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In a setup with multiple executor group set, Frontend will try to match a query with the smallest executor group set that can fit the memory and cpu requirement of the compiled query. There are kinds of query where the compiled plan will fit to any executor group set but not necessarily deliver the best performance. An example for this is Impala's COMPUTE STATS query. It does full table scan and aggregate the stats, have fairly simple query plan shape, but can benefit from higher scan parallelism. This patch relaxes the scan fragment parallelism on first round of query planning. This allows scan fragment to increase its parallelism based on its ProcessingCost estimation. If the relaxed plan fit in an executor group set, we replan once again with that executor group set but with scan fragment parallelism returned back to MT_DOP. This one extra round of query planning adds couple millisecond overhead depending on the complexity of the query plan, but necessary since the backend scheduler still expect at most MT_DOP amount of scan fragment instances. We can remove the extra replanning in the future once we can fully manage scan node parallelism without MT_DOP. This patch also adds some improvement, including: - Tune computeScanProcessingCost() to guard against scheduling too many scan fragments by comparing with the actual scan range count that Planner knows. - Use NUM_SCANNER_THREADS as a hint to cap scan node cost during the first round of planning. - Multiply memory related counters by num executors to make it per group set rather than per node. - Fix bug in doCreateExecRequest() about selection of num executors for planning. Testing: - Pass test_executor_groups.py - Add test cases in test_min_processing_per_thread_small. - Raised impala.admission-control.max-query-mem-limit.root.small from 64MB to 70MB in llama-site-3-groups.xml so that the new grouping query can fit in root.small pool. Change-Id: I7a2276fbd344d00caa67103026661a3644b9a1f9 Reviewed-on: http://gerrit.cloudera.org:8080/19656 Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Reviewed-by: Kurt Deschler <kdeschle@cloudera.com> Reviewed-by: Wenzhe Zhou <wzhou@cloudera.com>
1085 lines
55 KiB
Python
1085 lines
55 KiB
Python
#!/usr/bin/env impala-python
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#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from __future__ import absolute_import, division, print_function
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from builtins import range
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from tests.common.custom_cluster_test_suite import CustomClusterTestSuite
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from tests.util.concurrent_workload import ConcurrentWorkload
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import copy
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import json
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import logging
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import os
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import pytest
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from time import sleep
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LOG = logging.getLogger("test_auto_scaling")
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# Non-trivial query that gets scheduled on all executors within a group.
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TEST_QUERY = "select count(*) from functional.alltypes where month + random() < 3"
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# A query to test CPU requirement. Estimated memory per host is 37MB.
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CPU_TEST_QUERY = "select * from tpcds_parquet.store_sales where ss_item_sk = 1 limit 50;"
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# A query with full table scan characteristics.
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GROUPING_TEST_QUERY = ("select ss_item_sk from tpcds_parquet.store_sales"
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" group by (ss_item_sk) order by ss_item_sk limit 10")
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# Default query option to use for testing CPU requirement.
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CPU_DOP_OPTIONS = {'MT_DOP': '2', 'COMPUTE_PROCESSING_COST': 'true'}
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DEFAULT_RESOURCE_POOL = "default-pool"
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class TestExecutorGroups(CustomClusterTestSuite):
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"""This class contains tests that exercise the logic related to scaling clusters up and
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down by adding and removing groups of executors. All tests start with a base cluster
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containing a dedicated coordinator, catalog, and statestore. Tests will then start
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executor groups and run queries to validate the behavior."""
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def setup_method(self, method):
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# Always start the base cluster with the coordinator in its own executor group.
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existing_args = method.__dict__.get("impalad_args", "")
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method.__dict__["impalad_args"] = "%s -executor_groups=coordinator" % existing_args
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method.__dict__["cluster_size"] = 1
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method.__dict__["num_exclusive_coordinators"] = 1
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self.num_groups = 1
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self.num_impalads = 1
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super(TestExecutorGroups, self).setup_method(method)
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self.coordinator = self.cluster.impalads[0]
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def _group_name(self, resource_pool, name_suffix):
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# By convention, group names must start with their associated resource pool name
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# followed by a "-". Tests in this class mostly use the default resource pool.
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return "%s-%s" % (resource_pool, name_suffix)
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def _add_executor_group(self, name_suffix, min_size, num_executors=0,
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admission_control_slots=0, extra_args=None,
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resource_pool=DEFAULT_RESOURCE_POOL):
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"""Adds an executor group to the cluster. 'min_size' specifies the minimum size for
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the new group to be considered healthy. 'num_executors' specifies the number of
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executors to start and defaults to 'min_size' but can be different from 'min_size' to
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start an unhealthy group. 'admission_control_slots' can be used to override the
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default (num cores). If 'name_suffix' is empty, no executor group is specified for
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the new backends and they will end up in the default group."""
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self.num_groups += 1
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if num_executors == 0:
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num_executors = min_size
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self.num_impalads += num_executors
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name = self._group_name(resource_pool, name_suffix)
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LOG.info("Adding %s executors to group %s with minimum size %s" %
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(num_executors, name, min_size))
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cluster_args = ["--impalad_args=-admission_control_slots=%s" %
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admission_control_slots]
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if len(name_suffix) > 0:
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cluster_args.append("--impalad_args=-executor_groups=%s:%s" % (name, min_size))
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if extra_args:
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cluster_args.append("--impalad_args=%s" % extra_args)
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self._start_impala_cluster(options=cluster_args,
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cluster_size=num_executors,
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num_coordinators=0,
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add_executors=True,
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expected_num_impalads=self.num_impalads)
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def _add_executors(self, name_suffix, min_size, num_executors=0,
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extra_args=None, resource_pool=DEFAULT_RESOURCE_POOL,
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expected_num_impalads=0):
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"""Adds given number of executors to the cluster. 'min_size' specifies the minimum
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size for the group to be considered healthy. 'num_executors' specifies the number of
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executors to start. If 'name_suffix' is empty, no executor group is specified for
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the new backends and they will end up in the default group."""
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if num_executors == 0:
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return
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name = self._group_name(resource_pool, name_suffix)
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LOG.info("Adding %s executors to group %s with minimum size %s" %
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(num_executors, name, min_size))
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cluster_args = []
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if len(name_suffix) > 0:
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cluster_args.append("--impalad_args=-executor_groups=%s:%s" % (name, min_size))
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if extra_args:
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cluster_args.append("--impalad_args=%s" % extra_args)
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self._start_impala_cluster(options=cluster_args,
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cluster_size=num_executors,
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num_coordinators=0,
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add_executors=True,
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expected_num_impalads=expected_num_impalads)
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self.num_impalads += num_executors
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def _restart_coordinators(self, num_coordinators, extra_args=None):
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"""Restarts the coordinator spawned in setup_method and enables the caller to start
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more than one coordinator by specifying 'num_coordinators'"""
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LOG.info("Adding a coordinator")
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cluster_args = ["--impalad_args=-executor_groups=coordinator"]
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if extra_args:
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cluster_args.append("--impalad_args=%s" % extra_args)
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self._start_impala_cluster(options=cluster_args,
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cluster_size=num_coordinators,
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num_coordinators=num_coordinators,
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add_executors=False,
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expected_num_impalads=num_coordinators,
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use_exclusive_coordinators=True)
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self.coordinator = self.cluster.impalads[0]
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self.num_impalads = num_coordinators
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def _get_total_admitted_queries(self):
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"""Returns the total number of queries that have been admitted to the default resource
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pool."""
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return self.impalad_test_service.get_total_admitted_queries("default-pool")
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def _get_num_running_queries(self):
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"""Returns the number of queries that are currently running in the default resource
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pool."""
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return self.impalad_test_service.get_num_running_queries("default-pool")
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def _wait_for_num_executor_groups(self, num_exec_grps, only_healthy=False):
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"""Waits for the number of executor groups to reach 'num_exec_grps'. If 'only_healthy'
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is True, only the healthy executor groups are accounted for, otherwise all groups
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with at least one executor are accounted for."""
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if only_healthy:
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return self.coordinator.service.wait_for_metric_value(
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"cluster-membership.executor-groups.total-healthy", num_exec_grps, timeout=30)
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else:
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return self.coordinator.service.wait_for_metric_value(
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"cluster-membership.executor-groups.total", num_exec_grps, timeout=30)
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def _get_num_executor_groups(self, only_healthy=False, exec_group_set_prefix=None):
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"""Returns the number of executor groups with at least one executor. If
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'only_healthy' is True, only the number of healthy executor groups is returned.
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If exec_group_set_prefix is used, it returns the metric corresponding to that
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executor group set."""
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metric_name = ""
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if exec_group_set_prefix is None:
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if only_healthy:
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metric_name = "cluster-membership.executor-groups.total-healthy"
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else:
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metric_name = "cluster-membership.executor-groups.total"
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else:
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if only_healthy:
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metric_name = "cluster-membership.group-set.executor-groups.total-healthy.{0}"\
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.format(exec_group_set_prefix)
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else:
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metric_name = "cluster-membership.group-set.executor-groups.total.{0}"\
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.format(exec_group_set_prefix)
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return self.coordinator.service.get_metric_value(metric_name)
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def _get_num_queries_executing_for_exec_group(self, group_name_prefix):
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"""Returns the number of queries running on the executor group 'group_name_prefix'.
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None is returned if the group has no executors or does not exist."""
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METRIC_PREFIX = "admission-controller.executor-group.num-queries-executing.{0}"
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return self.coordinator.service.get_metric_value(
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METRIC_PREFIX.format(self._group_name(DEFAULT_RESOURCE_POOL, group_name_prefix)))
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def _assert_eventually_in_profile(self, query_handle, expected_str):
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"""Assert with a timeout of 60 sec and a polling interval of 1 sec that the
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expected_str exists in the query profile."""
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self.assert_eventually(
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60, 1, lambda: expected_str in self.client.get_runtime_profile(query_handle))
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@pytest.mark.execute_serially
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@CustomClusterTestSuite.with_args(impalad_args="-queue_wait_timeout_ms=1000")
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def test_no_group(self):
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"""Tests that a regular query submitted to a coordinator with no executor group
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times out but coordinator only queries can still run."""
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result = self.execute_query_expect_failure(self.client, TEST_QUERY)
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assert "Admission for query exceeded timeout" in str(result)
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assert self._get_num_executor_groups(only_healthy=True) == 0
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expected_group = "Executor Group: empty group (using coordinator only)"
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# Force the query to run on coordinator only.
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result = self.execute_query_expect_success(self.client, TEST_QUERY,
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query_options={'NUM_NODES': '1'})
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assert expected_group in str(result.runtime_profile)
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# Small query runs on coordinator only.
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result = self.execute_query_expect_success(self.client, "select 1")
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assert expected_group in str(result.runtime_profile)
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@pytest.mark.execute_serially
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def test_single_group(self):
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"""Tests that we can start a single executor group and run a simple query."""
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self._add_executor_group("group1", 2)
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self.execute_query_expect_success(self.client, TEST_QUERY)
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assert self._get_num_executor_groups(only_healthy=True) == 1
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@pytest.mark.execute_serially
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def test_executor_group_starts_while_qeueud(self):
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"""Tests that a query can stay in the queue of an empty cluster until an executor
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group comes online."""
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client = self.client
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handle = client.execute_async(TEST_QUERY)
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self._assert_eventually_in_profile(handle, "Waiting for executors to start")
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assert self._get_num_executor_groups(only_healthy=True) == 0
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self._add_executor_group("group1", 2)
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client.wait_for_finished_timeout(handle, 20)
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assert self._get_num_executor_groups(only_healthy=True) == 1
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@pytest.mark.execute_serially
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def test_executor_group_health(self):
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"""Tests that an unhealthy executor group will not run queries."""
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# Start cluster and group
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self._add_executor_group("group1", 2)
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self._wait_for_num_executor_groups(1, only_healthy=True)
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client = self.client
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# Run query to validate
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self.execute_query_expect_success(client, TEST_QUERY)
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# Kill an executor
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executor = self.cluster.impalads[1]
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executor.kill()
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self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 2,
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timeout=20)
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assert self._get_num_executor_groups(only_healthy=True) == 0
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# Run query and observe timeout
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handle = client.execute_async(TEST_QUERY)
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self._assert_eventually_in_profile(handle, "Waiting for executors to start")
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# Restart executor
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executor.start()
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# Query should now finish
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client.wait_for_finished_timeout(handle, 20)
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# Run query and observe success
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self.execute_query_expect_success(client, TEST_QUERY)
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self._wait_for_num_executor_groups(1, only_healthy=True)
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@pytest.mark.execute_serially
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def test_executor_group_min_size_update(self):
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"""Tests that we can update an executor group's min size without restarting
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coordinators."""
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# Start cluster and group
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self._add_executor_group("group1", min_size=1, num_executors=1)
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self._wait_for_num_executor_groups(1, only_healthy=True)
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client = self.client
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# Kill the executor
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executor = self.cluster.impalads[1]
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executor.kill()
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self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 1,
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timeout=20)
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assert self._get_num_executor_groups(only_healthy=True) == 0
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# Add a new executor to group1 with group min size 2
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self._add_executors("group1", min_size=2, num_executors=2, expected_num_impalads=3)
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assert self._get_num_executor_groups(only_healthy=True) == 1
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# Run query and observe success
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self.execute_query_expect_success(client, TEST_QUERY)
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@pytest.mark.execute_serially
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@CustomClusterTestSuite.with_args(impalad_args="-default_pool_max_requests=1")
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def test_executor_group_shutdown(self):
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"""Tests that an executor group can shutdown and a query in the queue can still run
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successfully when the group gets restored."""
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self._add_executor_group("group1", 2)
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client = self.client
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q1 = client.execute_async("select sleep(5000)")
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q2 = client.execute_async("select sleep(3)")
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# Verify that q2 is queued up behind q1
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self._assert_eventually_in_profile(
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q2, "Initial admission queue reason: number of running queries")
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# Kill an executor
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executor = self.cluster.impalads[1]
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executor.kill()
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self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 2)
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# Wait for q1 to finish (sleep runs on the coordinator)
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client.wait_for_finished_timeout(q1, 20)
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# Check that q2 still hasn't run
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profile = client.get_runtime_profile(q2)
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assert "Admission result: Queued" in profile, profile
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assert self._get_num_executor_groups(only_healthy=True) == 0
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# Restore executor group health
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executor.start()
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# Query should now finish
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client.wait_for_finished_timeout(q2, 20)
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assert self._get_num_executor_groups(only_healthy=True) == 1
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@pytest.mark.execute_serially
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def test_admission_slots(self):
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"""Tests that the admission_control_slots flag works as expected to
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specify the number of admission slots on the executors."""
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self._add_executor_group("group1", 2, admission_control_slots=1)
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# Query that runs on every executor
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QUERY = "select * from functional_parquet.alltypestiny \
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where month < 3 and id + random() < sleep(500);"
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client = self.client
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q1 = client.execute_async(QUERY)
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client.wait_for_admission_control(q1)
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q2 = client.execute_async(QUERY)
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self._assert_eventually_in_profile(q2, "Initial admission queue reason: Not enough "
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"admission control slots available on host")
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client.cancel(q1)
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client.cancel(q2)
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# Test that a query that would occupy too many slots gets rejected
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result = self.execute_query_expect_failure(self.client,
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"select min(ss_list_price) from tpcds_parquet.store_sales", {'mt_dop': 64})
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assert "number of admission control slots needed" in str(result)
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assert "is greater than total slots available" in str(result)
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@pytest.mark.execute_serially
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def test_multiple_executor_groups(self):
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"""Tests that two queries can run on two separate executor groups simultaneously."""
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# Query that runs on every executor
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QUERY = "select * from functional_parquet.alltypestiny \
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where month < 3 and id + random() < sleep(500);"
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self._add_executor_group("group1", 2, admission_control_slots=1)
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self._add_executor_group("group2", 2, admission_control_slots=1)
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self._wait_for_num_executor_groups(2, only_healthy=True)
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client = self.client
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q1 = client.execute_async(QUERY)
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client.wait_for_admission_control(q1)
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q2 = client.execute_async(QUERY)
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client.wait_for_admission_control(q2)
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profiles = [client.get_runtime_profile(q) for q in (q1, q2)]
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assert not any("Initial admission queue reason" in p for p in profiles), profiles
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client.cancel(q1)
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client.cancel(q2)
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@pytest.mark.execute_serially
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@CustomClusterTestSuite.with_args(impalad_args="-admission_control_slots=1")
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def test_coordinator_concurrency(self):
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"""Tests that the command line flag to limit the coordinator concurrency works as
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expected."""
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QUERY = "select sleep(1000)"
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# Add group with more slots than coordinator
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self._add_executor_group("group2", 2, admission_control_slots=3)
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# Try to run two queries and observe that one gets queued
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client = self.client
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q1 = client.execute_async(QUERY)
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client.wait_for_admission_control(q1)
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q2 = client.execute_async(QUERY)
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self._assert_eventually_in_profile(q2, "Initial admission queue reason")
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client.cancel(q1)
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client.cancel(q2)
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@pytest.mark.execute_serially
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def test_executor_concurrency(self):
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"""Tests that the command line flag to limit query concurrency on executors works as
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expected."""
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# Query that runs on every executor
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QUERY = "select * from functional_parquet.alltypestiny \
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where month < 3 and id + random() < sleep(500);"
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self._add_executor_group("group1", 2, admission_control_slots=3)
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workload = None
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try:
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workload = ConcurrentWorkload(QUERY, num_streams=5)
|
|
LOG.info("Starting workload")
|
|
workload.start()
|
|
|
|
RAMP_UP_TIMEOUT_S = 60
|
|
# Wait until we admitted at least 10 queries
|
|
assert any(self._get_total_admitted_queries() >= 10 or sleep(1)
|
|
for _ in range(RAMP_UP_TIMEOUT_S)), \
|
|
"Did not admit enough queries within %s s" % RAMP_UP_TIMEOUT_S
|
|
|
|
# Sample the number of admitted queries on each backend for while.
|
|
# Note that the total number of queries in the cluster can higher
|
|
# than 3 because resources may be released on some backends, allowing
|
|
# a new query to fit (see IMPALA-9073).
|
|
NUM_SAMPLES = 30
|
|
executor_slots_in_use = []
|
|
for _ in range(NUM_SAMPLES):
|
|
backends_json = json.loads(
|
|
self.impalad_test_service.read_debug_webpage('backends?json'))
|
|
for backend in backends_json['backends']:
|
|
if backend['is_executor']:
|
|
executor_slots_in_use.append(backend['admission_slots_in_use'])
|
|
sleep(1)
|
|
|
|
# Must reach 3 but not exceed it
|
|
assert max(executor_slots_in_use) == 3, \
|
|
"Unexpected number of slots in use: %s" % executor_slots_in_use
|
|
|
|
finally:
|
|
LOG.info("Stopping workload")
|
|
if workload:
|
|
workload.stop()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_sequential_startup_wait(self):
|
|
"""Tests that starting an executor group sequentially works as expected, i.e. queries
|
|
don't fail and no queries are admitted until the group is in a healthy state."""
|
|
# Start first executor
|
|
self._add_executor_group("group1", 3, num_executors=1)
|
|
self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 2)
|
|
assert self._get_num_executor_groups() == 1
|
|
assert self._get_num_executor_groups(only_healthy=True) == 0
|
|
# Run query and observe that it gets queued
|
|
client = self.client
|
|
handle = client.execute_async(TEST_QUERY)
|
|
self._assert_eventually_in_profile(handle, "Initial admission queue reason:"
|
|
" Waiting for executors to start")
|
|
initial_state = client.get_state(handle)
|
|
# Start another executor and observe that the query stays queued
|
|
self._add_executor_group("group1", 3, num_executors=1)
|
|
self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 3)
|
|
assert self._get_num_executor_groups(only_healthy=True) == 0
|
|
assert client.get_state(handle) == initial_state
|
|
# Start the remaining executor and observe that the query finishes
|
|
self._add_executor_group("group1", 3, num_executors=1)
|
|
self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 4)
|
|
assert self._get_num_executor_groups(only_healthy=True) == 1
|
|
client.wait_for_finished_timeout(handle, 20)
|
|
|
|
@pytest.mark.execute_serially
|
|
@CustomClusterTestSuite.with_args(impalad_args="-queue_wait_timeout_ms=2000")
|
|
def test_empty_default_group(self):
|
|
"""Tests that an empty default group is correctly marked as non-healthy and excluded
|
|
from scheduling."""
|
|
# Start default executor group
|
|
self._add_executor_group("", min_size=2, num_executors=2,
|
|
admission_control_slots=3)
|
|
# Run query to make sure things work
|
|
self.execute_query_expect_success(self.client, TEST_QUERY)
|
|
assert self._get_num_executor_groups(only_healthy=True) == 1
|
|
# Kill executors to make group empty
|
|
impalads = self.cluster.impalads
|
|
impalads[1].kill()
|
|
impalads[2].kill()
|
|
self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 1)
|
|
# Run query to make sure it times out
|
|
result = self.execute_query_expect_failure(self.client, TEST_QUERY)
|
|
expected_error = "Query aborted:Admission for query exceeded timeout 2000ms in " \
|
|
"pool default-pool. Queued reason: Waiting for executors to " \
|
|
"start. Only DDL queries and queries scheduled only on the " \
|
|
"coordinator (either NUM_NODES set to 1 or when small query " \
|
|
"optimization is triggered) can currently run."
|
|
assert expected_error in str(result)
|
|
assert self._get_num_executor_groups(only_healthy=True) == 0
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_executor_group_num_queries_executing_metric(self):
|
|
"""Tests the functionality of the metric keeping track of the query load of executor
|
|
groups."""
|
|
# Query that runs on every executor
|
|
QUERY = "select * from functional_parquet.alltypestiny \
|
|
where month < 3 and id + random() < sleep(500);"
|
|
group_names = ["group1", "group2"]
|
|
self._add_executor_group(group_names[0], min_size=1, num_executors=1,
|
|
admission_control_slots=1)
|
|
# Create an exec group of min size 2 to exercise the case where a group becomes
|
|
# unhealthy.
|
|
self._add_executor_group(group_names[1], min_size=2, num_executors=2,
|
|
admission_control_slots=1)
|
|
self._wait_for_num_executor_groups(2, only_healthy=True)
|
|
# Verify metrics for both groups appear.
|
|
assert all(
|
|
self._get_num_queries_executing_for_exec_group(name) == 0 for name in group_names)
|
|
|
|
# First query will run on the first group. Verify the metric updates accordingly.
|
|
client = self.client
|
|
q1 = client.execute_async(QUERY)
|
|
client.wait_for_admission_control(q1)
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[0]) == 1
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[1]) == 0
|
|
|
|
# Similarly verify the metric updates accordingly when a query runs on the next group.
|
|
q2 = client.execute_async(QUERY)
|
|
client.wait_for_admission_control(q2)
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[0]) == 1
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[1]) == 1
|
|
|
|
# Close both queries and verify metrics are updated accordingly.
|
|
client.close_query(q1)
|
|
client.close_query(q2)
|
|
assert all(
|
|
self._get_num_queries_executing_for_exec_group(name) == 0 for name in group_names)
|
|
|
|
# Kill an executor from group2 to make that group unhealthy, then verify that the
|
|
# metric is still there.
|
|
self.cluster.impalads[-1].kill()
|
|
self._wait_for_num_executor_groups(1, only_healthy=True)
|
|
assert self._get_num_executor_groups() == 2
|
|
assert all(
|
|
self._get_num_queries_executing_for_exec_group(name) == 0 for name in group_names)
|
|
|
|
# Kill the last executor from group2 so that it is removed from the exec group list,
|
|
# then verify that the metric disappears.
|
|
self.cluster.impalads[-2].kill()
|
|
self._wait_for_num_executor_groups(1)
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[0]) == 0
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[1]) is None
|
|
|
|
# Now make sure the metric accounts for already running queries that linger around
|
|
# from when the group was healthy.
|
|
# Block the query cancellation thread to allow the query to linger between exec group
|
|
# going down and coming back up.
|
|
client.set_configuration({"debug_action": "QUERY_CANCELLATION_THREAD:SLEEP@10000"})
|
|
q3 = client.execute_async(QUERY)
|
|
client.wait_for_admission_control(q3)
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[0]) == 1
|
|
impalad_grp1 = self.cluster.impalads[-3]
|
|
impalad_grp1.kill()
|
|
self._wait_for_num_executor_groups(0)
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[0]) is None
|
|
impalad_grp1.start()
|
|
self._wait_for_num_executor_groups(1, only_healthy=True)
|
|
assert self._get_num_queries_executing_for_exec_group(group_names[0]) == 1, \
|
|
"The lingering query should be accounted for when the group comes back up."
|
|
client.cancel(q3)
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.executor-group.num-queries-executing.{0}".format(
|
|
self._group_name(DEFAULT_RESOURCE_POOL, group_names[0])), 0, timeout=30)
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_join_strategy_single_executor(self):
|
|
"""Tests that the planner picks the correct join strategy based on the current cluster
|
|
size. This test uses an executor group with a minimum size of 1."""
|
|
TABLE = "functional.alltypes"
|
|
QUERY = "explain select * from %s a inner join %s b on a.id = b.id" % (TABLE, TABLE)
|
|
|
|
# Predicates to assert that a certain join type was picked.
|
|
def assert_broadcast_join():
|
|
ret = self.execute_query_expect_success(self.client, QUERY)
|
|
assert ":EXCHANGE [BROADCAST]" in str(ret)
|
|
|
|
def assert_hash_join():
|
|
ret = self.execute_query_expect_success(self.client, QUERY)
|
|
assert ":EXCHANGE [HASH(b.id)]" in str(ret)
|
|
|
|
# Without any executors we default to a hash join.
|
|
assert_hash_join()
|
|
|
|
# Add a healthy executor group of size 1 and observe that we switch to broadcast join.
|
|
self._add_executor_group("group1", min_size=1, num_executors=1)
|
|
assert_broadcast_join()
|
|
|
|
# Add another executor to the same group and observe that with two executors we pick a
|
|
# partitioned hash join.
|
|
self._add_executor_group("group1", min_size=1, num_executors=1)
|
|
assert_hash_join()
|
|
|
|
# Kill an executor. The group remains healthy but its size decreases and we revert
|
|
# back to a broadcast join.
|
|
self.cluster.impalads[-1].kill()
|
|
self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 2,
|
|
timeout=20)
|
|
assert_broadcast_join()
|
|
|
|
# Kill a second executor. The group becomes unhealthy and we go back to using the
|
|
# expected size to plan which would result in a hash join
|
|
self.cluster.impalads[-2].kill()
|
|
self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 1,
|
|
timeout=20)
|
|
assert_hash_join()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_join_strategy_multiple_executors(self):
|
|
"""Tests that the planner picks the correct join strategy based on the current cluster
|
|
size. This test uses an executor group which requires multiple executors to be
|
|
healthy."""
|
|
TABLE = "functional.alltypes"
|
|
QUERY = "explain select * from %s a inner join %s b on a.id = b.id" % (TABLE, TABLE)
|
|
|
|
# Predicate to assert that the planner decided on a hash join.
|
|
def assert_hash_join():
|
|
ret = self.execute_query_expect_success(self.client, QUERY)
|
|
assert ":EXCHANGE [HASH(b.id)]" in str(ret)
|
|
|
|
# Without any executors we default to a hash join.
|
|
assert_hash_join()
|
|
|
|
# Adding an unhealthy group will not affect the planner's decision.
|
|
self._add_executor_group("group1", min_size=2, num_executors=1)
|
|
assert_hash_join()
|
|
|
|
# Adding a second executor makes the group healthy (note that the resulting join
|
|
# strategy is the same though).
|
|
self._add_executor_group("group1", min_size=2, num_executors=1)
|
|
assert_hash_join()
|
|
|
|
# Kill an executor. The unhealthy group does not affect the planner's decision, even
|
|
# though only one executor is now online.
|
|
self.cluster.impalads[-1].kill()
|
|
self.coordinator.service.wait_for_metric_value("cluster-membership.backends.total", 2,
|
|
timeout=20)
|
|
assert_hash_join()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_admission_control_with_multiple_coords(self):
|
|
"""This test verifies that host level metrics like the num of admission slots used
|
|
and memory admitted is disseminated correctly across the cluster and accounted for
|
|
while making admission decisions. We run a query that takes up all of a particular
|
|
resource (slots or memory) and check if attempting to run a query on the other
|
|
coordinator results in queuing."""
|
|
# A long running query that runs on every executor
|
|
QUERY = "select * from functional_parquet.alltypes \
|
|
where month < 3 and id + random() < sleep(100);"
|
|
# default_pool_mem_limit is set to enable mem based admission.
|
|
self._restart_coordinators(num_coordinators=2,
|
|
extra_args="-default_pool_mem_limit=100g")
|
|
# Create fresh clients
|
|
second_coord_client = self.create_client_for_nth_impalad(1)
|
|
self.create_impala_clients()
|
|
# Add an exec group with a 4.001gb mem_limit, adding spare room for cancellation
|
|
self._add_executor_group("group1", 2, admission_control_slots=2,
|
|
extra_args="-mem_limit=4.001g")
|
|
assert self._get_num_executor_groups(only_healthy=True) == 1
|
|
second_coord_client.set_configuration({'mt_dop': '2'})
|
|
handle_for_second = second_coord_client.execute_async(QUERY)
|
|
# Verify that the first coordinator knows about the query running on the second
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.agg-num-running.default-pool", 1, timeout=30)
|
|
handle_for_first = self.execute_query_async(TEST_QUERY)
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.local-num-queued.default-pool", 1, timeout=30)
|
|
profile = self.client.get_runtime_profile(handle_for_first)
|
|
assert "queue reason: Not enough admission control slots available on host" in \
|
|
profile, profile
|
|
self.close_query(handle_for_first)
|
|
second_coord_client.close_query(handle_for_second)
|
|
# Wait for first coordinator to get the admission update.
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.agg-num-running.default-pool", 0, timeout=30)
|
|
# Now verify that mem based admission also works as intended. A max of mem_reserved
|
|
# and mem_admitted is used for this. Since mem_limit is being used here, both will be
|
|
# identical but this will at least test that code path as a sanity check.
|
|
second_coord_client.clear_configuration()
|
|
# The maximum memory can be used for query needs to subtract the codegen cache
|
|
# capacity, which is 4GB - 10% * 4GB = 3.6GB.
|
|
query_mem_limit = 4 * (1 - 0.1)
|
|
second_coord_client.set_configuration({'mem_limit': str(query_mem_limit) + 'g'})
|
|
handle_for_second = second_coord_client.execute_async(QUERY)
|
|
# Verify that the first coordinator knows about the query running on the second
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.agg-num-running.default-pool", 1, timeout=30)
|
|
handle_for_first = self.execute_query_async(TEST_QUERY)
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.local-num-queued.default-pool", 1, timeout=30)
|
|
profile = self.client.get_runtime_profile(handle_for_first)
|
|
assert "queue reason: Not enough memory available on host" in profile, profile
|
|
self.close_query(handle_for_first)
|
|
second_coord_client.close_query(handle_for_second)
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_admission_control_with_multiple_coords_and_exec_groups(self):
|
|
"""This test verifies that admission control accounting works when using multiple
|
|
coordinators and multiple executor groups mapped to different resource pools and
|
|
having different sizes."""
|
|
# A long running query that runs on every executor
|
|
LONG_QUERY = "select * from functional_parquet.alltypes \
|
|
where month < 3 and id + random() < sleep(100);"
|
|
# The path to resources directory which contains the admission control config files.
|
|
RESOURCES_DIR = os.path.join(os.environ['IMPALA_HOME'], "fe", "src", "test",
|
|
"resources")
|
|
fs_allocation_path = os.path.join(RESOURCES_DIR, "fair-scheduler-allocation.xml")
|
|
llama_site_path = os.path.join(RESOURCES_DIR, "llama-site-empty.xml")
|
|
# Start with a regular admission config with multiple pools and no resource limits.
|
|
self._restart_coordinators(num_coordinators=2,
|
|
extra_args="-vmodule admission-controller=3 "
|
|
"-expected_executor_group_sets=root.queue1:2,root.queue2:1 "
|
|
"-fair_scheduler_allocation_path %s "
|
|
"-llama_site_path %s" % (
|
|
fs_allocation_path, llama_site_path))
|
|
|
|
# Create fresh clients
|
|
second_coord_client = self.create_client_for_nth_impalad(1)
|
|
self.create_impala_clients()
|
|
# Add an exec group with a single admission slot and 2 executors.
|
|
self._add_executor_group("group", 2, admission_control_slots=1,
|
|
resource_pool="root.queue1", extra_args="-mem_limit=2g")
|
|
# Add an exec group with a single admission slot and only 1 executor.
|
|
self._add_executor_group("group", 1, admission_control_slots=1,
|
|
resource_pool="root.queue2", extra_args="-mem_limit=2g")
|
|
assert self._get_num_executor_groups(only_healthy=True) == 2
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.queue1") == 1
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.queue2") == 1
|
|
|
|
# Execute a long running query on group 'queue1'
|
|
self.client.set_configuration({'request_pool': 'queue1'})
|
|
handle_long_running_queue1 = self.execute_query_async(LONG_QUERY)
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.executor-group.num-queries-executing.root.queue1-group",
|
|
1, timeout=30)
|
|
profile = self.client.get_runtime_profile(handle_long_running_queue1)
|
|
"Executor Group: root.queue1-group" in profile
|
|
|
|
# Try to execute another query on group 'queue1'. This one should queue.
|
|
handle_queued_query_queue1 = self.execute_query_async(TEST_QUERY)
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.local-num-queued.root.queue1", 1, timeout=30)
|
|
profile = self.client.get_runtime_profile(handle_queued_query_queue1)
|
|
assert "queue reason: Not enough admission control slots available on host" in \
|
|
profile, profile
|
|
|
|
# Execute a query on group 'queue2'. This one will run as its running in another pool.
|
|
result = self.execute_query_expect_success(self.client, TEST_QUERY,
|
|
query_options={'request_pool': 'queue2'})
|
|
assert "Executor Group: root.queue2-group" in str(result.runtime_profile)
|
|
|
|
# Verify that multiple coordinators' accounting still works correctly in case of
|
|
# multiple executor groups.
|
|
|
|
# Run a query in group 'queue2' on the second coordinator
|
|
second_coord_client.set_configuration({'request_pool': 'queue2'})
|
|
second_coord_client.execute_async(LONG_QUERY)
|
|
# Verify that the first coordinator knows about the query running on the second
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.agg-num-running.root.queue2", 1, timeout=30)
|
|
|
|
# Check that attempting to run another query in 'queue2' will queue the query.
|
|
self.client.set_configuration({'request_pool': 'queue2'})
|
|
handle_queued_query_queue2 = self.execute_query_async(TEST_QUERY)
|
|
self.coordinator.service.wait_for_metric_value(
|
|
"admission-controller.local-num-queued.root.queue2", 1, timeout=30)
|
|
profile = self.client.get_runtime_profile(handle_queued_query_queue2)
|
|
assert "queue reason: Not enough admission control slots available on host" in \
|
|
profile, profile
|
|
|
|
self.client.close()
|
|
second_coord_client.close()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_query_assignment_with_two_exec_groups(self):
|
|
"""This test verifies that query assignment works with two executor groups with
|
|
different number of executors and memory limit in each."""
|
|
# A small query with estimated memory per host of 10MB that can run on the small
|
|
# executor group
|
|
SMALL_QUERY = "select count(*) from tpcds_parquet.date_dim where d_year=2022;"
|
|
# A large query with estimated memory per host of 132MB that can only run on
|
|
# the large executor group.
|
|
LARGE_QUERY = "select * from tpcds_parquet.store_sales where ss_item_sk = 1 limit 50;"
|
|
# The path to resources directory which contains the admission control config files.
|
|
RESOURCES_DIR = os.path.join(os.environ['IMPALA_HOME'], "fe", "src", "test",
|
|
"resources")
|
|
# Define three group sets: tiny, small and large
|
|
fs_allocation_path = os.path.join(RESOURCES_DIR, "fair-scheduler-3-groups.xml")
|
|
# Define the min-query-mem-limit, max-query-mem-limit,
|
|
# max-query-cpu-core-per-node-limit and max-query-cpu-core-coordinator-limit
|
|
# properties of the three sets:
|
|
# tiny: [0, 64MB, 4, 4]
|
|
# small: [0, 70MB, 8, 8]
|
|
# large: [64MB+1Byte, 8PB, 64, 64]
|
|
llama_site_path = os.path.join(RESOURCES_DIR, "llama-site-3-groups.xml")
|
|
# Start with a regular admission config with multiple pools and no resource limits.
|
|
# Only populate executor froup sets small and large.
|
|
self._restart_coordinators(num_coordinators=1,
|
|
extra_args="-vmodule admission-controller=3 "
|
|
"-expected_executor_group_sets=root.small:2,root.large:3 "
|
|
"-fair_scheduler_allocation_path %s "
|
|
"-llama_site_path %s" % (
|
|
fs_allocation_path, llama_site_path))
|
|
|
|
# Create fresh client
|
|
self.create_impala_clients()
|
|
# Add an exec group with a single admission slot and 2 executors.
|
|
self._add_executor_group("group", 2, admission_control_slots=1,
|
|
resource_pool="root.small", extra_args="-mem_limit=2g")
|
|
# Add another exec group with a single admission slot and 3 executors.
|
|
self._add_executor_group("group", 3, admission_control_slots=1,
|
|
resource_pool="root.large", extra_args="-mem_limit=2g")
|
|
assert self._get_num_executor_groups(only_healthy=True) == 2
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.small") == 1
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.large") == 1
|
|
|
|
# Expect to run the small query on the small group
|
|
result = self.execute_query_expect_success(self.client, SMALL_QUERY)
|
|
assert "Executor Group: root.small-group" in str(result.runtime_profile)
|
|
|
|
# Expect to run the large query on the large group
|
|
result = self.execute_query_expect_success(self.client, LARGE_QUERY)
|
|
assert "Executor Group: root.large-group" in str(result.runtime_profile)
|
|
|
|
# Force to run the large query on the small group.
|
|
# Query should run successfully since exec group memory limit is ignored.
|
|
self.client.set_configuration({'request_pool': 'small'})
|
|
result = self.execute_query_expect_success(self.client, LARGE_QUERY)
|
|
assert ("Verdict: query option REQUEST_POOL=small is set. "
|
|
"Memory and cpu limit checking is skipped.") in str(result.runtime_profile)
|
|
|
|
self.client.close()
|
|
|
|
def _setup_three_exec_group_cluster(self, coordinator_test_args):
|
|
# The path to resources directory which contains the admission control config files.
|
|
RESOURCES_DIR = os.path.join(os.environ['IMPALA_HOME'], "fe", "src", "test",
|
|
"resources")
|
|
# Define two group sets: tiny, small and large
|
|
fs_allocation_path = os.path.join(RESOURCES_DIR, "fair-scheduler-3-groups.xml")
|
|
# Define the min-query-mem-limit, max-query-mem-limit,
|
|
# max-query-cpu-core-per-node-limit and max-query-cpu-core-coordinator-limit
|
|
# properties of the three sets:
|
|
# tiny: [0, 64MB, 4, 4]
|
|
# small: [0, 70MB, 8, 8]
|
|
# large: [64MB+1Byte, 8PB, 64, 64]
|
|
llama_site_path = os.path.join(RESOURCES_DIR, "llama-site-3-groups.xml")
|
|
|
|
# extra args template to start coordinator
|
|
extra_args_template = ("-vmodule admission-controller=3 "
|
|
"-expected_executor_group_sets=root.tiny:1,root.small:2,root.large:3 "
|
|
"-fair_scheduler_allocation_path %s "
|
|
"-llama_site_path %s "
|
|
"%s ")
|
|
|
|
# Start with a regular admission config, multiple pools, no resource limits,
|
|
# and query_cpu_count_divisor=2.
|
|
self._restart_coordinators(num_coordinators=1,
|
|
extra_args=extra_args_template % (fs_allocation_path, llama_site_path,
|
|
coordinator_test_args))
|
|
|
|
# Create fresh client
|
|
self.create_impala_clients()
|
|
# Add an exec group with a 2 admission slot and 1 executors.
|
|
self._add_executor_group("group", 1, admission_control_slots=2,
|
|
resource_pool="root.tiny", extra_args="-mem_limit=2g")
|
|
# Add an exec group with a 2 admission slot and 2 executors.
|
|
self._add_executor_group("group", 2, admission_control_slots=2,
|
|
resource_pool="root.small", extra_args="-mem_limit=2g")
|
|
# Add another exec group with 2 admission slot and 3 executors.
|
|
self._add_executor_group("group", 3, admission_control_slots=2,
|
|
resource_pool="root.large", extra_args="-mem_limit=2g")
|
|
assert self._get_num_executor_groups(only_healthy=True) == 3
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.tiny") == 1
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.small") == 1
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.large") == 1
|
|
|
|
def _run_query_and_verify_profile(self, query, query_options,
|
|
expected_strings_in_profile, not_expected_in_profile=[]):
|
|
"""Run 'query' with given 'query_options'. Assert existence of
|
|
'expected_strings_in_profile' and nonexistence of 'not_expected_in_profile'
|
|
in query profile.
|
|
Caller is reponsible to close self.client at the end of test."""
|
|
for k, v in query_options.items():
|
|
self.execute_query_expect_success(self.client, "SET {}='{}';".format(k, v))
|
|
result = self.execute_query_expect_success(self.client, query)
|
|
for expected_profile in expected_strings_in_profile:
|
|
assert expected_profile in str(result.runtime_profile)
|
|
for not_expected in not_expected_in_profile:
|
|
assert not_expected not in str(result.runtime_profile)
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_query_cpu_count_divisor_default(self):
|
|
# Expect to run the query on the small group by default.
|
|
coordinator_test_args = ""
|
|
self._setup_three_exec_group_cluster(coordinator_test_args)
|
|
self._run_query_and_verify_profile(CPU_TEST_QUERY, CPU_DOP_OPTIONS,
|
|
["Executor Group: root.small-group", "EffectiveParallelism: 5",
|
|
"ExecutorGroupsConsidered: 2"])
|
|
|
|
# Test disabling COMPUTE_PROCESING_COST and not setting REQUEST_POOL
|
|
options = copy.deepcopy(CPU_DOP_OPTIONS)
|
|
options['COMPUTE_PROCESSING_COST'] = 'false'
|
|
self._run_query_and_verify_profile(CPU_TEST_QUERY, options,
|
|
["Executor Group: root.tiny-group", "ExecutorGroupsConsidered: 1",
|
|
"Verdict: Match"],
|
|
["EffectiveParallelism:", "CpuAsk:"])
|
|
|
|
# Test that REQUEST_POOL will override executor group selection
|
|
options['COMPUTE_PROCESSING_COST'] = 'true'
|
|
options['REQUEST_POOL'] = 'root.large'
|
|
self._run_query_and_verify_profile(CPU_TEST_QUERY, options,
|
|
["Executor Group: root.large-group",
|
|
("Verdict: query option REQUEST_POOL=root.large is set. "
|
|
"Memory and cpu limit checking is skipped."),
|
|
"EffectiveParallelism: 7", "ExecutorGroupsConsidered: 1"])
|
|
|
|
# Test setting REQUEST_POOL and disabling COMPUTE_PROCESSING_COST
|
|
options['COMPUTE_PROCESSING_COST'] = 'false'
|
|
options['REQUEST_POOL'] = 'root.large'
|
|
self._run_query_and_verify_profile(CPU_TEST_QUERY, options,
|
|
["Executor Group: root.large-group",
|
|
("Verdict: query option REQUEST_POOL=root.large is set. "
|
|
"Memory and cpu limit checking is skipped."),
|
|
"ExecutorGroupsConsidered: 1"],
|
|
["EffectiveParallelism:", "CpuAsk:"])
|
|
|
|
# Unset REQUEST_POOL.
|
|
self.execute_query_expect_success(self.client, "SET REQUEST_POOL='';")
|
|
|
|
# Test that GROUPING_TEST_QUERY will get assigned to the small group.
|
|
self._run_query_and_verify_profile(GROUPING_TEST_QUERY, CPU_DOP_OPTIONS,
|
|
["Executor Group: root.small-group", "ExecutorGroupsConsidered: 2",
|
|
"Verdict: Match", "CpuAsk: 4", "CpuAskUnbounded: 1"])
|
|
self.client.close()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_query_cpu_count_divisor_two(self):
|
|
# Expect to run the query on the tiny group
|
|
coordinator_test_args = "-query_cpu_count_divisor=2 "
|
|
self._setup_three_exec_group_cluster(coordinator_test_args)
|
|
self._run_query_and_verify_profile(CPU_TEST_QUERY, CPU_DOP_OPTIONS,
|
|
["Executor Group: root.tiny-group", "EffectiveParallelism: 3",
|
|
"ExecutorGroupsConsidered: 1"])
|
|
self.client.close()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_query_cpu_count_divisor_fraction(self):
|
|
# Expect to run the query on the large group
|
|
coordinator_test_args = "-query_cpu_count_divisor=0.03 "
|
|
self._setup_three_exec_group_cluster(coordinator_test_args)
|
|
options = copy.deepcopy(CPU_DOP_OPTIONS)
|
|
options['MT_DOP'] = '1'
|
|
self._run_query_and_verify_profile(CPU_TEST_QUERY, options,
|
|
["Executor Group: root.large-group", "EffectiveParallelism: 4",
|
|
"ExecutorGroupsConsidered: 3", "CpuAsk: 134",
|
|
"Verdict: Match"])
|
|
|
|
# Expect that a query still admitted to last group even if
|
|
# its resource requirement exceed the limit on that last executor group.
|
|
self._run_query_and_verify_profile(CPU_TEST_QUERY, CPU_DOP_OPTIONS,
|
|
["Executor Group: root.large-group", "EffectiveParallelism: 7",
|
|
"ExecutorGroupsConsidered: 3", "CpuAsk: 234",
|
|
"Verdict: no executor group set fit. Admit to last executor group set."])
|
|
self.client.close()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_no_skip_resource_checking(self):
|
|
"""This test check that executor group limit is enforced if
|
|
skip_resource_checking_on_last_executor_group_set=false."""
|
|
coordinator_test_args = ("-query_cpu_count_divisor=0.03 "
|
|
"-skip_resource_checking_on_last_executor_group_set=false ")
|
|
self._setup_three_exec_group_cluster(coordinator_test_args)
|
|
self.client.set_configuration(CPU_DOP_OPTIONS)
|
|
result = self.execute_query_expect_failure(self.client, CPU_TEST_QUERY)
|
|
assert ("AnalysisException: The query does not fit largest executor group sets. "
|
|
"Reason: not enough cpu cores (require=234, max=192).") in str(result)
|
|
self.client.close()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_min_processing_per_thread_small(self):
|
|
"""Test processing cost with min_processing_per_thread smaller than default"""
|
|
coordinator_test_args = "-min_processing_per_thread=500000"
|
|
self._setup_three_exec_group_cluster(coordinator_test_args)
|
|
|
|
# Test that GROUPING_TEST_QUERY will get assigned to the large group.
|
|
self._run_query_and_verify_profile(GROUPING_TEST_QUERY, CPU_DOP_OPTIONS,
|
|
["Executor Group: root.large-group", "ExecutorGroupsConsidered: 3",
|
|
"Verdict: Match", "CpuAsk: 6"],
|
|
["CpuAskUnbounded:"])
|
|
|
|
# Test that high_scan_cost_query will get assigned to the large group.
|
|
high_scan_cost_query = ("SELECT ss_item_sk FROM tpcds_parquet.store_sales "
|
|
"WHERE ss_item_sk < 1000000 GROUP BY ss_item_sk LIMIT 10")
|
|
options = copy.deepcopy(CPU_DOP_OPTIONS)
|
|
self._run_query_and_verify_profile(high_scan_cost_query, options,
|
|
["Executor Group: root.large-group", "ExecutorGroupsConsidered: 3",
|
|
"Verdict: Match", "CpuAsk: 6"],
|
|
["CpuAskUnbounded:"])
|
|
|
|
# Test that high_scan_cost_query will get assigned to the small group
|
|
# if NUM_SCANNER_THREADS is limited to 1.
|
|
options['NUM_SCANNER_THREADS'] = '1'
|
|
self._run_query_and_verify_profile(high_scan_cost_query, options,
|
|
["Executor Group: root.small-group", "ExecutorGroupsConsidered: 2",
|
|
"Verdict: Match", "CpuAsk: 4", "CpuAskUnbounded: 4"])
|
|
|
|
self.client.close()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_per_exec_group_set_metrics(self):
|
|
"""This test verifies that the metrics for each exec group set are updated
|
|
appropriately."""
|
|
self._restart_coordinators(num_coordinators=1,
|
|
extra_args="-expected_executor_group_sets=root.queue1:2,root.queue2:1")
|
|
|
|
# Add an unhealthy exec group in queue1 group set
|
|
self._add_executor_group("group", 2, num_executors=1,
|
|
resource_pool="root.queue1", extra_args="-mem_limit=2g")
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.queue1") == 0
|
|
assert self._get_num_executor_groups(exec_group_set_prefix="root.queue1") == 1
|
|
assert self.coordinator.service.get_metric_value(
|
|
"cluster-membership.group-set.backends.total.root.queue1") == 1
|
|
|
|
# Add another executor to the previous group to make healthy again
|
|
self._add_executor_group("group", 2, num_executors=1,
|
|
resource_pool="root.queue1", extra_args="-mem_limit=2g")
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.queue1") == 1
|
|
assert self._get_num_executor_groups(exec_group_set_prefix="root.queue1") == 1
|
|
assert self.coordinator.service.get_metric_value(
|
|
"cluster-membership.group-set.backends.total.root.queue1") == 2
|
|
|
|
# Add a healthy exec group in queue2 group set
|
|
self._add_executor_group("group", 1,
|
|
resource_pool="root.queue2", extra_args="-mem_limit=2g")
|
|
assert self._get_num_executor_groups(only_healthy=True,
|
|
exec_group_set_prefix="root.queue2") == 1
|
|
assert self._get_num_executor_groups(exec_group_set_prefix="root.queue2") == 1
|
|
assert self.coordinator.service.get_metric_value(
|
|
"cluster-membership.group-set.backends.total.root.queue2") == 1
|
|
|
|
def _setup_two_coordinator_two_exec_group_cluster(self, coordinator_test_args):
|
|
"""Start a cluster with two coordinators and two executor groups that mapped to
|
|
the same request pool 'root.queue1'."""
|
|
RESOURCES_DIR = os.path.join(os.environ['IMPALA_HOME'], "fe", "src", "test",
|
|
"resources")
|
|
fs_allocation_path = os.path.join(RESOURCES_DIR, "fair-scheduler-allocation.xml")
|
|
llama_site_path = os.path.join(RESOURCES_DIR, "llama-site-empty.xml")
|
|
# Start with a regular admission config with multiple pools and no resource limits.
|
|
self._restart_coordinators(num_coordinators=2,
|
|
extra_args="-fair_scheduler_allocation_path %s "
|
|
"-llama_site_path %s %s" %
|
|
(fs_allocation_path, llama_site_path, coordinator_test_args))
|
|
# Add two executor groups with 2 admission slots and 1 executor.
|
|
self._add_executor_group("group1", min_size=1, admission_control_slots=2,
|
|
resource_pool="root.queue1")
|
|
self._add_executor_group("group2", min_size=1, admission_control_slots=2,
|
|
resource_pool="root.queue1")
|
|
assert self._get_num_executor_groups(only_healthy=True) == 2
|
|
|
|
def _execute_query_async_using_client_and_verify_exec_group(self, client, query,
|
|
config_options, expected_group_str):
|
|
"""Execute 'query' asynchronously using 'client' with given 'config_options'.
|
|
Assert existence of expected_group_str in query profile."""
|
|
client.set_configuration(config_options)
|
|
query_handle = client.execute_async(query)
|
|
self.wait_for_state(query_handle, client.QUERY_STATES['RUNNING'], 30, client=client)
|
|
assert expected_group_str in client.get_runtime_profile(query_handle)
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_default_assign_policy_with_multiple_exec_groups_and_coordinators(self):
|
|
"""Tests that the default admission control assign policy is filling up executor
|
|
groups one by one."""
|
|
# A long running query that runs on every executor
|
|
QUERY = "select * from functional_parquet.alltypes \
|
|
where month < 3 and id + random() < sleep(100);"
|
|
coordinator_test_args = ""
|
|
self._setup_two_coordinator_two_exec_group_cluster(coordinator_test_args)
|
|
# Create fresh clients
|
|
self.create_impala_clients()
|
|
second_coord_client = self.create_client_for_nth_impalad(1)
|
|
# Check that the first two queries both run in 'group1'.
|
|
self._execute_query_async_using_client_and_verify_exec_group(self.client,
|
|
QUERY, {'request_pool': 'queue1'}, "Executor Group: root.queue1-group1")
|
|
self._execute_query_async_using_client_and_verify_exec_group(second_coord_client,
|
|
QUERY, {'request_pool': 'queue1'}, "Executor Group: root.queue1-group1")
|
|
self.client.close()
|
|
second_coord_client.close()
|
|
|
|
@pytest.mark.execute_serially
|
|
def test_load_balancing_with_multiple_exec_groups_and_coordinators(self):
|
|
"""Tests that the admission controller balance queries across multiple
|
|
executor groups that mapped to the same request pool when setting
|
|
balance_queries_across_executor_groups true."""
|
|
# A long running query that runs on every executor
|
|
QUERY = "select * from functional_parquet.alltypes \
|
|
where month < 3 and id + random() < sleep(100);"
|
|
coordinator_test_args = "-balance_queries_across_executor_groups=true"
|
|
self._setup_two_coordinator_two_exec_group_cluster(coordinator_test_args)
|
|
# Create fresh clients
|
|
self.create_impala_clients()
|
|
second_coord_client = self.create_client_for_nth_impalad(1)
|
|
# Check that two queries run in two different groups.
|
|
self._execute_query_async_using_client_and_verify_exec_group(self.client,
|
|
QUERY, {'request_pool': 'queue1'}, "Executor Group: root.queue1-group1")
|
|
self._execute_query_async_using_client_and_verify_exec_group(second_coord_client,
|
|
QUERY, {'request_pool': 'queue1'}, "Executor Group: root.queue1-group2")
|
|
self.client.close()
|
|
second_coord_client.close()
|