Add PSDH Q2-9 for cudf.pandas (rapidsai#20418)

mroeschke · web-flow · commit 03bf5862470d · 2025-11-03T15:08:56.000Z
closes rapidsai#19765 xref rapidsai#20170 I also realized that `cudf.pandas` needed to be loaded in `pdsh.py` discovered by Q7 accessing an API from the pandas module. I had thought loading cudf.pandas in `utils.py` was sufficient. <details> <summary>Outputs of Q0-9 SF0.1</summary> ```bash ~/cudf$ python /cudf/python/cudf/cudf/pandas/_benchmarks/pdsh.py 0,1,2,3,4,5,6,7,8,9 --scale=0.1 --iterations=1 --path "/cudf/sf0.1" Empty DataFrame Columns: [] Index: [] Query 0 - Iteration 0 finished in 0.0003s l_returnflag l_linestatus sum_qty sum_base_price sum_disc_price sum_charge avg_qty avg_price avg_disc count_order 0 A F 3774200.00 5320753880.69 -5.054096e+09 -5.256751e+09 25.537587 36002.123829 0.050145 147790 1 N F 95257.00 133737795.84 -1.271324e+08 -1.322863e+08 25.300664 35521.326916 0.049394 3765 2 N O 7459297.00 10512270008.90 -9.986238e+09 -1.038558e+10 25.545538 36000.924688 0.050096 292000 3 R F 3785523.00 5337950526.47 -5.071819e+09 -5.274406e+09 25.525944 35994.029214 0.049989 148301 Query 1 - Iteration 0 finished in 2.3091s s_acctbal s_name n_name p_partkey p_mfgr s_address s_phone s_comment 29 9828.21 Supplier#000000647 UNITED KINGDOM 13120 Manufacturer#5 x5U7MBZmwfG9 33-258-202-4782 s the slyly even ideas poach fluffily 5 9508.37 Supplier#000000070 FRANCE 3563 Manufacturer#1 INWNH2w,OOWgNDq0BRCcBwOMQc6PdFDc4 16-821-608-1166 ests sleep quickly express ideas. ironic ideas... 39 9508.37 Supplier#000000070 FRANCE 17268 Manufacturer#4 INWNH2w,OOWgNDq0BRCcBwOMQc6PdFDc4 16-821-608-1166 ests sleep quickly express ideas. ironic ideas... 21 9453.01 Supplier#000000802 ROMANIA 10021 Manufacturer#5 ,6HYXb4uaHITmtMBj4Ak57Pd 29-342-882-6463 gular frets. permanently special multipliers b... 31 9453.01 Supplier#000000802 ROMANIA 13275 Manufacturer#4 ,6HYXb4uaHITmtMBj4Ak57Pd 29-342-882-6463 gular frets. permanently special multipliers b... 32 9192.10 Supplier#000000115 UNITED KINGDOM 13325 Manufacturer#1 nJ 2t0f7Ve,wL1,6WzGBJLNBUCKlsV 33-597-248-1220 es across the carefully express accounts boost... 10 9032.15 Supplier#000000959 GERMANY 4958 Manufacturer#4 8grA EHBnwOZhO 17-108-642-3106 nding dependencies nag furiou 27 8702.02 Supplier#000000333 RUSSIA 11810 Manufacturer#3 MaVf XgwPdkiX4nfJGOis8Uu2zKiIZH 32-508-202-6136 oss the deposits cajole carefully even pinto b... 22 8615.50 Supplier#000000812 FRANCE 10551 Manufacturer#2 8qh4tezyScl5bidLAysvutB,,ZI2dn6xP 16-585-724-6633 y quickly regular deposits? quickly pending pa... 34 8615.50 Supplier#000000812 FRANCE 13811 Manufacturer#4 8qh4tezyScl5bidLAysvutB,,ZI2dn6xP 16-585-724-6633 y quickly regular deposits? quickly pending pa... 14 8488.53 Supplier#000000367 RUSSIA 6854 Manufacturer#4 E Sv9brQVf43Mzz 32-458-198-9557 ages. carefully final excuses nag finally. car... 26 8430.52 Supplier#000000646 FRANCE 11384 Manufacturer#3 IUzsmT,2oBgjhWP2TlXTL6IkJH,4h,1SJRt 16-601-220-5489 ites among the always final ideas kindle accor... 7 8271.39 Supplier#000000146 RUSSIA 4637 Manufacturer#5 rBDNgCr04x0sfdzD5,gFOutCiG2 32-792-619-3155 s cajole quickly special requests. quickly ent... 0 8096.98 Supplier#000000574 RUSSIA 323 Manufacturer#4 2O8 sy9g2mlBOuEjzj0pA2pevk, 32-866-246-8752 ully after the regular requests. slyly final d... 16 7392.78 Supplier#000000170 UNITED KINGDOM 7655 Manufacturer#2 RtsXQ,SunkA XHy9 33-803-340-5398 ake carefully across the quickly 24 7205.20 Supplier#000000477 GERMANY 10956 Manufacturer#5 VtaNKN5Mqui5yh7j2ldd5waf 17-180-144-7991 excuses wake express deposits. furiously care... 30 6820.35 Supplier#000000007 UNITED KINGDOM 13217 Manufacturer#5 s,4TicNGB4uO6PaSqNBUq 33-990-965-2201 s unwind silently furiously regular courts. fi... 6 6721.70 Supplier#000000954 FRANCE 4191 Manufacturer#3 P3O5p UFz1QsLmZX 16-537-341-8517 ect blithely blithely final acco 23 6329.90 Supplier#000000996 GERMANY 10735 Manufacturer#2 Wx4dQwOAwWjfSCGupfrM 17-447-811-3282 ironic forges cajole blithely agai 41 6173.87 Supplier#000000408 RUSSIA 18139 Manufacturer#1 qcor1u,vJXAokjnL5,dilyYNmh 32-858-724-2950 blithely pending packages cajole furiously sly... 33 5364.99 Supplier#000000785 RUSSIA 13784 Manufacturer#4 W VkHBpQyD3qjQjWGpWicOpmILFehmEdWy67kUGY 32-297-653-2203 packages boost carefully. express ideas along 36 5069.27 Supplier#000000328 GERMANY 16327 Manufacturer#1 SMm24d WG62 17-231-513-5721 he unusual ideas. slyly final packages a 15 4941.88 Supplier#000000321 ROMANIA 7320 Manufacturer#5 pLngFl5yeMcHyov 29-573-279-1406 y final requests impress s 28 4672.25 Supplier#000000239 RUSSIA 12238 Manufacturer#1 XO101kgHrJagK2FL1U6QCaTE ncCsMbeuTgK6o8 32-396-654-6826 arls wake furiously deposits. even, regular depen 11 4586.49 Supplier#000000680 RUSSIA 5679 Manufacturer#3 UhvDfdEfJh,Qbe7VZb8uSGO2TU 0jEa6nXZXE 32-522-382-1620 the regularly regular dependencies. carefully... 42 4518.31 Supplier#000000149 FRANCE 18344 Manufacturer#5 pVyWsjOidpHKp4NfKU4yLeym 16-660-553-2456 ts detect along the foxes. final Tiresias are.... 43 4315.15 Supplier#000000509 FRANCE 18972 Manufacturer#2 SF7dR8V5pK 16-298-154-3365 ronic orbits are furiously across the requests... 17 3526.53 Supplier#000000553 FRANCE 8036 Manufacturer#4 a,liVofXbCJ 16-599-552-3755 lar dinos nag slyly brave 37 3526.53 Supplier#000000553 FRANCE 17018 Manufacturer#3 a,liVofXbCJ 16-599-552-3755 lar dinos nag slyly brave 8 3294.68 Supplier#000000350 GERMANY 4841 Manufacturer#4 KIFxV73eovmwhh 17-113-181-4017 e slyly special foxes. furiously unusual depos... 1 2972.26 Supplier#000000016 RUSSIA 1015 Manufacturer#4 YjP5C55zHDXL7LalK27zfQnwejdpin4AMpvh 32-822-502-4215 ously express ideas haggle quickly dugouts? fu 4 2963.09 Supplier#000000840 ROMANIA 3080 Manufacturer#2 iYzUIypKhC0Y 29-781-337-5584 eep blithely regular dependencies. blithely re... 35 2221.25 Supplier#000000771 ROMANIA 13981 Manufacturer#2 lwZ I15rq9kmZXUNhl 29-986-304-9006 nal foxes eat slyly about the fluffily permane... 40 1381.97 Supplier#000000104 FRANCE 18103 Manufacturer#3 Dcl4yGrzqv3OPeRO49bKh78XmQEDR7PBXIs0m 16-434-972-6922 gular ideas. bravely bold deposits haggle thro... 18 906.07 Supplier#000000138 ROMANIA 8363 Manufacturer#4 utbplAm g7RmxVfYoNdhcrQGWuzRqPe0qHSwbKw 29-533-434-6776 ickly unusual requests cajole. accounts above ... 25 765.69 Supplier#000000799 RUSSIA 11276 Manufacturer#2 jwFN7ZB3T9sMF 32-579-339-1495 nusual requests. furiously unusual epitaphs in... 13 727.89 Supplier#000000470 ROMANIA 6213 Manufacturer#3 XckbzsAgBLbUkdfjgJEPjmUMTM8ebSMEvI 29-165-289-1523 gular excuses. furiously regular excuses sleep... 9 683.07 Supplier#000000651 RUSSIA 4888 Manufacturer#4 oWekiBV6s,1g 32-181-426-4490 ly regular requests cajole abou 2 167.56 Supplier#000000290 FRANCE 2037 Manufacturer#1 6Bk06GVtwZaKqg01 16-675-286-5102 the theodolites. ironic, ironic deposits above 19 91.39 Supplier#000000949 UNITED KINGDOM 9430 Manufacturer#2 a,UE,6nRVl2fCphkOoetR1ajIzAEJ1Aa1G1HV 33-332-697-2768 pinto beans. carefully express requests hagg 38 -314.06 Supplier#000000510 ROMANIA 17242 Manufacturer#4 VmXQl ,vY8JiEseo8Mv4zscvNCfsY 29-207-852-3454 bold deposits. carefully even d 3 -820.89 Supplier#000000409 GERMANY 2156 Manufacturer#5 LyXUYFz7aXrvy65kKAbTatGzGS,NDBcdtD 17-719-517-9836 y final, slow theodolites. furiously regular req 20 -845.44 Supplier#000000704 ROMANIA 9926 Manufacturer#5 hQvlBqbqqnA5Dgo1BffRBX78tkkRu 29-300-896-5991 ctions. carefully sly requ 12 -942.73 Supplier#000000563 GERMANY 5797 Manufacturer#1 Rc7U1cRUhYs03JD 17-108-537-2691 slyly furiously final decoys; silent, special ... Query 2 - Iteration 0 finished in 0.3807s l_orderkey revenue o_orderdate o_shippriority 435 223140 355369.0698 1995-03-14 0 1175 584291 354494.7318 1995-02-21 0 796 405063 353125.4577 1995-03-03 0 1150 573861 351238.2770 1995-03-09 0 1113 554757 349181.7426 1995-03-14 0 1019 506021 321075.5810 1995-03-10 0 218 121604 318576.4154 1995-03-07 0 197 108514 314967.0754 1995-02-20 0 928 462502 312604.5420 1995-03-08 0 346 178727 309728.9306 1995-02-25 0 Query 3 - Iteration 0 finished in 0.1659s o_orderpriority order_count 0 1-URGENT 999 1 2-HIGH 997 2 3-MEDIUM 1031 3 4-NOT SPECIFIED 989 4 5-LOW 1077 Query 4 - Iteration 0 finished in 0.1455s n_name revenue 4 VIETNAM -4.497841e+06 2 INDONESIA -5.580475e+06 3 JAPAN -6.000077e+06 1 INDIA -6.376122e+06 0 CHINA -7.822103e+06 Query 5 - Iteration 0 finished in 0.2009s revenue 0 11803420.2534 Query 6 - Iteration 0 finished in 0.0596s supp_nation cust_nation l_year revenue 0 FRANCE GERMANY 1995 -4.637235e+06 1 FRANCE GERMANY 1996 -5.224780e+06 2 GERMANY FRANCE 1995 -6.232819e+06 3 GERMANY FRANCE 1996 -5.557312e+06 Query 7 - Iteration 0 finished in 0.2901s o_year mkt_share 0 1995 0.03 1 1996 0.02 Query 8 - Iteration 0 finished in 0.6331s nation o_year sum_profit 0 ALGERIA 1998 4.716279e+06 1 ALGERIA 1997 8.071240e+06 2 ALGERIA 1996 9.273503e+06 3 ALGERIA 1995 8.472341e+06 4 ALGERIA 1994 8.718336e+06 .. ... ... ... 170 VIETNAM 1996 8.576511e+06 171 VIETNAM 1995 8.890273e+06 172 VIETNAM 1994 8.934413e+06 173 VIETNAM 1993 6.282243e+06 174 VIETNAM 1992 8.378368e+06 [175 rows x 3 columns] Query 9 - Iteration 0 finished in 0.4285s Iteration Summary ======================================= query: 0 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.0003 max time : 0.0003 mean time: 0.0003 ======================================= query: 1 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 2.3091 max time : 2.3091 mean time: 2.3091 ======================================= query: 2 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.3807 max time : 0.3807 mean time: 0.3807 ======================================= query: 3 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.1659 max time : 0.1659 mean time: 0.1659 ======================================= query: 4 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.1455 max time : 0.1455 mean time: 0.1455 ======================================= query: 5 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.2009 max time : 0.2009 mean time: 0.2009 ======================================= query: 6 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.0596 max time : 0.0596 mean time: 0.0596 ======================================= query: 7 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.2901 max time : 0.2901 mean time: 0.2901 ======================================= query: 8 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.6331 max time : 0.6331 mean time: 0.6331 ======================================= query: 9 path: /cudf/sf0.1 scale_factor: 0.1 executor: in-memory iterations: 1 --------------------------------------- min time : 0.4285 max time : 0.4285 mean time: 0.4285 ======================================= Total mean time across all queries: 4.6137 seconds ``` </details> Authors: - Matthew Roeschke (https://github.com/mroeschke) Approvers: - GALI PREM SAGAR (https://github.com/galipremsagar) URL: rapidsai#20418
diff --git a/python/cudf/cudf/pandas/_benchmarks/pdsh.py b/python/cudf/cudf/pandas/_benchmarks/pdsh.py
@@ -16,9 +16,13 @@
 from datetime import date
 from typing import TYPE_CHECKING
 
-import pandas as pd
+import cudf.pandas
 
-from cudf.pandas._benchmarks.utils import (
+cudf.pandas.install()
+
+import pandas as pd  # noqa: E402
+
+from cudf.pandas._benchmarks.utils import (  # noqa: E402
     get_data,
     run_pandas,
 )
@@ -72,6 +76,336 @@ def q1(run_config: RunConfig) -> pd.DataFrame:
 
         return agg.sort_values(["l_returnflag", "l_linestatus"])
 
+    @staticmethod
+    def q2(run_config: RunConfig) -> pd.DataFrame:
+        """Query 2."""
+        nation = get_data(run_config.dataset_path, "nation", run_config.suffix)
+        part = get_data(run_config.dataset_path, "part", run_config.suffix)
+        partsupp = get_data(
+            run_config.dataset_path, "partsupp", run_config.suffix
+        )
+        region = get_data(run_config.dataset_path, "region", run_config.suffix)
+        supplier = get_data(
+            run_config.dataset_path, "supplier", run_config.suffix
+        )
+
+        var1 = 15
+        var2 = "BRASS"
+        var3 = "EUROPE"
+
+        jn = (
+            part.merge(partsupp, left_on="p_partkey", right_on="ps_partkey")
+            .merge(supplier, left_on="ps_suppkey", right_on="s_suppkey")
+            .merge(nation, left_on="s_nationkey", right_on="n_nationkey")
+            .merge(region, left_on="n_regionkey", right_on="r_regionkey")
+        )
+
+        jn = jn[jn["p_size"] == var1]
+        jn = jn[jn["p_type"].str.endswith(var2)]
+        jn = jn[jn["r_name"] == var3]
+
+        gb = jn.groupby("p_partkey", as_index=False)
+        agg = gb["ps_supplycost"].min()
+        jn2 = agg.merge(jn, on=["p_partkey", "ps_supplycost"])
+
+        sel = jn2.loc[
+            :,
+            [
+                "s_acctbal",
+                "s_name",
+                "n_name",
+                "p_partkey",
+                "p_mfgr",
+                "s_address",
+                "s_phone",
+                "s_comment",
+            ],
+        ]
+
+        sort = sel.sort_values(
+            by=["s_acctbal", "n_name", "s_name", "p_partkey"],
+            ascending=[False, True, True, True],
+        )
+        return sort.head(100)
+
+    @staticmethod
+    def q3(run_config: RunConfig) -> pd.DataFrame:
+        """Query 3."""
+        customer = get_data(
+            run_config.dataset_path, "customer", run_config.suffix
+        )
+        lineitem = get_data(
+            run_config.dataset_path, "lineitem", run_config.suffix
+        )
+        orders = get_data(run_config.dataset_path, "orders", run_config.suffix)
+
+        var1 = "BUILDING"
+        var2 = date(1995, 3, 15)
+
+        fcustomer = customer[customer["c_mktsegment"] == var1]
+
+        jn1 = fcustomer.merge(
+            orders, left_on="c_custkey", right_on="o_custkey"
+        )
+        jn2 = jn1.merge(lineitem, left_on="o_orderkey", right_on="l_orderkey")
+
+        jn2 = jn2[jn2["o_orderdate"] < var2]
+        jn2 = jn2[jn2["l_shipdate"] > var2]
+        jn2["revenue"] = jn2.l_extendedprice * (1 - jn2.l_discount)
+
+        gb = jn2.groupby(
+            ["o_orderkey", "o_orderdate", "o_shippriority"], as_index=False
+        )
+        agg = gb["revenue"].sum()
+
+        sel = agg.loc[
+            :, ["o_orderkey", "revenue", "o_orderdate", "o_shippriority"]
+        ]
+        sel = sel.rename(columns={"o_orderkey": "l_orderkey"})
+
+        sorted_df = sel.sort_values(
+            by=["revenue", "o_orderdate"], ascending=[False, True]
+        )
+        return sorted_df.head(10)
+
+    @staticmethod
+    def q4(run_config: RunConfig) -> pd.DataFrame:
+        """Query 4."""
+        lineitem = get_data(
+            run_config.dataset_path, "lineitem", run_config.suffix
+        )
+        orders = get_data(run_config.dataset_path, "orders", run_config.suffix)
+
+        var1 = date(1993, 7, 1)
+        var2 = date(1993, 10, 1)
+
+        jn = lineitem.merge(
+            orders, left_on="l_orderkey", right_on="o_orderkey"
+        )
+
+        jn = jn[(jn["o_orderdate"] >= var1) & (jn["o_orderdate"] < var2)]
+        jn = jn[jn["l_commitdate"] < jn["l_receiptdate"]]
+
+        jn = jn.drop_duplicates(subset=["o_orderpriority", "l_orderkey"])
+
+        gb = jn.groupby("o_orderpriority", as_index=False)
+        agg = gb.agg(
+            order_count=pd.NamedAgg(column="o_orderkey", aggfunc="count")
+        )
+
+        return agg.sort_values(["o_orderpriority"])
+
+    @staticmethod
+    def q5(run_config: RunConfig) -> pd.DataFrame:
+        """Query 5."""
+        path = run_config.dataset_path
+        suffix = run_config.suffix
+        customer = get_data(path, "customer", suffix)
+        lineitem = get_data(path, "lineitem", suffix)
+        nation = get_data(path, "nation", suffix)
+        orders = get_data(path, "orders", suffix)
+        region = get_data(path, "region", suffix)
+        supplier = get_data(path, "supplier", suffix)
+
+        var1 = "ASIA"
+        var2 = date(1994, 1, 1)
+        var3 = date(1995, 1, 1)
+
+        jn1 = region.merge(
+            nation, left_on="r_regionkey", right_on="n_regionkey"
+        )
+        jn2 = jn1.merge(
+            customer, left_on="n_nationkey", right_on="c_nationkey"
+        )
+        jn3 = jn2.merge(orders, left_on="c_custkey", right_on="o_custkey")
+        jn4 = jn3.merge(lineitem, left_on="o_orderkey", right_on="l_orderkey")
+        jn5 = jn4.merge(
+            supplier,
+            left_on=["l_suppkey", "n_nationkey"],
+            right_on=["s_suppkey", "s_nationkey"],
+        )
+
+        jn5 = jn5[jn5["r_name"] == var1]
+        jn5 = jn5[(jn5["o_orderdate"] >= var2) & (jn5["o_orderdate"] < var3)]
+        jn5["revenue"] = jn5.l_extendedprice * (1.0 - jn5.l_discount)
+
+        gb = jn5.groupby("n_name", as_index=False)["revenue"].sum()
+        return gb.sort_values("revenue", ascending=False)
+
+    @staticmethod
+    def q6(run_config: RunConfig) -> pd.DataFrame:
+        """Query 6."""
+        path = run_config.dataset_path
+        suffix = run_config.suffix
+        lineitem = get_data(path, "lineitem", suffix)
+
+        var1 = date(1994, 1, 1)
+        var2 = date(1995, 1, 1)
+        var3 = 0.05
+        var4 = 0.07
+        var5 = 24
+
+        filt = lineitem[
+            (lineitem["l_shipdate"] >= var1) & (lineitem["l_shipdate"] < var2)
+        ]
+        filt = filt[
+            (filt["l_discount"] >= var3) & (filt["l_discount"] <= var4)
+        ]
+        filt = filt[filt["l_quantity"] < var5]
+        result_value = (filt["l_extendedprice"] * filt["l_discount"]).sum()
+        return pd.DataFrame({"revenue": [result_value]})
+
+    @staticmethod
+    def q7(run_config: RunConfig) -> pd.DataFrame:
+        """Query 7."""
+        customer = get_data(
+            run_config.dataset_path, "customer", run_config.suffix
+        )
+        lineitem = get_data(
+            run_config.dataset_path, "lineitem", run_config.suffix
+        )
+        nation = get_data(run_config.dataset_path, "nation", run_config.suffix)
+        orders = get_data(run_config.dataset_path, "orders", run_config.suffix)
+        supplier = get_data(
+            run_config.dataset_path, "supplier", run_config.suffix
+        )
+
+        var1 = "FRANCE"
+        var2 = "GERMANY"
+        var3 = date(1995, 1, 1)
+        var4 = date(1996, 12, 31)
+
+        n1 = nation[(nation["n_name"] == var1)]
+        n2 = nation[(nation["n_name"] == var2)]
+
+        # Part 1
+        jn1 = customer.merge(n1, left_on="c_nationkey", right_on="n_nationkey")
+        jn2 = jn1.merge(orders, left_on="c_custkey", right_on="o_custkey")
+        jn2 = jn2.rename(columns={"n_name": "cust_nation"})
+        jn3 = jn2.merge(lineitem, left_on="o_orderkey", right_on="l_orderkey")
+        jn4 = jn3.merge(supplier, left_on="l_suppkey", right_on="s_suppkey")
+        jn5 = jn4.merge(n2, left_on="s_nationkey", right_on="n_nationkey")
+        df1 = jn5.rename(columns={"n_name": "supp_nation"})
+
+        # Part 2
+        jn1 = customer.merge(n2, left_on="c_nationkey", right_on="n_nationkey")
+        jn2 = jn1.merge(orders, left_on="c_custkey", right_on="o_custkey")
+        jn2 = jn2.rename(columns={"n_name": "cust_nation"})
+        jn3 = jn2.merge(lineitem, left_on="o_orderkey", right_on="l_orderkey")
+        jn4 = jn3.merge(supplier, left_on="l_suppkey", right_on="s_suppkey")
+        jn5 = jn4.merge(n1, left_on="s_nationkey", right_on="n_nationkey")
+        df2 = jn5.rename(columns={"n_name": "supp_nation"})
+
+        # Combine
+        total = pd.concat([df1, df2])
+
+        total = total[
+            (total["l_shipdate"] >= var3) & (total["l_shipdate"] <= var4)
+        ]
+        total["volume"] = total["l_extendedprice"] * (
+            1.0 - total["l_discount"]
+        )
+        total["l_year"] = total["l_shipdate"].dt.year
+
+        gb = total.groupby(
+            ["supp_nation", "cust_nation", "l_year"], as_index=False
+        )
+        agg = gb.agg(revenue=pd.NamedAgg(column="volume", aggfunc="sum"))
+
+        return agg.sort_values(by=["supp_nation", "cust_nation", "l_year"])
+
+    @staticmethod
+    def q8(run_config: RunConfig) -> pd.DataFrame:
+        """Query 8."""
+        customer = get_data(
+            run_config.dataset_path, "customer", run_config.suffix
+        )
+        lineitem = get_data(
+            run_config.dataset_path, "lineitem", run_config.suffix
+        )
+        nation = get_data(run_config.dataset_path, "nation", run_config.suffix)
+        orders = get_data(run_config.dataset_path, "orders", run_config.suffix)
+        part = get_data(run_config.dataset_path, "part", run_config.suffix)
+        region = get_data(run_config.dataset_path, "region", run_config.suffix)
+        supplier = get_data(
+            run_config.dataset_path, "supplier", run_config.suffix
+        )
+
+        var1 = "BRAZIL"
+        var2 = "AMERICA"
+        var3 = "ECONOMY ANODIZED STEEL"
+        var4 = date(1995, 1, 1)
+        var5 = date(1996, 12, 31)
+
+        n1 = nation.loc[:, ["n_nationkey", "n_regionkey"]]
+        n2 = nation.loc[:, ["n_nationkey", "n_name"]]
+
+        jn1 = part.merge(lineitem, left_on="p_partkey", right_on="l_partkey")
+        jn2 = jn1.merge(supplier, left_on="l_suppkey", right_on="s_suppkey")
+        jn3 = jn2.merge(orders, left_on="l_orderkey", right_on="o_orderkey")
+        jn4 = jn3.merge(customer, left_on="o_custkey", right_on="c_custkey")
+        jn5 = jn4.merge(n1, left_on="c_nationkey", right_on="n_nationkey")
+        jn6 = jn5.merge(region, left_on="n_regionkey", right_on="r_regionkey")
+
+        jn6 = jn6[(jn6["r_name"] == var2)]
+
+        jn7 = jn6.merge(n2, left_on="s_nationkey", right_on="n_nationkey")
+
+        jn7 = jn7[(jn7["o_orderdate"] >= var4) & (jn7["o_orderdate"] <= var5)]
+        jn7 = jn7[jn7["p_type"] == var3]
+
+        jn7["o_year"] = jn7["o_orderdate"].dt.year
+        jn7["volume"] = jn7["l_extendedprice"] * (1.0 - jn7["l_discount"])
+        jn7 = jn7.rename(columns={"n_name": "nation"})
+
+        def udf(df: pd.DataFrame) -> float:
+            demonimator: float = df["volume"].sum()
+            df = df[df["nation"] == var1]
+            numerator: float = df["volume"].sum()
+            return round(numerator / demonimator, 2)
+
+        gb = jn7.groupby("o_year", as_index=False)
+        agg = gb.apply(udf, include_groups=False)
+        agg.columns = ["o_year", "mkt_share"]
+        return agg.sort_values("o_year")
+
+    @staticmethod
+    def q9(run_config: RunConfig) -> pd.DataFrame:
+        """Query 9."""
+        path = run_config.dataset_path
+        suffix = run_config.suffix
+        lineitem = get_data(path, "lineitem", suffix)
+        nation = get_data(path, "nation", suffix)
+        orders = get_data(path, "orders", suffix)
+        part = get_data(path, "part", suffix)
+        partsupp = get_data(path, "partsupp", suffix)
+        supplier = get_data(path, "supplier", suffix)
+
+        jn1 = part.merge(partsupp, left_on="p_partkey", right_on="ps_partkey")
+        jn2 = jn1.merge(supplier, left_on="ps_suppkey", right_on="s_suppkey")
+        jn3 = jn2.merge(
+            lineitem,
+            left_on=["p_partkey", "ps_suppkey"],
+            right_on=["l_partkey", "l_suppkey"],
+        )
+        jn4 = jn3.merge(orders, left_on="l_orderkey", right_on="o_orderkey")
+        jn5 = jn4.merge(nation, left_on="s_nationkey", right_on="n_nationkey")
+
+        jn5 = jn5[jn5["p_name"].str.contains("green", regex=False)]
+
+        jn5["o_year"] = jn5["o_orderdate"].dt.year
+        jn5["amount"] = jn5["l_extendedprice"] * (1.0 - jn5["l_discount"]) - (
+            jn5["ps_supplycost"] * jn5["l_quantity"]
+        )
+        jn5 = jn5.rename(columns={"n_name": "nation"})
+
+        gb = jn5.groupby(["nation", "o_year"], as_index=False, sort=False)
+        agg = gb.agg(sum_profit=pd.NamedAgg(column="amount", aggfunc="sum"))
+        sorted_df = agg.sort_values(
+            by=["nation", "o_year"], ascending=[True, False]
+        )
+        return sorted_df.reset_index(drop=True)
+
 
 if __name__ == "__main__":
     run_pandas(PDSHQueries)
