apdbCassandra.py

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# This file is part of dax_apdb.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (http://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
from __future__ import annotations
 
__all__ = ["ApdbCassandra"]
 
import dataclasses
import logging
import random
import warnings
from collections.abc import Iterable, Iterator, Mapping, Set
from typing import TYPE_CHECKING, Any, cast
 
import numpy as np
import pandas
 
# If cassandra-driver is not there the module can still be imported
# but ApdbCassandra cannot be instantiated.
try:
    import cassandra
    import cassandra.query
    from cassandra.auth import AuthProvider, PlainTextAuthProvider
    from cassandra.cluster import EXEC_PROFILE_DEFAULT, Cluster, ExecutionProfile, Session
    from cassandra.policies import AddressTranslator, RoundRobinPolicy, WhiteListRoundRobinPolicy
    from cassandra.query import UNSET_VALUE
 
    CASSANDRA_IMPORTED = True
except ImportError:
    CASSANDRA_IMPORTED = False
 
import astropy.time
import felis.datamodel
from lsst import sphgeom
from lsst.utils.db_auth import DbAuth, DbAuthNotFoundError
from lsst.utils.iteration import chunk_iterable
 
from ..apdb import Apdb, ApdbConfig
from ..apdbConfigFreezer import ApdbConfigFreezer
from ..apdbReplica import ApdbTableData, ReplicaChunk
from ..apdbSchema import ApdbTables
from ..monitor import MonAgent
from ..pixelization import Pixelization
from ..schema_model import Table
from ..timer import Timer
from ..versionTuple import IncompatibleVersionError, VersionTuple
from .apdbCassandraReplica import ApdbCassandraReplica
from .apdbCassandraSchema import ApdbCassandraSchema, CreateTableOptions, ExtraTables
from .apdbMetadataCassandra import ApdbMetadataCassandra
from .cassandra_utils import (
    PreparedStatementCache,
    literal,
    pandas_dataframe_factory,
    quote_id,
    raw_data_factory,
    select_concurrent,
)
from .config import ApdbCassandraConfig, ApdbCassandraConnectionConfig
 
if TYPE_CHECKING:
    from ..apdbMetadata import ApdbMetadata
 
_LOG = logging.getLogger(__name__)
 
_MON = MonAgent(__name__)
 
VERSION = VersionTuple(0, 1, 1)
"""Version for the code controlling non-replication tables. This needs to be
updated following compatibility rules when schema produced by this code
changes.
"""
 
 
def _dump_query(rf: Any) -> None:
    """Dump cassandra query to debug log."""
    _LOG.debug("Cassandra query: %s", rf.query)
 
 
def _dump_query(rf: Any) -> None: …
class CassandraMissingError(Exception):
    def __init__(self) -> None:
        super().__init__("cassandra-driver module cannot be imported")
 
 
@dataclasses.dataclass
    def __init__(self) -> None: …
class CassandraMissingError(Exception): …
class DatabaseInfo:
    """Collection of information about a specific database."""
 
    name: str
    """Keyspace name."""
 
    permissions: dict[str, set[str]] | None = None
    """Roles that can access the database and their permissions.
 
    `None` means that authentication information is not accessible due to
    system table permissions. If anonymous access is enabled then dictionary
    will be empty but not `None`.
    """
 
 
@dataclasses.dataclass
class DatabaseInfo: …
class _DbVersions:
    """Versions defined in APDB metadata table."""
 
    schema_version: VersionTuple
    """Version of the schema from which database was created."""
 
    code_version: VersionTuple
    """Version of ApdbCassandra with which database was created."""
 
    replica_version: VersionTuple | None
    """Version of ApdbCassandraReplica with which database was created, None
    if replication was not configured.
    """
 
 
class _DbVersions: …
if CASSANDRA_IMPORTED:
 
    class _AddressTranslator(AddressTranslator):
        """Translate internal IP address to external.
 
        Only used for docker-based setup, not viable long-term solution.
        """
 
        def __init__(self, public_ips: tuple[str, ...], private_ips: tuple[str, ...]):
            self._map = dict((k, v) for k, v in zip(private_ips, public_ips))
 
        def __init__(self, public_ips: tuple[str, ...], private_ips: tuple[str, ...]): …
        def translate(self, private_ip: str) -> str:
            return self._map.get(private_ip, private_ip)
 
 
        def translate(self, private_ip: str) -> str: …
    class _AddressTranslator(AddressTranslator): …
class ApdbCassandra(Apdb):
    """Implementation of APDB database on to of Apache Cassandra.
 
    The implementation is configured via standard ``pex_config`` mechanism
    using `ApdbCassandraConfig` configuration class. For an example of
    different configurations check config/ folder.
 
    Parameters
    ----------
    config : `ApdbCassandraConfig`
        Configuration object.
    """
 
    metadataSchemaVersionKey = "version:schema"
    """Name of the metadata key to store schema version number."""
 
    metadataCodeVersionKey = "version:ApdbCassandra"
    """Name of the metadata key to store code version number."""
 
    metadataReplicaVersionKey = "version:ApdbCassandraReplica"
    """Name of the metadata key to store replica code version number."""
 
    metadataConfigKey = "config:apdb-cassandra.json"
    """Name of the metadata key to store code version number."""
 
    _frozen_parameters = (
        "enable_replica",
        "ra_dec_columns",
        "replica_skips_diaobjects",
        "replica_sub_chunk_count",
        "partitioning.part_pixelization",
        "partitioning.part_pix_level",
        "partitioning.time_partition_tables",
        "partitioning.time_partition_days",
    )
    """Names of the config parameters to be frozen in metadata table."""
 
    partition_zero_epoch = astropy.time.Time(0, format="unix_tai")
    """Start time for partition 0, this should never be changed."""
 
    def __init__(self, config: ApdbCassandraConfig):
        if not CASSANDRA_IMPORTED:
            raise CassandraMissingError()
 
        self._keyspace = config.keyspace
 
        self._cluster, self._session = self._make_session(config)
 
        meta_table_name = ApdbTables.metadata.table_name(config.prefix)
        self._metadata = ApdbMetadataCassandra(
            self._session, meta_table_name, config.keyspace, "read_tuples", "write"
        )
 
        # Read frozen config from metadata.
        with self._timer("read_metadata_config"):
            config_json = self._metadata.get(self.metadataConfigKey)
            if config_json is not None:
                # Update config from metadata.
                freezer = ApdbConfigFreezer[ApdbCassandraConfig](self._frozen_parameters)
                self.config = freezer.update(config, config_json)
            else:
                self.config = config
 
        # Read versions stored in database.
        self._db_versions = self._readVersions(self._metadata)
        _LOG.debug("Database versions: %s", self._db_versions)
 
        # Since replica version 1.1.0 we use finer partitioning for replica
        # chunk tables.
        self._has_chunk_sub_partitions = False
        if self.config.enable_replica:
            assert self._db_versions.replica_version is not None, "Replica version must be defined"
            self._has_chunk_sub_partitions = ApdbCassandraReplica.hasChunkSubPartitions(
                self._db_versions.replica_version
            )
 
        self._pixelization = Pixelization(
            self.config.partitioning.part_pixelization,
            self.config.partitioning.part_pix_level,
            config.partitioning.part_pix_max_ranges,
        )
 
        self._schema = ApdbCassandraSchema(
            session=self._session,
            keyspace=self._keyspace,
            schema_file=self.config.schema_file,
            schema_name=self.config.schema_name,
            prefix=self.config.prefix,
            time_partition_tables=self.config.partitioning.time_partition_tables,
            enable_replica=self.config.enable_replica,
            has_chunk_sub_partitions=self._has_chunk_sub_partitions,
        )
        self._partition_zero_epoch_mjd = float(self.partition_zero_epoch.mjd)
 
        current_versions = _DbVersions(
            schema_version=self._schema.schemaVersion(),
            code_version=self.apdbImplementationVersion(),
            replica_version=(
                ApdbCassandraReplica.apdbReplicaImplementationVersion()
                if self.config.enable_replica
                else None
            ),
        )
        _LOG.debug("Current versions: %s", current_versions)
        self._versionCheck(current_versions, self._db_versions)
 
        # Since replica version 1.1.0 we use finer partitioning for replica
        # chunk tables.
        self._has_chunk_sub_partitions = False
        if self.config.enable_replica:
            assert self._db_versions.replica_version is not None, "Replica version must be defined"
            self._has_chunk_sub_partitions = ApdbCassandraReplica.hasChunkSubPartitions(
                self._db_versions.replica_version
            )
 
        # Support for DiaObjectLastToPartition was added at code version 0.1.1
        # in a backward-compatible way (we only use the table if it is there).
        self._has_dia_object_last_to_partition = self._db_versions.code_version >= VersionTuple(0, 1, 1)
 
        # Cache for prepared statements
        self._preparer = PreparedStatementCache(self._session)
 
        if _LOG.isEnabledFor(logging.DEBUG):
            _LOG.debug("ApdbCassandra Configuration: %s", self.config.model_dump())
 
    def __init__(self, config: ApdbCassandraConfig): …
    def __del__(self) -> None:
        if hasattr(self, "_cluster"):
            self._cluster.shutdown()
 
    def __del__(self) -> None: …
    def _timer(self, name: str, *, tags: Mapping[str, str | int] | None = None) -> Timer:
        """Create `Timer` instance given its name."""
        return Timer(name, _MON, tags=tags)
 
    def _timer(self, name: str, *, tags: Mapping[str, str | int] | None = None) -> Timer: …
    @classmethod
    def _make_session(cls, config: ApdbCassandraConfig) -> tuple[Cluster, Session]:
        """Make Cassandra session."""
        addressTranslator: AddressTranslator | None = None
        if config.connection_config.private_ips:
            addressTranslator = _AddressTranslator(
                config.contact_points, config.connection_config.private_ips
            )
 
        with Timer("cluster_connect", _MON):
            cluster = Cluster(
                execution_profiles=cls._makeProfiles(config),
                contact_points=config.contact_points,
                port=config.connection_config.port,
                address_translator=addressTranslator,
                protocol_version=config.connection_config.protocol_version,
                auth_provider=cls._make_auth_provider(config),
                **config.connection_config.extra_parameters,
            )
            session = cluster.connect()
 
        # Dump queries if debug level is enabled.
        if _LOG.isEnabledFor(logging.DEBUG):
            session.add_request_init_listener(_dump_query)
 
        # Disable result paging
        session.default_fetch_size = None
 
        return cluster, session
 
    def _make_session(cls, config: ApdbCassandraConfig) -> tuple[Cluster, Session]: …
    @classmethod
    def _make_auth_provider(cls, config: ApdbCassandraConfig) -> AuthProvider | None:
        """Make Cassandra authentication provider instance."""
        try:
            dbauth = DbAuth()
        except DbAuthNotFoundError:
            # Credentials file doesn't exist, use anonymous login.
            return None
 
        empty_username = True
        # Try every contact point in turn.
        for hostname in config.contact_points:
            try:
                username, password = dbauth.getAuth(
                    "cassandra",
                    config.connection_config.username,
                    hostname,
                    config.connection_config.port,
                    config.keyspace,
                )
                if not username:
                    # Password without user name, try next hostname, but give
                    # warning later if no better match is found.
                    empty_username = True
                else:
                    return PlainTextAuthProvider(username=username, password=password)
            except DbAuthNotFoundError:
                pass
 
        if empty_username:
            _LOG.warning(
                f"Credentials file ({dbauth.db_auth_path}) provided password but not "
                "user name, anonymous Cassandra logon will be attempted."
            )
 
        return None
 
    def _make_auth_provider(cls, config: ApdbCassandraConfig) -> AuthProvider | None: …
    def _readVersions(self, metadata: ApdbMetadataCassandra) -> _DbVersions:
        """Check schema version compatibility."""
 
        def _get_version(key: str) -> VersionTuple:
            """Retrieve version number from given metadata key."""
            version_str = metadata.get(key)
            if version_str is None:
                # Should not happen with existing metadata table.
                raise RuntimeError(f"Version key {key!r} does not exist in metadata table.")
            return VersionTuple.fromString(version_str)
 
        db_schema_version = _get_version(self.metadataSchemaVersionKey)
        db_code_version = _get_version(self.metadataCodeVersionKey)
 
        # Check replica code version only if replica is enabled.
        db_replica_version: VersionTuple | None = None
        if self.config.enable_replica:
            db_replica_version = _get_version(self.metadataReplicaVersionKey)
 
        return _DbVersions(
            schema_version=db_schema_version, code_version=db_code_version, replica_version=db_replica_version
        )
 
    def _readVersions(self, metadata: ApdbMetadataCassandra) -> _DbVersions: …
    def _versionCheck(self, current_versions: _DbVersions, db_versions: _DbVersions) -> None:
        """Check schema version compatibility."""
        if not current_versions.schema_version.checkCompatibility(db_versions.schema_version):
            raise IncompatibleVersionError(
                f"Configured schema version {current_versions.schema_version} "
                f"is not compatible with database version {db_versions.schema_version}"
            )
        if not current_versions.code_version.checkCompatibility(db_versions.code_version):
            raise IncompatibleVersionError(
                f"Current code version {current_versions.code_version} "
                f"is not compatible with database version {db_versions.code_version}"
            )
 
        # Check replica code version only if replica is enabled.
        match current_versions.replica_version, db_versions.replica_version:
            case None, None:
                pass
            case VersionTuple() as current, VersionTuple() as stored:
                if not current.checkCompatibility(stored):
                    raise IncompatibleVersionError(
                        f"Current replication code version {current} "
                        f"is not compatible with database version {stored}"
                    )
            case _:
                raise IncompatibleVersionError(
                    f"Current replication code version {current_versions.replica_version} "
                    f"is not compatible with database version {db_versions.replica_version}"
                )
 
    def _versionCheck(self, current_versions: _DbVersions, db_versions: _DbVersions) -> None: …
    @classmethod
    def apdbImplementationVersion(cls) -> VersionTuple:
        """Return version number for current APDB implementation.
 
        Returns
        -------
        version : `VersionTuple`
            Version of the code defined in implementation class.
        """
        return VERSION
 
    def apdbImplementationVersion(cls) -> VersionTuple: …
    def tableDef(self, table: ApdbTables) -> Table | None:
        # docstring is inherited from a base class
        return self._schema.tableSchemas.get(table)
 
    def tableDef(self, table: ApdbTables) -> Table | None: …
    @classmethod
    def init_database(
        cls,
        hosts: tuple[str, ...],
        keyspace: str,
        *,
        schema_file: str | None = None,
        schema_name: str | None = None,
        read_sources_months: int | None = None,
        read_forced_sources_months: int | None = None,
        enable_replica: bool = False,
        replica_skips_diaobjects: bool = False,
        port: int | None = None,
        username: str | None = None,
        prefix: str | None = None,
        part_pixelization: str | None = None,
        part_pix_level: int | None = None,
        time_partition_tables: bool = True,
        time_partition_start: str | None = None,
        time_partition_end: str | None = None,
        read_consistency: str | None = None,
        write_consistency: str | None = None,
        read_timeout: int | None = None,
        write_timeout: int | None = None,
        ra_dec_columns: tuple[str, str] | None = None,
        replication_factor: int | None = None,
        drop: bool = False,
        table_options: CreateTableOptions | None = None,
    ) -> ApdbCassandraConfig:
        """Initialize new APDB instance and make configuration object for it.
 
        Parameters
        ----------
        hosts : `tuple` [`str`, ...]
            List of host names or IP addresses for Cassandra cluster.
        keyspace : `str`
            Name of the keyspace for APDB tables.
        schema_file : `str`, optional
            Location of (YAML) configuration file with APDB schema. If not
            specified then default location will be used.
        schema_name : `str`, optional
            Name of the schema in YAML configuration file. If not specified
            then default name will be used.
        read_sources_months : `int`, optional
            Number of months of history to read from DiaSource.
        read_forced_sources_months : `int`, optional
            Number of months of history to read from DiaForcedSource.
        enable_replica : `bool`, optional
            If True, make additional tables used for replication to PPDB.
        replica_skips_diaobjects : `bool`, optional
            If `True` then do not fill regular ``DiaObject`` table when
            ``enable_replica`` is `True`.
        port : `int`, optional
            Port number to use for Cassandra connections.
        username : `str`, optional
            User name for Cassandra connections.
        prefix : `str`, optional
            Optional prefix for all table names.
        part_pixelization : `str`, optional
            Name of the MOC pixelization used for partitioning.
        part_pix_level : `int`, optional
            Pixelization level.
        time_partition_tables : `bool`, optional
            Create per-partition tables.
        time_partition_start : `str`, optional
            Starting time for per-partition tables, in yyyy-mm-ddThh:mm:ss
            format, in TAI.
        time_partition_end : `str`, optional
            Ending time for per-partition tables, in yyyy-mm-ddThh:mm:ss
            format, in TAI.
        read_consistency : `str`, optional
            Name of the consistency level for read operations.
        write_consistency : `str`, optional
            Name of the consistency level for write operations.
        read_timeout : `int`, optional
            Read timeout in seconds.
        write_timeout : `int`, optional
            Write timeout in seconds.
        ra_dec_columns : `tuple` [`str`, `str`], optional
            Names of ra/dec columns in DiaObject table.
        replication_factor : `int`, optional
            Replication factor used when creating new keyspace, if keyspace
            already exists its replication factor is not changed.
        drop : `bool`, optional
            If `True` then drop existing tables before re-creating the schema.
        table_options : `CreateTableOptions`, optional
            Options used when creating Cassandra tables.
 
        Returns
        -------
        config : `ApdbCassandraConfig`
            Resulting configuration object for a created APDB instance.
        """
        # Some non-standard defaults for connection parameters, these can be
        # changed later in generated config. Check Cassandra driver
        # documentation for what these parameters do. These parameters are not
        # used during database initialization, but they will be saved with
        # generated config.
        connection_config = ApdbCassandraConnectionConfig(
            extra_parameters={
                "idle_heartbeat_interval": 0,
                "idle_heartbeat_timeout": 30,
                "control_connection_timeout": 100,
            },
        )
        config = ApdbCassandraConfig(
            contact_points=hosts,
            keyspace=keyspace,
            enable_replica=enable_replica,
            replica_skips_diaobjects=replica_skips_diaobjects,
            connection_config=connection_config,
        )
        config.partitioning.time_partition_tables = time_partition_tables
        if schema_file is not None:
            config.schema_file = schema_file
        if schema_name is not None:
            config.schema_name = schema_name
        if read_sources_months is not None:
            config.read_sources_months = read_sources_months
        if read_forced_sources_months is not None:
            config.read_forced_sources_months = read_forced_sources_months
        if port is not None:
            config.connection_config.port = port
        if username is not None:
            config.connection_config.username = username
        if prefix is not None:
            config.prefix = prefix
        if part_pixelization is not None:
            config.partitioning.part_pixelization = part_pixelization
        if part_pix_level is not None:
            config.partitioning.part_pix_level = part_pix_level
        if time_partition_start is not None:
            config.partitioning.time_partition_start = time_partition_start
        if time_partition_end is not None:
            config.partitioning.time_partition_end = time_partition_end
        if read_consistency is not None:
            config.connection_config.read_consistency = read_consistency
        if write_consistency is not None:
            config.connection_config.write_consistency = write_consistency
        if read_timeout is not None:
            config.connection_config.read_timeout = read_timeout
        if write_timeout is not None:
            config.connection_config.write_timeout = write_timeout
        if ra_dec_columns is not None:
            config.ra_dec_columns = ra_dec_columns
 
        cls._makeSchema(config, drop=drop, replication_factor=replication_factor, table_options=table_options)
 
        return config
 
    def init_database( …
    @classmethod
    def list_databases(cls, host: str) -> Iterable[DatabaseInfo]:
        """Return the list of keyspaces with APDB databases.
 
        Parameters
        ----------
        host : `str`
            Name of one of the hosts in Cassandra cluster.
 
        Returns
        -------
        databases : `~collections.abc.Iterable` [`DatabaseInfo`]
            Information about databases that contain APDB instance.
        """
        # For DbAuth we need to use database name "*" to try to match any
        # database.
        config = ApdbCassandraConfig(contact_points=(host,), keyspace="*")
        cluster, session = cls._make_session(config)
 
        with cluster, session:
            # Get names of all keyspaces containing DiaSource table
            table_name = ApdbTables.DiaSource.table_name()
            query = "select keyspace_name from system_schema.tables where table_name = %s ALLOW FILTERING"
            result = session.execute(query, (table_name,))
            keyspaces = [row[0] for row in result.all()]
 
            if not keyspaces:
                return []
 
            # Retrieve roles for each keyspace.
            template = ", ".join(["%s"] * len(keyspaces))
            query = (
                "SELECT resource, role, permissions FROM system_auth.role_permissions "
                f"WHERE resource IN ({template}) ALLOW FILTERING"
            )
            resources = [f"data/{keyspace}" for keyspace in keyspaces]
            try:
                result = session.execute(query, resources)
                # If anonymous access is enabled then result will be empty,
                # set infos to have empty permissions dict in that case.
                infos = {keyspace: DatabaseInfo(name=keyspace, permissions={}) for keyspace in keyspaces}
                for row in result:
                    _, _, keyspace = row[0].partition("/")
                    role: str = row[1]
                    role_permissions: set[str] = set(row[2])
                    infos[keyspace].permissions[role] = role_permissions  # type: ignore[index]
            except cassandra.Unauthorized as exc:
                # Likely that access to role_permissions is not granted for
                # current user.
                warnings.warn(
                    f"Authentication information is not accessible to current user - {exc}", stacklevel=2
                )
                infos = {keyspace: DatabaseInfo(name=keyspace) for keyspace in keyspaces}
 
            # Would be nice to get size estimate, but this is not available
            # via CQL queries.
            return infos.values()
 
    def list_databases(cls, host: str) -> Iterable[DatabaseInfo]: …
    @classmethod
    def delete_database(cls, host: str, keyspace: str, *, timeout: int = 3600) -> None:
        """Delete APDB database by dropping its keyspace.
 
        Parameters
        ----------
        host : `str`
            Name of one of the hosts in Cassandra cluster.
        keyspace : `str`
            Name of keyspace to delete.
        timeout : `int`, optional
            Timeout for delete operation in seconds. Dropping a large keyspace
            can be a long operation, but this default value of one hour should
            be sufficient for most or all cases.
        """
        # For DbAuth we need to use database name "*" to try to match any
        # database.
        config = ApdbCassandraConfig(contact_points=(host,), keyspace="*")
        cluster, session = cls._make_session(config)
        with cluster, session:
            query = f"DROP KEYSPACE {quote_id(keyspace)}"
            session.execute(query, timeout=timeout)
 
    def delete_database(cls, host: str, keyspace: str, *, timeout: int = 3600) -> None: …
    def get_replica(self) -> ApdbCassandraReplica:
        """Return `ApdbReplica` instance for this database."""
        # Note that this instance has to stay alive while replica exists, so
        # we pass reference to self.
        return ApdbCassandraReplica(self, self._schema, self._session)
 
    def get_replica(self) -> ApdbCassandraReplica: …
    @classmethod
    def _makeSchema(
        cls,
        config: ApdbConfig,
        *,
        drop: bool = False,
        replication_factor: int | None = None,
        table_options: CreateTableOptions | None = None,
    ) -> None:
        # docstring is inherited from a base class
 
        if not isinstance(config, ApdbCassandraConfig):
            raise TypeError(f"Unexpected type of configuration object: {type(config)}")
 
        cluster, session = cls._make_session(config)
        with cluster, session:
            schema = ApdbCassandraSchema(
                session=session,
                keyspace=config.keyspace,
                schema_file=config.schema_file,
                schema_name=config.schema_name,
                prefix=config.prefix,
                time_partition_tables=config.partitioning.time_partition_tables,
                enable_replica=config.enable_replica,
            )
 
            # Ask schema to create all tables.
            if config.partitioning.time_partition_tables:
                time_partition_start = astropy.time.Time(
                    config.partitioning.time_partition_start, format="isot", scale="tai"
                )
                time_partition_end = astropy.time.Time(
                    config.partitioning.time_partition_end, format="isot", scale="tai"
                )
                part_epoch = float(cls.partition_zero_epoch.mjd)
                part_days = config.partitioning.time_partition_days
                part_range = (
                    cls._time_partition_cls(time_partition_start, part_epoch, part_days),
                    cls._time_partition_cls(time_partition_end, part_epoch, part_days) + 1,
                )
                schema.makeSchema(
                    drop=drop,
                    part_range=part_range,
                    replication_factor=replication_factor,
                    table_options=table_options,
                )
            else:
                schema.makeSchema(
                    drop=drop, replication_factor=replication_factor, table_options=table_options
                )
 
            meta_table_name = ApdbTables.metadata.table_name(config.prefix)
            metadata = ApdbMetadataCassandra(
                session, meta_table_name, config.keyspace, "read_tuples", "write"
            )
 
            # Fill version numbers, overrides if they existed before.
            metadata.set(cls.metadataSchemaVersionKey, str(schema.schemaVersion()), force=True)
            metadata.set(cls.metadataCodeVersionKey, str(cls.apdbImplementationVersion()), force=True)
 
            if config.enable_replica:
                # Only store replica code version if replica is enabled.
                metadata.set(
                    cls.metadataReplicaVersionKey,
                    str(ApdbCassandraReplica.apdbReplicaImplementationVersion()),
                    force=True,
                )
 
            # Store frozen part of a configuration in metadata.
            freezer = ApdbConfigFreezer[ApdbCassandraConfig](cls._frozen_parameters)
            metadata.set(cls.metadataConfigKey, freezer.to_json(config), force=True)
 
    def _makeSchema( …
    def getDiaObjects(self, region: sphgeom.Region) -> pandas.DataFrame:
        # docstring is inherited from a base class
 
        sp_where = self._spatial_where(region)
        _LOG.debug("getDiaObjects: #partitions: %s", len(sp_where))
 
        # We need to exclude extra partitioning columns from result.
        column_names = self._schema.apdbColumnNames(ApdbTables.DiaObjectLast)
        what = ",".join(quote_id(column) for column in column_names)
 
        table_name = self._schema.tableName(ApdbTables.DiaObjectLast)
        query = f'SELECT {what} from "{self._keyspace}"."{table_name}"'
        statements: list[tuple] = []
        for where, params in sp_where:
            full_query = f"{query} WHERE {where}"
            if params:
                statement = self._preparer.prepare(full_query)
            else:
                # If there are no params then it is likely that query has a
                # bunch of literals rendered already, no point trying to
                # prepare it because it's not reusable.
                statement = cassandra.query.SimpleStatement(full_query)
            statements.append((statement, params))
        _LOG.debug("getDiaObjects: #queries: %s", len(statements))
 
        with _MON.context_tags({"table": "DiaObject"}):
            _MON.add_record(
                "select_query_stats", values={"num_sp_part": len(sp_where), "num_queries": len(statements)}
            )
            with self._timer("select_time") as timer:
                objects = cast(
                    pandas.DataFrame,
                    select_concurrent(
                        self._session,
                        statements,
                        "read_pandas_multi",
                        self.config.connection_config.read_concurrency,
                    ),
                )
                timer.add_values(row_count=len(objects))
 
        _LOG.debug("found %s DiaObjects", objects.shape[0])
        return objects
 
    def getDiaObjects(self, region: sphgeom.Region) -> pandas.DataFrame: …
    def getDiaSources(
        self, region: sphgeom.Region, object_ids: Iterable[int] | None, visit_time: astropy.time.Time
    ) -> pandas.DataFrame | None:
        # docstring is inherited from a base class
        months = self.config.read_sources_months
        if months == 0:
            return None
        mjd_end = float(visit_time.mjd)
        mjd_start = mjd_end - months * 30
 
        return self._getSources(region, object_ids, mjd_start, mjd_end, ApdbTables.DiaSource)
 
    def getDiaSources( …
    def getDiaForcedSources(
        self, region: sphgeom.Region, object_ids: Iterable[int] | None, visit_time: astropy.time.Time
    ) -> pandas.DataFrame | None:
        # docstring is inherited from a base class
        months = self.config.read_forced_sources_months
        if months == 0:
            return None
        mjd_end = float(visit_time.mjd)
        mjd_start = mjd_end - months * 30
 
        return self._getSources(region, object_ids, mjd_start, mjd_end, ApdbTables.DiaForcedSource)
 
    def getDiaForcedSources( …
    def containsVisitDetector(
        self,
        visit: int,
        detector: int,
        region: sphgeom.Region,
        visit_time: astropy.time.Time,
    ) -> bool:
        # docstring is inherited from a base class
        # The order of checks corresponds to order in store(), on potential
        # store failure earlier tables have higher probability containing
        # stored records. With per-partition tables there will be many tables
        # in the list, but it is unlikely that we'll use that setup in
        # production.
        sp_where = self._spatial_where(region, use_ranges=True)
        visit_detector_where = ("visit = ? AND detector = ?", (visit, detector))
 
        # Sources are partitioned on their midPointMjdTai. To avoid precision
        # issues add some fuzzines to visit time.
        mjd_start = float(visit_time.mjd) - 1.0 / 24
        mjd_end = float(visit_time.mjd) + 1.0 / 24
 
        statements: list[tuple] = []
        for table_type in ApdbTables.DiaSource, ApdbTables.DiaForcedSource:
            tables, temporal_where = self._temporal_where(
                table_type, mjd_start, mjd_end, query_per_time_part=True
            )
            for table in tables:
                prefix = f'SELECT apdb_part FROM "{self._keyspace}"."{table}"'
                # Needs ALLOW FILTERING as there is no PK constraint.
                suffix = "PER PARTITION LIMIT 1 LIMIT 1 ALLOW FILTERING"
                statements += list(
                    self._combine_where(prefix, sp_where, temporal_where, visit_detector_where, suffix)
                )
 
        with self._timer("contains_visit_detector_time"):
            result = cast(
                list[tuple[int] | None],
                select_concurrent(
                    self._session,
                    statements,
                    "read_tuples",
                    self.config.connection_config.read_concurrency,
                ),
            )
        return bool(result)
 
    def containsVisitDetector( …
    def getSSObjects(self) -> pandas.DataFrame:
        # docstring is inherited from a base class
        tableName = self._schema.tableName(ApdbTables.SSObject)
        query = f'SELECT * from "{self._keyspace}"."{tableName}"'
 
        objects = None
        with self._timer("select_time", tags={"table": "SSObject"}) as timer:
            result = self._session.execute(query, execution_profile="read_pandas")
            objects = result._current_rows
            timer.add_values(row_count=len(objects))
 
        _LOG.debug("found %s SSObjects", objects.shape[0])
        return objects
 
    def getSSObjects(self) -> pandas.DataFrame: …
    def store(
        self,
        visit_time: astropy.time.Time,
        objects: pandas.DataFrame,
        sources: pandas.DataFrame | None = None,
        forced_sources: pandas.DataFrame | None = None,
    ) -> None:
        # docstring is inherited from a base class
        objects = self._fix_input_timestamps(objects)
        if sources is not None:
            sources = self._fix_input_timestamps(sources)
        if forced_sources is not None:
            forced_sources = self._fix_input_timestamps(forced_sources)
 
        replica_chunk: ReplicaChunk | None = None
        if self._schema.replication_enabled:
            replica_chunk = ReplicaChunk.make_replica_chunk(visit_time, self.config.replica_chunk_seconds)
            self._storeReplicaChunk(replica_chunk, visit_time)
 
        # fill region partition column for DiaObjects
        objects = self._add_apdb_part(objects)
        self._storeDiaObjects(objects, visit_time, replica_chunk)
 
        if sources is not None and len(sources) > 0:
            # copy apdb_part column from DiaObjects to DiaSources
            sources = self._add_apdb_part(sources)
            subchunk = self._storeDiaSources(ApdbTables.DiaSource, sources, replica_chunk)
            self._storeDiaSourcesPartitions(sources, visit_time, replica_chunk, subchunk)
 
        if forced_sources is not None and len(forced_sources) > 0:
            forced_sources = self._add_apdb_part(forced_sources)
            self._storeDiaSources(ApdbTables.DiaForcedSource, forced_sources, replica_chunk)
 
    def store( …
    def storeSSObjects(self, objects: pandas.DataFrame) -> None:
        # docstring is inherited from a base class
        objects = self._fix_input_timestamps(objects)
        self._storeObjectsPandas(objects, ApdbTables.SSObject)
 
    def storeSSObjects(self, objects: pandas.DataFrame) -> None: …
    def reassignDiaSources(self, idMap: Mapping[int, int]) -> None:
        # docstring is inherited from a base class
 
        # To update a record we need to know its exact primary key (including
        # partition key) so we start by querying for diaSourceId to find the
        # primary keys.
 
        table_name = self._schema.tableName(ExtraTables.DiaSourceToPartition)
        # split it into 1k IDs per query
        selects: list[tuple] = []
        for ids in chunk_iterable(idMap.keys(), 1_000):
            ids_str = ",".join(str(item) for item in ids)
            selects.append(
                (
                    (
                        'SELECT "diaSourceId", "apdb_part", "apdb_time_part", "apdb_replica_chunk" '
                        f'FROM "{self._keyspace}"."{table_name}" WHERE "diaSourceId" IN ({ids_str})'
                    ),
                    {},
                )
            )
 
        # No need for DataFrame here, read data as tuples.
        result = cast(
            list[tuple[int, int, int, int | None]],
            select_concurrent(
                self._session, selects, "read_tuples", self.config.connection_config.read_concurrency
            ),
        )
 
        # Make mapping from source ID to its partition.
        id2partitions: dict[int, tuple[int, int]] = {}
        id2chunk_id: dict[int, int] = {}
        for row in result:
            id2partitions[row[0]] = row[1:3]
            if row[3] is not None:
                id2chunk_id[row[0]] = row[3]
 
        # make sure we know partitions for each ID
        if set(id2partitions) != set(idMap):
            missing = ",".join(str(item) for item in set(idMap) - set(id2partitions))
            raise ValueError(f"Following DiaSource IDs do not exist in the database: {missing}")
 
        # Reassign in standard tables
        queries = cassandra.query.BatchStatement()
        table_name = self._schema.tableName(ApdbTables.DiaSource)
        for diaSourceId, ssObjectId in idMap.items():
            apdb_part, apdb_time_part = id2partitions[diaSourceId]
            values: tuple
            if self.config.partitioning.time_partition_tables:
                query = (
                    f'UPDATE "{self._keyspace}"."{table_name}_{apdb_time_part}"'
                    ' SET "ssObjectId" = ?, "diaObjectId" = NULL'
                    ' WHERE "apdb_part" = ? AND "diaSourceId" = ?'
                )
                values = (ssObjectId, apdb_part, diaSourceId)
            else:
                query = (
                    f'UPDATE "{self._keyspace}"."{table_name}"'
                    ' SET "ssObjectId" = ?, "diaObjectId" = NULL'
                    ' WHERE "apdb_part" = ? AND "apdb_time_part" = ? AND "diaSourceId" = ?'
                )
                values = (ssObjectId, apdb_part, apdb_time_part, diaSourceId)
            queries.add(self._preparer.prepare(query), values)
 
        # TODO: (DM-50190) Replication for updated records is not implemented.
        if id2chunk_id:
            warnings.warn("Replication of reassigned DiaSource records is not implemented.", stacklevel=2)
 
        _LOG.debug("%s: will update %d records", table_name, len(idMap))
        with self._timer("source_reassign_time") as timer:
            self._session.execute(queries, execution_profile="write")
            timer.add_values(source_count=len(idMap))
 
    def reassignDiaSources(self, idMap: Mapping[int, int]) -> None: …
    def dailyJob(self) -> None:
        # docstring is inherited from a base class
        pass
 
    def dailyJob(self) -> None: …
    def countUnassociatedObjects(self) -> int:
        # docstring is inherited from a base class
 
        # It's too inefficient to implement it for Cassandra in current schema.
        raise NotImplementedError()
 
    def countUnassociatedObjects(self) -> int: …
    @property
    def metadata(self) -> ApdbMetadata:
        # docstring is inherited from a base class
        return self._metadata
 
    def metadata(self) -> ApdbMetadata: …
    @classmethod
    def _makeProfiles(cls, config: ApdbCassandraConfig) -> Mapping[Any, ExecutionProfile]:
        """Make all execution profiles used in the code."""
        if config.connection_config.private_ips:
            loadBalancePolicy = WhiteListRoundRobinPolicy(hosts=config.contact_points)
        else:
            loadBalancePolicy = RoundRobinPolicy()
 
        read_tuples_profile = ExecutionProfile(
            consistency_level=getattr(cassandra.ConsistencyLevel, config.connection_config.read_consistency),
            request_timeout=config.connection_config.read_timeout,
            row_factory=cassandra.query.tuple_factory,
            load_balancing_policy=loadBalancePolicy,
        )
        read_pandas_profile = ExecutionProfile(
            consistency_level=getattr(cassandra.ConsistencyLevel, config.connection_config.read_consistency),
            request_timeout=config.connection_config.read_timeout,
            row_factory=pandas_dataframe_factory,
            load_balancing_policy=loadBalancePolicy,
        )
        read_raw_profile = ExecutionProfile(
            consistency_level=getattr(cassandra.ConsistencyLevel, config.connection_config.read_consistency),
            request_timeout=config.connection_config.read_timeout,
            row_factory=raw_data_factory,
            load_balancing_policy=loadBalancePolicy,
        )
        # Profile to use with select_concurrent to return pandas data frame
        read_pandas_multi_profile = ExecutionProfile(
            consistency_level=getattr(cassandra.ConsistencyLevel, config.connection_config.read_consistency),
            request_timeout=config.connection_config.read_timeout,
            row_factory=pandas_dataframe_factory,
            load_balancing_policy=loadBalancePolicy,
        )
        # Profile to use with select_concurrent to return raw data (columns and
        # rows)
        read_raw_multi_profile = ExecutionProfile(
            consistency_level=getattr(cassandra.ConsistencyLevel, config.connection_config.read_consistency),
            request_timeout=config.connection_config.read_timeout,
            row_factory=raw_data_factory,
            load_balancing_policy=loadBalancePolicy,
        )
        write_profile = ExecutionProfile(
            consistency_level=getattr(cassandra.ConsistencyLevel, config.connection_config.write_consistency),
            request_timeout=config.connection_config.write_timeout,
            load_balancing_policy=loadBalancePolicy,
        )
        # To replace default DCAwareRoundRobinPolicy
        default_profile = ExecutionProfile(
            load_balancing_policy=loadBalancePolicy,
        )
        return {
            "read_tuples": read_tuples_profile,
            "read_pandas": read_pandas_profile,
            "read_raw": read_raw_profile,
            "read_pandas_multi": read_pandas_multi_profile,
            "read_raw_multi": read_raw_multi_profile,
            "write": write_profile,
            EXEC_PROFILE_DEFAULT: default_profile,
        }
 
    def _makeProfiles(cls, config: ApdbCassandraConfig) -> Mapping[Any, ExecutionProfile]: …
    def _getSources(
        self,
        region: sphgeom.Region,
        object_ids: Iterable[int] | None,
        mjd_start: float,
        mjd_end: float,
        table_name: ApdbTables,
    ) -> pandas.DataFrame:
        """Return catalog of DiaSource instances given set of DiaObject IDs.
 
        Parameters
        ----------
        region : `lsst.sphgeom.Region`
            Spherical region.
        object_ids :
            Collection of DiaObject IDs
        mjd_start : `float`
            Lower bound of time interval.
        mjd_end : `float`
            Upper bound of time interval.
        table_name : `ApdbTables`
            Name of the table.
 
        Returns
        -------
        catalog : `pandas.DataFrame`, or `None`
            Catalog containing DiaSource records. Empty catalog is returned if
            ``object_ids`` is empty.
        """
        object_id_set: Set[int] = set()
        if object_ids is not None:
            object_id_set = set(object_ids)
            if len(object_id_set) == 0:
                return self._make_empty_catalog(table_name)
 
        sp_where = self._spatial_where(region)
        tables, temporal_where = self._temporal_where(table_name, mjd_start, mjd_end)
 
        # We need to exclude extra partitioning columns from result.
        column_names = self._schema.apdbColumnNames(table_name)
        what = ",".join(quote_id(column) for column in column_names)
 
        # Build all queries
        statements: list[tuple] = []
        for table in tables:
            prefix = f'SELECT {what} from "{self._keyspace}"."{table}"'
            statements += list(self._combine_where(prefix, sp_where, temporal_where))
        _LOG.debug("_getSources %s: #queries: %s", table_name, len(statements))
 
        with _MON.context_tags({"table": table_name.name}):
            _MON.add_record(
                "select_query_stats", values={"num_sp_part": len(sp_where), "num_queries": len(statements)}
            )
            with self._timer("select_time") as timer:
                catalog = cast(
                    pandas.DataFrame,
                    select_concurrent(
                        self._session,
                        statements,
                        "read_pandas_multi",
                        self.config.connection_config.read_concurrency,
                    ),
                )
                timer.add_values(row_count=len(catalog))
 
        # filter by given object IDs
        if len(object_id_set) > 0:
            catalog = cast(pandas.DataFrame, catalog[catalog["diaObjectId"].isin(object_id_set)])
 
        # precise filtering on midpointMjdTai
        catalog = cast(pandas.DataFrame, catalog[catalog["midpointMjdTai"] > mjd_start])
 
        _LOG.debug("found %d %ss", catalog.shape[0], table_name.name)
        return catalog
 
    def _getSources( …
    def _storeReplicaChunk(self, replica_chunk: ReplicaChunk, visit_time: astropy.time.Time) -> None:
        # Cassandra timestamp uses milliseconds since epoch
        timestamp = int(replica_chunk.last_update_time.unix_tai * 1000)
 
        # everything goes into a single partition
        partition = 0
 
        table_name = self._schema.tableName(ExtraTables.ApdbReplicaChunks)
 
        columns = ["partition", "apdb_replica_chunk", "last_update_time", "unique_id"]
        values = [partition, replica_chunk.id, timestamp, replica_chunk.unique_id]
        if self._has_chunk_sub_partitions:
            columns.append("has_subchunks")
            values.append(True)
 
        column_list = ", ".join(columns)
        placeholders = ",".join(["%s"] * len(columns))
        query = f'INSERT INTO "{self._keyspace}"."{table_name}" ({column_list}) VALUES ({placeholders})'
 
        self._session.execute(
            query,
            values,
            timeout=self.config.connection_config.write_timeout,
            execution_profile="write",
        )
 
    def _storeReplicaChunk(self, replica_chunk: ReplicaChunk, visit_time: astropy.time.Time) -> None: …
    def _queryDiaObjectLastPartitions(self, ids: Iterable[int]) -> Mapping[int, int]:
        """Return existing mapping of diaObjectId to its last partition."""
        table_name = self._schema.tableName(ExtraTables.DiaObjectLastToPartition)
        queries = []
        object_count = 0
        for id_chunk in chunk_iterable(ids, 10_000):
            id_chunk_list = list(id_chunk)
            query = (
                f'SELECT "diaObjectId", apdb_part FROM "{self._keyspace}"."{table_name}" '
                f'WHERE "diaObjectId" in ({",".join(str(oid) for oid in id_chunk_list)})'
            )
            queries.append((query, ()))
            object_count += len(id_chunk_list)
 
        with self._timer("query_object_last_partitions") as timer:
            data = cast(
                ApdbTableData,
                select_concurrent(
                    self._session, queries, "read_raw_multi", self.config.connection_config.read_concurrency
                ),
            )
            timer.add_values(object_count=object_count, row_count=len(data.rows()))
 
        if data.column_names() != ["diaObjectId", "apdb_part"]:
            raise RuntimeError(f"Unexpected column names in query result: {data.column_names()}")
 
        return {row[0]: row[1] for row in data.rows()}
 
    def _queryDiaObjectLastPartitions(self, ids: Iterable[int]) -> Mapping[int, int]: …
    def _deleteMovingObjects(self, objs: pandas.DataFrame) -> None:
        """Objects in DiaObjectsLast can move from one spatial partition to
        another. For those objects inserting new version does not replace old
        one, so we need to explicitly remove old versions before inserting new
        ones.
        """
        # Extract all object IDs.
        new_partitions = {oid: part for oid, part in zip(objs["diaObjectId"], objs["apdb_part"])}
        old_partitions = self._queryDiaObjectLastPartitions(objs["diaObjectId"])
 
        moved_oids: dict[int, tuple[int, int]] = {}
        for oid, old_part in old_partitions.items():
            new_part = new_partitions.get(oid, old_part)
            if new_part != old_part:
                moved_oids[oid] = (old_part, new_part)
        _LOG.debug("DiaObject IDs that moved to new partition: %s", moved_oids)
 
        if moved_oids:
            # Delete old records from DiaObjectLast.
            table_name = self._schema.tableName(ApdbTables.DiaObjectLast)
            query = f'DELETE FROM "{self._keyspace}"."{table_name}" WHERE apdb_part = ? AND "diaObjectId" = ?'
            statement = self._preparer.prepare(query)
            batch = cassandra.query.BatchStatement()
            for oid, (old_part, _) in moved_oids.items():
                batch.add(statement, (old_part, oid))
            with self._timer("delete_object_last") as timer:
                self._session.execute(
                    batch, timeout=self.config.connection_config.write_timeout, execution_profile="write"
                )
                timer.add_values(row_count=len(moved_oids))
 
        # Add all new records to the map.
        table_name = self._schema.tableName(ExtraTables.DiaObjectLastToPartition)
        query = f'INSERT INTO "{self._keyspace}"."{table_name}" ("diaObjectId", apdb_part) VALUES (?,?)'
        statement = self._preparer.prepare(query)
 
        batch_size = self._batch_size(ExtraTables.DiaObjectLastToPartition)
        batches = []
        for chunk in chunk_iterable(new_partitions.items(), batch_size):
            batch = cassandra.query.BatchStatement()
            for oid, new_part in chunk:
                batch.add(statement, (oid, new_part))
            batches.append(batch)
 
        with self._timer("update_object_last_partition") as timer:
            for batch in batches:
                self._session.execute(
                    batch, timeout=self.config.connection_config.write_timeout, execution_profile="write"
                )
                timer.add_values(row_count=len(batch))
 
    def _deleteMovingObjects(self, objs: pandas.DataFrame) -> None: …
    def _storeDiaObjects(
        self, objs: pandas.DataFrame, visit_time: astropy.time.Time, replica_chunk: ReplicaChunk | None
    ) -> None:
        """Store catalog of DiaObjects from current visit.
 
        Parameters
        ----------
        objs : `pandas.DataFrame`
            Catalog with DiaObject records
        visit_time : `astropy.time.Time`
            Time of the current visit.
        replica_chunk : `ReplicaChunk` or `None`
            Replica chunk identifier if replication is configured.
        """
        if len(objs) == 0:
            _LOG.debug("No objects to write to database.")
            return
 
        if self._has_dia_object_last_to_partition:
            self._deleteMovingObjects(objs)
 
        visit_time_dt = visit_time.datetime
        extra_columns = dict(lastNonForcedSource=visit_time_dt)
        self._storeObjectsPandas(objs, ApdbTables.DiaObjectLast, extra_columns=extra_columns)
 
        extra_columns["validityStart"] = visit_time_dt
        time_part: int | None = self._time_partition(visit_time)
        if not self.config.partitioning.time_partition_tables:
            extra_columns["apdb_time_part"] = time_part
            time_part = None
 
        # Only store DiaObects if not doing replication or explicitly
        # configured to always store them.
        if replica_chunk is None or not self.config.replica_skips_diaobjects:
            self._storeObjectsPandas(
                objs, ApdbTables.DiaObject, extra_columns=extra_columns, time_part=time_part
            )
 
        if replica_chunk is not None:
            extra_columns = dict(apdb_replica_chunk=replica_chunk.id, validityStart=visit_time_dt)
            table = ExtraTables.DiaObjectChunks
            if self._has_chunk_sub_partitions:
                table = ExtraTables.DiaObjectChunks2
                # Use a random number for a second part of partitioning key so
                # that different clients could wrtite to different partitions.
                # This makes it not exactly reproducible.
                extra_columns["apdb_replica_subchunk"] = random.randrange(self.config.replica_sub_chunk_count)
            self._storeObjectsPandas(objs, table, extra_columns=extra_columns)
 
    def _storeDiaObjects( …
    def _storeDiaSources(
        self,
        table_name: ApdbTables,
        sources: pandas.DataFrame,
        replica_chunk: ReplicaChunk | None,
    ) -> int | None:
        """Store catalog of DIASources or DIAForcedSources from current visit.
 
        Parameters
        ----------
        table_name : `ApdbTables`
            Table where to store the data.
        sources : `pandas.DataFrame`
            Catalog containing DiaSource records
        visit_time : `astropy.time.Time`
            Time of the current visit.
        replica_chunk : `ReplicaChunk` or `None`
            Replica chunk identifier if replication is configured.
 
        Returns
        -------
        subchunk : `int` or `None`
            Subchunk number for resulting replica data, `None` if relication is
            not enabled ot subchunking is not enabled.
        """
        # Time partitioning has to be based on midpointMjdTai, not visit_time
        # as visit_time is not really a visit time.
        tp_sources = sources.copy(deep=False)
        tp_sources["apdb_time_part"] = tp_sources["midpointMjdTai"].apply(self._time_partition)
        extra_columns: dict[str, Any] = {}
        if not self.config.partitioning.time_partition_tables:
            self._storeObjectsPandas(tp_sources, table_name)
        else:
            # Group by time partition
            partitions = set(tp_sources["apdb_time_part"])
            if len(partitions) == 1:
                # Single partition - just save the whole thing.
                time_part = partitions.pop()
                self._storeObjectsPandas(sources, table_name, time_part=time_part)
            else:
                # group by time partition.
                for time_part, sub_frame in tp_sources.groupby(by="apdb_time_part"):
                    sub_frame.drop(columns="apdb_time_part", inplace=True)
                    self._storeObjectsPandas(sub_frame, table_name, time_part=time_part)
 
        subchunk: int | None = None
        if replica_chunk is not None:
            extra_columns = dict(apdb_replica_chunk=replica_chunk.id)
            if self._has_chunk_sub_partitions:
                subchunk = random.randrange(self.config.replica_sub_chunk_count)
                extra_columns["apdb_replica_subchunk"] = subchunk
                if table_name is ApdbTables.DiaSource:
                    extra_table = ExtraTables.DiaSourceChunks2
                else:
                    extra_table = ExtraTables.DiaForcedSourceChunks2
            else:
                if table_name is ApdbTables.DiaSource:
                    extra_table = ExtraTables.DiaSourceChunks
                else:
                    extra_table = ExtraTables.DiaForcedSourceChunks
            self._storeObjectsPandas(sources, extra_table, extra_columns=extra_columns)
 
        return subchunk
 
    def _storeDiaSources( …
    def _storeDiaSourcesPartitions(
        self,
        sources: pandas.DataFrame,
        visit_time: astropy.time.Time,
        replica_chunk: ReplicaChunk | None,
        subchunk: int | None,
    ) -> None:
        """Store mapping of diaSourceId to its partitioning values.
 
        Parameters
        ----------
        sources : `pandas.DataFrame`
            Catalog containing DiaSource records
        visit_time : `astropy.time.Time`
            Time of the current visit.
        replica_chunk : `ReplicaChunk` or `None`
            Replication chunk, or `None` when replication is disabled.
        subchunk : `int` or `None`
            Replication sub-chunk, or `None` when replication is disabled or
            sub-chunking is not used.
        """
        id_map = cast(pandas.DataFrame, sources[["diaSourceId", "apdb_part"]])
        extra_columns = {
            "apdb_time_part": self._time_partition(visit_time),
            "apdb_replica_chunk": replica_chunk.id if replica_chunk is not None else None,
        }
        if self._has_chunk_sub_partitions:
            extra_columns["apdb_replica_subchunk"] = subchunk
 
        self._storeObjectsPandas(
            id_map, ExtraTables.DiaSourceToPartition, extra_columns=extra_columns, time_part=None
        )
 
    def _storeDiaSourcesPartitions( …
    def _storeObjectsPandas(
        self,
        records: pandas.DataFrame,
        table_name: ApdbTables | ExtraTables,
        extra_columns: Mapping | None = None,
        time_part: int | None = None,
    ) -> None:
        """Store generic objects.
 
        Takes Pandas catalog and stores a bunch of records in a table.
 
        Parameters
        ----------
        records : `pandas.DataFrame`
            Catalog containing object records
        table_name : `ApdbTables`
            Name of the table as defined in APDB schema.
        extra_columns : `dict`, optional
            Mapping (column_name, column_value) which gives fixed values for
            columns in each row, overrides values in ``records`` if matching
            columns exist there.
        time_part : `int`, optional
            If not `None` then insert into a per-partition table.
 
        Notes
        -----
        If Pandas catalog contains additional columns not defined in table
        schema they are ignored. Catalog does not have to contain all columns
        defined in a table, but partition and clustering keys must be present
        in a catalog or ``extra_columns``.
        """
        # use extra columns if specified
        if extra_columns is None:
            extra_columns = {}
        extra_fields = list(extra_columns.keys())
 
        # Fields that will come from dataframe.
        df_fields = [column for column in records.columns if column not in extra_fields]
 
        column_map = self._schema.getColumnMap(table_name)
        # list of columns (as in felis schema)
        fields = [column_map[field].name for field in df_fields if field in column_map]
        fields += extra_fields
 
        # check that all partitioning and clustering columns are defined
        required_columns = self._schema.partitionColumns(table_name) + self._schema.clusteringColumns(
            table_name
        )
        missing_columns = [column for column in required_columns if column not in fields]
        if missing_columns:
            raise ValueError(f"Primary key columns are missing from catalog: {missing_columns}")
 
        qfields = [quote_id(field) for field in fields]
        qfields_str = ",".join(qfields)
 
        batch_size = self._batch_size(table_name)
 
        with self._timer("insert_build_time", tags={"table": table_name.name}):
            table = self._schema.tableName(table_name)
            if time_part is not None:
                table = f"{table}_{time_part}"
 
            holders = ",".join(["?"] * len(qfields))
            query = f'INSERT INTO "{self._keyspace}"."{table}" ({qfields_str}) VALUES ({holders})'
            statement = self._preparer.prepare(query)
            # Cassandra has 64k limit on batch size, normally that should be
            # enough but some tests generate too many forced sources.
            queries = []
            for rec_chunk in chunk_iterable(records.itertuples(index=False), batch_size):
                batch = cassandra.query.BatchStatement()
                for rec in rec_chunk:
                    values = []
                    for field in df_fields:
                        if field not in column_map:
                            continue
                        value = getattr(rec, field)
                        if column_map[field].datatype is felis.datamodel.DataType.timestamp:
                            if isinstance(value, pandas.Timestamp):
                                value = value.to_pydatetime()
                            elif value is pandas.NaT:
                                value = None
                            else:
                                # Assume it's seconds since epoch, Cassandra
                                # datetime is in milliseconds
                                value = int(value * 1000)
                        value = literal(value)
                        values.append(UNSET_VALUE if value is None else value)
                    for field in extra_fields:
                        value = literal(extra_columns[field])
                        values.append(UNSET_VALUE if value is None else value)
                    batch.add(statement, values)
                queries.append(batch)
 
        _LOG.debug("%s: will store %d records", self._schema.tableName(table_name), records.shape[0])
        with self._timer("insert_time", tags={"table": table_name.name}) as timer:
            for batch in queries:
                self._session.execute(
                    batch, timeout=self.config.connection_config.write_timeout, execution_profile="write"
                )
            timer.add_values(row_count=len(records))
 
    def _storeObjectsPandas( …
    def _add_apdb_part(self, df: pandas.DataFrame) -> pandas.DataFrame:
        """Calculate spatial partition for each record and add it to a
        DataFrame.
 
        Parameters
        ----------
        df : `pandas.DataFrame`
            DataFrame which has to contain ra/dec columns, names of these
            columns are defined by configuration ``ra_dec_columns`` field.
 
        Returns
        -------
        df : `pandas.DataFrame`
            DataFrame with ``apdb_part`` column which contains pixel index
            for ra/dec coordinates.
 
        Notes
        -----
        This overrides any existing column in a DataFrame with the same name
        (``apdb_part``). Original DataFrame is not changed, copy of a DataFrame
        is returned.
        """
        # Calculate pixelization index for every record.
        apdb_part = np.zeros(df.shape[0], dtype=np.int64)
        ra_col, dec_col = self.config.ra_dec_columns
        for i, (ra, dec) in enumerate(zip(df[ra_col], df[dec_col])):
            uv3d = sphgeom.UnitVector3d(sphgeom.LonLat.fromDegrees(ra, dec))
            idx = self._pixelization.pixel(uv3d)
            apdb_part[i] = idx
        df = df.copy()
        df["apdb_part"] = apdb_part
        return df
 
    def _add_apdb_part(self, df: pandas.DataFrame) -> pandas.DataFrame: …
    @classmethod
    def _time_partition_cls(cls, time: float | astropy.time.Time, epoch_mjd: float, part_days: int) -> int:
        """Calculate time partition number for a given time.
 
        Parameters
        ----------
        time : `float` or `astropy.time.Time`
            Time for which to calculate partition number. Can be float to mean
            MJD or `astropy.time.Time`
        epoch_mjd : `float`
            Epoch time for partition 0.
        part_days : `int`
            Number of days per partition.
 
        Returns
        -------
        partition : `int`
            Partition number for a given time.
        """
        if isinstance(time, astropy.time.Time):
            mjd = float(time.mjd)
        else:
            mjd = time
        days_since_epoch = mjd - epoch_mjd
        partition = int(days_since_epoch) // part_days
        return partition
 
    def _time_partition_cls(cls, time: float | astropy.time.Time, epoch_mjd: float, part_days: int) -> int: …
    def _time_partition(self, time: float | astropy.time.Time) -> int:
        """Calculate time partition number for a given time.
 
        Parameters
        ----------
        time : `float` or `astropy.time.Time`
            Time for which to calculate partition number. Can be float to mean
            MJD or `astropy.time.Time`
 
        Returns
        -------
        partition : `int`
            Partition number for a given time.
        """
        if isinstance(time, astropy.time.Time):
            mjd = float(time.mjd)
        else:
            mjd = time
        days_since_epoch = mjd - self._partition_zero_epoch_mjd
        partition = int(days_since_epoch) // self.config.partitioning.time_partition_days
        return partition
 
    def _time_partition(self, time: float | astropy.time.Time) -> int: …
    def _make_empty_catalog(self, table_name: ApdbTables) -> pandas.DataFrame:
        """Make an empty catalog for a table with a given name.
 
        Parameters
        ----------
        table_name : `ApdbTables`
            Name of the table.
 
        Returns
        -------
        catalog : `pandas.DataFrame`
            An empty catalog.
        """
        table = self._schema.tableSchemas[table_name]
 
        data = {
            columnDef.name: pandas.Series(dtype=self._schema.column_dtype(columnDef.datatype))
            for columnDef in table.columns
        }
        return pandas.DataFrame(data)
 
    def _make_empty_catalog(self, table_name: ApdbTables) -> pandas.DataFrame: …
    def _combine_where(
        self,
        prefix: str,
        where1: list[tuple[str, tuple]],
        where2: list[tuple[str, tuple]],
        where3: tuple[str, tuple] | None = None,
        suffix: str | None = None,
    ) -> Iterator[tuple[cassandra.query.Statement, tuple]]:
        """Make cartesian product of two parts of WHERE clause into a series
        of statements to execute.
 
        Parameters
        ----------
        prefix : `str`
            Initial statement prefix that comes before WHERE clause, e.g.
            "SELECT * from Table"
        """
        # If lists are empty use special sentinels.
        if not where1:
            where1 = [("", ())]
        if not where2:
            where2 = [("", ())]
 
        for expr1, params1 in where1:
            for expr2, params2 in where2:
                full_query = prefix
                wheres = []
                params = params1 + params2
                if expr1:
                    wheres.append(expr1)
                if expr2:
                    wheres.append(expr2)
                if where3:
                    wheres.append(where3[0])
                    params += where3[1]
                if wheres:
                    full_query += " WHERE " + " AND ".join(wheres)
                if suffix:
                    full_query += " " + suffix
                if params:
                    statement = self._preparer.prepare(full_query)
                else:
                    # If there are no params then it is likely that query
                    # has a bunch of literals rendered already, no point
                    # trying to prepare it.
                    statement = cassandra.query.SimpleStatement(full_query)
                yield (statement, params)
 
    def _combine_where( …
    def _spatial_where(
        self, region: sphgeom.Region | None, use_ranges: bool = False
    ) -> list[tuple[str, tuple]]:
        """Generate expressions for spatial part of WHERE clause.
 
        Parameters
        ----------
        region : `sphgeom.Region`
            Spatial region for query results.
        use_ranges : `bool`
            If True then use pixel ranges ("apdb_part >= p1 AND apdb_part <=
            p2") instead of exact list of pixels. Should be set to True for
            large regions covering very many pixels.
 
        Returns
        -------
        expressions : `list` [ `tuple` ]
            Empty list is returned if ``region`` is `None`, otherwise a list
            of one or more (expression, parameters) tuples
        """
        if region is None:
            return []
        if use_ranges:
            pixel_ranges = self._pixelization.envelope(region)
            expressions: list[tuple[str, tuple]] = []
            for lower, upper in pixel_ranges:
                upper -= 1
                if lower == upper:
                    expressions.append(('"apdb_part" = ?', (lower,)))
                else:
                    expressions.append(('"apdb_part" >= ? AND "apdb_part" <= ?', (lower, upper)))
            return expressions
        else:
            pixels = self._pixelization.pixels(region)
            if self.config.partitioning.query_per_spatial_part:
                return [('"apdb_part" = ?', (pixel,)) for pixel in pixels]
            else:
                pixels_str = ",".join([str(pix) for pix in pixels])
                return [(f'"apdb_part" IN ({pixels_str})', ())]
 
    def _spatial_where( …
    def _temporal_where(
        self,
        table: ApdbTables,
        start_time: float | astropy.time.Time,
        end_time: float | astropy.time.Time,
        query_per_time_part: bool | None = None,
    ) -> tuple[list[str], list[tuple[str, tuple]]]:
        """Generate table names and expressions for temporal part of WHERE
        clauses.
 
        Parameters
        ----------
        table : `ApdbTables`
            Table to select from.
        start_time : `astropy.time.Time` or `float`
            Starting Datetime of MJD value of the time range.
        end_time : `astropy.time.Time` or `float`
            Starting Datetime of MJD value of the time range.
        query_per_time_part : `bool`, optional
            If None then use ``query_per_time_part`` from configuration.
 
        Returns
        -------
        tables : `list` [ `str` ]
            List of the table names to query.
        expressions : `list` [ `tuple` ]
            A list of zero or more (expression, parameters) tuples.
        """
        tables: list[str]
        temporal_where: list[tuple[str, tuple]] = []
        table_name = self._schema.tableName(table)
        time_part_start = self._time_partition(start_time)
        time_part_end = self._time_partition(end_time)
        time_parts = list(range(time_part_start, time_part_end + 1))
        if self.config.partitioning.time_partition_tables:
            tables = [f"{table_name}_{part}" for part in time_parts]
        else:
            tables = [table_name]
            if query_per_time_part is None:
                query_per_time_part = self.config.partitioning.query_per_time_part
            if query_per_time_part:
                temporal_where = [('"apdb_time_part" = ?', (time_part,)) for time_part in time_parts]
            else:
                time_part_list = ",".join([str(part) for part in time_parts])
                temporal_where = [(f'"apdb_time_part" IN ({time_part_list})', ())]
 
        return tables, temporal_where
 
    def _temporal_where( …
    def _fix_input_timestamps(self, df: pandas.DataFrame) -> pandas.DataFrame:
        """Update timestamp columns in input DataFrame to be naive datetime
        type.
 
        Clients may or may not generate aware timestamps, code in this class
        assumes that timestamps are naive, so we convert them to UTC and
        drop timezone.
        """
        # Find all columns with aware timestamps.
        columns = [column for column, dtype in df.dtypes.items() if isinstance(dtype, pandas.DatetimeTZDtype)]
        for column in columns:
            # tz_convert(None) will convert to UTC and drop timezone.
            df[column] = df[column].dt.tz_convert(None)
        return df
 
    def _fix_input_timestamps(self, df: pandas.DataFrame) -> pandas.DataFrame: …
    def _batch_size(self, table: ApdbTables | ExtraTables) -> int:
        """Calculate batch size based on config parameters."""
        # Cassandra limit on number of statements in a batch is 64k.
        batch_size = 65_535
        if 0 < self.config.batch_statement_limit < batch_size:
            batch_size = self.config.batch_statement_limit
        if self.config.batch_size_limit > 0:
            # The purpose of this limit is to try not to exceed batch size
            # threshold which is set on server side. Cassandra wire protocol
            # for prepared queries (and batches) only sends column values with
            # with an additional 4 bytes per value specifying size. Value is
            # not included for NULL or NOT_SET values, but the size is always
            # there. There is additional small per-query overhead, which we
            # ignore.
            row_size = self._schema.table_row_size(table)
            row_size += 4 * len(self._schema.getColumnMap(table))
            batch_size = min(batch_size, (self.config.batch_size_limit // row_size) + 1)
        return batch_size
    def _batch_size(self, table: ApdbTables | ExtraTables) -> int: …
class ApdbCassandra(Apdb): …