from __future__ import annotations
from functools import lru_cache
import typing as t
import logging
from sqlglot import exp
from sqlmesh.core.dialect import to_schema
from sqlmesh.utils.aws import validate_s3_uri, parse_s3_uri
from sqlmesh.core.engine_adapter.mixins import PandasNativeFetchDFSupportMixin, RowDiffMixin
from sqlmesh.core.engine_adapter.trino import TrinoEngineAdapter
from sqlmesh.core.node import IntervalUnit
import posixpath
from sqlmesh.utils.errors import SQLMeshError
from sqlmesh.core.engine_adapter.shared import (
    CatalogSupport,
    DataObject,
    DataObjectType,
    CommentCreationTable,
    CommentCreationView,
    SourceQuery,
    InsertOverwriteStrategy,
)

if t.TYPE_CHECKING:
    from sqlmesh.core._typing import SchemaName, TableName
    from sqlmesh.core.engine_adapter._typing import QueryOrDF

    TableType = t.Union[t.Literal["hive"], t.Literal["iceberg"]]

logger = logging.getLogger(__name__)


class AthenaEngineAdapter(PandasNativeFetchDFSupportMixin, RowDiffMixin):
    DIALECT = "athena"
    SUPPORTS_TRANSACTIONS = False
    SUPPORTS_REPLACE_TABLE = False
    # Athena's support for table and column comments is too patchy to consider "supported"
    # Hive tables: Table + Column comments are supported
    # Iceberg tables: Column comments only
    # CTAS, Views: No comment support at all
    COMMENT_CREATION_TABLE = CommentCreationTable.UNSUPPORTED
    COMMENT_CREATION_VIEW = CommentCreationView.UNSUPPORTED
    SCHEMA_DIFFER_KWARGS = TrinoEngineAdapter.SCHEMA_DIFFER_KWARGS
    MAX_TIMESTAMP_PRECISION = 3  # copied from Trino
    # Athena does not deal with comments well, e.g:
    # >>> self._execute('/* test */ DESCRIBE foo')
    #     pyathena.error.OperationalError: FAILED: ParseException line 1:0 cannot recognize input near '/' '*' 'test'
    ATTACH_CORRELATION_ID = False
    SUPPORTS_QUERY_EXECUTION_TRACKING = True
    SUPPORTED_DROP_CASCADE_OBJECT_KINDS = ["DATABASE", "SCHEMA"]

    def __init__(
        self, *args: t.Any, s3_warehouse_location: t.Optional[str] = None, **kwargs: t.Any
    ):
        # Need to pass s3_warehouse_location to the superclass so that it goes into _extra_config
        # which means that EngineAdapter.with_settings() keeps this property when it makes a clone
        super().__init__(*args, s3_warehouse_location=s3_warehouse_location, **kwargs)
        self.s3_warehouse_location = s3_warehouse_location

        self._default_catalog = self._default_catalog or "awsdatacatalog"

    @property
    def s3_warehouse_location(self) -> t.Optional[str]:
        return self._s3_warehouse_location

    @s3_warehouse_location.setter
    def s3_warehouse_location(self, value: t.Optional[str]) -> None:
        if value:
            value = validate_s3_uri(value, base=True)
        self._s3_warehouse_location = value

    @property
    def s3_warehouse_location_or_raise(self) -> str:
        # this makes tests easier to write without extra null checks to keep mypy happy
        if location := self.s3_warehouse_location:
            return location

        raise SQLMeshError("s3_warehouse_location was expected to be populated; it isnt")

    @property
    def catalog_support(self) -> CatalogSupport:
        # Athena has the concept of catalogs but the current catalog is set in the connection parameters with no way to query or change it after that
        # It also cant create new catalogs, you have to configure them in AWS. Typically, catalogs that are not "awsdatacatalog"
        # are pointers to the "awsdatacatalog" of other AWS accounts
        return CatalogSupport.SINGLE_CATALOG_ONLY

    def create_state_table(
        self,
        table_name: str,
        target_columns_to_types: t.Dict[str, exp.DataType],
        primary_key: t.Optional[t.Tuple[str, ...]] = None,
    ) -> None:
        self.create_table(
            table_name,
            target_columns_to_types,
            primary_key=primary_key,
            # it's painfully slow, but it works
            table_format="iceberg",
        )

    def _get_data_objects(
        self, schema_name: SchemaName, object_names: t.Optional[t.Set[str]] = None
    ) -> t.List[DataObject]:
        """
        Returns all the data objects that exist in the given schema and optionally catalog.
        """
        schema_name = to_schema(schema_name)
        schema = schema_name.db
        query = (
            exp.select(
                exp.column("table_catalog").as_("catalog"),
                exp.column("table_schema", table="t").as_("schema"),
                exp.column("table_name", table="t").as_("name"),
                exp.case()
                .when(
                    exp.column("table_type", table="t").eq("BASE TABLE"),
                    exp.Literal.string("table"),
                )
                .else_(exp.column("table_type", table="t"))
                .as_("type"),
            )
            .from_(exp.to_table("information_schema.tables", alias="t"))
            .where(exp.column("table_schema", table="t").eq(schema))
        )
        if object_names:
            query = query.where(exp.column("table_name", table="t").isin(*object_names))

        df = self.fetchdf(query)

        return [
            DataObject(
                catalog=row.catalog,  # type: ignore
                schema=row.schema,  # type: ignore
                name=row.name,  # type: ignore
                type=DataObjectType.from_str(row.type),  # type: ignore
            )
            for row in df.itertuples()
        ]

    def columns(
        self, table_name: TableName, include_pseudo_columns: bool = False
    ) -> t.Dict[str, exp.DataType]:
        table = exp.to_table(table_name)
        # note: the data_type column contains the full parameterized type, eg 'varchar(10)'
        query = (
            exp.select("column_name", "data_type")
            .from_("information_schema.columns")
            .where(exp.column("table_schema").eq(table.db), exp.column("table_name").eq(table.name))
            .order_by("ordinal_position")
        )
        result = self.fetchdf(query, quote_identifiers=True)
        return {
            str(r.column_name): exp.DataType.build(str(r.data_type))
            for r in result.itertuples(index=False)
        }

    def _create_schema(
        self,
        schema_name: SchemaName,
        ignore_if_exists: bool,
        warn_on_error: bool,
        properties: t.List[exp.Expression],
        kind: str,
    ) -> None:
        if location := self._table_location(table_properties=None, table=exp.to_table(schema_name)):
            # don't add extra LocationProperty's if one already exists
            if not any(p for p in properties if isinstance(p, exp.LocationProperty)):
                properties.append(location)

        return super()._create_schema(
            schema_name=schema_name,
            ignore_if_exists=ignore_if_exists,
            warn_on_error=warn_on_error,
            properties=properties,
            kind=kind,
        )

    def _build_create_table_exp(
        self,
        table_name_or_schema: t.Union[exp.Schema, TableName],
        expression: t.Optional[exp.Expression],
        exists: bool = True,
        replace: bool = False,
        target_columns_to_types: t.Optional[t.Dict[str, exp.DataType]] = None,
        table_description: t.Optional[str] = None,
        table_kind: t.Optional[str] = None,
        partitioned_by: t.Optional[t.List[exp.Expression]] = None,
        table_properties: t.Optional[t.Dict[str, exp.Expression]] = None,
        **kwargs: t.Any,
    ) -> exp.Create:
        exists = False if replace else exists

        table: exp.Table
        if isinstance(table_name_or_schema, str):
            table = exp.to_table(table_name_or_schema)
        elif isinstance(table_name_or_schema, exp.Schema):
            table = table_name_or_schema.this
        else:
            table = table_name_or_schema

        properties = self._build_table_properties_exp(
            table=table,
            expression=expression,
            target_columns_to_types=target_columns_to_types,
            partitioned_by=partitioned_by,
            table_properties=table_properties,
            table_description=table_description,
            table_kind=table_kind,
            **kwargs,
        )

        is_hive = self._table_type(kwargs.get("table_format", None)) == "hive"

        # Filter any PARTITIONED BY properties from the main column list since they cant be specified in both places
        # ref: https://docs.aws.amazon.com/athena/latest/ug/partitions.html
        if is_hive and partitioned_by and isinstance(table_name_or_schema, exp.Schema):
            partitioned_by_column_names = {e.name for e in partitioned_by}
            filtered_expressions = [
                e
                for e in table_name_or_schema.expressions
                if isinstance(e, exp.ColumnDef) and e.this.name not in partitioned_by_column_names
            ]
            table_name_or_schema.args["expressions"] = filtered_expressions

        return exp.Create(
            this=table_name_or_schema,
            kind=table_kind or "TABLE",
            replace=replace,
            exists=exists,
            expression=expression,
            properties=properties,
        )

    def _build_table_properties_exp(
        self,
        catalog_name: t.Optional[str] = None,
        table_format: t.Optional[str] = None,
        storage_format: t.Optional[str] = None,
        partitioned_by: t.Optional[t.List[exp.Expression]] = None,
        partition_interval_unit: t.Optional[IntervalUnit] = None,
        clustered_by: t.Optional[t.List[exp.Expression]] = None,
        table_properties: t.Optional[t.Dict[str, exp.Expression]] = None,
        target_columns_to_types: t.Optional[t.Dict[str, exp.DataType]] = None,
        table_description: t.Optional[str] = None,
        table_kind: t.Optional[str] = None,
        table: t.Optional[exp.Table] = None,
        expression: t.Optional[exp.Expression] = None,
        **kwargs: t.Any,
    ) -> t.Optional[exp.Properties]:
        properties: t.List[exp.Expression] = []
        table_properties = table_properties or {}

        is_hive = self._table_type(table_format) == "hive"
        is_iceberg = not is_hive

        if is_hive and not expression:
            # Hive tables are CREATE EXTERNAL TABLE, Iceberg tables are CREATE TABLE
            # Unless it's a CTAS, those are always CREATE TABLE
            properties.append(exp.ExternalProperty())

        if table_format:
            properties.append(
                exp.Property(this=exp.var("table_type"), value=exp.Literal.string(table_format))
            )

        if table_description:
            properties.append(exp.SchemaCommentProperty(this=exp.Literal.string(table_description)))

        if partitioned_by:
            schema_expressions: t.List[exp.Expression] = []
            if is_hive and target_columns_to_types:
                # For Hive-style tables, you cannot include the partitioned by columns in the main set of columns
                # In the PARTITIONED BY expression, you also cant just include the column names, you need to include the data type as well
                # ref: https://docs.aws.amazon.com/athena/latest/ug/partitions.html
                for match_name, match_dtype in self._find_matching_columns(
                    partitioned_by, target_columns_to_types
                ):
                    column_def = exp.ColumnDef(this=exp.to_identifier(match_name), kind=match_dtype)
                    schema_expressions.append(column_def)
            else:
                schema_expressions = partitioned_by

            properties.append(
                exp.PartitionedByProperty(this=exp.Schema(expressions=schema_expressions))
            )

        if clustered_by:
            # Athena itself supports CLUSTERED BY, via the syntax CLUSTERED BY (col) INTO <n> BUCKETS
            # However, SQLMesh is more closely aligned with BigQuery's notion of clustering and
            # defines `clustered_by` as a List[str] with no way of indicating the number of buckets
            #
            # Athena's concept of CLUSTER BY is more like Iceberg's `bucket(<num_buckets>, col)` partition transform
            logging.warning("clustered_by is not supported in the Athena adapter at this time")

        if storage_format:
            if is_iceberg:
                # TBLPROPERTIES('format'='parquet')
                table_properties["format"] = exp.Literal.string(storage_format)
            else:
                # STORED AS PARQUET
                properties.append(exp.FileFormatProperty(this=storage_format))

        if table and (location := self._table_location_or_raise(table_properties, table)):
            properties.append(location)

            if is_iceberg and expression:
                # To make a CTAS expression persist as iceberg, alongside setting `table_type=iceberg`, you also need to set is_external=false
                # Note that SQLGlot does the right thing with LocationProperty and writes it as `location` (Iceberg) instead of `external_location` (Hive)
                # ref: https://docs.aws.amazon.com/athena/latest/ug/create-table-as.html#ctas-table-properties
                properties.append(exp.Property(this=exp.var("is_external"), value="false"))

        for name, value in table_properties.items():
            properties.append(exp.Property(this=exp.var(name), value=value))

        if properties:
            return exp.Properties(expressions=properties)

        return None

    def drop_table(self, table_name: TableName, exists: bool = True, **kwargs: t.Any) -> None:
        table = exp.to_table(table_name)

        if self._query_table_type(table) == "hive":
            self._truncate_table(table)

        return super().drop_table(table_name=table, exists=exists, **kwargs)

    def _truncate_table(self, table_name: TableName) -> None:
        table = exp.to_table(table_name)

        # Truncating an Iceberg table is just DELETE FROM <table>
        if self._query_table_type(table) == "iceberg":
            return self.delete_from(table, exp.true())

        # Truncating a partitioned Hive table is dropping all partitions and deleting the data from S3
        if self._is_hive_partitioned_table(table):
            self._clear_partition_data(table, exp.true())
        elif s3_location := self._query_table_s3_location(table):
            # Truncating a non-partitioned Hive table is clearing out all data in its Location
            self._clear_s3_location(s3_location)

    def _table_type(self, table_format: t.Optional[str] = None) -> TableType:
        """
        Interpret the "table_format" property to check if this is a Hive or an Iceberg table
        """
        if table_format and table_format.lower() == "iceberg":
            return "iceberg"

        # if we cant detect any indication of Iceberg, this is a Hive table
        return "hive"

    def _query_table_type(self, table: exp.Table) -> t.Optional[TableType]:
        if self.table_exists(table):
            return self._query_table_type_or_raise(table)
        return None

    @lru_cache()
    def _query_table_type_or_raise(self, table: exp.Table) -> TableType:
        """
        Hit the DB to check if this is a Hive or an Iceberg table.

        Note that in order to @lru_cache() this method, we have the following assumptions:
         - The table must exist (otherwise we would cache None if this method was called before table creation and always return None after creation)
         - The table type will not change within the same SQLMesh session
        """
        # Note: SHOW TBLPROPERTIES gets parsed by SQLGlot as an exp.Command anyway so we just use a string here
        # This also means we need to use dialect="hive" instead of dialect="athena" so that the identifiers get the correct quoting (backticks)
        for row in self.fetchall(f"SHOW TBLPROPERTIES {table.sql(dialect='hive', identify=True)}"):
            # This query returns a single column with values like 'EXTERNAL\tTRUE'
            row_lower = row[0].lower()
            if "external" in row_lower and "true" in row_lower:
                return "hive"
        return "iceberg"

    def _is_hive_partitioned_table(self, table: exp.Table) -> bool:
        try:
            self._list_partitions(table=table, where=None, limit=1)
            return True
        except Exception as e:
            if "TABLE_NOT_FOUND" in str(e):
                return False
            raise e

    def _table_location_or_raise(
        self, table_properties: t.Optional[t.Dict[str, exp.Expression]], table: exp.Table
    ) -> exp.LocationProperty:
        location = self._table_location(table_properties, table)
        if not location:
            raise SQLMeshError(
                f"Cannot figure out location for table {table}. Please either set `s3_base_location` in `physical_properties` or set `s3_warehouse_location` in the Athena connection config"
            )
        return location

    def _table_location(
        self,
        table_properties: t.Optional[t.Dict[str, exp.Expression]],
        table: exp.Table,
    ) -> t.Optional[exp.LocationProperty]:
        base_uri: str

        # If the user has manually specified a `s3_base_location`, use it
        if table_properties and "s3_base_location" in table_properties:
            s3_base_location_property = table_properties.pop(
                "s3_base_location"
            )  # pop because it's handled differently and we dont want it to end up in the TBLPROPERTIES clause
            if isinstance(s3_base_location_property, exp.Expression):
                base_uri = s3_base_location_property.name
            else:
                base_uri = s3_base_location_property

        elif self.s3_warehouse_location:
            # If the user has set `s3_warehouse_location` in the connection config, the base URI is <s3_warehouse_location>/<catalog>/<schema>/
            base_uri = posixpath.join(
                self.s3_warehouse_location, table.catalog or "", table.db or ""
            )
        else:
            return None

        full_uri = validate_s3_uri(posixpath.join(base_uri, table.text("this") or ""), base=True)
        return exp.LocationProperty(this=exp.Literal.string(full_uri))

    def _find_matching_columns(
        self, partitioned_by: t.List[exp.Expression], columns_to_types: t.Dict[str, exp.DataType]
    ) -> t.List[t.Tuple[str, exp.DataType]]:
        matches = []
        for col in partitioned_by:
            # TODO: do we care about normalization?
            key = col.name
            if isinstance(col, exp.Column) and (match_dtype := columns_to_types.get(key)):
                matches.append((key, match_dtype))
        return matches

    def replace_query(
        self,
        table_name: TableName,
        query_or_df: QueryOrDF,
        target_columns_to_types: t.Optional[t.Dict[str, exp.DataType]] = None,
        table_description: t.Optional[str] = None,
        column_descriptions: t.Optional[t.Dict[str, str]] = None,
        source_columns: t.Optional[t.List[str]] = None,
        supports_replace_table_override: t.Optional[bool] = None,
        **kwargs: t.Any,
    ) -> None:
        table = exp.to_table(table_name)

        if self._query_table_type(table=table) == "hive":
            self.drop_table(table)

        return super().replace_query(
            table_name=table,
            query_or_df=query_or_df,
            target_columns_to_types=target_columns_to_types,
            table_description=table_description,
            column_descriptions=column_descriptions,
            source_columns=source_columns,
            **kwargs,
        )

    def _insert_overwrite_by_time_partition(
        self,
        table_name: TableName,
        source_queries: t.List[SourceQuery],
        target_columns_to_types: t.Dict[str, exp.DataType],
        where: exp.Condition,
        **kwargs: t.Any,
    ) -> None:
        table = exp.to_table(table_name)

        table_type = self._query_table_type(table)

        if table_type == "iceberg":
            # Iceberg tables work as expected, we can use the default behaviour
            return super()._insert_overwrite_by_time_partition(
                table, source_queries, target_columns_to_types, where, **kwargs
            )

        # For Hive tables, we need to drop all the partitions covered by the query and delete the data from S3
        self._clear_partition_data(table, where)

        # Now the data is physically gone, we can continue with inserting a new partition
        return super()._insert_overwrite_by_time_partition(
            table,
            source_queries,
            target_columns_to_types,
            where,
            insert_overwrite_strategy_override=InsertOverwriteStrategy.INTO_IS_OVERWRITE,  # since we already cleared the data
            **kwargs,
        )

    def _clear_partition_data(self, table: exp.Table, where: t.Optional[exp.Condition]) -> None:
        if partitions_to_drop := self._list_partitions(table, where):
            for _, s3_location in partitions_to_drop:
                logger.debug(
                    f"Clearing S3 location for '{table.sql(dialect=self.dialect)}': {s3_location}"
                )
                self._clear_s3_location(s3_location)

            partition_values = [k for k, _ in partitions_to_drop]
            logger.debug(
                f"Dropping partitions for '{table.sql(dialect=self.dialect)}' from metastore: {partition_values}"
            )
            self._drop_partitions_from_metastore(table, partition_values)

    def _list_partitions(
        self,
        table: exp.Table,
        where: t.Optional[exp.Condition] = None,
        limit: t.Optional[int] = None,
    ) -> t.List[t.Tuple[t.List[str], str]]:
        # Use Athena's magic "$partitions" metadata table to identify the partitions to drop
        # Doing it this way allows us to use SQL to filter the partition list
        partition_table_name = table.copy()
        partition_table_name.this.replace(
            exp.to_identifier(f"{table.name}$partitions", quoted=True)
        )

        query = exp.select("*").from_(partition_table_name).where(where)
        if limit:
            query = query.limit(limit)

        partition_values = [list(r) for r in self.fetchall(query, quote_identifiers=True)]

        if partition_values:
            response = self._glue_client.batch_get_partition(
                DatabaseName=table.db,
                TableName=table.name,
                PartitionsToGet=[{"Values": [str(v) for v in lst]} for lst in partition_values],
            )
            return sorted(
                [(p["Values"], p["StorageDescriptor"]["Location"]) for p in response["Partitions"]]
            )

        return []

    def _query_table_s3_location(self, table: exp.Table) -> str:
        response = self._glue_client.get_table(DatabaseName=table.db, Name=table.name)

        # Athena wont let you create a table without a location, so *theoretically* this should never be empty
        if location := response.get("Table", {}).get("StorageDescriptor", {}).get("Location", None):
            return location

        raise SQLMeshError(f"Table {table} has no location set in the metastore!")

    def _drop_partitions_from_metastore(
        self, table: exp.Table, partition_values: t.List[t.List[str]]
    ) -> None:
        # todo: switch to itertools.batched when our minimum supported Python is 3.12
        # 25 = maximum number of partitions that batch_delete_partition can process at once
        # ref: https://docs.aws.amazon.com/glue/latest/webapi/API_BatchDeletePartition.html#API_BatchDeletePartition_RequestParameters
        def _chunks() -> t.Iterable[t.List[t.List[str]]]:
            for i in range(0, len(partition_values), 25):
                yield partition_values[i : i + 25]

        for batch in _chunks():
            self._glue_client.batch_delete_partition(
                DatabaseName=table.db,
                TableName=table.name,
                PartitionsToDelete=[{"Values": v} for v in batch],
            )

    def delete_from(self, table_name: TableName, where: t.Union[str, exp.Expression]) -> None:
        table = exp.to_table(table_name)

        table_type = self._query_table_type(table)

        # If Iceberg, DELETE operations work as expected
        if table_type == "iceberg":
            return super().delete_from(table, where)

        # If Hive, DELETE is an error
        if table_type == "hive":
            # However, if there are no actual records to delete, we can make DELETE a no-op
            # This simplifies a bunch of calling code that just assumes DELETE works (which to be fair is a reasonable assumption since it does for every other engine)
            empty_check = (
                exp.select("*").from_(table).where(where).limit(1)
            )  # deliberately not count(*) because we want the engine to stop as soon as it finds a record
            if len(self.fetchall(empty_check)) > 0:
                raise SQLMeshError("Cannot delete individual records from a Hive table")

        return None

    def _clear_s3_location(self, s3_uri: str) -> None:
        s3 = self._s3_client

        bucket, key = parse_s3_uri(s3_uri)
        if not key.endswith("/"):
            key = f"{key}/"

        keys_to_delete = []

        # note: uses Delimiter=/ to prevent stepping into folders
        # the assumption is that all the files in a partition live directly at the partition `Location`
        for page in s3.get_paginator("list_objects_v2").paginate(
            Bucket=bucket, Prefix=key, Delimiter="/"
        ):
            # list_objects_v2() returns 1000 keys per page so that lines up nicely with delete_objects() being able to delete 1000 keys at a time
            keys = [item["Key"] for item in page.get("Contents", [])]
            if keys:
                keys_to_delete.append(keys)

        for chunk in keys_to_delete:
            s3.delete_objects(Bucket=bucket, Delete={"Objects": [{"Key": k} for k in chunk]})

    @property
    def _glue_client(self) -> t.Any:
        return self._boto3_client("glue")

    @property
    def _s3_client(self) -> t.Any:
        return self._boto3_client("s3")

    def _boto3_client(self, name: str) -> t.Any:
        # use the client factory from PyAthena which is already configured with the correct AWS details
        conn = self.connection
        return conn.session.client(
            name,
            region_name=conn.region_name,
            config=conn.config,
            **conn._client_kwargs,
        )  # type: ignore

    def get_current_catalog(self) -> t.Optional[str]:
        return self.connection.catalog_name
