# This workload tests repeatedly killing actors and submitting tasks to them.
import numpy as np
import sys
import time

import ray
from ray.cluster_utils import Cluster
from ray._private.test_utils import monitor_memory_usage, safe_write_to_results_json


def update_progress(result):
    result["last_update"] = time.time()
    safe_write_to_results_json(result)


num_redis_shards = 1
redis_max_memory = 10**8
object_store_memory = 10**8
num_nodes = 2

message = (
    "Make sure there is enough memory on this machine to run this "
    "workload. We divide the system memory by 2 to provide a buffer."
)
assert (
    num_nodes * object_store_memory + num_redis_shards * redis_max_memory
    < ray._private.utils.get_system_memory() / 2
), message

# Simulate a cluster on one machine.

cluster = Cluster()
for i in range(num_nodes):
    cluster.add_node(
        redis_port=6379 if i == 0 else None,
        num_redis_shards=num_redis_shards if i == 0 else None,
        num_cpus=8,
        num_gpus=0,
        resources={str(i): 2},
        object_store_memory=object_store_memory,
        redis_max_memory=redis_max_memory,
        dashboard_host="0.0.0.0",
    )
ray.init(address=cluster.address)
monitor_actor = monitor_memory_usage()

# Run the workload.

num_parents = 5
num_children = 5
death_probability = 0.95


@ray.remote
class Child(object):
    def __init__(self, death_probability):
        self.death_probability = death_probability

    def ping(self):
        # Exit process with some probability.
        exit_chance = np.random.rand()
        if exit_chance > self.death_probability:
            sys.exit(-1)


@ray.remote
class Parent(object):
    def __init__(self, num_children, death_probability):
        self.death_probability = death_probability
        self.children = [Child.remote(death_probability) for _ in range(num_children)]

    def ping(self, num_pings):
        children_outputs = []
        for _ in range(num_pings):
            children_outputs += [child.ping.remote() for child in self.children]
        try:
            ray.get(children_outputs)
        except Exception:
            # Replace the children if one of them died.
            self.__init__(len(self.children), self.death_probability)

    def kill(self):
        # Clean up children.
        try:
            ray.get([child.__ray_terminate__.remote() for child in self.children])
        except ray.exceptions.RayActorError as e:
            # Sleep for 30 more seconds so that drivers will get more
            # information from GCS when actors are unexpectedly failed.
            print("Failed to kill a children actor. Error: ", e)
            time.sleep(30)
            raise e


parents = [Parent.remote(num_children, death_probability) for _ in range(num_parents)]

iteration = 0
start_time = time.time()
previous_time = start_time
while True:
    ray.get([parent.ping.remote(10) for parent in parents])

    # Kill a parent actor with some probability.
    exit_chance = np.random.rand()
    if exit_chance > death_probability:
        parent_index = np.random.randint(len(parents))
        parents[parent_index].kill.remote()
        parents[parent_index] = Parent.remote(num_children, death_probability)

    new_time = time.time()
    print(
        "Iteration {}:\n"
        "  - Iteration time: {}.\n"
        "  - Absolute time: {}.\n"
        "  - Total elapsed time: {}.".format(
            iteration, new_time - previous_time, new_time, new_time - start_time
        )
    )
    update_progress(
        {
            "iteration": iteration,
            "iteration_time": new_time - previous_time,
            "absolute_time": new_time,
            "elapsed_time": new_time - start_time,
        }
    )
    previous_time = new_time
    iteration += 1
