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104 lines
4.0 KiB
Python
104 lines
4.0 KiB
Python
import threading
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import time
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import pytest
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from base.collection_wrapper import ApiCollectionWrapper
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from common.common_type import CaseLabel
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from common import common_func as cf
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from customize.milvus_operator import MilvusOperator
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from scale import constants
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from pymilvus import connections
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from utils.util_log import test_log as log
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from utils.util_k8s import wait_pods_ready
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prefix = "data_scale"
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default_schema = cf.gen_default_collection_schema()
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default_search_exp = "int64 >= 0"
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default_index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}}
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class TestDataNodeScale:
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@pytest.mark.tags(CaseLabel.L3)
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def test_scale_data_node(self):
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"""
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target: test scale dataNode
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method: 1.deploy milvus cluster with 2 dataNode
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2.create collection with shards_num=5
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3.continuously insert new data (daemon thread)
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4.expand dataNode from 2 to 5
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5.create new collection with shards_num=2
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6.continuously insert new collection new data (daemon thread)
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7.shrink dataNode from 5 to 3
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expected: Verify milvus remains healthy, Insert and flush successfully during scale
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Average dataNode memory usage
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"""
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release_name = "scale-data"
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image = f'{constants.IMAGE_REPOSITORY}:{constants.IMAGE_TAG}'
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data_config = {
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'metadata.namespace': constants.NAMESPACE,
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'metadata.name': release_name,
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'spec.components.image': image,
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'spec.components.proxy.serviceType': 'LoadBalancer',
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'spec.components.dataNode.replicas': 2,
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'spec.config.dataCoord.enableCompaction': True,
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'spec.config.dataCoord.enableGarbageCollection': True
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}
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mic = MilvusOperator()
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mic.install(data_config)
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healthy = mic.wait_for_healthy(release_name, constants.NAMESPACE, timeout=1200)
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log.info(f"milvus healthy: {healthy}")
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host = mic.endpoint(release_name, constants.NAMESPACE).split(':')[0]
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# host = '10.98.0.4'
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# connect
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connections.add_connection(default={"host": host, "port": 19530})
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connections.connect(alias='default')
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# create
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c_name = cf.gen_unique_str("scale_query")
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# c_name = 'scale_query_DymS7kI4'
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collection_w = ApiCollectionWrapper()
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collection_w.init_collection(name=c_name, schema=cf.gen_default_collection_schema(), shards_num=5)
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tmp_nb = 10000
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def do_insert():
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while True:
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tmp_df = cf.gen_default_dataframe_data(tmp_nb)
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collection_w.insert(tmp_df)
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log.debug(collection_w.num_entities)
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t_insert = threading.Thread(target=do_insert, args=(), daemon=True)
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t_insert.start()
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# scale dataNode to 5
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mic.upgrade(release_name, {'spec.components.dataNode.replicas': 5}, constants.NAMESPACE)
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time.sleep(300)
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log.debug("Expand dataNode test finished")
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# create new collection and insert
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new_c_name = cf.gen_unique_str("scale_query")
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collection_w_new = ApiCollectionWrapper()
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collection_w_new.init_collection(name=new_c_name, schema=cf.gen_default_collection_schema(), shards_num=2)
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def do_new_insert():
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while True:
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tmp_df = cf.gen_default_dataframe_data(tmp_nb)
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collection_w_new.insert(tmp_df)
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log.debug(collection_w_new.num_entities)
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t_insert_new = threading.Thread(target=do_new_insert, args=(), daemon=True)
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t_insert_new.start()
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# scale dataNode to 3
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mic.upgrade(release_name, {'spec.components.dataNode.replicas': 3}, constants.NAMESPACE)
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wait_pods_ready(constants.NAMESPACE, f"app.kubernetes.io/instance={release_name}")
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log.debug(collection_w.num_entities)
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time.sleep(300)
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log.debug("Shrink dataNode test finished")
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mic.uninstall(release_name, namespace=constants.NAMESPACE)
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