[BUG] compare_two_records fails in Spark if some values are None

[RobinL]

This fails in Spark:

r1 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": "1980-01-01",
}
r2 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": None,
}

linker.inference.compare_two_records(r1, r2).as_pandas_dataframe()

with

PySparkValueError: [CANNOT_DETERMINE_TYPE] Some of types cannot be determined after inferring.

Click to expand

from pyspark.context import SparkConf, SparkContext
from pyspark.sql import SparkSession

import splink.comparison_library as cl
from splink import (
    Linker,
    SettingsCreator,
    SparkAPI,
    block_on,
    splink_datasets,
)
from splink.backends.spark import similarity_jar_location

path = similarity_jar_location()
df_pandas = splink_datasets.fake_1000


conf = SparkConf()
conf.set("spark.jars", path)
conf.set("spark.driver.memory", "12g")
conf.set("spark.sql.shuffle.partitions", "12")
conf.set("spark.default.parallelism", "12")

sc = SparkContext.getOrCreate(conf=conf)
sc.setCheckpointDir("tmp_checkpoints/")
spark = SparkSession(sc)


df = spark.createDataFrame(df_pandas)


db_api = SparkAPI(
    spark_session=spark,
    break_lineage_method="parquet",
    num_partitions_on_repartition=6,
)


df = splink_datasets.fake_1000

settings = SettingsCreator(
    link_type="dedupe_only",
    comparisons=[
        cl.ExactMatch("first_name"),
        cl.ExactMatch("surname"),
        cl.ExactMatch("dob"),
    ],
    blocking_rules_to_generate_predictions=[
        block_on("first_name"),
        block_on("surname"),
    ],
    max_iterations=2,
)

linker = Linker(df, settings, db_api)


pairwise_predictions = linker.inference.predict(threshold_match_weight=-10)


r1 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": "1980-01-01",
}
r2 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": None,
}
pd.DataFrame([r1, r2])

linker.inference.compare_two_records(r1, r2).as_pandas_dataframe()

Ultimate issue is with

splink/splink/internals/spark/database_api.py

Lines 64 to 76 in 8b44ab5

	def _table_registration(
	self, input: AcceptableInputTableType, table_name: str
	) -> None:
	if isinstance(input, dict):
	input = pd.DataFrame(input)
	elif isinstance(input, list):
	input = self.spark.createDataFrame(input)
	if isinstance(input, pd.DataFrame):
	input = self._clean_pandas_df(input)
	input = self.spark.createDataFrame(input)
	input.createOrReplaceTempView(table_name)

[RobinL]

Fix is that table registration should accept an arrow row

splink/splink/internals/spark/database_api.py

Line 72 in 8b44ab5

if isinstance(input, pd.DataFrame):

Actually that's no good because you can't pass arrow directly to Spark

[RobinL]

from pyspark.sql.types import StructType, StructField, StringType

r1 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": None
}
r2 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": "1980-01-01",
}

schema = StructType([
    StructField("first_name", StringType(), True),
    StructField("surname", StringType(), True),
    StructField("dob", StringType(), True)
])

in_1 = spark.createDataFrame([r1], schema=schema)
in_2 = spark.createDataFrame([r2], schema=schema)

# linker.inference.compare_two_records(r1, r2).as_pandas_dataframe()


import pandas as pd

linker.inference.compare_two_records(
    in_1, in_2
).as_pandas_dataframe()

Should probably be alloewd

[RobinL]

The only reason you can't do that at the moment is that we add [] around the record! We should only do that if it's a dict

splink/splink/internals/linker_components/inference.py

Line 521 in 8b44ab5

[record_1], f"__splink__compare_two_records_left_{uid}", overwrite=True

That should fix

[RobinL]

I applied a fix that allows two schemas sparkdataframes to be passed in in compre two records:

        if isinstance(record_1, dict):
            record_1 = [record_1]
        if isinstance(record_2, dict):
            record_2 = [record_2]

        uid = ascii_uid(8)
        df_records_left = self._linker.table_management.register_table(
            record_1, f"__splink__compare_two_records_left_{uid}", overwrite=True
        )
        df_records_left.templated_name = "__splink__compare_two_records_left"

        df_records_right = self._linker.table_management.register_table(
            record_2, f"__splink__compare_two_records_right_{uid}", overwrite=True
        )
        df_records_right.templated_name = "__splink__compare_two_records_right"

But giving up for now because the number of paritions seems to explode even when running the query in plain spark:

Click to expand

from __future__ import annotations

import logging
import time
from typing import TYPE_CHECKING, Any

from splink.internals.blocking import (
    BlockingRule,
    block_using_rules_sqls,
    materialise_exploded_id_tables,
)
from splink.internals.blocking_rule_creator import BlockingRuleCreator
from splink.internals.blocking_rule_creator_utils import to_blocking_rule_creator
from splink.internals.comparison_vector_values import (
    compute_comparison_vector_values_from_id_pairs_sqls,
)
from splink.internals.database_api import AcceptableInputTableType
from splink.internals.find_matches_to_new_records import (
    add_unique_id_and_source_dataset_cols_if_needed,
)
from splink.internals.misc import (
    ascii_uid,
    ensure_is_list,
)
from splink.internals.pipeline import CTEPipeline
from splink.internals.predict import (
    predict_from_comparison_vectors_sqls_using_settings,
)
from splink.internals.splink_dataframe import SplinkDataFrame
from splink.internals.term_frequencies import (
    _join_new_table_to_df_concat_with_tf_sql,
    colname_to_tf_tablename,
)
from splink.internals.vertically_concatenate import (
    compute_df_concat_with_tf,
    enqueue_df_concat_with_tf,
    split_df_concat_with_tf_into_two_tables_sqls,
)

if TYPE_CHECKING:
    from splink.internals.linker import Linker

logger = logging.getLogger(__name__)


class LinkerInference:
    """Use your Splink model to make predictions (perform inference). Accessed via
    `linker.inference`.
    """

    def __init__(self, linker: Linker):
        self._linker = linker

    def deterministic_link(self) -> SplinkDataFrame:
        """Uses the blocking rules specified by
        `blocking_rules_to_generate_predictions` in your settings to
        generate pairwise record comparisons.

        For deterministic linkage, this should be a list of blocking rules which
        are strict enough to generate only true links.

        Deterministic linkage, however, is likely to result in missed links
        (false negatives).

        Returns:
            SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons.


        Examples:

            ```py
            settings = SettingsCreator(
                link_type="dedupe_only",
                blocking_rules_to_generate_predictions=[
                    block_on("first_name", "surname"),
                    block_on("dob", "first_name"),
                ],
            )

            linker = Linker(df, settings, db_api=db_api)
            splink_df = linker.inference.deterministic_link()
            ```
        """
        pipeline = CTEPipeline()
        # Allows clustering during a deterministic linkage.
        # This is used in `cluster_pairwise_predictions_at_threshold`
        # to set the cluster threshold to 1

        df_concat_with_tf = compute_df_concat_with_tf(self._linker, pipeline)
        pipeline = CTEPipeline([df_concat_with_tf])
        link_type = self._linker._settings_obj._link_type

        blocking_input_tablename_l = "__splink__df_concat_with_tf"
        blocking_input_tablename_r = "__splink__df_concat_with_tf"

        link_type = self._linker._settings_obj._link_type
        if (
            len(self._linker._input_tables_dict) == 2
            and self._linker._settings_obj._link_type == "link_only"
        ):
            sqls = split_df_concat_with_tf_into_two_tables_sqls(
                "__splink__df_concat_with_tf",
                self._linker._settings_obj.column_info_settings.source_dataset_column_name,
            )
            pipeline.enqueue_list_of_sqls(sqls)

            blocking_input_tablename_l = "__splink__df_concat_with_tf_left"
            blocking_input_tablename_r = "__splink__df_concat_with_tf_right"
            link_type = "two_dataset_link_only"

        exploding_br_with_id_tables = materialise_exploded_id_tables(
            link_type=link_type,
            blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
            db_api=self._linker._db_api,
            splink_df_dict=self._linker._input_tables_dict,
            source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
            unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
        )

        sqls = block_using_rules_sqls(
            input_tablename_l=blocking_input_tablename_l,
            input_tablename_r=blocking_input_tablename_r,
            blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
            link_type=link_type,
            source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
            unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
        )
        pipeline.enqueue_list_of_sqls(sqls)
        blocked_pairs = self._linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)

        pipeline = CTEPipeline([blocked_pairs, df_concat_with_tf])

        sqls = compute_comparison_vector_values_from_id_pairs_sqls(
            self._linker._settings_obj._columns_to_select_for_blocking,
            ["*"],
            input_tablename_l="__splink__df_concat_with_tf",
            input_tablename_r="__splink__df_concat_with_tf",
            source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
            unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        deterministic_link_df = self._linker._db_api.sql_pipeline_to_splink_dataframe(
            pipeline
        )
        deterministic_link_df.metadata["is_deterministic_link"] = True

        [b.drop_materialised_id_pairs_dataframe() for b in exploding_br_with_id_tables]
        blocked_pairs.drop_table_from_database_and_remove_from_cache()

        return deterministic_link_df

    def predict(
        self,
        threshold_match_probability: float = None,
        threshold_match_weight: float = None,
        materialise_after_computing_term_frequencies: bool = True,
        materialise_blocked_pairs: bool = True,
    ) -> SplinkDataFrame:
        """Create a dataframe of scored pairwise comparisons using the parameters
        of the linkage model.

        Uses the blocking rules specified in the
        `blocking_rules_to_generate_predictions` key of the settings to
        generate the pairwise comparisons.

        Args:
            threshold_match_probability (float, optional): If specified,
                filter the results to include only pairwise comparisons with a
                match_probability above this threshold. Defaults to None.
            threshold_match_weight (float, optional): If specified,
                filter the results to include only pairwise comparisons with a
                match_weight above this threshold. Defaults to None.
            materialise_after_computing_term_frequencies (bool): If true, Splink
                will materialise the table containing the input nodes (rows)
                joined to any term frequencies which have been asked
                for in the settings object.  If False, this will be
                computed as part of a large CTE pipeline.   Defaults to True
            materialise_blocked_pairs: In the blocking phase, materialise the table
                of pairs of records that will be scored

        Examples:
            ```py
            linker = linker(df, "saved_settings.json", db_api=db_api)
            splink_df = linker.inference.predict(threshold_match_probability=0.95)
            splink_df.as_pandas_dataframe(limit=5)
            ```
        Returns:
            SplinkDataFrame: A SplinkDataFrame of the scored pairwise comparisons.
        """

        pipeline = CTEPipeline()

        # If materialise_after_computing_term_frequencies=False and the user only
        # calls predict, it runs as a single pipeline with no materialisation
        # of anything.

        # In duckdb, calls to random() in a CTE pipeline cause problems:
        # https://gist.github.com/RobinL/d329e7004998503ce91b68479aa41139
        if (
            materialise_after_computing_term_frequencies
            or self._linker._sql_dialect.sql_dialect_str == "duckdb"
        ):
            df_concat_with_tf = compute_df_concat_with_tf(self._linker, pipeline)
            pipeline = CTEPipeline([df_concat_with_tf])
        else:
            pipeline = enqueue_df_concat_with_tf(self._linker, pipeline)

        start_time = time.time()

        blocking_input_tablename_l = "__splink__df_concat_with_tf"
        blocking_input_tablename_r = "__splink__df_concat_with_tf"

        link_type = self._linker._settings_obj._link_type
        if (
            len(self._linker._input_tables_dict) == 2
            and self._linker._settings_obj._link_type == "link_only"
        ):
            sqls = split_df_concat_with_tf_into_two_tables_sqls(
                "__splink__df_concat_with_tf",
                self._linker._settings_obj.column_info_settings.source_dataset_column_name,
            )
            pipeline.enqueue_list_of_sqls(sqls)

            blocking_input_tablename_l = "__splink__df_concat_with_tf_left"
            blocking_input_tablename_r = "__splink__df_concat_with_tf_right"
            link_type = "two_dataset_link_only"

        # If exploded blocking rules exist, we need to materialise
        # the tables of ID pairs

        exploding_br_with_id_tables = materialise_exploded_id_tables(
            link_type=link_type,
            blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
            db_api=self._linker._db_api,
            splink_df_dict=self._linker._input_tables_dict,
            source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
            unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
        )

        sqls = block_using_rules_sqls(
            input_tablename_l=blocking_input_tablename_l,
            input_tablename_r=blocking_input_tablename_r,
            blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
            link_type=link_type,
            source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
            unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
        )

        pipeline.enqueue_list_of_sqls(sqls)

        if materialise_blocked_pairs:
            blocked_pairs = self._linker._db_api.sql_pipeline_to_splink_dataframe(
                pipeline
            )

            pipeline = CTEPipeline([blocked_pairs, df_concat_with_tf])
            blocking_time = time.time() - start_time
            logger.info(f"Blocking time: {blocking_time:.2f} seconds")
            start_time = time.time()

        sqls = compute_comparison_vector_values_from_id_pairs_sqls(
            self._linker._settings_obj._columns_to_select_for_blocking,
            self._linker._settings_obj._columns_to_select_for_comparison_vector_values,
            input_tablename_l="__splink__df_concat_with_tf",
            input_tablename_r="__splink__df_concat_with_tf",
            source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
            unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        sqls = predict_from_comparison_vectors_sqls_using_settings(
            self._linker._settings_obj,
            threshold_match_probability,
            threshold_match_weight,
            sql_infinity_expression=self._linker._infinity_expression,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        predictions = self._linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)

        predict_time = time.time() - start_time
        logger.info(f"Predict time: {predict_time:.2f} seconds")

        self._linker._predict_warning()

        [b.drop_materialised_id_pairs_dataframe() for b in exploding_br_with_id_tables]
        if materialise_blocked_pairs:
            blocked_pairs.drop_table_from_database_and_remove_from_cache()

        return predictions

    def find_matches_to_new_records(
        self,
        records_or_tablename: AcceptableInputTableType | str,
        blocking_rules: list[BlockingRuleCreator | dict[str, Any] | str]
        | BlockingRuleCreator
        | dict[str, Any]
        | str = [],
        match_weight_threshold: float = -4,
    ) -> SplinkDataFrame:
        """Given one or more records, find records in the input dataset(s) which match
        and return in order of the Splink prediction score.

        This effectively provides a way of searching the input datasets
        for given record(s)

        Args:
            records_or_tablename (List[dict]): Input search record(s) as list of dict,
                or a table registered to the database.
            blocking_rules (list, optional): Blocking rules to select
                which records to find and score. If [], do not use a blocking
                rule - meaning the input records will be compared to all records
                provided to the linker when it was instantiated. Defaults to [].
            match_weight_threshold (int, optional): Return matches with a match weight
                above this threshold. Defaults to -4.

        Examples:
            ```py
            linker = Linker(df, "saved_settings.json", db_api=db_api)

            # You should load or pre-compute tf tables for any tables with
            # term frequency adjustments
            linker.table_management.compute_tf_table("first_name")
            # OR
            linker.table_management.register_term_frequency_lookup(df, "first_name")

            record = {'unique_id': 1,
                'first_name': "John",
                'surname': "Smith",
                'dob': "1971-05-24",
                'city': "London",
                'email': "[email protected]"
                }
            df = linker.inference.find_matches_to_new_records(
                [record], blocking_rules=[]
            )
            ```

        Returns:
            SplinkDataFrame: The pairwise comparisons.
        """

        original_blocking_rules = (
            self._linker._settings_obj._blocking_rules_to_generate_predictions
        )
        original_link_type = self._linker._settings_obj._link_type

        blocking_rule_list = ensure_is_list(blocking_rules)

        if not isinstance(records_or_tablename, str):
            uid = ascii_uid(8)
            new_records_tablename = f"__splink__df_new_records_{uid}"
            self._linker.table_management.register_table(
                records_or_tablename, new_records_tablename, overwrite=True
            )

        else:
            new_records_tablename = records_or_tablename

        new_records_df = self._linker._db_api.table_to_splink_dataframe(
            "__splink__df_new_records", new_records_tablename
        )

        pipeline = CTEPipeline()
        nodes_with_tf = compute_df_concat_with_tf(self._linker, pipeline)

        pipeline = CTEPipeline([nodes_with_tf, new_records_df])
        if len(blocking_rule_list) == 0:
            blocking_rule_list = ["1=1"]

        blocking_rule_list = [
            to_blocking_rule_creator(br).get_blocking_rule(
                self._linker._db_api.sql_dialect.sql_dialect_str
            )
            for br in blocking_rule_list
        ]
        for n, br in enumerate(blocking_rule_list):
            br.add_preceding_rules(blocking_rule_list[:n])

        self._linker._settings_obj._blocking_rules_to_generate_predictions = (
            blocking_rule_list
        )

        pipeline = add_unique_id_and_source_dataset_cols_if_needed(
            self._linker,
            new_records_df,
            pipeline,
            in_tablename="__splink__df_new_records",
            out_tablename="__splink__df_new_records_uid_fix",
        )
        settings = self._linker._settings_obj
        sqls = block_using_rules_sqls(
            input_tablename_l="__splink__df_concat_with_tf",
            input_tablename_r="__splink__df_new_records_uid_fix",
            blocking_rules=blocking_rule_list,
            link_type="two_dataset_link_only",
            source_dataset_input_column=settings.column_info_settings.source_dataset_input_column,
            unique_id_input_column=settings.column_info_settings.unique_id_input_column,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        blocked_pairs = self._linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)

        pipeline = CTEPipeline([blocked_pairs, new_records_df, nodes_with_tf])

        cache = self._linker._intermediate_table_cache
        for tf_col in self._linker._settings_obj._term_frequency_columns:
            tf_table_name = colname_to_tf_tablename(tf_col)
            if tf_table_name in cache:
                tf_table = cache.get_with_logging(tf_table_name)
                pipeline.append_input_dataframe(tf_table)
            else:
                if "__splink__df_concat_with_tf" not in cache:
                    logger.warning(
                        f"No term frequencies found for column {tf_col.name}.\n"
                        "To apply term frequency adjustments, you need to register"
                        " a lookup using "
                        "`linker.table_management.register_term_frequency_lookup`."
                    )

        sql = _join_new_table_to_df_concat_with_tf_sql(
            self._linker, "__splink__df_new_records"
        )
        pipeline.enqueue_sql(sql, "__splink__df_new_records_with_tf_before_uid_fix")

        pipeline = add_unique_id_and_source_dataset_cols_if_needed(
            self._linker,
            new_records_df,
            pipeline,
            in_tablename="__splink__df_new_records_with_tf_before_uid_fix",
            out_tablename="__splink__df_new_records_with_tf",
        )

        sqls = compute_comparison_vector_values_from_id_pairs_sqls(
            self._linker._settings_obj._columns_to_select_for_blocking,
            self._linker._settings_obj._columns_to_select_for_comparison_vector_values,
            input_tablename_l="__splink__df_concat_with_tf",
            input_tablename_r="__splink__df_new_records_with_tf",
            source_dataset_input_column=settings.column_info_settings.source_dataset_input_column,
            unique_id_input_column=settings.column_info_settings.unique_id_input_column,
        )

        pipeline.enqueue_list_of_sqls(sqls)

        sqls = predict_from_comparison_vectors_sqls_using_settings(
            self._linker._settings_obj,
            sql_infinity_expression=self._linker._infinity_expression,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        sql = f"""
        select * from __splink__df_predict
        where match_weight > {match_weight_threshold}
        """

        pipeline.enqueue_sql(sql, "__splink__find_matches_predictions")

        predictions = self._linker._db_api.sql_pipeline_to_splink_dataframe(
            pipeline, use_cache=False
        )

        self._linker._settings_obj._blocking_rules_to_generate_predictions = (
            original_blocking_rules
        )
        self._linker._settings_obj._link_type = original_link_type

        blocked_pairs.drop_table_from_database_and_remove_from_cache()

        return predictions

    def compare_two_records(
        self, record_1: dict[str, Any], record_2: dict[str, Any]
    ) -> SplinkDataFrame:
        """Use the linkage model to compare and score a pairwise record comparison
        based on the two input records provided

        Args:
            record_1 (dict): dictionary representing the first record.  Columns names
                and data types must be the same as the columns in the settings object
            record_2 (dict): dictionary representing the second record.  Columns names
                and data types must be the same as the columns in the settings object

        Examples:
            ```py
            linker = Linker(df, "saved_settings.json", db_api=db_api)

            # You should load or pre-compute tf tables for any tables with
            # term frequency adjustments
            linker.table_management.compute_tf_table("first_name")
            # OR
            linker.table_management.register_term_frequency_lookup(df, "first_name")

            record_1 = {'unique_id': 1,
                'first_name': "John",
                'surname': "Smith",
                'dob': "1971-05-24",
                'city': "London",
                'email': "[email protected]"
                }

            record_2 = {'unique_id': 1,
                'first_name': "Jon",
                'surname': "Smith",
                'dob': "1971-05-23",
                'city': "London",
                'email': "[email protected]"
                }
            df = linker.inference.compare_two_records(record_1, record_2)

            ```

        Returns:
            SplinkDataFrame: Pairwise comparison with scored prediction
        """

        cache = self._linker._intermediate_table_cache

        if isinstance(record_1, dict):
            record_1 = [record_1]
        if isinstance(record_2, dict):
            record_2 = [record_2]

        uid = ascii_uid(8)
        df_records_left = self._linker.table_management.register_table(
            record_1, f"__splink__compare_two_records_left_{uid}", overwrite=True
        )
        df_records_left.templated_name = "__splink__compare_two_records_left"

        df_records_right = self._linker.table_management.register_table(
            record_2, f"__splink__compare_two_records_right_{uid}", overwrite=True
        )
        df_records_right.templated_name = "__splink__compare_two_records_right"

        pipeline = CTEPipeline([df_records_left, df_records_right])

        if "__splink__df_concat_with_tf" in cache:
            nodes_with_tf = cache.get_with_logging("__splink__df_concat_with_tf")
            pipeline.append_input_dataframe(nodes_with_tf)

        for tf_col in self._linker._settings_obj._term_frequency_columns:
            tf_table_name = colname_to_tf_tablename(tf_col)
            if tf_table_name in cache:
                tf_table = cache.get_with_logging(tf_table_name)
                pipeline.append_input_dataframe(tf_table)
            else:
                if "__splink__df_concat_with_tf" not in cache:
                    logger.warning(
                        f"No term frequencies found for column {tf_col.name}.\n"
                        "To apply term frequency adjustments, you need to register"
                        " a lookup using "
                        "`linker.table_management.register_term_frequency_lookup`."
                    )

        sql_join_tf = _join_new_table_to_df_concat_with_tf_sql(
            self._linker, "__splink__compare_two_records_left"
        )

        pipeline.enqueue_sql(sql_join_tf, "__splink__compare_two_records_left_with_tf")

        sql_join_tf = _join_new_table_to_df_concat_with_tf_sql(
            self._linker, "__splink__compare_two_records_right"
        )

        pipeline.enqueue_sql(sql_join_tf, "__splink__compare_two_records_right_with_tf")

        source_dataset_ic = (
            self._linker._settings_obj.column_info_settings.source_dataset_input_column
        )
        uid_ic = self._linker._settings_obj.column_info_settings.unique_id_input_column

        pipeline = add_unique_id_and_source_dataset_cols_if_needed(
            self._linker,
            df_records_left,
            pipeline,
            in_tablename="__splink__compare_two_records_left_with_tf",
            out_tablename="__splink__compare_two_records_left_with_tf_uid_fix",
            uid_str="_left",
        )
        pipeline = add_unique_id_and_source_dataset_cols_if_needed(
            self._linker,
            df_records_right,
            pipeline,
            in_tablename="__splink__compare_two_records_right_with_tf",
            out_tablename="__splink__compare_two_records_right_with_tf_uid_fix",
            uid_str="_right",
        )

        sqls = block_using_rules_sqls(
            input_tablename_l="__splink__compare_two_records_left_with_tf_uid_fix",
            input_tablename_r="__splink__compare_two_records_right_with_tf_uid_fix",
            blocking_rules=[BlockingRule("1=1")],
            link_type=self._linker._settings_obj._link_type,
            source_dataset_input_column=source_dataset_ic,
            unique_id_input_column=uid_ic,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        sqls = compute_comparison_vector_values_from_id_pairs_sqls(
            self._linker._settings_obj._columns_to_select_for_blocking,
            self._linker._settings_obj._columns_to_select_for_comparison_vector_values,
            input_tablename_l="__splink__compare_two_records_left_with_tf_uid_fix",
            input_tablename_r="__splink__compare_two_records_right_with_tf_uid_fix",
            source_dataset_input_column=source_dataset_ic,
            unique_id_input_column=uid_ic,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        sqls = predict_from_comparison_vectors_sqls_using_settings(
            self._linker._settings_obj,
            sql_infinity_expression=self._linker._infinity_expression,
        )
        pipeline.enqueue_list_of_sqls(sqls)

        predictions = self._linker._db_api.sql_pipeline_to_splink_dataframe(
            pipeline, use_cache=False
        )

        return predictions

which results inexplicably in something like
[Stage 0:> (252 + 12) / 20736]

even though predict() is basically the same query.

I've tried repartitioning, going through pandas, etc and the result always seems to be the same

This seems to fix:

spark.conf.set("spark.sql.adaptive.enabled", "true")
spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true")
spark.conf.set("spark.sql.adaptive.advisoryPartitionSizeInBytes", "1MB")

[RobinL]

In Splink 4 the thing that changed is that blocking results in a pairwise table of records.

That's probably the cause of the bug

It's a bit of hassle, but the fix is probably to cut the blocking step entirely out of compare_two_records. Since we know what the result is - we just need it to be a table iwth a single row of like _left = _right

[RobinL]

This now works:

from pyspark.sql.types import StructType, StructField, StringType

r1 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": None
}
r2 = {
    "first_name": "John",
    "surname": "Smith",
    "dob": "1980-01-01",
}

schema = StructType([
    StructField("first_name", StringType(), True),
    StructField("surname", StringType(), True),
    StructField("dob", StringType(), True)
])

in_1 = spark.createDataFrame([r1], schema=schema)
in_2 = spark.createDataFrame([r2], schema=schema)

linker.inference.compare_two_records(in_1, in_2).as_pandas_dataframe()

Closed by #2498