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eb66d00f9f
Python 3 changes list operators such as range, map, and filter to be lazy. Some code that expects the list operators to happen immediately will fail. e.g. Python 2: range(0,5) == [0,1,2,3,4] True Python 3: range(0,5) == [0,1,2,3,4] False The fix is to wrap locations with list(). i.e. Python 3: list(range(0,5)) == [0,1,2,3,4] True Since the base operators are now lazy, Python 3 also removes the old lazy versions (e.g. xrange, ifilter, izip, etc). This uses future's builtins package to convert the code to the Python 3 behavior (i.e. xrange -> future's builtins.range). Most of the changes were done via these futurize fixes: - libfuturize.fixes.fix_xrange_with_import - lib2to3.fixes.fix_map - lib2to3.fixes.fix_filter This eliminates the pylint warnings: - xrange-builtin - range-builtin-not-iterating - map-builtin-not-iterating - zip-builtin-not-iterating - filter-builtin-not-iterating - reduce-builtin - deprecated-itertools-function Testing: - Ran core job Change-Id: Ic7c082711f8eff451a1b5c085e97461c327edb5f Reviewed-on: http://gerrit.cloudera.org:8080/19589 Reviewed-by: Joe McDonnell <joemcdonnell@cloudera.com> Tested-by: Joe McDonnell <joemcdonnell@cloudera.com>
94 lines
4.1 KiB
Python
94 lines
4.1 KiB
Python
# 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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import os
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import pytest
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from subprocess import call
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from tests.common.custom_cluster_test_suite import CustomClusterTestSuite
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from tests.common.skip import SkipIf
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TBL_NAME = "widetable_2000_cols_partitioned"
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NUM_PARTS = 50000
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@SkipIf.not_hdfs
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class TestWideTableOperations(CustomClusterTestSuite):
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@classmethod
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def get_workload(cls):
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return 'functional-query'
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@classmethod
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def setup_class(cls):
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if cls.exploration_strategy() != 'exhaustive':
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pytest.skip('runs only in exhaustive since it takes more than 20 mins')
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super(TestWideTableOperations, cls).setup_class()
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@pytest.mark.execute_serially
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@CustomClusterTestSuite.with_args(
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jvm_args="-Xmx2g -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath="
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+ os.getenv("LOG_DIR", "/tmp"))
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def test_wide_table_operations(self, vector, unique_database):
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"""Regression test for IMPALA-11812. Test DDL/DML operations on wide table.
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Use a small heap size (2GB) to make sure memory consumption is optimized.
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Each FieldSchema instance takes 24 bytes in a small heap (<32GB). Without the fix,
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catalogd will hold at least 50,000 (parts) * 2,000 (cols) = 100,000,000 FieldSchema
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instances in memory for execDdl or table loading, which already takes more than 2GB
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and will results in OOM failures."""
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# Create partition dirs and files locally
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tmp_dir = "/tmp/" + TBL_NAME
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os.mkdir(tmp_dir)
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for i in range(NUM_PARTS):
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part_dir = tmp_dir + "/p=" + str(i)
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data_file = part_dir + "/data.txt"
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os.mkdir(part_dir)
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with open(data_file, 'w') as local_file:
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local_file.write("true")
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# Upload files to HDFS
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hdfs_dir = self._get_table_location("functional." + TBL_NAME, vector)
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call(["hdfs", "dfs", "-rm", "-r", "-skipTrash", hdfs_dir])
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# Use 1 replica to save space, 8 threads to speed up
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call(["hdfs", "dfs", "-Ddfs.replication=1", "-put", "-t", "8", tmp_dir, hdfs_dir])
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# Create a new table so we don't need to drop partitions at the end.
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# It will be dropped when 'unique_database' is dropped.
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create_tbl_ddl =\
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"create external table {db}.{tbl} like functional.{tbl} " \
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"location '{location}'".format(
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db=unique_database, tbl=TBL_NAME, location=hdfs_dir)
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self.execute_query_expect_success(
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self.client, create_tbl_ddl.format(db=unique_database, tbl=TBL_NAME))
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# Recover partitions first. This takes 10mins for 50k partitions.
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recover_stmt = "alter table {db}.{tbl} recover partitions"
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# Invalidate the table to test initial metadata loading
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invalidate_stmt = "invalidate metadata {db}.{tbl}"
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# Test initial table loading and get all partitions
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show_parts_stmt = "show partitions {db}.{tbl}"
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try:
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self.execute_query_expect_success(
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self.client, recover_stmt.format(db=unique_database, tbl=TBL_NAME))
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self.execute_query_expect_success(
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self.client, invalidate_stmt.format(db=unique_database, tbl=TBL_NAME))
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res = self.execute_query_expect_success(
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self.client, show_parts_stmt.format(db=unique_database, tbl=TBL_NAME))
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# Last line is 'Total'
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assert len(res.data) == NUM_PARTS + 1
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finally:
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call(["rm", "-rf", tmp_dir])
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call(["hdfs", "dfs", "-rm", "-r", "-skipTrash", hdfs_dir])
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