incubator-superset0.99/superset/db_engine_specs/presto.py

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# to you under the Apache License, Version 2.0 (the
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import logging
import re
import textwrap
import time
from collections import defaultdict, deque
from contextlib import closing
from datetime import datetime
from distutils.version import StrictVersion
from typing import Any, cast, Dict, List, Optional, Tuple, TYPE_CHECKING
from urllib import parse

import pandas as pd
import simplejson as json
from sqlalchemy import Column, literal_column
from sqlalchemy.engine.base import Engine
from sqlalchemy.engine.reflection import Inspector
from sqlalchemy.engine.result import RowProxy
from sqlalchemy.engine.url import URL
from sqlalchemy.orm import Session
from sqlalchemy.sql.expression import ColumnClause, Select

from superset import app, cache, is_feature_enabled, security_manager
from superset.db_engine_specs.base import BaseEngineSpec
from superset.exceptions import SupersetTemplateException
from superset.models.sql_lab import Query
from superset.models.sql_types.presto_sql_types import type_map as presto_type_map
from superset.sql_parse import ParsedQuery
from superset.utils import core as utils

if TYPE_CHECKING:
    # prevent circular imports
    from superset.models.core import Database  # pylint: disable=unused-import

QueryStatus = utils.QueryStatus
config = app.config
logger = logging.getLogger(__name__)


def get_children(column: Dict[str, str]) -> List[Dict[str, str]]:
    """
    Get the children of a complex Presto type (row or array).

    For arrays, we return a single list with the base type:

        >>> get_children(dict(name="a", type="ARRAY(BIGINT)"))
        [{"name": "a", "type": "BIGINT"}]

    For rows, we return a list of the columns:

        >>> get_children(dict(name="a", type="ROW(BIGINT,FOO VARCHAR)"))
        [{'name': 'a._col0', 'type': 'BIGINT'}, {'name': 'a.foo', 'type': 'VARCHAR'}]

    :param column: dictionary representing a Presto column
    :return: list of dictionaries representing children columns
    """
    pattern = re.compile(r"(?P<type>\w+)\((?P<children>.*)\)")
    match = pattern.match(column["type"])
    if not match:
        raise Exception(f"Unable to parse column type {column['type']}")

    group = match.groupdict()
    type_ = group["type"].upper()
    children_type = group["children"]
    if type_ == "ARRAY":
        return [{"name": column["name"], "type": children_type}]
    elif type_ == "ROW":
        nameless_columns = 0
        columns = []
        for child in utils.split(children_type, ","):
            parts = list(utils.split(child.strip(), " "))
            if len(parts) == 2:
                name, type_ = parts
                name = name.strip('"')
            else:
                name = f"_col{nameless_columns}"
                type_ = parts[0]
                nameless_columns += 1
            columns.append({"name": f"{column['name']}.{name.lower()}", "type": type_})
        return columns
    else:
        raise Exception(f"Unknown type {type_}!")


class PrestoEngineSpec(BaseEngineSpec):
    engine = "presto"

    _time_grain_functions = {
        None: "{col}",
        "PT1S": "date_trunc('second', CAST({col} AS TIMESTAMP))",
        "PT1M": "date_trunc('minute', CAST({col} AS TIMESTAMP))",
        "PT1H": "date_trunc('hour', CAST({col} AS TIMESTAMP))",
        "P1D": "date_trunc('day', CAST({col} AS TIMESTAMP))",
        "P1W": "date_trunc('week', CAST({col} AS TIMESTAMP))",
        "P1M": "date_trunc('month', CAST({col} AS TIMESTAMP))",
        "P0.25Y": "date_trunc('quarter', CAST({col} AS TIMESTAMP))",
        "P1Y": "date_trunc('year', CAST({col} AS TIMESTAMP))",
        "P1W/1970-01-03T00:00:00Z": "date_add('day', 5, date_trunc('week', "
        "date_add('day', 1, CAST({col} AS TIMESTAMP))))",
        "1969-12-28T00:00:00Z/P1W": "date_add('day', -1, date_trunc('week', "
        "date_add('day', 1, CAST({col} AS TIMESTAMP))))",
    }

    @classmethod
    def get_allow_cost_estimate(cls, version: Optional[str] = None) -> bool:
        return version is not None and StrictVersion(version) >= StrictVersion("0.319")

    @classmethod
    def get_table_names(
        cls, database: "Database", inspector: Inspector, schema: Optional[str]
    ) -> List[str]:
        tables = super().get_table_names(database, inspector, schema)
        if not is_feature_enabled("PRESTO_SPLIT_VIEWS_FROM_TABLES"):
            return tables

        views = set(cls.get_view_names(database, inspector, schema))
        actual_tables = set(tables) - views
        return list(actual_tables)

    @classmethod
    def get_view_names(
        cls, database: "Database", inspector: Inspector, schema: Optional[str]
    ) -> List[str]:
        """Returns an empty list

        get_table_names() function returns all table names and view names,
        and get_view_names() is not implemented in sqlalchemy_presto.py
        https://github.com/dropbox/PyHive/blob/e25fc8440a0686bbb7a5db5de7cb1a77bdb4167a/pyhive/sqlalchemy_presto.py
        """
        if not is_feature_enabled("PRESTO_SPLIT_VIEWS_FROM_TABLES"):
            return []

        if schema:
            sql = (
                "SELECT table_name FROM information_schema.views "
                "WHERE table_schema=%(schema)s"
            )
            params = {"schema": schema}
        else:
            sql = "SELECT table_name FROM information_schema.views"
            params = {}

        engine = cls.get_engine(database, schema=schema)
        with closing(engine.raw_connection()) as conn:
            with closing(conn.cursor()) as cursor:
                cursor.execute(sql, params)
                results = cursor.fetchall()

        return [row[0] for row in results]

    @classmethod
    def _create_column_info(cls, name: str, data_type: str) -> dict:
        """
        Create column info object
        :param name: column name
        :param data_type: column data type
        :return: column info object
        """
        return {"name": name, "type": f"{data_type}"}

    @classmethod
    def _get_full_name(cls, names: List[Tuple[str, str]]) -> str:
        """
        Get the full column name
        :param names: list of all individual column names
        :return: full column name
        """
        return ".".join(column[0] for column in names if column[0])

    @classmethod
    def _has_nested_data_types(cls, component_type: str) -> bool:
        """
        Check if string contains a data type. We determine if there is a data type by
        whitespace or multiple data types by commas
        :param component_type: data type
        :return: boolean
        """
        comma_regex = r",(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)"
        white_space_regex = r"\s(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)"
        return (
            re.search(comma_regex, component_type) is not None
            or re.search(white_space_regex, component_type) is not None
        )

    @classmethod
    def _split_data_type(cls, data_type: str, delimiter: str) -> List[str]:
        """
        Split data type based on given delimiter. Do not split the string if the
        delimiter is enclosed in quotes
        :param data_type: data type
        :param delimiter: string separator (i.e. open parenthesis, closed parenthesis,
               comma, whitespace)
        :return: list of strings after breaking it by the delimiter
        """
        return re.split(
            r"{}(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)".format(delimiter), data_type
        )

    @classmethod
    def _parse_structural_column(  # pylint: disable=too-many-locals,too-many-branches
        cls, parent_column_name: str, parent_data_type: str, result: List[dict]
    ) -> None:
        """
        Parse a row or array column
        :param result: list tracking the results
        """
        formatted_parent_column_name = parent_column_name
        # Quote the column name if there is a space
        if " " in parent_column_name:
            formatted_parent_column_name = f'"{parent_column_name}"'
        full_data_type = f"{formatted_parent_column_name} {parent_data_type}"
        original_result_len = len(result)
        # split on open parenthesis ( to get the structural
        # data type and its component types
        data_types = cls._split_data_type(full_data_type, r"\(")
        stack: List[Tuple[str, str]] = []
        for data_type in data_types:
            # split on closed parenthesis ) to track which component
            # types belong to what structural data type
            inner_types = cls._split_data_type(data_type, r"\)")
            for inner_type in inner_types:
                # We have finished parsing multiple structural data types
                if not inner_type and stack:
                    stack.pop()
                elif cls._has_nested_data_types(inner_type):
                    # split on comma , to get individual data types
                    single_fields = cls._split_data_type(inner_type, ",")
                    for single_field in single_fields:
                        single_field = single_field.strip()
                        # If component type starts with a comma, the first single field
                        # will be an empty string. Disregard this empty string.
                        if not single_field:
                            continue
                        # split on whitespace to get field name and data type
                        field_info = cls._split_data_type(single_field, r"\s")
                        # check if there is a structural data type within
                        # overall structural data type
                        if field_info[1] == "array" or field_info[1] == "row":
                            stack.append((field_info[0], field_info[1]))
                            full_parent_path = cls._get_full_name(stack)
                            result.append(
                                cls._create_column_info(
                                    full_parent_path, presto_type_map[field_info[1]]()
                                )
                            )
                        else:  # otherwise this field is a basic data type
                            full_parent_path = cls._get_full_name(stack)
                            column_name = "{}.{}".format(
                                full_parent_path, field_info[0]
                            )
                            result.append(
                                cls._create_column_info(
                                    column_name, presto_type_map[field_info[1]]()
                                )
                            )
                    # If the component type ends with a structural data type, do not pop
                    # the stack. We have run across a structural data type within the
                    # overall structural data type. Otherwise, we have completely parsed
                    # through the entire structural data type and can move on.
                    if not (inner_type.endswith("array") or inner_type.endswith("row")):
                        stack.pop()
                # We have an array of row objects (i.e. array(row(...)))
                elif inner_type == "array" or inner_type == "row":
                    # Push a dummy object to represent the structural data type
                    stack.append(("", inner_type))
                # We have an array of a basic data types(i.e. array(varchar)).
                elif stack:
                    # Because it is an array of a basic data type. We have finished
                    # parsing the structural data type and can move on.
                    stack.pop()
        # Unquote the column name if necessary
        if formatted_parent_column_name != parent_column_name:
            for index in range(original_result_len, len(result)):
                result[index]["name"] = result[index]["name"].replace(
                    formatted_parent_column_name, parent_column_name
                )

    @classmethod
    def _show_columns(
        cls, inspector: Inspector, table_name: str, schema: Optional[str]
    ) -> List[RowProxy]:
        """
        Show presto column names
        :param inspector: object that performs database schema inspection
        :param table_name: table name
        :param schema: schema name
        :return: list of column objects
        """
        quote = inspector.engine.dialect.identifier_preparer.quote_identifier
        full_table = quote(table_name)
        if schema:
            full_table = "{}.{}".format(quote(schema), full_table)
        columns = inspector.bind.execute("SHOW COLUMNS FROM {}".format(full_table))
        return columns

    @classmethod
    def get_columns(
        cls, inspector: Inspector, table_name: str, schema: Optional[str]
    ) -> List[Dict[str, Any]]:
        """
        Get columns from a Presto data source. This includes handling row and
        array data types
        :param inspector: object that performs database schema inspection
        :param table_name: table name
        :param schema: schema name
        :return: a list of results that contain column info
                (i.e. column name and data type)
        """
        columns = cls._show_columns(inspector, table_name, schema)
        result: List[dict] = []
        for column in columns:
            try:
                # parse column if it is a row or array
                if is_feature_enabled("PRESTO_EXPAND_DATA") and (
                    "array" in column.Type or "row" in column.Type
                ):
                    structural_column_index = len(result)
                    cls._parse_structural_column(column.Column, column.Type, result)
                    result[structural_column_index]["nullable"] = getattr(
                        column, "Null", True
                    )
                    result[structural_column_index]["default"] = None
                    continue
                else:  # otherwise column is a basic data type
                    column_type = presto_type_map[column.Type]()
            except KeyError:
                logger.info(
                    "Did not recognize type {} of column {}".format(  # pylint: disable=logging-format-interpolation
                        column.Type, column.Column
                    )
                )
                column_type = "OTHER"
            column_info = cls._create_column_info(column.Column, column_type)
            column_info["nullable"] = getattr(column, "Null", True)
            column_info["default"] = None
            result.append(column_info)
        return result

    @classmethod
    def _is_column_name_quoted(cls, column_name: str) -> bool:
        """
        Check if column name is in quotes
        :param column_name: column name
        :return: boolean
        """
        return column_name.startswith('"') and column_name.endswith('"')

    @classmethod
    def _get_fields(cls, cols: List[dict]) -> List[ColumnClause]:
        """
        Format column clauses where names are in quotes and labels are specified
        :param cols: columns
        :return: column clauses
        """
        column_clauses = []
        # Column names are separated by periods. This regex will find periods in a
        # string if they are not enclosed in quotes because if a period is enclosed in
        # quotes, then that period is part of a column name.
        dot_pattern = r"""\.                # split on period
                          (?=               # look ahead
                          (?:               # create non-capture group
                          [^\"]*\"[^\"]*\"  # two quotes
                          )*[^\"]*$)        # end regex"""
        dot_regex = re.compile(dot_pattern, re.VERBOSE)
        for col in cols:
            # get individual column names
            col_names = re.split(dot_regex, col["name"])
            # quote each column name if it is not already quoted
            for index, col_name in enumerate(col_names):
                if not cls._is_column_name_quoted(col_name):
                    col_names[index] = '"{}"'.format(col_name)
            quoted_col_name = ".".join(
                col_name if cls._is_column_name_quoted(col_name) else f'"{col_name}"'
                for col_name in col_names
            )
            # create column clause in the format "name"."name" AS "name.name"
            column_clause = literal_column(quoted_col_name).label(col["name"])
            column_clauses.append(column_clause)
        return column_clauses

    @classmethod
    def select_star(  # pylint: disable=too-many-arguments
        cls,
        database: "Database",
        table_name: str,
        engine: Engine,
        schema: Optional[str] = None,
        limit: int = 100,
        show_cols: bool = False,
        indent: bool = True,
        latest_partition: bool = True,
        cols: Optional[List[Dict[str, Any]]] = None,
    ) -> str:
        """
        Include selecting properties of row objects. We cannot easily break arrays into
        rows, so render the whole array in its own row and skip columns that correspond
        to an array's contents.
        """
        cols = cols or []
        presto_cols = cols
        if is_feature_enabled("PRESTO_EXPAND_DATA") and show_cols:
            dot_regex = r"\.(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)"
            presto_cols = [
                col for col in presto_cols if not re.search(dot_regex, col["name"])
            ]
        return super().select_star(
            database,
            table_name,
            engine,
            schema,
            limit,
            show_cols,
            indent,
            latest_partition,
            presto_cols,
        )

    @classmethod
    def estimate_statement_cost(  # pylint: disable=too-many-locals
        cls, statement: str, database: "Database", cursor: Any, user_name: str
    ) -> Dict[str, Any]:
        """
        Run a SQL query that estimates the cost of a given statement.

        :param statement: A single SQL statement
        :param database: Database instance
        :param cursor: Cursor instance
        :param username: Effective username
        :return: JSON response from Presto
        """
        parsed_query = ParsedQuery(statement)
        sql = parsed_query.stripped()

        sql_query_mutator = config["SQL_QUERY_MUTATOR"]
        if sql_query_mutator:
            sql = sql_query_mutator(sql, user_name, security_manager, database)

        sql = f"EXPLAIN (TYPE IO, FORMAT JSON) {sql}"
        cursor.execute(sql)

        # the output from Presto is a single column and a single row containing
        # JSON:
        #
        #   {
        #     ...
        #     "estimate" : {
        #       "outputRowCount" : 8.73265878E8,
        #       "outputSizeInBytes" : 3.41425774958E11,
        #       "cpuCost" : 3.41425774958E11,
        #       "maxMemory" : 0.0,
        #       "networkCost" : 3.41425774958E11
        #     }
        #   }
        result = json.loads(cursor.fetchone()[0])
        return result

    @classmethod
    def query_cost_formatter(
        cls, raw_cost: List[Dict[str, Any]]
    ) -> List[Dict[str, str]]:
        """
        Format cost estimate.

        :param raw_cost: JSON estimate from Presto
        :return: Human readable cost estimate
        """

        def humanize(value: Any, suffix: str) -> str:
            try:
                value = int(value)
            except ValueError:
                return str(value)

            prefixes = ["K", "M", "G", "T", "P", "E", "Z", "Y"]
            prefix = ""
            to_next_prefix = 1000
            while value > to_next_prefix and prefixes:
                prefix = prefixes.pop(0)
                value //= to_next_prefix

            return f"{value} {prefix}{suffix}"

        cost = []
        columns = [
            ("outputRowCount", "Output count", " rows"),
            ("outputSizeInBytes", "Output size", "B"),
            ("cpuCost", "CPU cost", ""),
            ("maxMemory", "Max memory", "B"),
            ("networkCost", "Network cost", ""),
        ]
        for row in raw_cost:
            estimate: Dict[str, float] = row.get("estimate", {})
            statement_cost = {}
            for key, label, suffix in columns:
                if key in estimate:
                    statement_cost[label] = humanize(estimate[key], suffix).strip()
            cost.append(statement_cost)

        return cost

    @classmethod
    def adjust_database_uri(
        cls, uri: URL, selected_schema: Optional[str] = None
    ) -> None:
        database = uri.database
        if selected_schema and database:
            selected_schema = parse.quote(selected_schema, safe="")
            if "/" in database:
                database = database.split("/")[0] + "/" + selected_schema
            else:
                database += "/" + selected_schema
            uri.database = database

    @classmethod
    def convert_dttm(cls, target_type: str, dttm: datetime) -> Optional[str]:
        tt = target_type.upper()
        if tt == "DATE":
            return f"""from_iso8601_date('{dttm.date().isoformat()}')"""
        if tt == "TIMESTAMP":
            return f"""from_iso8601_timestamp('{dttm.isoformat(timespec="microseconds")}')"""  # pylint: disable=line-too-long
        return None

    @classmethod
    def epoch_to_dttm(cls) -> str:
        return "from_unixtime({col})"

    @classmethod
    def get_all_datasource_names(
        cls, database: "Database", datasource_type: str
    ) -> List[utils.DatasourceName]:
        datasource_df = database.get_df(
            "SELECT table_schema, table_name FROM INFORMATION_SCHEMA.{}S "
            "ORDER BY concat(table_schema, '.', table_name)".format(
                datasource_type.upper()
            ),
            None,
        )
        datasource_names: List[utils.DatasourceName] = []
        for _unused, row in datasource_df.iterrows():
            datasource_names.append(
                utils.DatasourceName(
                    schema=row["table_schema"], table=row["table_name"]
                )
            )
        return datasource_names

    @classmethod
    def expand_data(  # pylint: disable=too-many-locals
        cls, columns: List[dict], data: List[dict]
    ) -> Tuple[List[dict], List[dict], List[dict]]:
        """
        We do not immediately display rows and arrays clearly in the data grid. This
        method separates out nested fields and data values to help clearly display
        structural columns.

        Example: ColumnA is a row(nested_obj varchar) and ColumnB is an array(int)
        Original data set = [
            {'ColumnA': ['a1'], 'ColumnB': [1, 2]},
            {'ColumnA': ['a2'], 'ColumnB': [3, 4]},
        ]
        Expanded data set = [
            {'ColumnA': ['a1'], 'ColumnA.nested_obj': 'a1', 'ColumnB': 1},
            {'ColumnA': '',     'ColumnA.nested_obj': '',   'ColumnB': 2},
            {'ColumnA': ['a2'], 'ColumnA.nested_obj': 'a2', 'ColumnB': 3},
            {'ColumnA': '',     'ColumnA.nested_obj': '',   'ColumnB': 4},
        ]
        :param columns: columns selected in the query
        :param data: original data set
        :return: list of all columns(selected columns and their nested fields),
                 expanded data set, listed of nested fields
        """
        if not is_feature_enabled("PRESTO_EXPAND_DATA"):
            return columns, data, []

        # process each column, unnesting ARRAY types and
        # expanding ROW types into new columns
        to_process = deque((column, 0) for column in columns)
        all_columns: List[dict] = []
        expanded_columns = []
        current_array_level = None
        while to_process:
            column, level = to_process.popleft()
            if column["name"] not in [column["name"] for column in all_columns]:
                all_columns.append(column)

            # When unnesting arrays we need to keep track of how many extra rows
            # were added, for each original row. This is necessary when we expand
            # multiple arrays, so that the arrays after the first reuse the rows
            # added by the first. every time we change a level in the nested arrays
            # we reinitialize this.
            if level != current_array_level:
                unnested_rows: Dict[int, int] = defaultdict(int)
                current_array_level = level

            name = column["name"]

            if column["type"].startswith("ARRAY("):
                # keep processing array children; we append to the right so that
                # multiple nested arrays are processed breadth-first
                to_process.append((get_children(column)[0], level + 1))

                # unnest array objects data into new rows
                i = 0
                while i < len(data):
                    row = data[i]
                    values = row.get(name)
                    if values:
                        # how many extra rows we need to unnest the data?
                        extra_rows = len(values) - 1

                        # how many rows were already added for this row?
                        current_unnested_rows = unnested_rows[i]

                        # add any necessary rows
                        missing = extra_rows - current_unnested_rows
                        for _ in range(missing):
                            data.insert(i + current_unnested_rows + 1, {})
                            unnested_rows[i] += 1

                        # unnest array into rows
                        for j, value in enumerate(values):
                            data[i + j][name] = value

                        # skip newly unnested rows
                        i += unnested_rows[i]

                    i += 1

            if column["type"].startswith("ROW("):
                # expand columns; we append them to the left so they are added
                # immediately after the parent
                expanded = get_children(column)
                to_process.extendleft((column, level) for column in expanded)
                expanded_columns.extend(expanded)

                # expand row objects into new columns
                for row in data:
                    for value, col in zip(row.get(name) or [], expanded):
                        row[col["name"]] = value

        data = [
            {k["name"]: row.get(k["name"], "") for k in all_columns} for row in data
        ]

        return all_columns, data, expanded_columns

    @classmethod
    def extra_table_metadata(
        cls, database: "Database", table_name: str, schema_name: str
    ) -> Dict[str, Any]:
        metadata = {}

        indexes = database.get_indexes(table_name, schema_name)
        if indexes:
            cols = indexes[0].get("column_names", [])
            full_table_name = table_name
            if schema_name and "." not in table_name:
                full_table_name = "{}.{}".format(schema_name, table_name)
            pql = cls._partition_query(full_table_name, database)
            col_names, latest_parts = cls.latest_partition(
                table_name, schema_name, database, show_first=True
            )

            if not latest_parts:
                latest_parts = tuple([None] * len(col_names))  # type: ignore
            metadata["partitions"] = {
                "cols": cols,
                "latest": dict(zip(col_names, latest_parts)),  # type: ignore
                "partitionQuery": pql,
            }

        # flake8 is not matching `Optional[str]` to `Any` for some reason...
        metadata["view"] = cast(
            Any, cls.get_create_view(database, schema_name, table_name)
        )

        return metadata

    @classmethod
    def get_create_view(
        cls, database: "Database", schema: str, table: str
    ) -> Optional[str]:
        """
        Return a CREATE VIEW statement, or `None` if not a view.

        :param database: Database instance
        :param schema: Schema name
        :param table: Table (view) name
        """
        from pyhive.exc import DatabaseError

        engine = cls.get_engine(database, schema)
        with closing(engine.raw_connection()) as conn:
            with closing(conn.cursor()) as cursor:
                sql = f"SHOW CREATE VIEW {schema}.{table}"
                try:
                    cls.execute(cursor, sql)
                    polled = cursor.poll()

                    while polled:
                        time.sleep(0.2)
                        polled = cursor.poll()
                except DatabaseError:  # not a VIEW
                    return None
                rows = cls.fetch_data(cursor, 1)
        return rows[0][0]

    @classmethod
    def handle_cursor(cls, cursor: Any, query: Query, session: Session) -> None:
        """Updates progress information"""
        query_id = query.id
        logger.info(f"Query {query_id}: Polling the cursor for progress")
        polled = cursor.poll()
        # poll returns dict -- JSON status information or ``None``
        # if the query is done
        # https://github.com/dropbox/PyHive/blob/
        # b34bdbf51378b3979eaf5eca9e956f06ddc36ca0/pyhive/presto.py#L178
        while polled:
            # Update the object and wait for the kill signal.
            stats = polled.get("stats", {})

            query = session.query(type(query)).filter_by(id=query_id).one()
            if query.status in [QueryStatus.STOPPED, QueryStatus.TIMED_OUT]:
                cursor.cancel()
                break

            if stats:
                state = stats.get("state")

                # if already finished, then stop polling
                if state == "FINISHED":
                    break

                completed_splits = float(stats.get("completedSplits"))
                total_splits = float(stats.get("totalSplits"))
                if total_splits and completed_splits:
                    progress = 100 * (completed_splits / total_splits)
                    logger.info(
                        "Query {} progress: {} / {} "  # pylint: disable=logging-format-interpolation
                        "splits".format(query_id, completed_splits, total_splits)
                    )
                    if progress > query.progress:
                        query.progress = progress
                    session.commit()
            time.sleep(1)
            logger.info(f"Query {query_id}: Polling the cursor for progress")
            polled = cursor.poll()

    @classmethod
    def _extract_error_message(cls, e: Exception) -> Optional[str]:
        if (
            hasattr(e, "orig")
            and type(e.orig).__name__ == "DatabaseError"  # type: ignore
            and isinstance(e.orig[0], dict)  # type: ignore
        ):
            error_dict = e.orig[0]  # type: ignore
            return "{} at {}: {}".format(
                error_dict.get("errorName"),
                error_dict.get("errorLocation"),
                error_dict.get("message"),
            )
        if type(e).__name__ == "DatabaseError" and hasattr(e, "args") and e.args:
            error_dict = e.args[0]
            return error_dict.get("message")
        return utils.error_msg_from_exception(e)

    @classmethod
    def _partition_query(  # pylint: disable=too-many-arguments,too-many-locals
        cls,
        table_name: str,
        database: "Database",
        limit: int = 0,
        order_by: Optional[List[Tuple[str, bool]]] = None,
        filters: Optional[Dict[Any, Any]] = None,
    ) -> str:
        """Returns a partition query

        :param table_name: the name of the table to get partitions from
        :type table_name: str
        :param limit: the number of partitions to be returned
        :type limit: int
        :param order_by: a list of tuples of field name and a boolean
            that determines if that field should be sorted in descending
            order
        :type order_by: list of (str, bool) tuples
        :param filters: dict of field name and filter value combinations
        """
        limit_clause = "LIMIT {}".format(limit) if limit else ""
        order_by_clause = ""
        if order_by:
            l = []
            for field, desc in order_by:
                l.append(field + " DESC" if desc else "")
            order_by_clause = "ORDER BY " + ", ".join(l)

        where_clause = ""
        if filters:
            l = []
            for field, value in filters.items():
                l.append(f"{field} = '{value}'")
            where_clause = "WHERE " + " AND ".join(l)

        presto_version = database.get_extra().get("version")

        # Partition select syntax changed in v0.199, so check here.
        # Default to the new syntax if version is unset.
        partition_select_clause = (
            f'SELECT * FROM "{table_name}$partitions"'
            if not presto_version
            or StrictVersion(presto_version) >= StrictVersion("0.199")
            else f"SHOW PARTITIONS FROM {table_name}"
        )

        sql = textwrap.dedent(
            f"""\
            {partition_select_clause}
            {where_clause}
            {order_by_clause}
            {limit_clause}
        """
        )
        return sql

    @classmethod
    def where_latest_partition(  # pylint: disable=too-many-arguments
        cls,
        table_name: str,
        schema: Optional[str],
        database: "Database",
        query: Select,
        columns: Optional[List] = None,
    ) -> Optional[Select]:
        try:
            col_names, values = cls.latest_partition(
                table_name, schema, database, show_first=True
            )
        except Exception:  # pylint: disable=broad-except
            # table is not partitioned
            return None

        if values is None:
            return None

        column_names = {column.get("name") for column in columns or []}
        for col_name, value in zip(col_names, values):
            if col_name in column_names:
                query = query.where(Column(col_name) == value)
        return query

    @classmethod
    def _latest_partition_from_df(  # pylint: disable=invalid-name
        cls, df: pd.DataFrame
    ) -> Optional[List[str]]:
        if not df.empty:
            return df.to_records(index=False)[0].item()
        return None

    @classmethod
    def latest_partition(
        cls,
        table_name: str,
        schema: Optional[str],
        database: "Database",
        show_first: bool = False,
    ) -> Tuple[List[str], Optional[List[str]]]:
        """Returns col name and the latest (max) partition value for a table

        :param table_name: the name of the table
        :param schema: schema / database / namespace
        :param database: database query will be run against
        :type database: models.Database
        :param show_first: displays the value for the first partitioning key
          if there are many partitioning keys
        :type show_first: bool

        >>> latest_partition('foo_table')
        (['ds'], ('2018-01-01',))
        """
        indexes = database.get_indexes(table_name, schema)
        if not indexes:
            raise SupersetTemplateException(
                f"Error getting partition for {schema}.{table_name}. "
                "Verify that this table has a partition."
            )

        if len(indexes[0]["column_names"]) < 1:
            raise SupersetTemplateException(
                "The table should have one partitioned field"
            )
        elif not show_first and len(indexes[0]["column_names"]) > 1:
            raise SupersetTemplateException(
                "The table should have a single partitioned field "
                "to use this function. You may want to use "
                "`presto.latest_sub_partition`"
            )
        column_names = indexes[0]["column_names"]
        part_fields = [(column_name, True) for column_name in column_names]
        sql = cls._partition_query(table_name, database, 1, part_fields)
        df = database.get_df(sql, schema)
        return column_names, cls._latest_partition_from_df(df)

    @classmethod
    def latest_sub_partition(
        cls, table_name: str, schema: Optional[str], database: "Database", **kwargs: Any
    ) -> Any:
        """Returns the latest (max) partition value for a table

        A filtering criteria should be passed for all fields that are
        partitioned except for the field to be returned. For example,
        if a table is partitioned by (``ds``, ``event_type`` and
        ``event_category``) and you want the latest ``ds``, you'll want
        to provide a filter as keyword arguments for both
        ``event_type`` and ``event_category`` as in
        ``latest_sub_partition('my_table',
            event_category='page', event_type='click')``

        :param table_name: the name of the table, can be just the table
            name or a fully qualified table name as ``schema_name.table_name``
        :type table_name: str
        :param schema: schema / database / namespace
        :type schema: str
        :param database: database query will be run against
        :type database: models.Database

        :param kwargs: keyword arguments define the filtering criteria
            on the partition list. There can be many of these.
        :type kwargs: str
        >>> latest_sub_partition('sub_partition_table', event_type='click')
        '2018-01-01'
        """
        indexes = database.get_indexes(table_name, schema)
        part_fields = indexes[0]["column_names"]
        for k in kwargs.keys():  # pylint: disable=consider-iterating-dictionary
            if k not in k in part_fields:
                msg = "Field [{k}] is not part of the portioning key"
                raise SupersetTemplateException(msg)
        if len(kwargs.keys()) != len(part_fields) - 1:
            msg = (
                "A filter needs to be specified for {} out of the " "{} fields."
            ).format(len(part_fields) - 1, len(part_fields))
            raise SupersetTemplateException(msg)

        for field in part_fields:
            if field not in kwargs.keys():
                field_to_return = field

        sql = cls._partition_query(
            table_name, database, 1, [(field_to_return, True)], kwargs
        )
        df = database.get_df(sql, schema)
        if df.empty:
            return ""
        return df.to_dict()[field_to_return][0]

    @classmethod
    @cache.memoize()
    def get_function_names(cls, database: "Database") -> List[str]:
        """
        Get a list of function names that are able to be called on the database.
        Used for SQL Lab autocomplete.

        :param database: The database to get functions for
        :return: A list of function names useable in the database
        """
        return database.get_df("SHOW FUNCTIONS")["Function"].tolist()