diff --git a/benchmarks/db-benchmark/README.md b/benchmarks/db-benchmark/README.md
new file mode 100644
index 000000000000..e1fc2a3504cf
--- /dev/null
+++ b/benchmarks/db-benchmark/README.md
@@ -0,0 +1,29 @@
+<!---
+  Licensed to the Apache Software Foundation (ASF) under one
+  or more contributor license agreements.  See the NOTICE file
+  distributed with this work for additional information
+  regarding copyright ownership.  The ASF licenses this file
+  to you under the Apache License, Version 2.0 (the
+  "License"); you may not use this file except in compliance
+  with the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing,
+  software distributed under the License is distributed on an
+  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+  KIND, either express or implied.  See the License for the
+  specific language governing permissions and limitations
+  under the License.
+-->
+
+# Run db-benchmark
+
+## Directions
+
+Run the following from root `arrow-datafusion` directory
+
+```bash
+$ docker buildx build -t db-benchmark -f benchmarks/db-benchmark/db-benchmark.dockerfile .
+$ docker run --privileged db-benchmark
+```
diff --git a/benchmarks/db-benchmark/db-benchmark.dockerfile b/benchmarks/db-benchmark/db-benchmark.dockerfile
new file mode 100644
index 000000000000..b21d3a0d1e0e
--- /dev/null
+++ b/benchmarks/db-benchmark/db-benchmark.dockerfile
@@ -0,0 +1,106 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+FROM ubuntu
+ARG DEBIAN_FRONTEND=noninteractive
+ARG TARGETPLATFORM
+
+RUN apt-get update && \
+    apt-get install -y git build-essential
+
+# Install R, curl, and python deps
+RUN apt-get -y install --no-install-recommends --no-install-suggests \
+    ca-certificates software-properties-common gnupg2 gnupg1 \
+    && apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 \
+    && add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/' \
+    && apt-get -y install r-base \
+    && apt-get -y install curl \
+    && apt-get -y install python3.8 \
+    && apt-get -y install python3-pip
+
+# Install R libraries
+RUN R -e "install.packages('data.table',dependencies=TRUE, repos='http://cran.rstudio.com/')" \
+    && R -e "install.packages('dplyr',dependencies=TRUE, repos='http://cran.rstudio.com/')"
+
+# Install Rust
+RUN curl https://sh.rustup.rs -sSf | bash -s -- -y
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+# Clone db-benchmark and download data
+RUN git clone https://github.com/h2oai/db-benchmark \
+    && cd db-benchmark \
+    && Rscript _data/groupby-datagen.R 1e7 1e2 0 0 \
+    && Rscript _data/join-datagen.R 1e7 0 0 0 \
+    && mkdir data \
+    && mv G1_1e7_1e2_0_0.csv data \
+    && mv J1_1e7_1e1_0_0.csv data \
+    && mv J1_1e7_1e4_0_0.csv data \
+    && mv J1_1e7_1e7_0_0.csv data \
+    && mv J1_1e7_NA_0_0.csv data \
+    && cd ..
+
+# Clone datafusion-python and build python library
+# Not sure if the wheel will be the same on all computers
+RUN git clone https://github.com/datafusion-contrib/datafusion-python \
+    && cd datafusion-python && git reset --hard 368b50ed9662d5e93c70b539f94cceace685265e \
+    && python3 -m pip install pip \
+    && python3 -m pip install pandas \
+    && python3 -m pip install -r requirements.txt \
+    && cd ..
+
+# Copy local arrow-datafusion
+COPY . arrow-datafusion
+
+# 1. datafusion-python that builds from datafusion version referenced datafusion-python
+RUN cd datafusion-python \
+    && maturin build --release \
+    && case "${TARGETPLATFORM}" in \
+    */amd64) CPUARCH=x86_64 ;; \
+    */arm64) CPUARCH=aarch64 ;; \
+    *) exit 1 ;; \
+    esac \
+    # Version will need to be updated in conjunction with datafusion-python version
+    && python3 -m pip install target/wheels/datafusion-0.4.0-cp36-abi3-linux_${CPUARCH}.whl \
+    && cd ..
+
+# 2. datafusion-python that builds from local datafusion.  use this when making local changes to datafusion.
+# Currently, as of March 5th 2022, this done not build (i think) because datafusion is being split into multiple crates
+# and datafusion-python has not yet been updated to reflect this.
+# RUN cd datafusion-python \
+# && sed -i '/datafusion =/c\datafusion = { path = "../arrow-datafusion/datafusion", features = ["pyarrow"] }' Cargo.toml \
+# && sed -i '/fuzz-utils/d' ../arrow-datafusion/datafusion/Cargo.toml \
+# && maturin build --release \
+# && case "${TARGETPLATFORM}" in \
+#     */amd64) CPUARCH=x86_64 ;; \
+#     */amd64) CPUARCH=aarch64 ;; \
+#     *) exit 1 ;; \
+# esac \
+# && python3 -m pip install target/wheels/datafusion-0.4.0-cp36-abi3-linux_${CPUARCH}.whl \
+# && cd ..
+
+# Make datafusion directory in db-benchmark
+RUN mkdir db-benchmark/datafusion \
+    && cp ../arrow-datafusion/benchmarks/db-benchmark/groupby-datafusion.py db-benchmark/datafusion \
+    && cp ../arrow-datafusion/benchmarks/db-benchmark/join-datafusion.py db-benchmark/datafusion \
+    && cp ../arrow-datafusion/benchmarks/db-benchmark/run-bench.sh db-benchmark/ \
+    && chmod +x db-benchmark/run-bench.sh
+
+WORKDIR /db-benchmark
+
+RUN ls && ls -al data/
+
+ENTRYPOINT ./run-bench.sh
\ No newline at end of file
diff --git a/benchmarks/db-benchmark/groupby-datafusion.py b/benchmarks/db-benchmark/groupby-datafusion.py
new file mode 100644
index 000000000000..5bf6c36a0e66
--- /dev/null
+++ b/benchmarks/db-benchmark/groupby-datafusion.py
@@ -0,0 +1,242 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+#!/usr/bin/env python
+
+print("# groupby-datafusion.py", flush=True)
+
+import os
+import gc
+import timeit
+import datafusion as df
+from datafusion import functions as f
+from datafusion import col
+from pyarrow import csv as pacsv
+
+# exec(open("./_helpers/helpers.py").read())
+
+def ans_shape(batches):
+    rows, cols = 0, 0
+    for batch in batches:
+        rows += batch.num_rows
+        if cols == 0:
+            cols = batch.num_columns
+        else:
+            assert(cols == batch.num_columns)
+    
+    return rows, cols
+
+# ver = df.__version__
+ver = "7.0.0"
+git = ""
+task = "groupby"
+solution = "datafusion"
+fun = ".groupby"
+cache = "TRUE"
+on_disk = "FALSE"
+
+data_name = os.environ["SRC_DATANAME"]
+src_grp = os.path.join("data", data_name + ".csv")
+print("loading dataset %s" % src_grp, flush=True)
+
+data = pacsv.read_csv(src_grp, convert_options=pacsv.ConvertOptions(auto_dict_encode=True))
+print("dataset loaded")
+
+ctx = df.ExecutionContext()
+ctx.register_record_batches("x", [data.to_batches()])
+print("registered record batches")
+# cols = ctx.sql("SHOW columns from x")
+# ans.show()
+
+in_rows = data.num_rows
+# print(in_rows, flush=True)
+
+task_init = timeit.default_timer()
+
+question = "sum v1 by id1" # q1
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id1, SUM(v1) AS v1 FROM x GROUP BY id1").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q1: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "sum v1 by id1:id2" # q2
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id1, id2, SUM(v1) AS v1 FROM x GROUP BY id1, id2").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q2: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "sum v1 mean v3 by id3" # q3
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id3, SUM(v1) AS v1, AVG(v3) AS v3 FROM x GROUP BY id3").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q3: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1")), f.sum(col("v3"))]).collect()[0].to_pandas().to_numpy()[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "mean v1:v3 by id4" # q4
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id4, AVG(v1) AS v1, AVG(v2) AS v2, AVG(v3) AS v3 FROM x GROUP BY id4").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q4: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1")), f.sum(col("v2")), f.sum(col("v3"))]).collect()[0].to_pandas().to_numpy()[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "sum v1:v3 by id6" # q5
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id6, SUM(v1) AS v1, SUM(v2) AS v2, SUM(v3) AS v3 FROM x GROUP BY id6").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q5: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1")), f.sum(col("v2")), f.sum(col("v3"))]).collect()[0].to_pandas().to_numpy()[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "median v3 sd v3 by id4 id5" # q6
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id4, id5, approx_percentile_cont(v3, .5) AS median_v3, stddev(v3) AS stddev_v3 FROM x GROUP BY id4, id5").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q6: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("median_v3")), f.sum(col("stddev_v3"))]).collect()[0].to_pandas().to_numpy()[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "max v1 - min v2 by id3" # q7
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id3, MAX(v1) - MIN(v2) AS range_v1_v2 FROM x GROUP BY id3").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q7: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("range_v1_v2"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "largest two v3 by id6" # q8
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id6, v3 from (SELECT id6, v3, row_number() OVER (PARTITION BY id6 ORDER BY v3 DESC) AS row FROM x) t WHERE row <= 2").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q8: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v3"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "regression v1 v2 by id2 id4" # q9
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT corr(v1, v2) as corr FROM x GROUP BY id2, id4").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q9: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("corr"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "sum v3 count by id1:id6" # q10
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT id1, id2, id3, id4, id5, id6, SUM(v3) as v3, COUNT(*) AS cnt FROM x GROUP BY id1, id2, id3, id4, id5, id6").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q10: {t}")
+# m = memory_usage()
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v3")), f.sum(col("cnt"))]).collect()[0].to_pandas().to_numpy()[0]
+chkt = timeit.default_timer() - t_start
+# write_log(task=task, data=data_name, in_rows=in_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+print("grouping finished, took %0.fs" % (timeit.default_timer() - task_init), flush=True)
+
+exit(0)
diff --git a/benchmarks/db-benchmark/join-datafusion.py b/benchmarks/db-benchmark/join-datafusion.py
new file mode 100755
index 000000000000..1a83b1f60d69
--- /dev/null
+++ b/benchmarks/db-benchmark/join-datafusion.py
@@ -0,0 +1,175 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+#!/usr/bin/env python
+
+print("# join-datafusion.py", flush=True)
+
+import os
+import gc
+import timeit
+import datafusion as df
+from datafusion import functions as f
+from datafusion import col
+from pyarrow import csv as pacsv
+
+# exec(open("./_helpers/helpers.py").read())
+
+def join_to_tbls(data_name):
+    x_n = int(float(data_name.split("_")[1]))
+    y_n = ["{:.0e}".format(x_n/1e6), "{:.0e}".format(x_n/1e3), "{:.0e}".format(x_n)]
+    y_n = [y_n[0].replace('+0', ''), y_n[1].replace('+0', ''), y_n[2].replace('+0', '')]
+    return [data_name.replace('NA', y_n[0]), data_name.replace('NA', y_n[1]), data_name.replace('NA', y_n[2])]
+
+
+def ans_shape(batches):
+    rows, cols = 0, 0
+    for batch in batches:
+        rows += batch.num_rows
+        if cols == 0:
+            cols = batch.num_columns
+        else:
+            assert(cols == batch.num_columns)
+    
+    return rows, cols
+
+ver = "6.0.0"
+task = "join"
+git = ""
+solution = "datafusion"
+fun = ".join"
+cache = "TRUE"
+on_disk = "FALSE"
+
+data_name = os.environ["SRC_DATANAME"]
+src_jn_x = os.path.join("data", data_name + ".csv")
+y_data_name = join_to_tbls(data_name)
+src_jn_y = [os.path.join("data", y_data_name[0] + ".csv"), os.path.join("data", y_data_name[1] + ".csv"), os.path.join("data", y_data_name[2] + ".csv")]
+if len(src_jn_y) != 3:
+  raise Exception("Something went wrong in preparing files used for join")
+
+print("loading datasets " + data_name + ", " + y_data_name[0] + ", " + y_data_name[2] + ", " + y_data_name[2], flush=True)
+
+ctx = df.ExecutionContext()
+
+x_data = pacsv.read_csv(src_jn_x, convert_options=pacsv.ConvertOptions(auto_dict_encode=True))
+ctx.register_record_batches("x", [x_data.to_batches()])
+small_data = pacsv.read_csv(src_jn_y[0], convert_options=pacsv.ConvertOptions(auto_dict_encode=True))
+ctx.register_record_batches("small", [small_data.to_batches()])
+medium_data = pacsv.read_csv(src_jn_y[1], convert_options=pacsv.ConvertOptions(auto_dict_encode=True))
+ctx.register_record_batches("medium", [medium_data.to_batches()])
+large_data = pacsv.read_csv(src_jn_y[2], convert_options=pacsv.ConvertOptions(auto_dict_encode=True))
+ctx.register_record_batches("large", [large_data.to_batches()])
+
+print(x_data.num_rows, flush=True)
+print(small_data.num_rows, flush=True)
+print(medium_data.num_rows, flush=True)
+print(large_data.num_rows, flush=True)
+
+task_init = timeit.default_timer()
+print("joining...", flush=True)
+
+question = "small inner on int" # q1
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT x.id1, x.id2, x.id3, x.id4 as xid4, small.id4 as smallid4, x.id5, x.id6, x.v1, small.v2 FROM x INNER JOIN small ON x.id1 = small.id1").collect()
+# ans = ctx.sql("SELECT * FROM x INNER JOIN small ON x.id1 = small.id1").collect()
+# print(set([b.schema for b in ans]))
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q1: {t}")
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# m = memory_usage()
+# write_log(task=task, data=data_name, in_rows=x_data.num_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "medium inner on int" # q2
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT x.id1 as xid1, medium.id1 as mediumid1, x.id2, x.id3, x.id4 as xid4, medium.id4 as mediumid4, x.id5 as xid5, medium.id5 as mediumid5, x.id6, x.v1, medium.v2 FROM x INNER JOIN medium ON x.id2 = medium.id2").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q2: {t}")
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1")), f.sum(col("v2"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# m = memory_usage()
+# write_log(task=task, data=data_name, in_rows=x_data.num_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "medium outer on int" # q3
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT x.id1 as xid1, medium.id1 as mediumid1, x.id2, x.id3, x.id4 as xid4, medium.id4 as mediumid4, x.id5 as xid5, medium.id5 as mediumid5, x.id6, x.v1, medium.v2 FROM x LEFT JOIN medium ON x.id2 = medium.id2").collect()
+shape = ans_shape(ans)
+# print(shape, flush=True)
+t = timeit.default_timer() - t_start
+print(f"q3: {t}")
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1")), f.sum(col("v2"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# m = memory_usage()
+# write_log(task=task, data=data_name, in_rows=x_data.num_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "medium inner on factor" # q4
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT x.id1 as xid1, medium.id1 as mediumid1, x.id2, x.id3, x.id4 as xid4, medium.id4 as mediumid4, x.id5 as xid5, medium.id5 as mediumid5, x.id6, x.v1, medium.v2 FROM x LEFT JOIN medium ON x.id5 = medium.id5").collect()
+shape = ans_shape(ans)
+# print(shape)
+t = timeit.default_timer() - t_start
+print(f"q4: {t}")
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1")), f.sum(col("v2"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# m = memory_usage()
+# write_log(task=task, data=data_name, in_rows=x_data.num_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+question = "big inner on int" # q5
+gc.collect()
+t_start = timeit.default_timer()
+ans = ctx.sql("SELECT x.id1 as xid1, large.id1 as largeid1, x.id2 as xid2, large.id2 as largeid2, x.id3, x.id4 as xid4, large.id4 as largeid4, x.id5 as xid5, large.id5 as largeid5, x.id6 as xid6, large.id6 as largeid6, x.v1, large.v2 FROM x LEFT JOIN large ON x.id3 = large.id3").collect()
+shape = ans_shape(ans)
+# print(shape)
+t = timeit.default_timer() - t_start
+print(f"q5: {t}")
+t_start = timeit.default_timer()
+df = ctx.create_dataframe([ans])
+chk = df.aggregate([], [f.sum(col("v1")), f.sum(col("v2"))]).collect()[0].column(0)[0]
+chkt = timeit.default_timer() - t_start
+# m = memory_usage()
+# write_log(task=task, data=data_name, in_rows=x_data.num_rows, question=question, out_rows=shape[0], out_cols=shape[1], solution=solution, version=ver, git=git, fun=fun, run=1, time_sec=t, mem_gb=m, cache=cache, chk=make_chk([chk]), chk_time_sec=chkt, on_disk=on_disk)
+del ans
+gc.collect()
+
+print("joining finished, took %0.fs" % (timeit.default_timer() - task_init), flush=True)
+
+exit(0)
diff --git a/benchmarks/db-benchmark/run-bench.sh b/benchmarks/db-benchmark/run-bench.sh
new file mode 100644
index 000000000000..9ccc26804d19
--- /dev/null
+++ b/benchmarks/db-benchmark/run-bench.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+set -e
+
+SRC_DATANAME=G1_1e7_1e2_0_0 python3 datafusion/groupby-datafusion.py
+SRC_DATANAME=J1_1e7_NA_0_0 python3 datafusion/join-datafusion.py