impala/tests/query_test/test_fetch.py

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from __future__ import absolute_import, division, print_function
import re

from time import sleep
from tests.common.impala_test_suite import ImpalaTestSuite
from tests.common.test_dimensions import extend_exec_option_dimension
from tests.util.parse_util import parse_duration_string_ms, \
    parse_duration_string_ns, get_time_summary_stats_counter


class TestFetch(ImpalaTestSuite):
  """Tests that are independent of whether result spooling is enabled or not."""

  @classmethod
  def add_test_dimensions(cls):
    super(TestFetch, cls).add_test_dimensions()
    # Result fetching should be independent of file format, so only test against
    # Parquet files.
    cls.ImpalaTestMatrix.add_constraint(lambda v:
        v.get_value('table_format').file_format == 'parquet')

  @classmethod
  def get_workload(cls):
    return 'functional-query'

  def test_rows_sent_counters(self, vector):
    """Validate that ClientFetchWaitTimer, NumRowsFetched, RowMaterializationRate,
    and RowMaterializationTimer are set to valid values in the ImpalaServer section
    of the runtime profile."""
    num_rows = 25
    query = "select sleep(100) from functional.alltypes limit {0}".format(num_rows)
    handle = self.execute_query_async(query, vector.get_value('exec_option'))
    try:
      # Wait until the query is 'FINISHED' and results are available for fetching.
      self.wait_for_state(handle, self.client.QUERY_STATES['FINISHED'], 30)
      # Sleep for 2.5 seconds so that the ClientFetchWaitTimer is >= 1s.
      sleep(2.5)
      # Fetch the results so that the fetch related counters are updated.
      assert self.client.fetch(query, handle).success

      runtime_profile = self.client.get_runtime_profile(handle)
      fetch_timer = re.search("ClientFetchWaitTimer: (.*)", runtime_profile)
      assert fetch_timer and len(fetch_timer.groups()) == 1 and \
          parse_duration_string_ms(fetch_timer.group(1)) > 1000
      assert "NumRowsFetched: {0} ({0})".format(num_rows) in runtime_profile
      assert re.search("RowMaterializationRate: [1-9]", runtime_profile)
      # The query should take at least 1s to materialize all rows since it should sleep
      # for at least 1s during materialization.
      materialization_timer = re.search("RowMaterializationTimer: (.*)", runtime_profile)
      assert materialization_timer and len(materialization_timer.groups()) == 1 and \
          parse_duration_string_ms(materialization_timer.group(1)) > 1000
      assert re.search("RPCCount: [5-9]", runtime_profile)

      rpc_read_timer = re.search("RPCReadTimer: (.*)", runtime_profile)
      assert rpc_read_timer and len(rpc_read_timer.groups()) == 1
      rpc_read_ns = parse_duration_string_ns(rpc_read_timer.group(1))
      assert 0 < rpc_read_ns and rpc_read_ns < 1000000

      rpc_write_timer = re.search("RPCWriteTimer: (.*)", runtime_profile)
      assert rpc_write_timer and len(rpc_write_timer.groups()) == 1
      rpc_write_ns = parse_duration_string_ns(rpc_write_timer.group(1))
      assert 0 < rpc_write_ns and rpc_write_ns < 10000000

      create_result_time = re.search("CreateResultSetTime: (.*)", runtime_profile)
      assert create_result_time and len(create_result_time.groups()) == 1
      create_result_ms = parse_duration_string_ms(create_result_time.group(1))
      assert 2400 < create_result_ms and create_result_ms < 2600

    finally:
      self.client.close_query(handle)

  def test_client_fetch_time_stats(self, vector):
    num_rows = 27
    query = "select sleep(10) from functional.alltypes limit {0}".format(num_rows)
    handle = self.execute_query_async(query, vector.get_value('exec_option'))
    try:
      # Wait until the query is 'FINISHED' and results are available for fetching.
      self.wait_for_state(handle, self.client.QUERY_STATES['FINISHED'], 30)

      # This loop will do 6 fetches that contain data and a final fetch with
      # no data. The last fetch is after eos has been set, so it does not count.
      rows_fetched = 0
      while True:
        result = self.client.fetch(query, handle, max_rows=5)
        assert result.success
        rows_fetched += len(result.data)
        # If no rows are returned, we are done.
        if len(result.data) == 0:
          break
        sleep(0.1)

      # After fetching all rows, sleep before closing the query. This should not
      # count as client wait time, because the query is already done.
      sleep(2.5)
    finally:
      self.client.close_query(handle)

    runtime_profile = self.client.get_runtime_profile(handle)
    summary_stats = get_time_summary_stats_counter("ClientFetchWaitTimeStats",
                                                   runtime_profile)
    assert len(summary_stats) == 1
    assert summary_stats[0].total_num_values == 6
    # The 2.5 second sleep should not count, so the max must be less than 2.5 seconds.
    assert summary_stats[0].max_value < 2500000000
    assert summary_stats[0].min_value > 0

  def test_client_fetch_time_stats_incomplete(self, vector):
    num_rows = 27
    query = "select sleep(10) from functional.alltypes limit {0}".format(num_rows)
    handle = self.execute_query_async(query, vector.get_value('exec_option'))
    try:
      # Wait until the query is 'FINISHED' and results are available for fetching.
      self.wait_for_state(handle, self.client.QUERY_STATES['FINISHED'], 30)

      # This loop will do 5 fetches for a total of 25 rows. This is incomplete.
      for i in range(5):
        result = self.client.fetch(query, handle, max_rows=5)
        assert result.success
        sleep(0.1)

      # Sleep before closing the query. For an incomplete fetch, this still counts
      # towards the query time, so this does show up in the counters.
      sleep(2.5)
    finally:
      self.client.close_query(handle)

    runtime_profile = self.client.get_runtime_profile(handle)

    summary_stats = get_time_summary_stats_counter("ClientFetchWaitTimeStats",
                                                   runtime_profile)
    assert len(summary_stats) == 1
    # There are 5 fetches and the finalization sample for a total of 6.
    assert summary_stats[0].total_num_values == 6
    # The 2.5 second sleep does count for an incomplete fetch, verify the max is higher.
    assert summary_stats[0].max_value >= 2500000000
    assert summary_stats[0].min_value > 0


class TestFetchAndSpooling(ImpalaTestSuite):
  """Tests that apply when result spooling is enabled or disabled."""

  @classmethod
  def add_test_dimensions(cls):
    super(TestFetchAndSpooling, cls).add_test_dimensions()
    # Result fetching should be independent of file format, so only test against
    # Parquet files.
    cls.ImpalaTestMatrix.add_constraint(lambda v:
        v.get_value('table_format').file_format == 'parquet')
    # spool_query_results is set as true by default.
    extend_exec_option_dimension(cls, 'spool_query_results', 'false')

  @classmethod
  def get_workload(cls):
    return 'functional-query'

  def test_rows_sent_counters(self, vector):
    """Validate that RowsSent and RowsSentRate are set to valid values in
    the PLAN_ROOT_SINK section of the runtime profile."""
    num_rows = 10
    if ('spool_query_results' in vector.get_value('exec_option') and
          vector.get_value('exec_option')['spool_query_results'] == 'false'):
      vector.get_value('exec_option')['debug_action'] = "BPRS_BEFORE_ADD_ROWS:SLEEP@1000"
    else:
      vector.get_value('exec_option')['debug_action'] = "BPRS_BEFORE_ADD_BATCH:SLEEP@1000"
    result = self.execute_query("select id from functional.alltypes limit {0}"
        .format(num_rows), vector.get_value('exec_option'))
    assert "RowsSent: {0} ({0})".format(num_rows) in result.runtime_profile
    rows_sent_rate = re.search("RowsSentRate: (\d*\.?\d*)", result.runtime_profile)
    assert rows_sent_rate
    assert float(rows_sent_rate.group(1)) > 0


class TestFetchTimeout(ImpalaTestSuite):
  """A few basic tests for FETCH_ROWS_TIMEOUT_MS that are not specific to the HS2 protocol
  (e.g. in contrast to the tests in tests/hs2/test_fetch_timeout.py). These tests are
  necessary because part of the FETCH_ROWS_TIMEOUT_MS code is HS2/Beeswax specific.
  Unlike the tests in hs2/test_fetch_timeout.py, these tests do not validate that
  individual RPC calls timeout, instead they set a low value for the timeout and assert
  that the query works end-to-end."""

  @classmethod
  def add_test_dimensions(cls):
    super(TestFetchTimeout, cls).add_test_dimensions()
    # Result fetching should be independent of file format, so only test against
    # Parquet files.
    cls.ImpalaTestMatrix.add_constraint(lambda v:
        v.get_value('table_format').file_format == 'parquet')
    extend_exec_option_dimension(cls, 'spool_query_results', 'true')

  @classmethod
  def get_workload(cls):
    return 'functional-query'

  def test_fetch_timeout(self, vector):
    """A simple test that runs a query with a low timeout and introduces delays in
    RowBatch production. Asserts that the query succeeds and returns the expected number
    of rows."""
    num_rows = 100
    query = "select * from functional.alltypes limit {0}".format(num_rows)
    vector.get_value('exec_option')['batch_size'] = 1
    vector.get_value('exec_option')['fetch_rows_timeout_ms'] = 1
    vector.get_value('exec_option')['debug_action'] = '0:GETNEXT:DELAY'
    results = self.execute_query(query, vector.get_value('exec_option'))
    assert results.success
    assert len(results.data) == num_rows

  def test_fetch_before_finished_timeout(self, vector):
    """Tests that the FETCH_ROWS_TIMEOUT_MS timeout applies to queries that are not in
    the 'finished' state. Similar to the test tests/hs2/test_fetch_timeout.py::
    TestFetchTimeout::test_fetch_before_finished_timeout(_with_result_spooling)."""
    num_rows = 10
    query = "select * from functional.alltypes limit {0}".format(num_rows)
    vector.get_value('exec_option')['debug_action'] = 'CRS_BEFORE_COORD_STARTS:SLEEP@5000'
    vector.get_value('exec_option')['fetch_rows_timeout_ms'] = '1000'
    results = self.execute_query(query, vector.get_value('exec_option'))
    assert results.success
    assert len(results.data) == num_rows