impala/tests/common/test_dimensions.py

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# Common test dimensions and associated utility functions.

from __future__ import absolute_import, division, print_function
from builtins import range
import copy
import os
from itertools import product

from tests.common.test_vector import (
    ImpalaTestDimension, ImpalaTestVector, assert_exec_option_key)
from tests.util.filesystem_utils import (
    IS_HDFS)

WORKLOAD_DIR = os.environ['IMPALA_WORKLOAD_DIR']

# Map from the test dimension file_format string to the SQL "STORED AS" or "STORED BY"
# argument.
FILE_FORMAT_TO_STORED_AS_MAP = {
  'text': 'TEXTFILE',
  'seq': 'SEQUENCEFILE',
  'rc': 'RCFILE',
  'orc': 'ORC',
  'parquet': 'PARQUET',
  'hudiparquet': 'HUDIPARQUET',
  'avro': 'AVRO',
  'hbase': "'org.apache.hadoop.hive.hbase.HBaseStorageHandler'",
  'kudu': "KUDU",
  'iceberg': "ICEBERG",
  'json': "JSONFILE",
}


# Describes the configuration used to execute a single tests. Contains both the details
# of what specific table format to target along with the exec options (num_nodes, etc)
# to use when running the query.
class TableFormatInfo(object):
  KNOWN_FILE_FORMATS = ['text', 'seq', 'rc', 'parquet', 'orc', 'avro', 'hbase',
                        'kudu', 'iceberg', 'json']
  KNOWN_COMPRESSION_CODECS = ['none', 'snap', 'gzip', 'bzip', 'def', 'zstd', 'lz4']
  KNOWN_COMPRESSION_TYPES = ['none', 'block', 'record']

  def __init__(self, **kwargs):
    self.dataset = kwargs.get('dataset', 'UNKNOWN')
    self.file_format = kwargs.get('file_format', 'text')
    self.compression_codec = kwargs.get('compression_codec', 'none')
    self.compression_type = kwargs.get('compression_type', 'none')
    self.__validate()

  def __validate(self):
    if self.file_format not in TableFormatInfo.KNOWN_FILE_FORMATS:
      raise ValueError('Unknown file format: %s' % self.file_format)
    if self.compression_codec not in TableFormatInfo.KNOWN_COMPRESSION_CODECS:
      raise ValueError('Unknown compression codec: %s' % self.compression_codec)
    if self.compression_type not in TableFormatInfo.KNOWN_COMPRESSION_TYPES:
      raise ValueError('Unknown compression type: %s' % self.compression_type)
    if (self.compression_codec == 'none' or self.compression_type == 'none') and\
        self.compression_codec != self.compression_type:
      raise ValueError('Invalid combination of compression codec/type: %s' % str(self))

  @staticmethod
  def create_from_string(dataset, table_format_string):
    """
    Parses a table format string and creates a table format info object from the string

    Expected  input is file_format/compression_codec/[compression_type]. The
    compression_type is optional, defaulting to 'block' if the table is compressed
    or 'none' if the table is uncompressed.
    """
    if table_format_string is None:
      raise ValueError('Table format string cannot be None')

    format_parts = table_format_string.strip().split('/')
    if len(format_parts) not in list(range(2, 4)):
      raise ValueError('Invalid table format %s' % table_format_string)

    file_format, compression_codec = format_parts[:2]
    if len(format_parts) == 3:
      compression_type = format_parts[2]
    else:
      # Assume the default compression type is block (of the table is compressed)
      compression_type = 'none' if compression_codec == 'none' else 'block'

    return TableFormatInfo(dataset=dataset, file_format=file_format,
                           compression_codec=compression_codec,
                           compression_type=compression_type)

  def __str__(self):
    compression_str = '%s/%s' % (self.compression_codec, self.compression_type)
    if self.compression_codec == 'none' and self.compression_type == 'none':
      compression_str = 'none'
    return '%s/%s' % (self.file_format, compression_str)

  def db_suffix(self):
    if self.file_format == 'text' and self.compression_codec == 'none':
      return ''
    elif self.compression_codec == 'none':
      return '_%s' % (self.file_format)
    elif self.compression_type == 'record':
      return '_%s_record_%s' % (self.file_format, self.compression_codec)
    else:
      return '_%s_%s' % (self.file_format, self.compression_codec)


def create_table_format_dimension(workload, table_format_string):
  dataset = get_dataset_from_workload(workload)
  return ImpalaTestDimension('table_format',
      TableFormatInfo.create_from_string(dataset, table_format_string))


def create_uncompressed_text_dimension(workload):
  return create_table_format_dimension(workload, 'text/none')


def create_uncompressed_json_dimension(workload):
  return create_table_format_dimension(workload, 'json/none')


def create_parquet_dimension(workload):
  return create_table_format_dimension(workload, 'parquet/none')


def create_orc_dimension(workload):
  return create_table_format_dimension(workload, 'orc/def')


def create_avro_snappy_dimension(workload):
  return create_table_format_dimension(workload, 'avro/snap/block')


def create_kudu_dimension(workload):
  return create_table_format_dimension(workload, 'kudu/none')


def create_client_protocol_dimension():
  # IMPALA-8864: Older python versions do not support SSLContext object that the thrift
  # http client implementation depends on. Falls back to a dimension without http
  # transport.
  import ssl
  if not hasattr(ssl, "create_default_context"):
    return ImpalaTestDimension('protocol', 'beeswax', 'hs2')
  return ImpalaTestDimension('protocol', 'beeswax', 'hs2', 'hs2-http')


def create_client_protocol_http_transport():
  return ImpalaTestDimension('protocol', 'hs2-http')


def create_client_protocol_strict_dimension():
  # only support strict dimensions if the file system is HDFS, since that is
  # where the hive cluster is run.
  if IS_HDFS:
    return ImpalaTestDimension('strict_hs2_protocol', False, True)
  else:
    return create_client_protocol_no_strict_dimension()


def create_client_protocol_no_strict_dimension():
  return ImpalaTestDimension('strict_hs2_protocol', False)


def hs2_parquet_constraint(v):
  """Constraint function, used to only run HS2 against Parquet format, because file format
  and the client protocol are orthogonal."""
  return (v.get_value('protocol') == 'beeswax' or
          v.get_value('table_format').file_format == 'parquet' and
          v.get_value('table_format').compression_codec == 'none')


def hs2_text_constraint(v):
  """Constraint function, used to only run HS2 against uncompressed text, because file
  format and the client protocol are orthogonal."""
  return (v.get_value('protocol') == 'beeswax' or
          v.get_value('table_format').file_format == 'text' and
          v.get_value('table_format').compression_codec == 'none')


def orc_schema_resolution_constraint(v):
  """ Constraint to use multiple orc_schema_resolution only in case of orc files"""
  file_format = v.get_value('table_format').file_format
  orc_schema_resolution = v.get_value('orc_schema_resolution')
  return file_format == 'orc' or orc_schema_resolution == 0


# Common sets of values for the exec option vectors
ALL_BATCH_SIZES = [0]

# Test SingleNode and Distributed Planners
ALL_CLUSTER_SIZES = [0, 1]

SINGLE_NODE_ONLY = [1]
ALL_NODES_ONLY = [0]
ALL_DISABLE_CODEGEN_OPTIONS = [True, False]


def create_single_exec_option_dimension(num_nodes=0, disable_codegen_rows_threshold=5000):
  """Creates an exec_option dimension that will produce a single test vector"""
  return create_exec_option_dimension(cluster_sizes=[num_nodes],
      disable_codegen_options=[False],
      # Make sure codegen kicks in for functional.alltypes.
      disable_codegen_rows_threshold_options=[disable_codegen_rows_threshold],
      batch_sizes=[0])


# TODO IMPALA-12394: switch to ALL_CLUSTER_SIZES
def create_exec_option_dimension(cluster_sizes=ALL_NODES_ONLY,
                                 disable_codegen_options=ALL_DISABLE_CODEGEN_OPTIONS,
                                 batch_sizes=ALL_BATCH_SIZES,
                                 sync_ddl=None, exec_single_node_option=[0],
                                 # We already run with codegen on and off explicitly -
                                 # don't need automatic toggling.
                                 disable_codegen_rows_threshold_options=[0],
                                 debug_action_options=None):
  exec_option_dimensions = {
      'abort_on_error': [1],
      'exec_single_node_rows_threshold': exec_single_node_option,
      'batch_size': batch_sizes,
      'disable_codegen': disable_codegen_options,
      'disable_codegen_rows_threshold': disable_codegen_rows_threshold_options,
      'num_nodes': cluster_sizes,
      'test_replan': [1]}

  if sync_ddl is not None:
    exec_option_dimensions['sync_ddl'] = sync_ddl
  if debug_action_options is not None:
    exec_option_dimensions['debug_action'] = debug_action_options
  return create_exec_option_dimension_from_dict(exec_option_dimensions)


def create_exec_option_dimension_from_dict(exec_option_dimensions):
  """
  Builds a query exec option test dimension

  Exhaustively goes through all combinations of the given query option values.
  For each combination create an exec option dictionary and add it as a value in the
  exec option test dimension. Each dictionary can then be passed via Beeswax to control
  Impala query execution behavior.

  TODO: In the future we could generate these values using pairwise to reduce total
  execution time.
  """
  # Generate the cross product (all combinations) of the exec options specified. Then
  # store them in exec_option dictionary format.
  keys = sorted(exec_option_dimensions)
  for name in keys:
    assert_exec_option_key(name)
  combinations = product(*(exec_option_dimensions[name] for name in keys))
  exec_option_dimension_values = [dict(zip(keys, prod)) for prod in combinations]

  # Build a test vector out of it
  return ImpalaTestDimension('exec_option', *exec_option_dimension_values)


def add_exec_option_dimension(test_suite, key, values):
  """
  Takes an ImpalaTestSuite object 'test_suite' and register new exec option dimension.
  'key' must be a query option known to Impala, and 'values' must be a list of more than
  one element. Exec option 'key' must not be declared before.
  If writing constraint against 'key', the value should be looked up at:
    vector.get_value('key')
  instead of:
    vector.get_value('exec_option')['key']
  """
  test_suite.ImpalaTestMatrix.add_exec_option_dimension(
    ImpalaTestDimension(key, *values))


def add_mandatory_exec_option(test_suite, key, value):
  """
  Takes an ImpalaTestSuite object 'test_suite' and adds 'key=value' to every exec option
  test dimension, leaving the number of tests that will be run unchanged. Exec option
  'key' must not be declared before.
  """
  test_suite.ImpalaTestMatrix.add_mandatory_exec_option(key, value)


def extend_exec_option_dimension(test_suite, key, value):
  """
  Takes an ImpalaTestSuite object 'test_suite' and extends the exec option test dimension
  by creating a copy of each existing exec option value that has 'key' set to 'value',
  doubling the number of tests that will be run. Exec option 'key' must not be declared
  before.
  """
  test_suite.ImpalaTestMatrix.assert_unique_exec_option_key(key)
  dim = test_suite.ImpalaTestMatrix.dimensions["exec_option"]
  new_value = []
  for v in dim:
    new_value.append(ImpalaTestVector.Value(v.name, copy.copy(v.value)))
    new_value[-1].value[key] = value
  dim.extend(new_value)
  test_suite.ImpalaTestMatrix.add_dimension(dim)


def get_dataset_from_workload(workload):
  # TODO: We need a better way to define the workload -> dataset mapping so we can
  # extract it without reading the actual test vector file
  return load_table_info_dimension(workload, 'exhaustive')[0].value.dataset


def load_table_info_dimension(workload_name, exploration_strategy, file_formats=None,
                      compression_codecs=None):
  """Loads test vector corresponding to the given workload and exploration strategy"""
  test_vector_file = os.path.join(
      WORKLOAD_DIR, workload_name, '%s_%s.csv' % (workload_name, exploration_strategy))

  if not os.path.isfile(test_vector_file):
    raise RuntimeError('Vector file not found: ' + test_vector_file)

  vector_values = []

  with open(test_vector_file, 'r') as vector_file:
    for line in vector_file.readlines():
      if line.strip().startswith('#'):
        continue

      # Extract each test vector and add them to a dictionary
      vals = dict((key.strip(), value.strip()) for key, value in
          (item.split(':') for item in line.split(',')))

      # If only loading specific file formats skip anything that doesn't match
      if file_formats is not None and vals['file_format'] not in file_formats:
        continue
      if compression_codecs is not None and\
         vals['compression_codec'] not in compression_codecs:
        continue
      vector_values.append(TableFormatInfo(**vals))

  return ImpalaTestDimension('table_format', *vector_values)


def is_supported_insert_format(table_format):
  # Returns true if the given table_format is a supported Impala INSERT format
  return table_format.compression_codec == 'none' and\
      table_format.file_format in ['text', 'parquet']