Merge remote-tracking branch 'apache/main' into alamb/union_sorting

.github/workflows/rust.yml

@@ -521,7 +521,7 @@ jobs:
         run: taplo format --check
 
   config-docs-check:
-    name: check configs.md is up-to-date
+    name: check configs.md and ***_functions.md is up-to-date
     needs: [ linux-build-lib ]
     runs-on: ubuntu-latest
     container:
@@ -542,6 +542,11 @@ jobs:
           # If you encounter an error, run './dev/update_config_docs.sh' and commit
           ./dev/update_config_docs.sh
           git diff --exit-code
+      - name: Check if any of the ***_functions.md has been modified
+        run: |
+          # If you encounter an error, run './dev/update_function_docs.sh' and commit
+          ./dev/update_function_docs.sh
+          git diff --exit-code
 
   # Verify MSRV for the crates which are directly used by other projects:
   # - datafusion

README.md

@@ -42,14 +42,25 @@
 </a>
 
 DataFusion is an extensible query engine written in [Rust] that
-uses [Apache Arrow] as its in-memory format. DataFusion's target users are
+uses [Apache Arrow] as its in-memory format.
+
+The DataFusion libraries in this repository are used to build data-centric system software. DataFusion also provides the
+following subprojects, which are packaged versions of DataFusion intended for end users.
+
+- [DataFusion Python](https://github.com/apache/datafusion-python/) offers a Python interface for SQL and DataFrame
+  queries.
+- [DataFusion Ray](https://github.com/apache/datafusion-ray/) provides a distributed version of DataFusion that scales
+  out on Ray clusters.
+- [DataFusion Comet](https://github.com/apache/datafusion-comet/) is an accelerator for Apache Spark based on
+  DataFusion.
+
+The target audience for the DataFusion crates in this repository are
 developers building fast and feature rich database and analytic systems,
 customized to particular workloads. See [use cases] for examples.
 
-"Out of the box," DataFusion offers [SQL] and [`Dataframe`] APIs,
+DataFusion offers [SQL] and [`Dataframe`] APIs,
 excellent [performance], built-in support for CSV, Parquet, JSON, and Avro,
 extensive customization, and a great community.
-[Python Bindings] are also available.
 
 DataFusion features a full query planner, a columnar, streaming, multi-threaded,
 vectorized execution engine, and partitioned data sources. You can

benchmarks/bench.sh

@@ -211,6 +211,7 @@ main() {
                     run_clickbench_1
                     run_clickbench_partitioned
                     run_clickbench_extended
+                    run_imdb
                     ;;
                 tpch)
                     run_tpch "1"
@@ -239,6 +240,9 @@ main() {
                 clickbench_extended)
                     run_clickbench_extended
                     ;;
+                imdb)
+                    run_imdb
+                    ;;
                 *)
                     echo "Error: unknown benchmark '$BENCHMARK' for run"
                     usage
@@ -510,6 +514,16 @@ data_imdb() {
     fi
 }
 
+# Runs the imdb benchmark
+run_imdb() {
+    IMDB_DIR="${DATA_DIR}/imdb"
+
+    RESULTS_FILE="${RESULTS_DIR}/imdb.json"
+    echo "RESULTS_FILE: ${RESULTS_FILE}"
+    echo "Running imdb benchmark..."
+    $CARGO_COMMAND --bin imdb -- benchmark datafusion --iterations 5 --path "${IMDB_DIR}" --prefer_hash_join "${PREFER_HASH_JOIN}" --format parquet -o "${RESULTS_FILE}"
+}
+
 
 
 

benchmarks/queries/imdb/10a.sql

@@ -0,0 +1 @@
+SELECT MIN(chn.name) AS uncredited_voiced_character, MIN(t.title) AS russian_movie FROM char_name AS chn, cast_info AS ci, company_name AS cn, company_type AS ct, movie_companies AS mc, role_type AS rt, title AS t WHERE ci.note  like '%(voice)%' and ci.note like '%(uncredited)%' AND cn.country_code  = '[ru]' AND rt.role  = 'actor' AND t.production_year > 2005 AND t.id = mc.movie_id AND t.id = ci.movie_id AND ci.movie_id = mc.movie_id AND chn.id = ci.person_role_id AND rt.id = ci.role_id AND cn.id = mc.company_id AND ct.id = mc.company_type_id;

benchmarks/queries/imdb/10b.sql

@@ -0,0 +1 @@
+SELECT MIN(chn.name) AS character, MIN(t.title) AS russian_mov_with_actor_producer FROM char_name AS chn, cast_info AS ci, company_name AS cn, company_type AS ct, movie_companies AS mc, role_type AS rt, title AS t WHERE ci.note  like '%(producer)%' AND cn.country_code  = '[ru]' AND rt.role  = 'actor' AND t.production_year > 2010 AND t.id = mc.movie_id AND t.id = ci.movie_id AND ci.movie_id = mc.movie_id AND chn.id = ci.person_role_id AND rt.id = ci.role_id AND cn.id = mc.company_id AND ct.id = mc.company_type_id;

benchmarks/queries/imdb/10c.sql

@@ -0,0 +1 @@
+SELECT MIN(chn.name) AS character, MIN(t.title) AS movie_with_american_producer FROM char_name AS chn, cast_info AS ci, company_name AS cn, company_type AS ct, movie_companies AS mc, role_type AS rt, title AS t WHERE ci.note  like '%(producer)%' AND cn.country_code  = '[us]' AND t.production_year > 1990 AND t.id = mc.movie_id AND t.id = ci.movie_id AND ci.movie_id = mc.movie_id AND chn.id = ci.person_role_id AND rt.id = ci.role_id AND cn.id = mc.company_id AND ct.id = mc.company_type_id;

benchmarks/queries/imdb/11a.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS from_company, MIN(lt.link) AS movie_link_type, MIN(t.title) AS non_polish_sequel_movie FROM company_name AS cn, company_type AS ct, keyword AS k, link_type AS lt, movie_companies AS mc, movie_keyword AS mk, movie_link AS ml, title AS t WHERE cn.country_code !='[pl]' AND (cn.name LIKE '%Film%' OR cn.name LIKE '%Warner%') AND ct.kind ='production companies' AND k.keyword ='sequel' AND lt.link LIKE '%follow%' AND mc.note IS NULL AND t.production_year BETWEEN 1950 AND 2000 AND lt.id = ml.link_type_id AND ml.movie_id = t.id AND t.id = mk.movie_id AND mk.keyword_id = k.id AND t.id = mc.movie_id AND mc.company_type_id = ct.id AND mc.company_id = cn.id AND ml.movie_id = mk.movie_id AND ml.movie_id = mc.movie_id AND mk.movie_id = mc.movie_id;

benchmarks/queries/imdb/11b.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS from_company, MIN(lt.link) AS movie_link_type, MIN(t.title) AS sequel_movie FROM company_name AS cn, company_type AS ct, keyword AS k, link_type AS lt, movie_companies AS mc, movie_keyword AS mk, movie_link AS ml, title AS t WHERE cn.country_code !='[pl]' AND (cn.name LIKE '%Film%' OR cn.name LIKE '%Warner%') AND ct.kind ='production companies' AND k.keyword ='sequel' AND lt.link LIKE '%follows%' AND mc.note IS NULL AND t.production_year  = 1998 and t.title like '%Money%' AND lt.id = ml.link_type_id AND ml.movie_id = t.id AND t.id = mk.movie_id AND mk.keyword_id = k.id AND t.id = mc.movie_id AND mc.company_type_id = ct.id AND mc.company_id = cn.id AND ml.movie_id = mk.movie_id AND ml.movie_id = mc.movie_id AND mk.movie_id = mc.movie_id;

benchmarks/queries/imdb/11c.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS from_company, MIN(mc.note) AS production_note, MIN(t.title) AS movie_based_on_book FROM company_name AS cn, company_type AS ct, keyword AS k, link_type AS lt, movie_companies AS mc, movie_keyword AS mk, movie_link AS ml, title AS t WHERE cn.country_code  !='[pl]' and (cn.name like '20th Century Fox%' or cn.name like 'Twentieth Century Fox%') AND ct.kind  != 'production companies' and ct.kind is not NULL AND k.keyword  in ('sequel', 'revenge', 'based-on-novel') AND mc.note  is not NULL AND t.production_year  > 1950 AND lt.id = ml.link_type_id AND ml.movie_id = t.id AND t.id = mk.movie_id AND mk.keyword_id = k.id AND t.id = mc.movie_id AND mc.company_type_id = ct.id AND mc.company_id = cn.id AND ml.movie_id = mk.movie_id AND ml.movie_id = mc.movie_id AND mk.movie_id = mc.movie_id;

benchmarks/queries/imdb/11d.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS from_company, MIN(mc.note) AS production_note, MIN(t.title) AS movie_based_on_book FROM company_name AS cn, company_type AS ct, keyword AS k, link_type AS lt, movie_companies AS mc, movie_keyword AS mk, movie_link AS ml, title AS t WHERE cn.country_code  !='[pl]' AND ct.kind  != 'production companies' and ct.kind is not NULL AND k.keyword  in ('sequel', 'revenge', 'based-on-novel') AND mc.note  is not NULL AND t.production_year  > 1950 AND lt.id = ml.link_type_id AND ml.movie_id = t.id AND t.id = mk.movie_id AND mk.keyword_id = k.id AND t.id = mc.movie_id AND mc.company_type_id = ct.id AND mc.company_id = cn.id AND ml.movie_id = mk.movie_id AND ml.movie_id = mc.movie_id AND mk.movie_id = mc.movie_id;

benchmarks/queries/imdb/12a.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS movie_company, MIN(mi_idx.info) AS rating, MIN(t.title) AS drama_horror_movie FROM company_name AS cn, company_type AS ct, info_type AS it1, info_type AS it2, movie_companies AS mc, movie_info AS mi, movie_info_idx AS mi_idx, title AS t WHERE cn.country_code  = '[us]' AND ct.kind  = 'production companies' AND it1.info = 'genres' AND it2.info = 'rating' AND mi.info  in ('Drama', 'Horror') AND mi_idx.info  > '8.0' AND t.production_year  between 2005 and 2008 AND t.id = mi.movie_id AND t.id = mi_idx.movie_id AND mi.info_type_id = it1.id AND mi_idx.info_type_id = it2.id AND t.id = mc.movie_id AND ct.id = mc.company_type_id AND cn.id = mc.company_id AND mc.movie_id = mi.movie_id AND mc.movie_id = mi_idx.movie_id AND mi.movie_id = mi_idx.movie_id;

benchmarks/queries/imdb/12b.sql

@@ -0,0 +1 @@
+SELECT MIN(mi.info) AS budget, MIN(t.title) AS unsuccsessful_movie FROM company_name AS cn, company_type AS ct, info_type AS it1, info_type AS it2, movie_companies AS mc, movie_info AS mi, movie_info_idx AS mi_idx, title AS t WHERE cn.country_code ='[us]' AND ct.kind  is not NULL and (ct.kind ='production companies' or ct.kind = 'distributors') AND it1.info ='budget' AND it2.info ='bottom 10 rank' AND t.production_year >2000 AND (t.title LIKE 'Birdemic%' OR t.title LIKE '%Movie%') AND t.id = mi.movie_id AND t.id = mi_idx.movie_id AND mi.info_type_id = it1.id AND mi_idx.info_type_id = it2.id AND t.id = mc.movie_id AND ct.id = mc.company_type_id AND cn.id = mc.company_id AND mc.movie_id = mi.movie_id AND mc.movie_id = mi_idx.movie_id AND mi.movie_id = mi_idx.movie_id;

benchmarks/queries/imdb/12c.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS movie_company, MIN(mi_idx.info) AS rating, MIN(t.title) AS mainstream_movie FROM company_name AS cn, company_type AS ct, info_type AS it1, info_type AS it2, movie_companies AS mc, movie_info AS mi, movie_info_idx AS mi_idx, title AS t WHERE cn.country_code  = '[us]' AND ct.kind  = 'production companies' AND it1.info = 'genres' AND it2.info = 'rating' AND mi.info  in ('Drama', 'Horror', 'Western', 'Family') AND mi_idx.info  > '7.0' AND t.production_year  between 2000 and 2010 AND t.id = mi.movie_id AND t.id = mi_idx.movie_id AND mi.info_type_id = it1.id AND mi_idx.info_type_id = it2.id AND t.id = mc.movie_id AND ct.id = mc.company_type_id AND cn.id = mc.company_id AND mc.movie_id = mi.movie_id AND mc.movie_id = mi_idx.movie_id AND mi.movie_id = mi_idx.movie_id;

benchmarks/queries/imdb/13a.sql

@@ -0,0 +1 @@
+SELECT MIN(mi.info) AS release_date, MIN(miidx.info) AS rating, MIN(t.title) AS german_movie FROM company_name AS cn, company_type AS ct, info_type AS it, info_type AS it2, kind_type AS kt, movie_companies AS mc, movie_info AS mi, movie_info_idx AS miidx, title AS t WHERE cn.country_code ='[de]' AND ct.kind ='production companies' AND it.info ='rating' AND it2.info ='release dates' AND kt.kind ='movie' AND mi.movie_id = t.id AND it2.id = mi.info_type_id AND kt.id = t.kind_id AND mc.movie_id = t.id AND cn.id = mc.company_id AND ct.id = mc.company_type_id AND miidx.movie_id = t.id AND it.id = miidx.info_type_id AND mi.movie_id = miidx.movie_id AND mi.movie_id = mc.movie_id AND miidx.movie_id = mc.movie_id;

benchmarks/queries/imdb/13b.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS producing_company, MIN(miidx.info) AS rating, MIN(t.title) AS movie_about_winning FROM company_name AS cn, company_type AS ct, info_type AS it, info_type AS it2, kind_type AS kt, movie_companies AS mc, movie_info AS mi, movie_info_idx AS miidx, title AS t WHERE cn.country_code ='[us]' AND ct.kind ='production companies' AND it.info ='rating' AND it2.info ='release dates' AND kt.kind ='movie' AND t.title  != '' AND (t.title LIKE '%Champion%' OR t.title LIKE '%Loser%') AND mi.movie_id = t.id AND it2.id = mi.info_type_id AND kt.id = t.kind_id AND mc.movie_id = t.id AND cn.id = mc.company_id AND ct.id = mc.company_type_id AND miidx.movie_id = t.id AND it.id = miidx.info_type_id AND mi.movie_id = miidx.movie_id AND mi.movie_id = mc.movie_id AND miidx.movie_id = mc.movie_id;

benchmarks/queries/imdb/13c.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS producing_company, MIN(miidx.info) AS rating, MIN(t.title) AS movie_about_winning FROM company_name AS cn, company_type AS ct, info_type AS it, info_type AS it2, kind_type AS kt, movie_companies AS mc, movie_info AS mi, movie_info_idx AS miidx, title AS t WHERE cn.country_code ='[us]' AND ct.kind ='production companies' AND it.info ='rating' AND it2.info ='release dates' AND kt.kind ='movie' AND t.title  != '' AND (t.title LIKE 'Champion%' OR t.title LIKE 'Loser%') AND mi.movie_id = t.id AND it2.id = mi.info_type_id AND kt.id = t.kind_id AND mc.movie_id = t.id AND cn.id = mc.company_id AND ct.id = mc.company_type_id AND miidx.movie_id = t.id AND it.id = miidx.info_type_id AND mi.movie_id = miidx.movie_id AND mi.movie_id = mc.movie_id AND miidx.movie_id = mc.movie_id;

benchmarks/queries/imdb/13d.sql

@@ -0,0 +1 @@
+SELECT MIN(cn.name) AS producing_company, MIN(miidx.info) AS rating, MIN(t.title) AS movie FROM company_name AS cn, company_type AS ct, info_type AS it, info_type AS it2, kind_type AS kt, movie_companies AS mc, movie_info AS mi, movie_info_idx AS miidx, title AS t WHERE cn.country_code ='[us]' AND ct.kind ='production companies' AND it.info ='rating' AND it2.info ='release dates' AND kt.kind ='movie' AND mi.movie_id = t.id AND it2.id = mi.info_type_id AND kt.id = t.kind_id AND mc.movie_id = t.id AND cn.id = mc.company_id AND ct.id = mc.company_type_id AND miidx.movie_id = t.id AND it.id = miidx.info_type_id AND mi.movie_id = miidx.movie_id AND mi.movie_id = mc.movie_id AND miidx.movie_id = mc.movie_id;

benchmarks/queries/imdb/14a.sql

@@ -0,0 +1 @@
+SELECT MIN(mi_idx.info) AS rating, MIN(t.title) AS northern_dark_movie FROM info_type AS it1, info_type AS it2, keyword AS k, kind_type AS kt, movie_info AS mi, movie_info_idx AS mi_idx, movie_keyword AS mk, title AS t WHERE it1.info  = 'countries' AND it2.info  = 'rating' AND k.keyword  in ('murder', 'murder-in-title', 'blood', 'violence') AND kt.kind  = 'movie' AND mi.info IN ('Sweden', 'Norway', 'Germany', 'Denmark', 'Swedish', 'Denish', 'Norwegian', 'German', 'USA', 'American') AND mi_idx.info  < '8.5' AND t.production_year  > 2010 AND kt.id = t.kind_id AND t.id = mi.movie_id AND t.id = mk.movie_id AND t.id = mi_idx.movie_id AND mk.movie_id = mi.movie_id AND mk.movie_id = mi_idx.movie_id AND mi.movie_id = mi_idx.movie_id AND k.id = mk.keyword_id AND it1.id = mi.info_type_id AND it2.id = mi_idx.info_type_id;

benchmarks/queries/imdb/14b.sql

@@ -0,0 +1 @@
+SELECT MIN(mi_idx.info) AS rating, MIN(t.title) AS western_dark_production FROM info_type AS it1, info_type AS it2, keyword AS k, kind_type AS kt, movie_info AS mi, movie_info_idx AS mi_idx, movie_keyword AS mk, title AS t WHERE it1.info  = 'countries' AND it2.info  = 'rating' AND k.keyword  in ('murder', 'murder-in-title') AND kt.kind  = 'movie' AND mi.info IN ('Sweden', 'Norway', 'Germany', 'Denmark', 'Swedish', 'Denish', 'Norwegian', 'German', 'USA', 'American') AND mi_idx.info  > '6.0' AND t.production_year  > 2010 and (t.title like '%murder%' or t.title like '%Murder%' or t.title like '%Mord%') AND kt.id = t.kind_id AND t.id = mi.movie_id AND t.id = mk.movie_id AND t.id = mi_idx.movie_id AND mk.movie_id = mi.movie_id AND mk.movie_id = mi_idx.movie_id AND mi.movie_id = mi_idx.movie_id AND k.id = mk.keyword_id AND it1.id = mi.info_type_id AND it2.id = mi_idx.info_type_id;

benchmarks/queries/imdb/14c.sql

@@ -0,0 +1 @@
+SELECT MIN(mi_idx.info) AS rating, MIN(t.title) AS north_european_dark_production FROM info_type AS it1, info_type AS it2, keyword AS k, kind_type AS kt, movie_info AS mi, movie_info_idx AS mi_idx, movie_keyword AS mk, title AS t WHERE it1.info  = 'countries' AND it2.info  = 'rating' AND k.keyword  is not null and k.keyword in ('murder', 'murder-in-title', 'blood', 'violence') AND kt.kind  in ('movie', 'episode') AND mi.info IN ('Sweden', 'Norway', 'Germany', 'Denmark', 'Swedish', 'Danish', 'Norwegian', 'German', 'USA', 'American') AND mi_idx.info  < '8.5' AND t.production_year  > 2005 AND kt.id = t.kind_id AND t.id = mi.movie_id AND t.id = mk.movie_id AND t.id = mi_idx.movie_id AND mk.movie_id = mi.movie_id AND mk.movie_id = mi_idx.movie_id AND mi.movie_id = mi_idx.movie_id AND k.id = mk.keyword_id AND it1.id = mi.info_type_id AND it2.id = mi_idx.info_type_id;

benchmarks/queries/imdb/15a.sql

@@ -0,0 +1 @@
+SELECT MIN(mi.info) AS release_date, MIN(t.title) AS internet_movie FROM aka_title AS at, company_name AS cn, company_type AS ct, info_type AS it1, keyword AS k, movie_companies AS mc, movie_info AS mi, movie_keyword AS mk, title AS t WHERE cn.country_code  = '[us]' AND it1.info  = 'release dates' AND mc.note  like '%(200%)%' and mc.note like '%(worldwide)%' AND mi.note  like '%internet%' AND mi.info  like 'USA:% 200%' AND t.production_year  > 2000 AND t.id = at.movie_id AND t.id = mi.movie_id AND t.id = mk.movie_id AND t.id = mc.movie_id AND mk.movie_id = mi.movie_id AND mk.movie_id = mc.movie_id AND mk.movie_id = at.movie_id AND mi.movie_id = mc.movie_id AND mi.movie_id = at.movie_id AND mc.movie_id = at.movie_id AND k.id = mk.keyword_id AND it1.id = mi.info_type_id AND cn.id = mc.company_id AND ct.id = mc.company_type_id;

benchmarks/queries/imdb/15b.sql

@@ -0,0 +1 @@
+SELECT MIN(mi.info) AS release_date, MIN(t.title) AS youtube_movie FROM aka_title AS at, company_name AS cn, company_type AS ct, info_type AS it1, keyword AS k, movie_companies AS mc, movie_info AS mi, movie_keyword AS mk, title AS t WHERE cn.country_code  = '[us]' and cn.name = 'YouTube' AND it1.info  = 'release dates' AND mc.note  like '%(200%)%' and mc.note like '%(worldwide)%' AND mi.note  like '%internet%' AND mi.info  like 'USA:% 200%' AND t.production_year  between 2005 and 2010 AND t.id = at.movie_id AND t.id = mi.movie_id AND t.id = mk.movie_id AND t.id = mc.movie_id AND mk.movie_id = mi.movie_id AND mk.movie_id = mc.movie_id AND mk.movie_id = at.movie_id AND mi.movie_id = mc.movie_id AND mi.movie_id = at.movie_id AND mc.movie_id = at.movie_id AND k.id = mk.keyword_id AND it1.id = mi.info_type_id AND cn.id = mc.company_id AND ct.id = mc.company_type_id;

benchmarks/queries/imdb/15c.sql

@@ -0,0 +1 @@
+SELECT MIN(mi.info) AS release_date, MIN(t.title) AS modern_american_internet_movie FROM aka_title AS at, company_name AS cn, company_type AS ct, info_type AS it1, keyword AS k, movie_companies AS mc, movie_info AS mi, movie_keyword AS mk, title AS t WHERE cn.country_code  = '[us]' AND it1.info  = 'release dates' AND mi.note  like '%internet%' AND mi.info  is not NULL and (mi.info like 'USA:% 199%' or mi.info like 'USA:% 200%') AND t.production_year  > 1990 AND t.id = at.movie_id AND t.id = mi.movie_id AND t.id = mk.movie_id AND t.id = mc.movie_id AND mk.movie_id = mi.movie_id AND mk.movie_id = mc.movie_id AND mk.movie_id = at.movie_id AND mi.movie_id = mc.movie_id AND mi.movie_id = at.movie_id AND mc.movie_id = at.movie_id AND k.id = mk.keyword_id AND it1.id = mi.info_type_id AND cn.id = mc.company_id AND ct.id = mc.company_type_id;

benchmarks/queries/imdb/15d.sql

@@ -0,0 +1 @@
+SELECT MIN(at.title) AS aka_title, MIN(t.title) AS internet_movie_title FROM aka_title AS at, company_name AS cn, company_type AS ct, info_type AS it1, keyword AS k, movie_companies AS mc, movie_info AS mi, movie_keyword AS mk, title AS t WHERE cn.country_code  = '[us]' AND it1.info  = 'release dates' AND mi.note  like '%internet%' AND t.production_year  > 1990 AND t.id = at.movie_id AND t.id = mi.movie_id AND t.id = mk.movie_id AND t.id = mc.movie_id AND mk.movie_id = mi.movie_id AND mk.movie_id = mc.movie_id AND mk.movie_id = at.movie_id AND mi.movie_id = mc.movie_id AND mi.movie_id = at.movie_id AND mc.movie_id = at.movie_id AND k.id = mk.keyword_id AND it1.id = mi.info_type_id AND cn.id = mc.company_id AND ct.id = mc.company_type_id;

benchmarks/queries/imdb/16a.sql

@@ -0,0 +1 @@
+SELECT MIN(an.name) AS cool_actor_pseudonym, MIN(t.title) AS series_named_after_char FROM aka_name AS an, cast_info AS ci, company_name AS cn, keyword AS k, movie_companies AS mc, movie_keyword AS mk, name AS n, title AS t WHERE cn.country_code ='[us]' AND k.keyword ='character-name-in-title' AND t.episode_nr >= 50 AND t.episode_nr < 100 AND an.person_id = n.id AND n.id = ci.person_id AND ci.movie_id = t.id AND t.id = mk.movie_id AND mk.keyword_id = k.id AND t.id = mc.movie_id AND mc.company_id = cn.id AND an.person_id = ci.person_id AND ci.movie_id = mc.movie_id AND ci.movie_id = mk.movie_id AND mc.movie_id = mk.movie_id;