to_get, timeout_ms); + bool is_from_worker; + RAY_RETURN_NOT_OK(ReadGetRequest(input, input_size, object_ids_to_get, &timeout_ms, + &is_from_worker)); + ProcessGetRequest(client, object_ids_to_get, timeout_ms, is_from_worker); } break; case fb::MessageType::PlasmaReleaseRequest: { RAY_RETURN_NOT_OK(ReadReleaseRequest(input, input_size, &object_id)); @@ -1020,6 +1030,11 @@ void PlasmaStore::ReplyToCreateClient(const std::shared_ptr &client, } } +int64_t PlasmaStore::GetReferencedBytes() { + std::lock_guard guard(mutex_); + return total_referenced_bytes_; +} + bool PlasmaStore::IsObjectSpillable(const ObjectID &object_id) { // The lock is acquired when a request is received to the plasma store. // recursive mutex is used here to allow diff --git a/src/ray/object_manager/plasma/store.h b/src/ray/object_manager/plasma/store.h index 214cf9763bf6d..cbe4e221acdbe 100644 --- a/src/ray/object_manager/plasma/store.h +++ b/src/ray/object_manager/plasma/store.h @@ -139,7 +139,8 @@ class PlasmaStore { /// \param object_ids Object IDs of the objects to be gotten. /// \param timeout_ms The timeout for the get request in milliseconds. void ProcessGetRequest(const std::shared_ptr &client, - const std::vector &object_ids, int64_t timeout_ms); + const std::vector &object_ids, int64_t timeout_ms, + bool is_from_worker); /// Seal a vector of objects. The objects are now immutable and can be accessed with /// get. @@ -190,6 +191,9 @@ class PlasmaStore { /// before the object is pinned by raylet for the first time. bool IsObjectSpillable(const ObjectID &object_id); + /// Return the plasma object bytes that are referenced by core workers. + int64_t GetReferencedBytes(); + void SetNotificationListener( const std::shared_ptr ¬ification_listener) { notification_listener_ = notification_listener; @@ -316,6 +320,9 @@ class PlasmaStore { std::recursive_mutex mutex_; size_t num_bytes_in_use_ = 0; + + /// Total plasma object bytes that are referenced by core workers. + int64_t total_referenced_bytes_ = 0; }; } // namespace plasma diff --git a/src/ray/object_manager/plasma/store_runner.cc b/src/ray/object_manager/plasma/store_runner.cc index 34e08080cced6..6923e449bb244 100644 --- a/src/ray/object_manager/plasma/store_runner.cc +++ b/src/ray/object_manager/plasma/store_runner.cc @@ -123,6 +123,8 @@ bool PlasmaStoreRunner::IsPlasmaObjectSpillable(const ObjectID &object_id) { return store_->IsObjectSpillable(object_id); } +int64_t PlasmaStoreRunner::GetReferencedBytes() { return store_->GetReferencedBytes(); } + std::unique_ptr plasma_store_runner; } // namespace plasma diff --git a/src/ray/object_manager/plasma/store_runner.h b/src/ray/object_manager/plasma/store_runner.h index 7ac7be59bbc58..61a00fc683adb 100644 --- a/src/ray/object_manager/plasma/store_runner.h +++ b/src/ray/object_manager/plasma/store_runner.h @@ -22,6 +22,8 @@ class PlasmaStoreRunner { } bool IsPlasmaObjectSpillable(const ObjectID &object_id); + int64_t GetReferencedBytes(); + void GetAvailableMemoryAsync(std::function callback) const { main_service_.post([this, callback]() { store_->GetAvailableMemory(callback); }); } diff --git a/src/ray/protobuf/node_manager.proto b/src/ray/protobuf/node_manager.proto index 386ed988ade31..6c6bafca1d395 100644 --- a/src/ray/protobuf/node_manager.proto +++ b/src/ray/protobuf/node_manager.proto @@ -138,6 +138,8 @@ message ObjectStoreStats { int64 object_store_bytes_avail = 8; // The number of local objects total. int64 num_local_objects = 9; + // The number of plasma object bytes that are referenced by core workers. + int64 referenced_bytes = 10; } message GetNodeStatsReply { diff --git a/src/ray/raylet/node_manager.cc b/src/ray/raylet/node_manager.cc index cbe287ef721d4..5084cc50be149 100644 --- a/src/ray/raylet/node_manager.cc +++ b/src/ray/raylet/node_manager.cc @@ -2383,7 +2383,9 @@ bool NodeManager::GetObjectsFromPlasma(const std::vector &object_ids, // heavy load, then this request can still block the NodeManager event loop // since we must wait for the plasma store's reply. We should consider using // an `AsyncGet` instead. - if (!store_client_.Get(object_ids, /*timeout_ms=*/0, &plasma_results).ok()) { + if (!store_client_ + .Get(object_ids, /*timeout_ms=*/0, &plasma_results, /*is_from_worker=*/false) + .ok()) { return false; } @@ -2545,6 +2547,8 @@ rpc::ObjectStoreStats AccumulateStoreStats( cur_store.object_store_bytes_avail()); store_stats.set_num_local_objects(store_stats.num_local_objects() + cur_store.num_local_objects()); + store_stats.set_referenced_bytes(store_stats.referenced_bytes() + + cur_store.referenced_bytes()); } return store_stats; }