diff --git a/nomenklatura/index/duckdb_index.py b/nomenklatura/index/duckdb_index.py
index e8aa60c9..e4dfb9df 100644
--- a/nomenklatura/index/duckdb_index.py
+++ b/nomenklatura/index/duckdb_index.py
@@ -4,7 +4,7 @@
 import logging
 from itertools import combinations
 from tempfile import mkdtemp
-from typing import Any, Dict, Generator, List, Set, Tuple
+from typing import Any, Dict, Generator, Iterable, List, Set, Tuple
 from followthemoney.types import registry
 import duckdb
 
@@ -19,6 +19,8 @@
 
 log = logging.getLogger(__name__)
 
+BATCH_SIZE = 1000
+
 
 class DuckDBIndex(BaseIndex[DS, CE]):
     """
@@ -57,6 +59,7 @@ def __init__(self, view: View[DS, CE], data_dir: Path):
         self.con.execute("SET max_memory = '2GB';")
         self.con.execute("SET threads = 1;")
         self.con.execute("CREATE TABLE entries (id TEXT, field TEXT, token TEXT)")
+        self.con.execute("CREATE TABLE boosts (field TEXT, boost FLOAT)")
 
     def dump(self, writer, entity: CE) -> None:
 
@@ -77,52 +80,17 @@ def build(self) -> None:
                 self.dump(writer, entity)
                 if idx % 10000 == 0:
                     log.info("Dumped %s entities" % idx)
+        for field, boost in self.BOOSTS.items():
+            self.con.execute("INSERT INTO boosts VALUES (?, ?)", [field, boost])
 
         log.info("Loading data...")
         self.con.execute(f"COPY entries from '{csv_path}'")
 
         log.info("Index built.")
 
-    def pairs(self, max_pairs: int = BaseIndex.MAX_PAIRS) -> List[Tuple[Pair, float]]:
-        csv_path = self.path / "cooccurrences.csv"
-        with open(csv_path, "w") as fh:
-            writer = csv.writer(fh)
-            writer.writerow(["left", "right", "score"])
-            for pair, score in self.cooccurring_tokens():
-                writer.writerow([pair[0], pair[1], score])
-        log.info("Loading co-occurrences...")
-        self.con.execute('CREATE TABLE cooccurrences ("left" TEXT, "right" TEXT, score FLOAT)')
-        self.con.execute(f"COPY cooccurrences from '{csv_path}'")
-        pairs_query = """
-            SELECT "left", "right", sum(score) as score
-            FROM cooccurrences
-            GROUP BY "left", "right"
-            ORDER BY score DESC
-            LIMIT ?
-        """
-        pairs_rel = self.con.execute(pairs_query, [max_pairs])
-        pairs: List[Tuple[Pair, float]] = []
-        for left, right, score in pairs_rel.fetchall():
-            pairs.append(((Identifier.get(left), Identifier.get(right)), score))
-        return pairs
-
-    def cooccurring_tokens(self):
-        logged = defaultdict(int)
-        for field_name, token, entities in self.frequencies():
-            logged[field_name] += 1
-            if logged[field_name] % 10000 == 0:
-                log.info("Pairwise xref [%s]: %d" % (field_name, logged[field_name]))
-            boost = self.BOOSTS.get(field_name, 1.0)
-            for (left, lw), (right, rw) in combinations(entities, 2):
-                if lw == 0.0 or rw == 0.0:
-                    continue
-                pair = (max(left, right), min(left, right))
-                score = (lw + rw) * boost
-                yield pair, score
-
-    def frequencies(
-        self,
-    ) -> Generator[Tuple[str, str, List[Tuple[Identifier, float]]], None, None]:
+    def pairs(
+        self, max_pairs: int = BaseIndex.MAX_PAIRS
+    ) -> Iterable[Tuple[Pair, float]]:
         field_len_query = """
             SELECT field, id, count(*) as field_len from entries
             GROUP BY field, id
@@ -140,33 +108,32 @@ def frequencies(
             GROUP BY field, token
         """
         token_freq = self.con.sql(token_freq_query)
-        query = """
-            SELECT mentions.field, mentions.token, mentions.id, mentions/field_len
+        term_frequencies_query = """
+            SELECT mentions.field, mentions.token, mentions.id, mentions/field_len as tf
             FROM field_len
             JOIN mentions
             ON field_len.field = mentions.field AND field_len.id = mentions.id
             JOIN token_freq
             ON token_freq.field = mentions.field AND token_freq.token = mentions.token
             where token_freq < 100
-            ORDER BY mentions.field, mentions.token
         """
-        rel = self.con.sql(query)
-        row = rel.fetchone()
-        entities = []  # the entities in this field, token group
-        field_name = None
-        token = None
-        while row is not None:
-            field_name, token, id, freq = row
-            entities.append((Identifier.get(id), freq))
-
-            row = rel.fetchone()
-            if row is None:
-                yield field_name, token, entities
-                break
-            new_field_name, new_token, _, _ = row
-            if new_field_name != field_name or new_token != token:
-                yield field_name, token, entities
-                entities = []
+        term_frequencies = self.con.sql(term_frequencies_query)
+        pairs_query = """
+            SELECT "left".id, "right".id, sum(("left".tf + "right".tf) * boost) as score
+            FROM term_frequencies as "left"
+            JOIN term_frequencies as "right"
+            ON "left".field = "right".field AND "left".token = "right".token
+            JOIN boosts
+            ON "left".field = boosts.field
+            WHERE "left".id > "right".id
+            GROUP BY "left".id, "right".id
+            ORDER BY score DESC
+            LIMIT ?
+        """
+        results = self.con.execute(pairs_query, [max_pairs])
+        while batch := results.fetchmany(BATCH_SIZE):
+            for left, right, score in batch:
+                yield (Identifier.get(left), Identifier.get(right)), score
 
     def __repr__(self) -> str:
         return "<DuckDBIndex(%r, %r)>" % (